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BUSINESS FORECASTING

It’s Coming, So Get Ready: Demand Planning During An Economic Recession

By Patrick Bower

2 0 1 9 | S U M M E R V O L U M E 3 8 | I S S U E 2

9 The Predictive Analytics Tool Box—Understanding the Tools and Algorithms By Eric Wilson, CPF

Fostering Collaboration— A Framework For Understanding Mindsets In S&OP By Larry Lapide

An Implementation Framework for Integrated Planning Transformations By Conrad den Hertog

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3 Answers to Your Forecasting Questions

4 It’s Coming, So Get Ready: Demand Planning During An Economic RecessionBy Patrick Bower

9 The Predictive Analytics Tool Box—Understanding the Tools and Algorithms By Eric Wilson, CPF

16 How the Digital Economy is Impacting the Supply ChainBy Charles Chase, CPF

21 Fostering Collaboration— A Framework For Understanding Mindsets In S&OP By Larry Lapide

26 An Implementation Framework for Integrated Planning Transformations By Conrad den Hertog

31 The Untapped Potential of Programmatic Advertising in Demand Planning and ForecastingBy Ryan A. Harris

34 Realignments in Capex and Export Flows Keep Global Growth on a Plateau By Evangelos Otto Simos

39 U. S. Economy Breaks Recovery Cycle Record—But Are Cracks Starting To Appear?By Jamal Nahavandi

48 IBF Calendar 2019

J O U R N A L O F

BUSINESS FORECASTINGV O L U M E 3 8 I S S U E 2 | S U M M E R 2 0 1 9

2 Copyright © 2019 Journal of Business Forecasting | All Rights Reserved | Spring 2019 | www.ibf.org

Answers to Your Forecasting Questions

Happy Forecasting!

Chaman L. Jain, EditorSt. John’s University |[email protected]

[ Q ] How would you plan for a supply chain with the following characteristics: API (active pharmaceutical ingredient) manufacturing with 9-month firm forecast requirement. API Product is sent to CMO (Contract Manufacturing Organization) with 3-month firm forecast requirement. Final product is then sent to 3PL who also requires a 3-month firm forecast. How should demand planning consider all these different requirements from the point of view of the supply chain?

[ A ] It is indeed a most difficult situation where, for one product, the forecaster must generate three different forecasts—for API, CMO and 3PL—and each requires a different commitment. The key to it, as Jeff Marthins from Takstykake points out, is knowing the risk exposer. The ultimate objective is to maximize profit and minimize losses. The biggest problem here is with the API, which needs to be forecasted nine months ahead. The question here is, what would be the cost of overstocking API? Can it be stored for a longer periods? If yes, what would be the cost? Is it a specialized ingredient, which can be used only for one or two products? If the cost of storing is too high and/or is a specialized component, we need to be a little more cautious in preparing its forecasts. In that case, the best strategy should be to prepare forecasts nine months ahead and update it every month. In other words, we will start with making a commitment for the first nine months. Thereafter, we will commit for one additional month at a time. When one month passes, we will commit for another month, that is, the tenth month, and so on. We can do the same with CMO and 3PL. It may increase the cost, but it will be well worth it.

[ Q ] We are struggling to improve forecasts; can you give some tips for doing it?

[ A ] Here are few things we can do:

1. Make sure there is consistency in the data. If we are forecasting demand, make sure all the data is demand data. In addition, make sure the data is cleaned and treated for such things as outliers, structural changes, missing values, etc.

2. Selection of the forecasting model also makes a difference. If there is a cause-and-effect relationship, make sure the model selected captures that relationship. If there is a seasonality in the data, choose a model that captures that too.

3. Assumptions including amount to be spent on promotion, price to be charged and state of the economy made in generating a forecast shouldn’t change.

4. In addition to all the diagnostic tests, use ex-post forecasts to see whether the model selected is giving the results we are looking for.

5. Finalize the forecast with a consensus process. If necessary, overlay judgment over the statistical forecast. There are some elements that cannot be quantified but have a bearing on the forecast. In so doing, make sure no one is biasing the number.

[ Q ] Our demand is usually much more than what we can deliver, do we still need to prepare a forecast?

[ A ] Yes, you do. This will help in knowing how much you are leaving on the table. With that information, you may be encouraged to do something to capitalize on it by expanding the capacity and/or by outsourcing.

Copyright © 2019 Journal of Business Forecasting | All Rights Reserved | Spring 2019 | www.ibf.org 32 Copyright © 2019 Journal of Business Forecasting | All Rights Reserved | Spring 2019 | www.ibf.org

It’s Coming, So Get Ready: Demand Planning During An Economic RecessionBy Patrick Bower

4 Copyright © 2019 Journal of Business Forecasting | All Rights Reserved | Summer 2019 | www.ibf.org

When I wrote this article at the end of 2018, the equity mar-kets were plummeting, threat-

ening to enter a bear market. We’ve since bounced back but that dramatic drop serves as a warning shot, letting us know we’re approaching the end of the cur-rent economic cycle. With economic ex-

pansion cresting, unemployment at his-torically low levels, and with the Federal Reserve considering interest rate adjust-ments to extend the span of this historic GDP growth, it is only a matter of time before the economy cools off. And with two-thirds of the US economy based on consumerism, the impact of any eco-

nomic decline will disproportionately impact consumer products and brands.

I have worked three recessions dur-ing my career in demand planning, so I know a little about what to expect. I also know that each recession is unique. The recession of 2008 was different from 2001, and both were different from

It’s Coming, So Get Ready: Demand Planning During An Economic RecessionBy Patrick Bower

E X E C U T I V E S U M M A R Y | As the Federal Reserve vacillates between tightening and loosening monetary policy, signals from economists suggest that we are approaching the end of the current economic cycle and that a recession is likely approaching. When the slowdown will happen remains unclear, but a recession is a meaningful event for most organizations, which necessitates that we, as demand planning and S&OP leaders, prepare for the inevitable impact on our businesses. Recessionary times can lead to many changes in consumer behavior such as shifting to different and lower grade products, inventory contractions, as well as lower retailer and consumer acceptance of new products. This article hopes to present practical ways to prepare before a recession starts, including ways to identify what specific economic indicators affect which products, using predictive analytics and econometric data to correlate your demand curve with different severities of economic downturn, and digging into your own company’s data to understand how your portfolio fared in previous downturns.

PAT R I C K B O W E R | Patrick is Senior Director, Global Supply Chain Planning & Customer Service at Combe Incorporated, producer of high-quality personal care products. A valued and frequent writer and speaker on Supply Chain subjects, he is a recognized demand planning and S&OP expert and a self-professed “S&OP geek.” Prior to Combe, he served as the Practice Manager of Supply Chain Planning at a boutique Supply Chain consulting firm, where his client list included Diageo, Bayer, Unilever, Glaxo Smith Kline, Pfizer, Foster Farms, Farley’s and Sather, Cabot Industries, and American Girl. His experience also includes roles at Cadbury, Kraft Foods, Unisys, and Snapple. In addition, he has worked for the Supply Chain software company, Numetrix, and was Vice President of R&D at Atrion International. He was recognized four times by Supply and Demand Chain Executive magazine as a “Pro to Know,” and Consumer Goods Technology magazine considered him one of their 2014 Visionaries. He is the recipient of the inaugural IBF’s Excellence in Business Forecasting and Planning Award.

Copyright © 2019 Journal of Business Forecasting | All Rights Reserved | Summer 2019 | www.ibf.org 5

1991. Some are bubble-influenced, like the housing bust of ’08, while others are simply soft-landing hangovers from rap-id expansion like in 2001. Despite differ-ences in the underlying causes, there are common recessionary themes that im-pact the demand curves of most compa-nies. Knowing these commonalities may help you prepare for the next downturn.

THE COMMONEST OF THEMES

There are some common themes and generalizations about the way products, consumers and retailers interact dur-ing a recession. One way or another, a recession will alter your demand curve. Your customers—whether large retailers or OEM parts suppliers—will cut their forecasts, reduce their inventory, and be-come more pessimistic in their forecast-ing of the future. Like you, they will not get the timing right, and the result will be fits and starts in their ordering pat-terns. And if you use point-of-sale data (POS) to help you forecast demand or estimate trade inventory, you will start to see a disconnect or a divergence between POS trends and orders from your customers. There will be a lot more “noise” in the data and a true demand signal will be harder to discern.

So why the noise? Well, to start, if history is a guide, activity relating to discounting and other retail trade will increase, and customers will offer more frequent pricing reductions, or other ways to stimulate demand on either the virtual or physical shelf for the value conscious consumer. Of course, your competitors will do the same, and the result will be a much more volatile demand pattern, which will make planning for both supply and demand more suspect. As you try to navigate these rough waters, it will be helpful to openly discuss the potential impact of

scenarios such as these. This will allow for at least some understanding of shifts in key performance indicators (KPIs), such as buffer inventories increasing to handle the greater demand volatility and forecast error.

During a recession, value becomes a dominant consumer theme. Cash stressed consumers will seek the best price. Generally, this results in both private and store brands, as well as off-brand or commodity products, picking up market share as consumers and customers move towards value. From a planning and S&OP perspective, your units might stay the same, but your revenue may decline due to a shift toward lower-priced goods. And with a mix shift in the products consumed towards value, strategies for competing or participating with products offering better value to the end consumer should be part of your S&OP decision-making process.

Managing new products will present a challenge as consumers are less likely to expose limited financial resources to try a new product. When my employer launched a new hair coloring product in 2008, it began to founder. Our initial demand sensing of POS results reflected a serious gap to expectations. We realized we had to take drastic measures, so we gave away free product—offering “free-bates” to help stimulate trial activity among our consumers. It worked. Noting the economic downturn with historically high unemployment, we also focused our advertising creative on how this product might help in a job interview, to directly appeal to the unemployed segment. This too also helped drive trials and interest in the product. The key learning is that in anticipation of a sure-to-come downturn, it is reasonable to expect your customers or consumers to be hesitant to shift to—or even buy—

new products without some compelling reason to do so. And to the extent possible, it would be wise to anticipate this type of dynamic throughout all your new product planning processes.

It is not just the consumers that are averse to new products—traditional brick and mortar retailer acceptance of new products will also be a challenge. These retailers tend to “batten down the hatches”, preferring to lean into known brands and products and lower-priced store-brand or private-label offerings during recessionary times. Not only will this make obtaining new product dis-tribution more difficult, but it is likely to result in some marginal items being delisted. Such activity indicates why ex-amining risk in your product portfolio is central to planning before and during a recession.

Similarly, you are likely to notice a shift in your product mix. While lower-priced offerings might sell better, so too will larger-size/better-value offerings. Bonus packs, upsized offerings, on-pack couponing, multipacks, and similar strat-egies will prove themselves to be smart, tactical alternatives for increasing con-sumer interest at shelf, and for holding ground against private-label offerings. Being prepared for this potential mix of shifts—if only on paper—will help you improve your reaction time if and when response tactics are called for.

And finally, trade inventory will drop - if only because your customers will lower their forecasts. For example, if your customer keeps four weeks of supply based on weekly demand of 100 units, then normal inventory would hold 400 units. If the forecast is cut to 90 units per week, however, the inventory target will drop to 360 units. In short, you should be prepared to ad-dress unexplainable drops in your cus-tomer’s inventory that are not aligned with historical trends.


CHANNELS WILL SHIFT

In what is probably the most obvious of statements, sales volume levels with mass discounters, club stores, and dollar outlets tend to swell during a recession, while specialty outlets will see a decline. Estimating and improving relationships in recession-friendly channels—prior to a downturn—may help you weather the economic storm. Consumers have always been less willing to pay a con-venience-level premium during tough times. When I worked with Snapple dur-ing the 2001 recession, fewer people made street-level lunch time purchases, preferring instead to buy multipack of-ferings of our product in grocery stores as they “brown-bagged” their lunches as a way to reduce day-to-day expenses. Interestingly, these multi-pack products were very sensitive to price-based pro-motions and sold tremendously well in discount grocer and big boxes outlets when on deal, a huge channel shift away from convenience stores and local delis. As we were constantly digging deep into our point of sale and shipment data, we were able to react and alter our sales and promotional strategy during that particular recession.

And while we are discussing channels it remains to be seen the impact a recession will have on the emerging e-commerce channels. These have vastly expanded since the last downturn and the impacts are hard to anticipate. Because of this unknown impact, more so than ever it is imperative for consumer goods companies to sense any shift in channels with consumers.

PLANNING FOR A RECESSION

While some of these recessionary

effects may seem like broad generaliza-tions, they are merely the most common impacts. The reality is that recession hits each business in unique ways. So where can you find guidance to determine how to plan better? What can you do before a recession? Here are some action items to consider:

Dig into your own data. Burrow deeply into all institutional data re-tained from prior recessions and try to curate the facts into an economic narrative of sorts. Find old S&OP con-tent, consensus reporting, or ask veter-ans of the business their opinions on the subject. These will all offer some guidance for the future. But make sure your analysis is not simply “What hap-pened?” Try to incorporate all the dy-namics of your firm’s reaction—an as-sessment of what worked (and didn’t), an assessment of competitor activities and reactions, and maybe even a snap-shot of economic indicators before, during, and after the recessionary pe-riod. If you don’t expand your analysis to paint a complete picture, you will be short-changing your own research. Wade neck-deep into your own data lake and immerse yourself fully into the past.

Reset your thinking. While most forecasters have a tendency toward a positive bias, force the stakeholders of your operational processes such as S&OP and financial planning and analysis (FP&A) to look at most plans with greater levels of scrutiny and skepticism. Use the results of your own historical data dig to enlighten the discussion. Make upside forecast moves based only on hard facts, not conjecture or opinion. Expect mix shifts in products. Use shorter trending metrics to forecast forward. Work on building different demand scenarios to estimate impact on the business, both top-line and bottom-line.

Examine your product portfolio. Are

you thinking of launching a high-priced premium offering sometime within the next year? How will you propose to punch through the economic noise and gain acceptance of your product when consumer dissonance for anything “new” and expensive may be heightened during a recession? Do you have products already at risk that may go under during a recession, or is there some way to make such items more desirable to retailers or resellers from a margin perspective? Ask yourself tougher and harder questions about your product portfolio to prepare for the inevitable downturn. Prepare your commercial innovation backlog with tactical options such as bonus or instant redeemable coupons, so you can be agile in the wake of declining economic results.

Use predictive analytics (PA) tools to see how your demand curve reacts to differing economic stimuli. Some of the PA products leverage large econometric databases. Prepare to align emerging economic factors against your own POS or shipment histories and look for correlations, latency, and inflection points. Look for products or product families that are counter–cyclical and may see an uptick and plan to leverage this dynamic. Understanding the leading economic indicators and their latency on your business will help you plan better in good times and in recessionary times.

Monitor key indicators of economic activity. During both the 2001 and 2008 recessions, my planning group provided an informal analysis of 25 or so key economic indicators—from housing starts to unemployment to consumer confidence. We looked for the aforementioned correlation and latency impacts to determine what items were impacted by specific economic indicators and how long it took these results to manifest themselves within demand.

Coutinued on page 15


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DRIVING VISIBILITY IN A WORLD OF COMPLEXITY

An optimized supply chain doesn’t just happen.

It is planned.

www.logility.comWorldwide Headquarters: 800.762.5207EMEA Headquarters: +44 (0) 121 629 7866

The Predictive Analytics Tool Box—Understanding the Tools and Algorithms By Eric Wilson, CPF

E X E C U T I V E S U M M A R Y | There’s a lot of excitement lately about AI, new models, and machine learning algorithms and the accompanying idea that they will replace all human judgement. This misconception may be due to lack of understanding about how all the tools and methods now available fit together, and how we need all of them if we’re to forecast all datasets accurately. In this article we will look at the full spectrum of forecasting methods from pure judgment to machine learning, and classify each of them so that they are easy to understand. I also provide an explanation of each of the broader classes of methods, so demand planners can add different models to their toolkit, knowing when to use which one for maximum effect.

E R I C W I L S O N | Eric is a predictive analytics and business planning innovator, author and speaker. He is the Director of Thought Leadership at The Institute of Business Forecasting (IBF), as well as the Director of Demand Planning at Escalade Sports. In 2016, he received the Excellence in Business Forecasting and Planning award from the IBF. He is a Certified Professional Forecaster (CPF) with over 20 years’ experience. Eric is a visionary in his field, a frequent speaker and panelist for many executive forums and professional conferences and has written numerous articles in publications such as The Journal of Business Forecasting and APICS Magazine. He is also the author of the book Cultural Cycles.

Abraham Maslow is reputed to have said, “If all you have is a hammer, everything looks like a

nail.” The meaning behind this aphorism is simple: If we have only one way to do things (hammer), then we might think

all problems (nails) need the same tool (hammer). Therefore, we always choose the hammer even if the problem needs a totally different kind of solution. The lesson learned from this is to understand that different problems require different

techniques—not just one approach, and not just one tool.

Broadly speaking, there are two approaches to demand forecasting—one is to obtain information and make assumptions about the likely purchase


behavior of buyers, the other is to obtain information and make assumptions about patterns of past purchases. This is not to imply that there are only two tools the demand planner can use; there are a wide variety of models that come from an assortment of techniques which fall under different methods. What’s more, all of these demand forecasting techniques rely to varying degrees on judgment and statistics. Unfortunately, many people see every data point as another nail and ignore the tools they need to develop a new forecast. For some executives and people in sales, the hammer is their subjective opinion about this year’s sales based on what we did last year. For others, it is looking at averages or smoothing techniques and only looking at the past. Others neglect the past and only look at relationships to other variables and may neglect the independence of what they are forecasting. Others even try to teach or train the models to no longer depend on judgment at all and think more sophisticated techniques must be better than simpler ones.

The truth is that data comes in all forms and volumes—and the different insights it is used for and gained may be just as vast. Data may be cross-sectional or time-series and stationary or non-stationary. Insights may be for classification, clustering or prediction, and then used for different time horizons. One size does not fit all, and neither is one method the right tool for every demand planning or predictive analytics problem. We need to have a large tool box, and we need to understand all the tools we have at our disposal and how each one can be used.

CLASSIFYING BUSINESS FORECASTING METHODS

There are many different criteria we could have used to classify the different types of methods, but the use of knowledge and how the algorithms gain insight from said knowledge may provide the best way of understanding

how these models differ. We cannot go into every model out there, instead I offer the broader categories of each method according to how it uses or doesn’t use data, how much knowledge internally or externally it relies on, and what it does with the information it has. In Figure 1 you will see at the far left pure qualitative human opinion, and moving right to almost pure quantitative and machine based. Keep in mind, every method serves a purpose, so for each we will provide pros and cons as well as list some of the most widely used algorithms or models used for each.

KNOWLEDGE (THEORY-BASED)

A theory is a supposition, or a system of ideas intended to explain something. By nature, a knowledge-based forecast uses qualitative or subject inputs to generate a prediction of the future. They rely on opinions of experts, people’s experiences, judgment, intuition, or just plain guesses. These techniques are often used when no other data is

Figure 1 | Forecasting Models


Figure 2 | Data, Skill and Precision Required for Judgmental and Experimental Models

available. We often use these qualitative judgments when forecasting new products, and in forecasting the impact of fundamental changes such as new technologies, environmental changes, cultural changes, legal changes, or during completely new and unique market conditions.

“Good” data refers to the amount of data needed being smaller or even not needed, and the quality of what you have is stable or reliable. “Poor” or “bad” is the opposite, necessitating the use of more unstable, much larger datasets. For skill, “good” is in relation to the amount of complexity in the methods or abilities needed to perform calculations as well as time to develop models. “Good” data means models can be created relatively easily and quickly while “poor” would take more experience and/or time to produce. Precision is looking at the accuracy or reliability of the results where “good” would be lower error or consistency of outputs. Inconsistent, highly variable or biased results would be labeled “poor.” (See Figure 2)

EMPIRICAL (JUDGMENTAL)

These are the most qualitative and subjective methods. They are used either as a replacement for statistical methods or to augment a statistical forecast with judgment. While there are concerns that judgmental approaches to forecasting can be less accurate, add bias and are

a poor method for developing a robust demand plan, they still have merit and a place in our tool box. Not only do we need to understand them (because they are already being widely used) but they can add value with the right input from the right people using the right method. Many times, sales and marketing people have valuable information about sales promotions, new products, competi-tor activity and so forth which should be incorporated into the forecast. More often than not, these can only be col-lected as subjective inputs and added to, or compared with, other quantitative outputs. Understanding this and imple-menting systematic and well-structured approaches to incorporating judgmen-tal data to forecasts can limit these is-sues and improve forecast accuracy. Examples of judgment-based methods include: Expert opinion, Jury method, Sales Build-Up and Delphi method.

POSTERIORI (EXPERIMENTAL)

These are still judgmental or theory based, but we can test our hypotheses and put much of the subjectivity onto the consumer. Here we conduct a test on a small group of consumers to extrapo-late those results to a larger population. These methods and techniques on their own may be better than pure judgment but they still come with limitations. This is a great example of how a classical method is being adapted with new data

and new technologies. Traditionally this approach consisted of inferences made by planners using conventional surveys or bringing people in for blind market testing. While these core methods are still subjective, many times they are based on data and are now combined with more statistical methods like caus-al modeling or even machine learning algorithms. With adequate sample sizes they can produce robust forecasts. Ex-amples of experimental-based methods include: Concept method, Scenario, Sur-vey, Panel Data.

DEDUCTIVE LOGIC (NUMERICAL-BASED)

Whilst qualitative forecasting methods rely on judgment of experts, quantitative techniques like causal and time-series methods rely on mathematical models and data. Op-posed to opinion and gut feel, these methods use correlation and objec-tive logic. These techniques rely on predefined models and data sets to replicate patterns or relationships that the model uses to predict a future point or relationship. Adding quanti-tative forecasting tools helps temper enthusiasm or flat-out false wish-casts or guestimates. It also statistically provides more insights into expected variability and a truer picture of con-sumer influences. That’s not to men-tion historical trend, seasonality, and impact of external events.


OBSERVATION (NAIVE)

This method is simple to compute, re-quiring the minimum of resources. That said, it is also the first true quantitative use of time-series data. It is a real meth-od, though many people assume it can only be used for comparison, not for pre-diction. Yes, it is based on limited obser-vation and is not designed for extended or long-term forecasts, but it should not be discounted entirely. This, like all methods, has its place and may be ex-cellent for very tactical or operational forecasts. In addition, there have been studies that show that a naive forecast actually does better than many demand planners’ best consensus efforts, and better even than sophisticated software. If this being the case, why not consider it not only at the end for evaluation of other methods (under the right circum-stances), but also at the start? Use it as a baseline using composite naive or add other components such as trend and seasonality and judgment. Examples of naive methods include: Random walk, Seasonal random walk and Historical analogy. (See Figure 3)

PERCEPTIVE (CAUSAL ANALYSIS)

A model based on the cause-and-

effect relationship. If, for example, sales (effect) depend on price (cause), the method can statistically calculate the relationship between them, and then use that relationship to prepare a fore-cast if you offer a certain price. With the increase of uses of forecasting and pre-dictive analytics inside organizations for decision making purposes, causal mod-els and understanding the relationship of variables become much more impor-tant. With this type of analysis and the right data, we can uncover consumer sentiment, plan e-commerce business more efficiently, and provide insights to others on drivers and variables that impact the business. Examples of Causal Analysis methods include: Regression, Ratio and Monte Carlo.

PERCEPTIVE (TIME SERIES ANALYSIS)

In these models, it is assumed that the past data pattern will continue in the future. Forecasts are generated by extrapolating the data from the past into the future. Data may have differ-ent components of level or stationary, trend, seasonality or cycles, and noise. Assumptions are made about different patterns that they will continue in the same or in a similar way in the future. Depending on how much historical data exists (and because past patterns do of-ten repeat in the future) you can use a

time series to make long-term predic-tions. For business forecasting, time se-ries models are often the most utilized. For this reason, and for their ability to forecast off the company’s own historical data, it is a staple for anyone in demand planning or business forecasting. It is a great first technique to learn for anyone new to the field. Examples of Time Series Analysis methods include: Averaging, Exponential smoothing, Decomposition and ARIMA (Box-Jenkins).

INDUCTIVE LEARNER (MACHINE-BASED)

Up until recently we would forecast based on either what we thought (quantitative) or what the data thought (qualitative). Either way the forecast would be generated on its own merits each time from the dataset or judgment without past iterations. It may do a great job but in essence it is solving the problem for the first time, every time it forecasts. A learning algorithm differs in that it will take information from previous iterations or a training dataset and applies it to help build the current or future model. Another way to think about the difference between traditional forecasting methods and machine learning is to consider learning algorithms as employing inductive logic where it starts with observations and arrives at general conclusions. (See

Figure 3 | Data, Skill and Precision Required for Naïve, Causal and Time-series Models


Figure 4)

COGNIZANCE (SUPERVISED LEARNING ALGORITHMS)

With judgmental, we relied on ex-perts and their opinions and with tradi-tional statistical methods, we relied only on the dataset at hand. With Supervised Learning Algorithms we rely on experts who act as teachers to feed the training data to help the model know what the right answers should be so that it learns and improves. Once the model gets trained it can start making a prediction or decision when new data is given to it. Supervised learning algorithms try to model relationships and dependencies between the target prediction output and the input features such that we can predict the output values for new data based on those relationships learned from the previous data sets. This can be used for either classification problems or predictive analytics that uses familiar algorithms like regression and ARIMA with labeled data and the ability to han-dle more multi-dimensional and multi-variety environments. It also is power-ful to use Supervised Learning to build models composed of multiple weaker models that are independently trained and whose predictions are combined in some way to make an overall bet-ter prediction. Examples of Supervised

Learning methods include: Instance-based Algorithms like Nearest Neighbor or Support Vector, Bayesian (Generative Learning Model), Regression, Decision Trees, Neural Network, ARIMAX, Ensem-ble Algorithms like Boosting, Stacking, and Random Forest.

COGNIZANCE (UNSUPERVISED LEARNING ALGORITHMS)

Unsupervised learning algorithms learn from plain examples without any associated response, leaving it to the al-gorithm to determine the data patterns on its own. Whereas with supervised learning we may provide a good data-set to use to compare to future inputs, this looks at the dataset provided and finds structures in that dataset on its own. This type of algorithm tends to re-structure the data into something else, such as new features that may represent a class or a new series of uncorrelated values. Here there’s no teacher at all, or maybe the data is the teacher instead. Using these techniques and algorithms, the computer might be able to teach us new things after it learns patterns in data. They are helpful with clustering and providing insights and meaning of the data that then can be used with other tools and methods in our toolbox.

Examples of Unsupervised Learn-ing methods include: Association Rule

Learning Algorithms; Clustering Algo-rithms like k-means and Hierarchical Clus-tering; and the Hidden Markov Model.

COMBINING WHAT WE HAVE LEARNED

You may think that the more complex the model or algorithm the better the forecast, and simpler statistical methods means worse forecasts. That is not at all true. There are many simple forecasting methods in our tool box that have specific purposes and provide even better insights and results than more sophisticated techniques. At the same time, there may also be (or should be) some more complex methods that may be very useful in other cases. The issue of complexity versus accuracy versus resources and effort is particularly important and should be evaluated in every situation. Complex methods may do well under conditions for which they were designed but tend to suffer when applied to new conditions, and judgment may have limitations or bias which machines may help eliminate or even discover new insights humans could not imagine on their own.

All methods help you better under-stand data and make predictions and are used in demand planning and predictive analytics, but each may take a very dif-ferent approach. By understanding the difference, we see that they are comple-mentary. And because they are comple-mentary, we see that the ideal approach

Figure 4 | Data, Skill and Precision Required for Supervised Learning and Unsupervised Learning


Start tracking these indicators now.Bring it to S&OP—now. Sooner

than later, proactive planners should escalate conversation about reces-sionary contingencies to the executive review phase of their S&OP processes, since the topic is a strategic issue that needs to be part of the executive con-versation. Create a one-slide summary of key factors likely to have the great-est impact on your business and track them in each meeting.

There is no magic to understanding and navigating the potential impacts of a recession, whenever the next one may come. The solution is the hard work of becoming intimately knowledgeable about past impacts on your business, tempered with updated knowledge of changes in your business model (such as the growth of e-commerce) and in your product offerings.

When you’ve weathered as many recessions as I have, you learn what to

look for and you recognize promising responses that have worked in the past. Some of the most interesting dialogues I ever had in the S&OP process occurred during difficult economic times.

Demand planners and S&OP leaders should take action now to initiate forward-looking conversations about recessionary impacts. It is a fiduciary responsibility of the planning role to facilitate this difficult discussion.

—Send Comments to: [email protected]

It’s Coming, So Get Ready: Demand Planning During An Economic RecessionCoutinued from page 7

is understanding the strengths and trade-offs and finding the best parts of each. Ultimately the best result may be to combine the different methods, algo-rithms and models in a way that plays to each other’s strengths and together provide the best result. You may just

find you use a k-means unsupervised algorithm to gain insights on promo-tions and other data that can be used in an ARIMAX supervised learning model along with a Judgmental Expert Opin-ion, comparing it to a naive Random Seasonal Walk after actuals are ascer-

tained for Forecast Value Added (FVA%). In other words, everything may not be a nail and you may find you need a ham-mer, screwdriver, pliers, and a mechani-cal auto-calibrated impact wrench to complete the job.


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E X E C U T I V E S U M M A R Y | The digital economy refers to an economy that is based on digital computing technologies where business is conducted through online and mobile devices using the internet-of-things (IoT). In the digital economy, value is created through the technology-enabled links between people, machines, channels and organizations. All this is giving rise to an awareness and willingness to apply analytics to everything, not just to strategic initiatives, but to day-to-day tasks. Advanced analytics aided by machine learning algorithms will automate the repetitive work demand planners do regarding managing data and information as well as uncovering key insights allowing them to work smarter and more efficiently. As such, digitalization of the supply chain will require companies to manage product replenishment based on actual consumption rather than transactions.

C H A R L E S C H A S E | Charles is the Executive Industry Consultant and Trusted Advisor for the Retail/CPG Practice at the SAS Institute. He is also the principal solutions architect and thought leader for delivering Demand Planning and Forecasting solutions to improve SAS customers’ Supply Chain efficiencies. Prior to that, he worked for various companies, including the Mennen Company, Johnson & Johnson, Consumer Products Inc., Reckitt Benckiser PLC, Polaroid Corporation, Coca Cola, Wyeth-Ayerst Pharmaceuticals, and Heineken USA. He has more than 20 years of experience in the consumer-packaged goods industry, and is an expert in sales forecasting, market response modeling, econometrics, and Supply Chain Management. He is the author of several books, including Next Generation Demand Management: People, Process, Analytics, and Technology, and DemandDriven Forecasting: A Structured Approach to Forecasting. In addition, he is co-author of Bricks Matter: The Role of Supply Chains in Building MarketDriven Differentiation. He is the second recipient of IBF’s Lifetime Achievement in Business Forecasting and Planning Award, as well as a Certified Professional Forecaster (CPF).

How the Digital Economy is Impacting the Supply ChainBy Charles Chase, CPF

Today’s business challenges are numerous due to globalization pressures, supply chain complex-

ity, rising consumer demands, and the

need to increase revenues across global markets while continuing to cut costs. Adding to these challenges is the new digital economy driven by the Internet

of Things (IOT) where devices are con-nected to everything, collecting real-time information about where we shop, what we buy, and how we purchase products.


Now we have digital assistants like Siri (also known as chatbots) who know our likes and dislikes, and remind us what we need to do daily.

The digital economy refers to an economy that is based on digital com-puting technologies where business is conducted through online and mobile devices using the IoT. The digital econ-omy is intertwined with the traditional brick-and-mortar retail economy, mak-ing a clear delineation tougher to deci-pher. In the digital economy, value is cre-ated through the technology-enabled links between people, machines, chan-nels and organizations. As a result, ev-ery company must change the way they operate.

WHAT KEEPS EXECUTIVES UP AT NIGHT?

There are new realities that compa-nies are facing that are driving how con-sumers are interacting with their brands. Because of this, companies will have to drastically change the way they have been behaving and truly embrace being a data driven culture. Those new realities are: 1. There are more data streams available

than ever before. Companies are gathering internal data from sensors, streaming from the manufacturing line to external data sources like weather and social media. It is important for companies to be able to harness that data into actionable information and insights.

2. CPG companies no longer control their brand image. Consumers can share experiences with large audi-ences very quickly. However, their internal quality metrics need to be aligned with how the consumer per-ceives their brand.

3. CPG companies must have a nimble and agile supply chain to be able to quickly adjust to the demand signal. Companies need to be able to optimize the E2E supply chain and simulate possibilities in order to make a profitable and proactive decision as how to meet those demand signals with the most efficient supply response.

4. Margins are being squeezed from all channels and supply partners. It is critical that they automate as much of their redundant decision making as they can, and truly focus on exceptions that need attention.

5. Consumers continue to expect CPG companies to innovate with new products and services to experience the brand.

DIGITAL TRENDS IMPACTING SUPPLY AND DEMAND

Over the next decade, 90% of industry growth will be captured by companies that successfully engage directly with consumers. Consumers rule the world; they are ubiquitously connected, crave individuality/personalization, and are intolerant of complexity and latency. They are a retailer’s and consumer goods company’s worst nightmare—and their greatest opportunity. It seems intuitively obvious that the companies that figure out how to best engage with these consumers will get more than their fair share of growth. And, by the way, as older consumers give way in masses to millennials, the “problem” just gets worse.

According to recent research, by 2020, one-third of retailers and consumer goods companies will have benefited from digital transformation (DX), with the remainder held back by inflexible/

outdated business models, processes or functional structures. According to recent research, one of the biggest gaps between digital success and survivors is in the area of leadership. Leadership holds responsibility for creating a vision of where their companies are headed in the next decade, but most are not ready to change direction by adopting new business models or functional structures.

As barriers to entry fall over the next five years, smaller “lateral” competitors will grab 10 to 15 share points from tra-ditional and established large enterprise players. Industry estimates find that, of the $35 billion in net growth over the past three years, only $1 billion has come from traditional, large enterprise players. That’s an anomaly perhaps, but if you look at the ranks of new competi-tors to traditional businesses, they are appearing in record numbers. Part of the reason for this is that the historical barri-ers to entry (technology, manufacturing facilities, etc.) have fallen—they’re now either “commodities” or available via the cloud.

A good example of a lateral competi-tor is Stitch Fix—“Stitch Fix came along, identified a largely unmet consumer need (curated apparel shopping), and leveraged a proprietary analytics plat-form to become a billion-dollar company in less than seven years.” Another lateral competitor is Harry’s Razors—”Harry’s razors company was founded in March 2013 by Andy Katz-Mayfield and Jeff Raider. In January 2014, Harry’s pur-chased the Feintechnik razor factory in Germany for a reported $100 million in an effort to provide control over the entire process of manufacturing their products. In July 2015 the company re-ceived a third-round financing of $75.6 million. Now not only can you purchase Harry’s razors on-line, but also in brick and-mortar stores like Walmart.”


Finally, not every company with a vision will be able to fully leverage its digital transformation investments in the supply chain, since there are many dimensions. There are already many supply chain leaders who are creating a roadmap for their companies to evolve in the next decade. Let’s take for ex-ample Bayer’s move to join forces with Monsanto to increase its innovation in agriculture, or Under Armour’s invest-ments in technology-enabled products and health information. These moves re-quired executive leadership to recognize how both their business models and en-abling processes must change—and not just incrementally, but fundamentally.

Digitization is making the Supply Chain faster, more intelligent, connected and autonomous. This trend is front and center with C-Level decision-makers and presents opportunities for companies to embrace the digital ecosystem as a strategic partner with their customers.

There is an undeniable movement happening right now. The convergence of 3 factors: powerful (and affordable!) computing power; abundance of data; and availability of analytics and algo-rithms—all the more so with open Source Analytics. All this is giving rise to an awareness and willingness to apply analytics to everything, not just to stra-tegic initiatives, but to day-to-day tasks. We see more and more business analysts embracing advanced analytics—know-ing how to use them, how to interpret and leverage the results, effectively be-coming citizen data scientists.

A recent McKinsey & Company study indicates that products and services are more digitalized than supply chains. However, that same study finds that digitalized supply chains are likely to have a major influence on future revenues and profits. The question becomes not if, but when supply chain organizations will embrace a culture of

analytics.

TRANSITIONING TO AN ANALYTICS-DRIVEN COMPANY

Companies are embracing analytics capabilities and new data streams as an opportunity to understand consumers and demand as never before. While the pursuit of data continues and remains invaluable to these missions, direct customer relationships and new data sources are opening new sources of insights. However, it takes time to transform from a limited analytics role to a broader role, which is an enterprise effort, requiring a different corporate culture, analytics, technology, and process changes. Consumer packaged goods companies have a head start over retailers in the race to optimize analytics use, but as both improve, the opportunities that lie in increased collaboration continue to multiply.

THE DIGITAL IMPACT ON DEMAND FORECASTING AND PLANNING

Future demand forecasting and planning will largely benefit from data being captured digitally from devices imbedded internally and externally utilizing the Internet of Things (IoT). Advanced analytics aided by machine learning algorithms will automate the repetitive work demand planners do regarding managing data and information as well as uncovering key insights allowing them to work smarter and more efficiently. Demand planners will leverage chatbots (like Siri) to answer their questions, create reports, and serve as a digital assistant

utilizing machine learning (ML), adding value at the point of execution deeper in the business hierarchy. These levers using predictive analytics and ML will play a significant role in improving the accuracy of the statistical forecast and “closed-loop planning.”

Predictive analytics will allow the demand planner to analyze hundreds of thousands to millions of internal as well as external demand influencing variables (e.g., price, sales promotions, in-store merchandizing tactics, digi-tal marketing programming, weather, trends from social networks, and sensor data) with advanced analytics aided by machine learning to uncover and model complex relationships and derive accu-rate and granular demand plans. These new technologies enable a significant improvement in demand forecast ac-curacy, often reducing the forecasting error by as much as 10 to 30 percent. This allows for targeted discussions, in-cluding upside potential and downside exposure in the S&OP/IBP process signif-icantly improving the overall inventory management process.

COMPANIES CAN NO LONGER FOCUS ON TRANSACTIONS

In typical consumer packaged goods (CPG) companies, the demand planning process is a collaborative activity that can involve everyone from raw materi-als suppliers to manufacturers to dis-tributors. The one group that is not di-rectly engaged in most cases is the end consumer who purchases and uses the products. The advent of IoT and access to point-of-sale (POS) and consumption (Nielsen/IRI) data will give CPG compa-nies the ability to evolve from traditional forecasting to planning based on actual consumer demand (also referred to as


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consumption). Those companies who transition from a transactional-based demand planning to a consumption-based modeling and planning process will thrive in the new digital economy.

The Marketing Science Institute quoted Intel Corporation’s Peter Levin: “With cheap sensors, democratized an-alytics, and new platform tools, we are moving from a ‘models’ to a ‘measures’ world.” This is a good way to see exactly how things are changing. Information will be readily available for manufac-turers and suppliers to use to make key decisions regarding products. No lon-ger are we focused on forecasting, but instead on real-time consumption.

WHAT IS REAL-TIME CONSUMPTION?

Today, most CPG manufacturers and distributors are using antiquated forecasting methods and solutions to create their “demand plan”. Demand planning has long been thought of as the only option. Projecting transactional data for products is a highly manual, subjective activity. When companies get demand forecasts wrong, it creates huge challenges upstream across the supply chain network.

Demand forecasting is critical to marketing (brand management) who want to sell as much product as possi-ble without oversaturating the market. Many consumer products tend to sell out on promotion in just one day. With multiple SKUs available across the mar-ket, those companies could have prof-ited more with better production and planning of the product being promot-ed. Not only does this limit sales, but it

also impacts the company’s reputation with consumers, encouraging them to turn to competitors’ products that were readily available in stock.

The advent of IoT and sensors that live stream data changes the playing field. It empowers companies by pro-viding a way to capture real data, in real time, directly from consumers. You know when products are being purchased, you know their buying patterns, demo-graphics, and who is consuming those products, as well as the velocity of use. This all translates into a highly effective way of forecasting and planning prod-uct replenishment.

The question is, how is all data and information captured, stored and pro-cessed? Let’s consider, for example, purchasing clothes. A consumer ex-changes information on Facebook with friends. Then, goes online to research where to get the best price and who has what they want in stock. Then, they either go into the brick-and mortar store or buy online. If that person opts into the manufacturers online program that information can then be sent back to the company. The company can use this information to adjust its product line, perhaps boosting overall quality, or it can send promotional information about new styles, colors, or other simi-lar offerings to encourage consumers to come back for additional purchases. This also works for other categories including food and beverages, house-hold products, and others.

Those companies who understand their consumers, their product usage and how often they purchase those products will thrive in the new digital economy. All of this translates into

highly usable data and information that can transform a company’s future production, new product deployment, as well as product design.

WHY CONSUMPTION-BASED FORECASTING AND PLANNING WILL IMPROVE BUSINESS PROCESSES.

Today’s consumers are becoming more and more digital. They connect through Facebook, Twitter, smart-phones, smart homes, and dozens of online mobile apps. Consumers benefit from digital services designed to work seamlessly with products that have IoT sensor technology built in.

For consumer-packaged goods com-panies, there are substantial benefits in terms of better supply chain visibility. Real-time data sensing and analysis is becoming the norm. As such, digitali-zation of the supply chain will require companies to manage product replen-ishment based on actual consumption rather than transactions.

With IoT capturing information and data in real time through sensors from everywhere, CPG companies must oper-ate in a more agile fashion, redirecting products to multiple locations quickly based on actual consumer demand. They will be required to model and fore-cast consumption to better understand when to increase/decrease product pro-duction and where to send more inven-tory to replenish retailer stores, or direct-ly to consumers.


TO BE CONSIDERED, please EMAIL: [email protected] with your interest in submitting an article


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Fostering Collaboration— A Framework For Understanding Mindsets In S&OP By Larry Lapide

I have been writing and talking about Sales and Operations Planning (S&OP) for the past 20 years, so it will come as

no surprise when I say that I believe that the S&OP process is one of the most crucial practices for business success. What may come as a surprise are the reasons why I

have focused so much on this process rather than other types of Supply Chain Management (SCM) processes.

The first (no surprise) reason is that the S&OP process aims to optimally match supply and demand from a planning perspective. This balancing of supply

E X E C U T I V E S U M M A R Y | This column is a modified version of an article I wrote, “Sales and Operations Planning (S&OP) Mindsets,” published in the JBF back in April 2007. While the content is largely the same as the one written 12 years ago, it is probably even more relevant today. Back then, S&OP was not as widely used as it is now but many of the challenges of running a well-functioning S&OP process remain the same. The major recommendation here is that one should establish clearly defined roles for various functional managers on an S&OP team—ones that are based on their psychologies or mindsets. I present a framework for understanding different stakeholders’ personalities in the S&OP meeting that can foster collaboration and help in achieving consensus.

L A R R Y L A P I D E | Dr. Lapide is a lecturer at the University of Massachusetts and an MIT Research Affiliate. He has extensive experience in industry, consulting, business research, and academia as well as a broad range of forecasting, planning, and supply chain experiences. He was an industry forecaster for many years, led supply chain consulting projects for clients across a variety of industries, and has researched supply chain and forecasting software as an analyst. He is the recipient of IBF’s inaugural Lifetime Achievement in Business Forecasting and Planning Award. He welcomes comments on his columns at [email protected].

(This ongoing column in the Journal is intended to give a brief view on a topic of potential interest to practitioners of business forecasting and planning. Suggestions on topics that you would like to see covered should be sent via email to [email protected].)

and demand isn’t just a goal of any SCM organization, it is the reason the SCM organization exists in the first place.

The second reason involves the fact that S&OP is the most prevalent cross-functional process that bridges a team of customer-facing managers from Sales,


Marketing, and Customer Services with Finance and supply-facing managers from Manufacturing, Operations, Logis-tics, and Supply Chain. My career expe-riences have been on both sides of this process bridge. While most know me as a supply-side professional, the reality is that the first part of my career was spent on the customer-facing side—as a man-ager and consultant in marketing and sales research, as well as in program mar-keting and demand forecasting.

Throughout my experience, I have learned that it is very difficult to align the goals and views of demand and supply facing managers so they can agree when making joint decisions. This makes S&OP a “work-in-progress” since the personalities of the participants frequently get in the way of developing consensus-based plans, often resulting in a dysfunctional cross-functional team. At times, the so-called “S&OP bridge” blows up.

Because I have been on both sides of the bridge, I believe that if a company can get its S&OP process to work well, there are substantial benefits to be obtained from jointly optimized decisions made by S&OP team members. The major roadblock to this seems to be team dynamics.

A while back, I never had a formal way

to think about how to leverage different personalities engaged in an S&OP team until I met Dr. Shalom Saada Saar, a senior fellow at Harvard University’s Center for Public Leadership. He had been teaching leadership to our graduate students in MIT’s Master’s of Engineering in Logistics (MLOG) program for some time.

UNDERSTANDING MINDSETS

I ran a Demand Management Sympo-sium at MIT in September 2006 and in-vited Dr. Saar to lead a closing session en-titled, “Critical Success Factors for Bridge Building in Demand Management.” His session was well received by the par-ticipants who were largely supply chain managers. As I watched him run that session, I realized he had a structured ap-proach to addressing the S&OP dilemma. He described corporate research from the 1970s that had identified 220 differ-ent mindsets among managers. He went on to describe how all these mindsets can be distilled into the following three color-coded types of people. (See Figure 1)

Blue Mindset: These people are very focused on “doing what is right.” They tend to be decisive because, based on their historical perspective, they inherently

“know” what the right thing to do is. Of course, they also tend to be judgmental and have little patience for the people who don’t get it.

Red Mindset: People of a red mindset aim for “doing what is true.” They are the analytical types who rely only on numbers and facts to make decisions. Moreover, they do not want to make decisions until they can get the most recent data to support them.

Green Mindset: These people are into “doing what is new.” They are futuristic in their thinking and can imagine all the possibilities and opportunities of the future. They tend to be the dreamers and creative types who don’t let the facts get in the way of a good story. They also tend to consider the other mindsets to be too rigid in their thinking. People of a green mindset like to think and stay outside the box.

While most managers have mindsets that are multi-colored, they usually have a dominant color that draws them to a specific functional discipline. It is what makes them good at what they do. However, when you have a cross-functional process, such as S&OP, which requires a team of Sales, Marketing, Supply Chain, Operations, Logistics, and Finance managers to engage in joint planning, the process can get out of hand when the views diverge.

Varied thinking and perceptions can quickly make the team dysfunctional if the team members don’t understand, value and leverage the strengths that each teammate brings to the table. Leveraging these strengths involves recognizing that there are natural roles each functional area can play during the S&OP process.

NATURAL ROLES IN S&OP

In order for an S&OP process to be

Exhibit 1Three Types of Manager Mindsets

BLUEDoing what

is right

GREENDoing what

is new

REDDoing what

is true

• Decisive • Judgmental• Impatient

• Facts• Numbers• Analyses

• Futuristic• Possibilities• Creative

Figure 1 | Three Types of Manager Mindsets


successful, the strengths of all three mindsets must be leveraged. If just “blue” managers drive the process, it will lead to plans totally rooted in the past because, to them, that is “what is right.” It could also lead to chaos if team members have divergent views of what right is. Similarly, if all members of the team are “red” managers, it will lead to plans that must be based only on today’s reality, which can lead to analysis paralysis and indecisiveness, particularly if all the so-called necessary facts are not available or if “the facts are too old.” Lastly, if all the team members are “green” managers, it can lead to plans rooted only in possibilities and dreams, with little relation to reality.

Recognizing that all types of mind-set managers need to play a role in the S&OP process, a key question arises, “What are those roles?” I offer the following recom-mended roles that I believe to be natural and logical, based on the color-coded mindset structure shown in Figure 2.

Operations and Logistics: Managers in these areas tend to have red mindsets because they are largely analytical and focused on getting facts to make decisions. Their responsibility and natural role is to develop supply plans that are predicated on demand plans and forecasts provided to them. These managers are very comfortable when they are systematically developing manufacturing, inventory, logistics, and procurement plans based on what is provided to them.

Supply Chain: Managers in this area tend to be a combination of red and blue mindsets. They are analytical, yet they also recognize that their job is to coordinate and harmonize all the plans on behalf of the whole company. They need to ensure that demand and supply chain activities are synchronized. While this is difficult to do, they know it is the right thing to do. These managers are

naturals for moderating a disciplined and collaborative S&OP process.

Marketing: These managers tend to be a combination of red and green mindsets. They are analytical in that they rely on facts and figures, gleaned from market research, sales, and competitive analyses, to develop demand plans—including future pricing, promotional, and new product launch activities designed to shape and create demand. The green in them allows them to create new types of demand plans needed to stay competitive.

Sales: Managers in this area tend to be pure green. They are steeped in optimistic thoughts about opportunities and the possibility of windfall sales in the future. They are the best at developing sales plans that identify the sales possibilities and future market conditions. One mistake others on the S&OP team can make when dealing with their green teammates is to try to get these green people to be analytical and quantify the possibilities. In doing so, other S&OP team members usually wind up being frustrated by them.

Finance: These managers are pure red. For them, it is all about what is true—“just the numbers, please.” Their natural role in the S&OP process is to monetize the demand and supply plans developed by others, so that everyone can see

the future financial picture vis-à-vis the financial plans and budgets in place.

If S&OP team members are given the roles that I’ve recommended above, the process stands a good chance of working well. (By its nature, a functioning S&OP process must be contentious, to an extent!). It is important to have each S&OP team member recognize the roles played by others, as well as their own, to avoid infringing on each other’s. This approach can avoid a dysfunctional S&OP team with a great deal of discord. Moreover, in the final analysis, it can lead to consensus-based demand and supply plans that are accurate and attainable.

THE FORECASTING ROLE

Demand forecasters will note that I left out the important role they play in the S&OP process. Simply put, this role is to quantify the unconstrained demand that would be generated by the demand plans put in place. Doing this requires a red mindset that can produce an information-based forecast predicated on historical demand, the demand plans, and the assumptions about future market conditions and sales possibilities. Thus, the role of forecasting can be taken

Exhibit 2Dominant Functional Manager Mindsets

BLUEDoing what

is right

GREENDoing what

is new

REDDoing what

is true

Sales

Marketing

Supply Chain

Finance, Logistics and

Operations

Figure 2 | Dominant Functional Manager Mindsets

Continued on page 33


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Integrated planning is defined here as the complete set of an organiza-tion’s planning activities which seam-

lessly convert business strategy into results execution—vertically aligning through tactical (S&OP), operational and executional planning tiers; and horizon-tally coordinating all functional areas to achieve quality, delivery, service and/or efficiency. In contrast to Integrated Busi-ness Planning (IBP), integrated planning excludes strategic goal setting activities. However, within the scope of integrated planning is contained Sales and Opera-tions Planning (S&OP) which Tuomikan-gas and Kaipia define as “an instrument

for the vertical alignment of business strategy and operational planning, and for the horizontal alignment of demand and supply plans.”

The distinction between S&OP and integrated planning is that the latter ad-ditionally includes operational (demand, supply and life cycle management plan-ning) and executional (sales order man-agement, rapid response as well as regu-lar purchase, production and transport order) planning activities, which under-pin and make possible the tactical S&OP cycle of review meetings. Thus, whilst S&OP is a crucial component of a modern organization’s planning processes, with-

out operational and executional planning processes, the organization will only par-tially address its planning challenges.

An integrated planning transformation program should aim to create a step change enhancement in planning activities which will aid in achieving KPI targets for on-time launches, forecast accuracy, order fulfillment, inventory turnover, supply reliability and lower supply chain costs, within the overarching goals of sustainably growing competitive advantage, revenue and profitability. However, many transformations use poorly defined implementation plans and underestimate the degree of dependency

An Implementation Framework for Integrated Planning Transformations By Conrad den Hertog

E X E C U T I V E S U M M A R Y | This article asserts that standard integrated planning implementation requires three generic templates: a process model, a transformation framework, and an information systems roadmap. It makes the case for S&OP and IBP program managers to tailor these templates to their own organizations in order to assess current capabilities and guide the next phase of their business transformation. The results of implementation are improvements in financial results and key performance indicators.

C O N R A D D E N H E R T O G | Conrad is director of Strategica, a management consulting bureau based in The Netherlands. His experience includes managing supply chains, business transformations and software implementations across a variety of industries including automotive, food & beverage, consumer electronics, medical devices, medical equipment, pharmaceuticals, consumer packaged goods and agriculture. He believes that a mature integrated planning process is essential for successfully executing the strategy of any organization.


between leadership culture, business processes and the information systems that support them. Indeed, according to McKinsey, organizations that develop clearly structured change initiatives are 3 ½ times more likely to achieve an effective and sustained cross-functional approach.

Furthermore, Patrick Bower and Niels van Hove have expressed the need for a standard reference model accessible to company executives, consultants and academics to optimize planning processes. Optimizing an organization’s planning processes is invariably a complex task, and a well thought out starting point will minimize the risk that the unavoidable complexity morphs into avoidable complications.

We need a standard reference point of generic templates including an integrated planning process model, transformation framework and information systems roadmap. These templates are intended

to provide a comprehensive high-level foundation to kick-start the next phase of an organization’s integrated planning transformation. Let’s examine these templates one by one.

INTEGRATED PLANNING—PROCESS MODEL

Figure 1 illustrates a process model template of the three integrated plan-ning cycles, highlighting the main pro-cess dependencies’ relationships with each other, as well as to the core organi-zational strategy and execution activities. In order to transform corporate strategy into successful results execution, top to bottom alignment is required across all organizational tiers: from strategy to three tiers of integrated planning—monthly tactical (S&OP), weekly opera-

tional and real-time executional (ERP) planning—then down to execution ac-tions such as product design, manufac-turing, logistics and sales transactions.

The top segment of Figure 1 illus-trates that strategic processes such as vision, strategic plan, annual operating plan and quarterly operating review and rolling forecast updates need to be set up well to drive organization planning and execution outcomes. Two key out-comes of the quarterly operating review are revised human resource planning needs and within year rolling forecasts to update the annual operating plan (AOP) targets. McKinsey recommend quarterly 12 to 18-month financial roll-ing forecasts in order to increase man-ager accountability for gaps between forecasts and performance.

Of the three integrated planning tiers, the S&OP or tactical cycle plays a crucial role in coordinating planning processes

Figure 1 | Integrated Planning – Process Model

© Strategica

Executional Control

OutlookTargets

SKUs & BOMs Orders & Actuals

Execution

Real Time Execution CycleExecution Software

( © Strategica )( © Strategica )

CAD


Figure 2 | Integrated Planning–Transformation Framework

by creating a structured monthly forum for senior managers to document insights and assumptions, assess risks and opportunities, and drive action plans to achieve business targets.

The S&OP process also facilitates the vertical alignment of planning activities with the organization’s strategy as follows:

Cascading down high level AOP and rolling forecast financial targets and KPIs through the global organization structure from head office to regional offices and factories, boosting employee engagement and clarifying trade-off decision rules.

Rolling up the consensus financial outlook signed off by regional offices, into the global quarterly operating review.

The S&OP process also facilitates horizontal alignment of cross-function-al activities through a series of review meetings at portfolio, channel, category, demand, supply, S&OP and executive S&OP levels, as illustrated in Figure 1. As Tom Wallace reminds us, planning does not end with S&OP, however, as the link-age of S&OP monthly aggregated vol-ume plans to detailed supply schedules

and demand plans “helps to ensure that decisions made in the Sales & Operations Planning are being transmitted down-wards into detailed schedules that affect what happens in the customer order de-partment and on the receiving dock, the plant floor and the shipping dock.” Also, in Figure 1, several detailed planning pro-cesses which support the S&OP process are illustrated, such as demand planning, supply planning, statistical forecasting, customer collaboration and product life-cycle management.

INTEGRATED PLANNING—TRANSFORMATION FRAMEWORK

A successful integrated planning im-plementation requires a well-coordinat-ed deployment of organizational change elements with individual project phases optimally tied to program stage gate plans, as outlined in Figure 2. Each row of the figure corresponds to an organiza-tional change element, which are aimed

at improving organizational effective-ness.

For the purposes of integrated plan-ning, three change elements are taken from the McKinsey 7S Framework: in-formation systems, staff resources and skills training. To these are added three change elements from Tuomikangas and Kaipia: leadership and culture, busi-ness processes and performance man-agement. Together, these six change elements are both necessary and suf-ficient to successfully implement an integrated planning program as dem-onstrated by global change programs I have been involved in. Note ongoing change actors such as business strategy and organizational structure are here classified under strategy and are there-fore beyond the scope of the integrated planning program. Following McKin-sey, these six change elements can be further categorized into hard and soft elements. “Hard” change elements are easier to quantify and comprise busi-ness processes, information systems and performance management. These are summarized as follows:

Integrated Planning - Operating Model

Culture & Governance[Behaviors, KPI Suite, Drumbeat]

Tactical Planning (S&OP) Process[Tactical KPIs]

Operational & Reponse Planning Processes[Operational & Response KPIs]

BI Software[Data Visualisation]

SCP Software[Worksheets]

Organization Structure[Job Roles, Descriptions & Rewards]

Competencies Training[Process, Systems & Behavior Modules]

Performance Management[KPI Tracking, CI & CoP]

Integrated Planning - Operating Model

Functional Requirements

Learning Needs Analysis

Change Adoption

Core Design Principles

User Roles & Authorizations

© Strategica


Business processes: Tiered process designs from high level process definitions down to detailed activities and steps across the tactical, operational and executional cycles. All process interdependencies should be mapped here.

Information systems: Designing, building, testing and deploying the BI, SCP, ERP and related tools required to support the business processes via automated interfaces, data consolidation and enhanced data navigation.

Performance management: The final change element requiring deployment. Its main focus is tracking program benefits against the business case and continually improving achievements against a suite of KPI targets aligned to the overarching business strategy.

“Soft” change elements are harder to quantify, revolve around interacting with people, and here comprise: leader-ship and culture, staff resources and skills training. Together these make the change sustainable after the transformation by helping to embed the hard change ele-ments into the organization’s new operat-ing model. The soft change elements are summarized as follows:

Leadership and culture: Senior exec-utive commitment to the cultural change and new ways of working is fundamental to any integrated planning program suc-cess. This can be supported by introduc-ing a rewards and recognition system to incentivize the most desirable S&OP mindset behaviors, such as:

– Cross-functional collaboration to break down silo behaviors;

– A high trust environment where employees are encouraged to speak out; and

– Employee engagement to generate enthusiastic support for the new ways of working.

Mindset behaviors effectively mod-eled by leadership will normally cascade

down through the layers of the organiza-tion.

Staff resources: Necessitate map-ping the required organizational design against the new ways of working. De-tailed process definitions drive new RACI (responsible, accountable, consulted, in-formed) roles and responsibilities, which in turn impact job descriptions and re-ward structures. This is also the place to consider centralizing standardized re-petitive tasks into global centers of excel-lence. Also, recruitment policies need be adjusted accordingly to support the new culture.

Skills training: Includes education in the end to end integrated planning process and the key role of S&OP to co-ordinate that process. Learning jour-neys across the training modules can be tailored to each staff role and should include building staff capability in the new mindset behaviors, business process knowledge and software user training.

In order to transform the organization by the desired quantum leap in perfor-mance, these six change elements should form the basis of program workstreams. Figure 2 also indicates the key depen-dencies between these change element workstreams and the high-level sequence in which they should be kicked off. At the end of the deployment, the transforma-tion framework has to turn into an oper-ating model when the transformation is finished and business as usual takes over.

INTEGRATED PLANNING—INFORMATION SYSTEMS ROADMAP

Information system investments are typically the most complex, time con-suming and costly change element to deploy within an integrated planning program and are therefore worthy of

extra planning attention. A lack of an in-formation systems roadmap will lead to a piecemeal approach that neglects the benefit of optimizing system dependen-cies along the maturity path, incurring risks of costly rework, program delays and team demotivation.

A high-level information systems roadmap should be defined to optimally sequence the design, build, test and de-ployment of the three key integrated planning systems tiers shown in Figure 2: BI (Business Intelligence), SCP (Supply Chain Planning) and ERP (Enterprise Re-source Planning). A generic integrated planning information systems road-map is illustrated in Figure 3. This fig-ure illustrates a list of milestones across functional attributes, data analytics and software releases mapped against the Gartner five stage scale of systems land-scape maturity, plus an additional “react” stage in position one.

The following features need to be con-sidered to convert this generic roadmap into a live roadmap for a specific organi-zation:

Milestone planning will depend upon the program preference to go wide (commence new software releases for high priority markets or factories) or deep (complete mass deployment of existing software releases across all markets and factories, before commencing lead deployment of the next software release).

At any one time, an organization may be active with multiple maturity stages in different global regions, depending upon program preferences and resource availability. As long as the roadmap de-ployment sequence rules are maintained for each market and factory within the organization, then costly rework will be minimized.

The logic behind the roadmap matu-rity stage sequence in Figure 3 is outlined as follows:

Maturity Stage 1 (React): The ERP


platform normally provides the data foundation of the entire integrated planning landscape. This stage pro-vides the opportunity to harmonize transactional data into a real time data warehouse and/or rationalize existing ERP kernels, which provides direct effi-ciency benefits and also supports sub-sequent roadmap stages.

Maturity Stage 2 (Anticipate): De-mand and supply planning modules should be designed and built in parallel with each other and should be imple-mented here. Advanced spreadsheet tools including a statistical forecast Excel add-in, and macro driven demand plan aggregation can deliver improved func-tionality beyond legacy spreadsheets whilst minimizing the change impact on end users.

Maturity Stage 3 (Integrate): SCP planning data and ERP transactional data can now be fed into visually attractive, exceptions based and drillable BI data visualization reports, to structure the S&OP

cycle of review meetings. A successful BI deployment can catalyze change adoption by allowing commercial and financial management to view monetized demand plans drilled down to customer planning group level, to improve sales accountability and performance.

Maturity Stage 4 (Collaborate): This phase generally requires a significant investment to upgrade the earlier spreadsheet-based demand and supply planning and scheduling tools into state-of-the-art advanced planning and scheduling (APS) tools. Besides improving user navigation and insight, APS tools can automate and improve previous manual activities, for example an algorithmic engine to obtain the optimal master production schedule, and a forecast netting engine to correct the demand plan for open sales orders and pipeline deliveries. Also, what-if scenarios can be configured to compare the financial impacts of alternative events and actions.

Maturity Stage 5 (Orchestrate): This

is the appropriate stage to add advanced optimization modules to complement the core SCP tools deployed in the pre-vious stage, such as product lifecycle management (to optimize product port-folio strength), inventory optimization (to optimize working capital), point of sale (POS) data (demand sensing insight into key customer sell out and trade stock data), order management (to improve gross margins) and rapid response (to re-duce stock outs).

By the end of maturity stage 5, the suite of ERP, SCP, BI and advanced sys-tems should be functioning seamlessly and only require incremental release up-grades based upon technology enhance-ments, and change requests based upon continuous improvement initiatives. Further, the tactical, operational and ex-ecutional planning processes should be working in synchronization and connect-ed by automated interfaces, to ensure one number planning up and down all

Functional Features

--

Purchase, Production, Transport and Sales Order Automation

-

Master Production Schedules, Detailed SchedulesPurchase, Production,Transport and Sales Order Automation

Master Production Schedules, Detailed SchedulesPurchase, Production, Transport and Sales Order Automation

Master Production Schedules, Detailed Schedules, Financial ScenariosPurchase, Production, Transport and Sales Order Automation

Optimize Product Portfolio Strength, Multi-Echelon Inventory Optimzation,

Master Production Schedules, Detailed Schedules, Financial ScenariosPurchase, Production, Transport and Sales Order Automation

Software Releases

Maturity Stage 1: React--

Core ERP Modules

Maturity Stage 2: Anticipate-

Spreadsheet, Fcst Add-in Tool

Core ERP Modules

Maturity Stage 3: Integrate

Spreadsheet, Fcst Add-in Tool

Core ERP Modules

Maturity Stage 4: Collaborate

Advanced Planning & Scheduling

Core ERP Modules

Maturity Stage 5: OrchestrateLifecycle Mgmt, Inv Opt, POS,Order Mgmt, Rapid Response

Data Visualization

Advanced Planning & Scheduling,

Product Lifecycle Management Core ERP Modules

( © Strategica )

SCP System

ERP System

SCP System

ERP System

BI System

ERP System

BI System

Advanced Modules

SCP System

SCP System

BI System

Systems Landscape

SCP SystemERP System

BI System

BI System

ERP System

Data Visualization

Data Visualization

Figure 3 | Integrated Planning—Information Systems Roadmap

© Strategica

Continued on page 33


Television advertising budgets used to be huge but focus has shifted away from this traditional

advertising medium to digital media. Included within digital media advertis-ing spend is programmatic advertis-ing, which is becoming a popular way to reach audiences. The benefit of this medium is that advertisement engage-ments (clicks and buys etc.) are track-able, allowing companies to understand

the customer journey, i.e., how they came to land on the company’s site, as well as their behavior while on the site. It also gives companies the ability to amass vast amounts of data and target consumers on an individual, highly per-sonalized basis.

In terms of forecasting demand, one thing many forecasters will agree on is that “You’re either wrong or you’re lucky.” That luck, however, is a result of supply

chain professionals collecting different data points, quantifying those variables, and generating insights for better decision-making. Big data mining and predictive analytics increase the chances of a “lucky” forecast.

We are at a point now where predic-tive analytics helps us understand our customers better than ever. Due to inter-net usage growing dramatically in recent years, consumption of digital media has

The Untapped Potential of Programmatic Advertising in Demand Planning and ForecastingBy Ryan A. Harris

E X E C U T I V E S U M M A R Y | Programmatic advertising allows companies to advertise to target markets and understand demand drivers and consumer behavior like never before. What’s more, this kind of advertising allows demand planners to shape demand in near real-time. By connecting programmatic advertising with demand and supply planning, we can use it to boost demand at will. It represents the next stage of demand planning and analytics, and offers forward-thinking companies the opportunity to gain a competitive advantage in the years to come.

R YA N A . H A R R I S | Ryan is currently an Assortment & Space Management Analyst at BJ’s Wholesale Club. Previously, he was an associate analyst for Xaxis, the world’s largest programmatic media company with revenue of $1billion. Prior to joining Xaxis in New York, he interned at BJ’s Wholesale Club analyzing their seasonal space utilization to promote incremental sales, as well as interning at Brew-Tek in Zhuhai, China. He recently graduated Summa Cum Laude from Bryant University with a dual concentration in Global Supply Chain Management and Applied Analytics. While at Bryant, he led the Supply Chain Leadership Association (SCLA), an academic organization, to provide workshops and info sessions on disruptive technologies in supply chain. Ryan continues to conduct research and thought leadership in analytics and supply chain, and sits on the board of directors of the APICS Providence chapter as VP of Marketing.


also increased, giving us a wealth of data to utilize. Through means of data col-lection, publishers of digital media and other players in the ecosystem now have a better understanding of who’s viewing the content and how they behave. This information has led to improved deci-sions on how to advertise to various tar-get markets, but the real gamechanger is the visibility into how advertisements affect the final purchase. This is where demand planning and programmatic ad-vertising meet to generate a great effect.

PROGRAMMATIC ADVERTISING HELPS TO INFLUENCE DEMAND IN REAL-TIME

Programmatic advertising gives us the ability to tailor advertisements to target markets by leveraging artificial intelligence. The next time you see an ad on a webpage, mobile app or TV commercial, it’s possible that the advertisements have shown up just for you and others like you. But, how does this happen? Within the milliseconds it took for the webpage to load, the publisher of the website hosts a real-time auction to determine which advertiser bids the highest. Based on information about who you are, advertisers will bid for people that align with their target market. This process is called “real-time bidding.” Jeff Green of The Trade Desk, a Demand-Side Platform used by programmatic advertising agencies, compares it to the bid/offer system in electronic financial trading. With that comparison, there is also a way to mitigate risk with the “direct guarantee” method. This alternative is comparable to the futures market in that an advertiser can reserve a set amount of ad delivery in advance for a publisher at a

static price.When it comes to magazine ads, high-

way billboards, or television commercials, marketing efforts may increase sales but little evidence exists to visualize how they directly affect sales. We may know that ad-vertising increases sales, but not exactly why. Nowadays there are data points such as geolocation, viewer characteristics and limited rolling history of interests etc. alongside other external variables such as weather and days of the week. Players in digital media understand who their con-sumers are, they track what ads they see, and determine their actions afterwards. Such visibility can extend across devices and aid in coordinating marketing efforts. This data driven environment is the foun-dation of programmatic advertising.

HOW DOES PROGRAMMATIC ADVERTISING HELP DEMAND PLANNING?

Even though programmatic advertis-ing trims off the fat of beefy budgets by targeting only valuable audiences, it re-mains primarily a third-party activity. AI is being leveraged successfully to assess if a person fits a company’s target market and provides instantaneous bid valuations based on their value as a customer. This involves automatically profiling the user, analyzing data collected about their de-mographic and behavior to create an es-timated ROI, which is then cross-checked with the cost of acquiring that user (i.e., how much you have to pay when that user clicks on an ad and arrives on your site). However, the usage of this medium so far has been limited to generating de-mand with little-to-no coordination of the supply.

Imagine forecasting models being able to process this data or being able to produce the needed algorithms to

connect supply to demand. Currently, third-party analysts in programmatic advertising see what audience segments purchase more, under what conditions, and which path of ads they followed to get there. With their findings, the programmatic advertiser optimizes their bidding model, and a report is sent to the client company. This use of a middle man (the third-party agencies) means companies themselves are not incorporating the findings into their own forecasting models, and so cannot use this insight for better demand planning.

But how does programmatic advertis-ing insight help demand planning spe-cifically? Imagine that inventory levels are very high at a physical store or distri-bution center. With programmatic adver-tising integrated into our demand plan-ning process, we could boost the bids on people in a 15-mile radius to increase de-mand levels and shift some of that excess stock. Similarly, we can reduce advertis-ing when stockouts are a concern. If lead times and destinations are well defined, we can prepare bidding according to the goods’ transportation status. Service-lev-els, bottleneck concerns and other con-siderations can now be controlled by de-mand generation activities. In short, we shape demand at the touch of button to solve particular problems and boost rev-enue. It is as close to real-time problem solving and demand shaping as we can get. The benefits of this approach with its control and coordination go much fur-ther than standard time series and cause and effect forecasting models.

PROGRAMMATIC ADVERTISING WILL DOMINATE

Lucy Tesseras of the MarketingWeek.com website has outlined some of the success stories. Scs, a U.S.-based furniture


retailer, showed “a 57% increase in store footfall during March and June in 2016 compared with the same period the previous year” with programmatic activity. Similarly, O2, a telecommunications service provider, used collected data to customize each of their mobile ads to inform customers of upgrades, explain O2’s value proposition, and offer directions to the nearest store. The results were “a 128% increase in click-through rate over generic video ads,” which was the KPI for this campaign.

PROGRAMMATIC ADVERTISING NOT YET ALIGNED WITH DEMAND PLANNING

Unfortunately, constrained primary KPIs like the above-mentioned are the

consequence of not tying programmatic advertising to core business objectives. While a higher click-through rate may represent improved advertising effec-tiveness, there is still an absence of using it to deliver holistic business outcomes. The saying goes “What gets measured gets done.” So why not connect this real-time demand shaping with our op-erational strategies? We can use the inte-gration of programmatic advertising and demand planning to deliver company strategies.

There are a few reasons why program-matic advertising hasn’t become an in-house activity. It is worth noting that larger companies have been attempting to move it in-house to avoid the “tech tax” (whereby agencies retain a large percent-age of the ad spend) but have encoun-tered difficulties. It is an immature indus-try with no blueprint for implementation

or established best practices, and it is currently an easier, lower risk strategy to outsource rather than develop in-house. High overhead costs and sparse expertise are a barrier to implementation.

Although it may be a while before programmatic advertising becomes an in-house capability, there are ways to take advantage of it now. Earlier this year, eMarketer estimated that over 80% of digital display ad spend in the US in 2018 will be through programmatic means. Companies can work closely with programmatic agencies by sharing CRM data and communicating supply constraints and issues. While this won’t provide the direct connection of data, planning and action, it’s a feasible way to get started with this technology and get ahead of the competition.


on by any of the disciplines that have significantly red mindsets—even by those members of Sales who might be somewhat red-minded.

However, the managers who take on the forecasting responsibilities should be aware that incorporating all the information into the forecast is easy once

there is a consensus on the demand plans and assumptions. Heated discussion, however, is likely to occur once other S&OP team members see the demand implications in hard numbers. Because some will disagree with the numbers, a forecaster’s only recourse is to stay red-minded and get them focused on

the facts, figures and assumptions that were incorporated in the forecast, rather than on the numbers themselves. After all, you are the expert at quantification and forecasting; and your forecasts are only as good as the information used to develop them.


Fostering Collaboration—A Framework For Understanding Mindsets In S&OPContinued from page 23

tiers of the planning hierarchy and sup-port high quality business decisions.

CONCLUSIONBy setting aside time to define these

key templates, an organization will likely

increase its probability of moving up the integrated planning maturity scale. In particular, alignment on key process, program and system dependencies can save significant program time amounting to months or even years. The end result of applying tailored versions of these

generic templates at the beginning of an integrated planning transformation effort will improve competitive advantage and enhance the capability of the organization to convert business strategy into desired results execution.


An Implementation Framework for Integrated Planning Transformations Continued from page 30


regions and “economic clubs” around the world are not expected to be in the same phase of their business cycle during 2019-21. Consequently, reallocation of resources, geographic realignment of capital expenditures (CAPEX), redirection of export flows, and lastly increased financial risks from unexpected changes in monetary, fiscal and trade policies will all affect in different directions and intensities the economic outlook of each country and region around the globe.

Here are some geo highlights derived from the second quarter’s executives’ survey. There will be a pause in the investment boom in the United States. Despite the so-called trade wars, U. S. export growth will slightly decline and import growth will hold steady; as the domestic economy is expected to grow faster than exports,

INTERNATIONAL

ECONOMIC OUTLOOK

Time series forward-looking predictive analytics for GDP and its drivers highlight the risks to the near-term outlook for turning points in the global growth cycle. Evidence from the second quarter of 2019 and expectations for the last two quarters of the year foreshadow a high probability for a stable and moderate path for overall global economic growth. Following an annual growth rate of 3.6% in 2018, worldwide GDP is estimated to decrease slightly to 3.4% this year. Our global model predicts worldwide output to advance at a rate of 3.6% in both 2020 and 2021.

Global economic growth was not synchronized in the last two years and it is not expected to follow a convergent path in the next two years. Desynchronization implies that countries,

I. GLOBAL ASSESSMENT AND OUTLOOK

Realignments in Capex and Export Flows Keep Global

Dr. Simos is Director of Forecasting and Predictive Analytics at e-forecasting.com, a division of Infometrica’s Data Center, 65

Newmarket Road, Durham, NH 03824, U.S.A. and professor of economics at Paul College, University of New Hampshire,

www.infometrica.com, [email protected]. This report does not purport to be a complete description of global

economic conditions and financial markets. Neither the Journal nor Infometrica, Inc. guarantee the accuracy of the projections,

nor do they warrant in any way that the use of information or data appearing herein will enhance operational or investment

performance of individuals or companies who use it. The views presented here are those of the author, and in no way represent

the views, analysis, or models of Infometrica, Inc. or any organization that the author may be associated with.

Growth on a Plateau By Evangelos Otto Simos


The baseline forecast incorporates predictive analytics modeled from the World Economic Survey carried out by the Ifo Institute, Leibniz Institute for Economic Research at the University of Munich, which was conducted in the second quarter of 2019. About 1300 executives from 119 countries have indicated that in the second quarter of 2019, the “assessment of the current situation held constant but expectations for the rest of 2019 have “brightened.” The major findings of the second quarter’s survey are as follows:• Worldwide, executives evaluated the current economic

situation, second quarter of 2019, to be slightly above satisfactory levels, caused by significant declines in capital expenditures. Executives found economic activity in their countries in the second quarter of 2019 to be lower than in the second quarter of 2018. Most important, regarding the future, executives are expecting economic conditions in the next two quarters to be below the levels of economic activity assessed for the second quarter of 2019.

• On a regional basis, in North America (United States and Canada) executives assessed the current economic situation to be above satisfactory levels, led by strong gains in consumer spending, but economic growth will be below the readings of a year ago. Looking forward, business executives expect economic conditions in both the United States and Canada to deteriorate over the next two quarters. In emerging Asia, executives appraised the current economic situation to be below satisfactory levels and significantly worse than a year ago; Asia executives were optimistic about the future, expecting economic activity in the next two quarters to be better than in the second quarter of 2019. In the European Union, executives’ appraisals of current economic conditions were above satisfactory levels; the present was assessed to be below performance levels at the same time a year ago. Looking forward at the next two quarters, expectations of European business executives indicate a change from optimism to pessimism, predicting a significant slowdown in European economic growth.

• With respect to prices, survey participants expect average worldwide consumer inflation rates in the next six months to slightly accelerate from current levels.

• Looking at world trade, the business executives’ combined expectations predict the volume of exports to slightly decline and the volume of imports to increase over the next six months, compared to the second quarter of 2019.

• Regarding financial markets, survey participants expect short-term rates to decline and long-term rates to edge up over the next two quarters. Using the ‘soft data’ findings of the World Economic Survey,

a 119-country composite global diffusion predictive analytic is modeled by e-forecasting.com to evaluate and forecast the short-term worldwide business cycle. A reading of 50, the flatline, is used as reference in evaluating the wave of alternating booms and busts, which mark the global economy. In the second quarter of 2019, our global business predictive analytic registered a reading of 56, which is above the zero-GDP growth value of 50, and the same reading as in the first quarter of 2019. In other words, in the second quarter worldwide GDP growth will be about the same as in the first quarter.

e-forecasting.com’s global business predictive analytic of business activity tracks quarterly and in a timely manner economic conditions around the world. Its historical behavior is consistent with the index of industrial production for a group of twenty-three advanced economies, also called industrial countries, constructed by ‘hard data’ and maintained by the International Monetary Fund (IMF). However, IMF’s industrial production index lags our predictive analytic indicator in terms of timeliness by one to two quarters. The e-forecasting.com global business activity index is a “real time” predictive analytic providing readings at the end of the last month of the reference quarter as well as predictions for two quarters ahead.

Historically, changes in our global activity index mirror the year-to-year growth rate of worldwide industrial production. In the fourth quarter of 2018, the last available reading of the IMF production index (hard data), industrial production in the

II. SHORT-TERM PREDICTIVE ANALYTICS

there will be no significant adverse effects of trade on U. S. economic growth. The BRICS club (Brazil, Russia, India, China and South Africa) are expected to enter a CAPEX boom in the second half of 2019, after a freefall in investment spending in the last two years. Trade balance in the BRICS club is expected

to significantly deteriorate in the second half of 2019. Additionally, Euro Area CAPEX growth is expected to decline,

while investment in the 13-member OPEC club is expected to recover, following years of declines. OPEC exports are expected to significantly rise from higher oil exports.


Table 1 | Global Economic Growth and Inflation

REGIONMarket Size

2018 GDP$PPP Billion

Economic GrowthPercent Change in Real GDP

InflationPercent Change in Consumer Prices

2018 2019 2020 2021 2018 2019 2020 2021 WORLD 122,974 3.6 3.4 3.6 3.6 5.8 5.1 4.3 3.9 EUROPEAN UNION (28) 22,023 2.1 1.7 1.9 1.7 1.9 1.7 1.7 1.8 Euro Area (19) 15,435 1.9 1.3 1.6 1.5 1.8 1.5 1.5 1.6 Austria 463 2.7 1.5 1.4 1.6 2.1 1.9 2.0 2.0 Belgium 551 1.4 1.0 1.3 1.5 2.3 2.0 1.9 1.9 Cyprus 35 3.9 4.8 4.2 3.1 0.8 0.6 1.0 1.3 Estonia 45 3.9 3.1 2.5 2.8 3.4 3.1 3.1 2.8 Finland 256 2.4 1.2 1.1 1.4 1.2 1.3 1.3 1.6 France 2,963 1.5 1.2 1.6 1.5 2.1 1.6 1.6 1.6 Germany 4,356 1.5 1.2 1.8 1.5 1.9 1.5 1.6 1.7 Greece 313 2.1 2.8 2.5 1.9 0.8 1.0 1.2 1.4 Ireland 386 6.8 3.5 3.2 3.1 0.7 1.1 1.1 1.4 Italy 2,397 0.9 -0.2 0.5 0.7 1.2 0.9 1.1 1.1 Latvia 58 4.8 3.1 2.6 3.1 2.6 2.5 2.5 2.3 Lithuania 97 3.4 3.7 3.2 2.6 2.5 2.3 2.5 2.3 Luxembourg 64 3.0 3.0 2.6 2.7 2.0 1.7 1.8 1.8 Malta 21 6.4 7.1 6.0 4.6 1.7 1.8 1.8 1.9 Netherlands 969 2.5 1.5 1.4 1.5 1.6 2.1 1.8 1.8 Portugal 329 2.1 2.2 2.4 1.4 1.2 1.1 1.4 1.5 Slovak Republic 191 4.1 3.1 2.8 3.0 2.5 2.4 2.2 2.2 Slovenia 76 4.5 3.8 4.0 3.4 1.7 1.5 1.6 1.7 Spain 1,864 2.5 2.0 1.8 1.7 1.7 1.4 1.5 1.6 Non-Euro Area (9) 6,588 2.7 2.7 2.4 2.1 2.4 2.1 2.1 2.1 Bulgaria 162 3.2 4.6 3.9 3.3 2.6 2.5 2.3 2.3 Croatia 107 2.7 4.0 3.5 3.0 1.5 1.5 1.5 1.6 Czech Republic 396 2.9 3.3 2.9 2.4 2.2 2.3 2.2 2.1 Denmark 301 1.2 1.4 1.9 1.8 0.7 1.0 1.1 1.3 Hungary 312 4.9 5.5 3.1 2.5 2.8 3.1 3.0 3.0 Poland 1,213 5.1 4.6 4.0 3.3 1.6 1.9 1.9 1.9 Romania 516 4.1 4.5 3.9 3.2 4.6 3.7 3.2 3.1 Sweden 542 2.3 0.7 1.1 1.6 2.0 1.9 1.8 1.8 United Kingdom 3,038 1.4 1.6 1.5 1.4 2.5 2.0 2.1 2.0 OTHER EUROPE 7,841 2.4 0.8 2.5 2.3 7.1 7.9 7.2 7.1 Norway 396 1.4 1.8 2.0 1.8 2.8 2.2 2.0 1.9 Russia 4,213 2.3 1.8 2.0 1.7 2.9 4.4 4.2 4.3 Switzerland 548 2.5 1.6 2.0 1.6 0.9 0.9 0.9 0.9 Turkey 2,293 2.6 -1.9 3.4 3.8 16.3 17.1 15.3 14.7 Ukraine 390 3.3 3.0 3.4 3.1 10.9 8.9 8.4 6.7 NORTH AMERICA 24,901 2.7 3.0 3.1 3.1 2.7 2.3 2.5 2.4 Canada 1,837 1.8 1.3 2.0 1.8 2.2 1.9 1.9 1.9 Mexico 2,570 2.0 2.0 1.8 2.4 4.9 4.1 4.0 3.4 United States 20,494 2.9 3.3 3.4 3.3 2.4 2.1 2.4 2.4 SOUTH AMERICA 6,366 0.3 0.5 2.1 2.4 52.9 40.3 25.8 16.0 Argentina 915 -2.5 -1.6 2.2 3.2 34.3 40.9 31.8 26.5 Brazil 3,365 1.1 1.8 2.3 2.2 3.7 3.6 3.8 3.9 Chile 482 4.0 3.6 3.2 3.0 2.3 2.3 2.6 2.8 Colombia 745 2.7 3.2 3.6 3.7 3.2 3.3 3.4 3.2 Peru 457 4.0 4.0 4.3 4.0 1.3 2.1 2.1 2.0 Uruguay 82 2.1 2.1 2.8 3.4 7.6 7.6 7.3 7.3 Venezuela 320 -18.0 -25.0 -10.0 -5.0 900.0 676.0 411.0 218.0 ASIA & PACIFIC INDUSTRIAL 9,247 1.6 1.3 1.4 1.5 1.3 1.3 1.4 1.5 Australia 1,318 2.8 1.9 2.5 2.8 2.0 2.0 2.1 2.3 Japan 5,594 0.8 0.8 0.6 0.5 1.0 1.0 1.2 1.2 Korea 2,136 2.7 2.2 2.5 2.9 1.5 1.4 1.5 1.6 New Zealand 198 3.0 2.1 2.4 2.8 1.6 1.9 1.9 1.9 EMERGING ASIA 45,982 6.2 5.8 5.5 5.5 2.5 2.7 2.8 3.0 China 25,270 6.6 6.2 5.5 5.0 2.1 2.2 2.3 2.5 Hong Kong 480 3.0 2.1 2.2 2.5 2.4 2.4 2.3 2.4 India 10,505 7.1 6.7 6.8 7.7 3.5 3.8 3.9 4.1 Indonesia 3,495 5.2 5.0 4.9 5.2 3.2 3.3 3.4 3.4 Malaysia 999 4.7 4.4 4.6 4.8 1.0 1.7 2.2 2.3 Pakistan 1,141 5.2 2.6 2.8 2.8 3.9 6.5 6.3 6.0 Philippines 953 6.2 6.0 6.1 6.7 5.2 4.2 3.8 3.5 Singapore 566 3.2 2.8 3.0 2.5 0.4 1.1 1.1 1.3 Taiwan 1,251 2.6 2.0 2.2 2.4 1.5 1.2 1.2 1.3 Thailand 1,320 4.1 3.1 3.3 3.5 1.1 1.0 1.1 1.3 MIDDLE EAST & AFRICA 6,615 1.0 0.9 2.1 2.5 13.3 12.4 11.4 10.9 Egypt 1,296 5.3 6.0 6.5 6.0 20.9 16.4 15.8 11.9 Iran 1,611 -3.9 -5.5 -1.2 0.9 31.2 35.4 30.7 31.5 Israel 337 3.3 3.1 3.3 3.1 0.8 0.9 1.1 1.6 Saudi Arabia 1,858 2.2 1.9 2.0 2.2 2.5 0.3 1.1 1.6 South Africa 789 0.8 1.3 1.6 1.8 4.6 4.9 5.1 5.3 United Arab Emirates 724 1.7 3.0 3.3 3.0 3.1 2.4 2.3 2.2The 63 countries in this table account for 92% of world’s estimated GDP expressed in PPPs in 2017. Source: www.e-forecasting.com


Chart 2 | Predicting World TradeChart 1 | Predicting Global Industrial Activity

Global Activity Index, left scale

World Industrial Production, right scale World Trade Volume, right scale

e-forecasting.com Index, change from previous year IMF World Industrial Production, % change from previous year

e-forecasting.com Index, change from previous year IMF World Industrial Production, % change from previous year

Global Activity Index, left scale

advanced economies rose by 0.8% from a year ago. Based on the real-time behavior of our business predictive analytic (soft data), year-to-year growth in industrial activity in the world’s advanced economies is estimated to decline in the first of quarter 2019 by 2.7% and then drop further in the second quarter of 2019 by 2.8%.

By modeling business executives’ two-quarters-ahead expectations into a dynamic high frequency forecast, our global activity predictive analytic anticipates year-over-year declines in industrial production at a rate of 2.1% in the third quarter and 1.9 % in the fourth quarter of 2019.

Our composite predictive analytic of global business activity also serves as a gauge of worldwide demand and, consequently, its change from a year ago mirrors the year-to-year growth rate in the demand for internationally traded goods. Derived from

the opinions of about 1, 300 business experts from 119 countries, e-forecasting.com’s predictive analytic of global business activity has shown a strong performance record in tracking the volume of international trade, measured by the dollar value of global exports adjusted for export price changes.

In the fourth quarter of 2018, the volume of international trade, measured by merchandise exports adjusted for changes in export prices, rose by a year-over-year rate of 0.6% to a reading of 158.1 (2005: Q1 = 100). The predictive power of our global business activity index suggests that the volume of global exports will decrease in the first quarter of 2019 by 2.5% and then decline in the second quarter by 2%. In addition, based on executives’ expectations, our global growth predictive analytic forecasts worldwide exports to decline in the last two quarters of this year by an average year-over-year rate of 1.9%.

In our baseline annual forecast, global output - a worldwide composite of 60 countries that account for 92% of the world’s GDP using as weights each country’s relative GDP converted to international dollars at purchasing-power-parity (PPP) – is estimated to post a growth rate of 3.4% in 2019. Growth in global output is forecasted to slightly accelerate to an annual rate of 3.6% in both 2020 and 2021, the same as the growth rate in 2018.

Given the relative economic size and expected output growth in each of the major regional blocs, the contribution of each

region to global economic growth is computed so that we may identify the distribution of worldwide growth and, consequently, the allocation of global demand among geographic areas along with its changing pattern over the forecast horizon.

The baseline forecast calls for output in the countries of the North America region to advance by 3.1% in both 2020 and 2021. Thus, the region will contribute 19.5% in 2020 and 17.5% in 2021 to the growth of global demand as measured by worldwide GDP.

III. GLOBAL FORECAST AND REGIONAL CONTRIBUTIONS TO GROWTH

92 94 96 98 00 02 04 06 08 10 12 14 16 18 20 92 94 96 98 00 02 04 06 08 10 12 14 16 18 20

70

35

0

-35

-70

70

35

0

-35

-70

20

10

0

-10

-20

50

25

0

-25

-50


Table 2 | Contribution of Regions to Global Growth

R E G I O NPercentage Points Contribution Relative Contribution, Percent

2018 2019 2020 2021 2018 2019 2020 2021

EUROPEAN UNION (EU28) 0.40 0.26 0.28 0.32 10.9 9.6 8.1 8.4

Euro Area (euro19) 0.25 0.17 0.16 0.19 6.8 6.0 4.7 5.0

Non-Euro Members (9) 0.15 0.10 0.12 0.13 4.1 3.6 3.4 3.4

OTHER EUROPE 0.15 0.05 0.13 0.18 4.0 1.9 3.8 4.7

NORTH AMERICA 0.55 0.48 0.67 0.66 15.1 17.3 19.5 17.5

United States 0.47 0.41 0.59 0.57 12.9 15.0 17.3 15.1

SOUTH AMERICA 0.01 0.06 0.11 0.12 0.4 2.3 3.2 3.3

ASIA & PACIFIC INDUSTRIAL 0.13 0.09 0.10 0.12 3.5 3.3 2.9 3.1

EMERGING ASIA 2.27 1.70 1.99 2.19 62.3 62.0 58.0 58.1

China & India 1.88 1.43 1.66 1.83 51.7 51.9 48.3 48.5

MIDDLE EAST & AFRICA 0.09 0.06 0.11 0.14 2.4 2.1 3.3 3.7

WORLD GROWTH1 3.6 2.7 3.4 3.8 100.0 100.0 100.0 100.0

1Sum of Regional Contributions Source: www.e-forecasting.com

In the Euro Area, the combined real GDP of the 19 members of the European Union (EU) that use the euro as common currency, GDP is forecast to edge up 1.6% in 2020 and 1.5% in 2021. As a result, the Euro Area will contribute 4.7% in 2020 and 5% in 2021 to the growth of worldwide demand measured by global GDP.

In the Emerging Asia region – which includes the two most populous and fastest growing countries, China and India – growth in output is predicted to average 5.5% in both 2020 and 2021, faster than any other economic bloc. Accordingly, the Emerging Asia region will contribute 58% in 2020 and 58.1% in 2021 to the growth of global GDP.

In the industrial bloc of Asia and Pacific region – which includes Japan, Korea, Australia and New Zealand - growth in output is forecast to post a rate of 1.4% in 2020 and 1.5% in 2021. Consequently, the Asia and Pacific industrial club will contribute just 2.9% in 2020 and 3.1% in 2021 to the growth of global demand as measured by worldwide GDP.

Real GDP in the major countries in South America is forecast to advance by 2.1% in 2020 and 2.4% in 2021. As a result, South America is expected to have a positive contribution to the growth of global demand of 3.2% in 2020 and 3.3% in 2021.



In June, the U. S. economic recovery that began in June of 2009 hit its tenth year, matching the previous longest recovery cycle of March 1991-March 2001. There are signs of

slowdown but no signs of recession in the foreseeable future, making the current cycle the expected longest recovery period in U. S. history. What is equally noteworthy is that despite the fifty-year low unemployment, there is no inflationary pressure

in sight. We believe that both the U. S. labor and businesses have matured as a result of the great recession shock and are conducting themselves with great restraint. Similar to the Germans, no one seems to be interested in living in an inflationary cycle. The national net worth has rebounded from the biggest drop since 2004, by $3.96 trillion in 2018: Q1 to $4.69 trillion growth in 2019: Q2 with equity and home prices

U. S. Economy Breaks Recovery Cycle Record—But Are Cracks Starting To Appear?By Jamal Nahavandi

Dr. Nahavandi is Associate Professor of economics at Pfeiffer University School of Graduate Studies, special izing in Business Economics, International Business, and Healthcare Economics. The information in these forecasts is gathered by the Journal from sources it considers reliable. Neither the Journal nor the individual institutions providing the data guarantee accuracy, nor do they claim that use of the data appearing herein will enhance the business or investment performance of companies or individuals who use them.

PARTICIPANTS | Conf. Board = Conference Board, New York, New York; Fannie Mae = Fannie Mae, Washington, D.C.; GSU - EFC = Georgia State

University, Economic Forecasting Center, Atlanta, Georgia; ICIMS=Holmdel, New Jersey; Moody’s Economy = Moody’s Economy.com, Westchester,

Pennsylvania; Mortgage = Mortgage Bankers Association, Washington, D.C.; NAM = National Association of Manufacturers, Washington, D.C.;

Perryman Gp = The Perryman Group, Waco, Texas; Royal Bank of Canada, Toronto, Ontario, Canada; S&P = Standard & Poor’s, New York, New York; US

Chamber = U.S. Chamber of Commerce, Washington, D.C.; Wells Fargo = Wells Fargo Bank, San Francisco, California.


PARTICIPANTS GROSS DOMESTIC PRODUCT (GDP)Bil. of Chained 2012 Dollars | Level

PERSONAL DISPOSABLE INCOMEBased on GDP Concept | Curr. Bil. of $, Level (SAAR)

QUARTER 19-3 19-4 20-1 20-2 19-3 19-4 20-1 20-2

Conf. Board | Gad Levanon 19093.35 19190.79 19289.63 19383.52 16569.55 16758.45 16945.45 17126.79

Fannie Mae | Doug Duncan 19097.00 19194.00 19282.00 19373.00 16187.00 16373.00 16576.00 16779.00

GSU-EFC | Rajeev Dhawan 19081.87 19167.45 19259.17 19358.16 16264.65 16446.23 16633.53 16809.99

ICIMS | Josh Wright 19100.31 19195.10 19280.90 19371.84 16259.15 16421.74 16553.11 16702.09

Moody’s Economy | Mark Zandi 19107.85 19214.82 19306.65 19383.70 16304.08 16497.17 16667.96 16833.93

Mortgage | Mike Fratantoni NA NA NA NA NA NA NA NA

NAM | Chad Moutray 19110.00 19215.00 19305.00 19385.00 16305.00 16500.00 16668.00 16835.00

Perryman Gp | Ray Perryman 19189.20 19320.60 19441.50 19570.40 16270.40 16444.90 16603.80 16770.40

Royal Bank of Canada | Craig Wright NA NA NA NA NA NA NA NA

S & P | Beth Ann Bovino 19027.88 19116.12 19175.72 19271.79 16229.43 16391.63 16558.66 16754.36

US Chamber | J.D. Foster 19050.94 19172.06 19287.28 19396.67 NA NA NA NA

Wells Fargo | Jay Bryson 19131.20 19248.30 19348.70 19451.20 NA NA NA NA

Consensus 19098.96 19203.42 19297.66 19394.53 16298.66 16479.14 16650.81 16826.44

PARTICIPANTS PERSONAL CONSUMPTION EXPENDITUREBased on GDP Concept | Curr. Bil. of $ | Level (SAAR)

CONSUMER PRICE INDEX 1982-1984=100 | LEVEL

QUARTER 19-3 19-4 20-1 20-2 19-3 19-4 20-1 20-2





Moody’s Economy | Mark Zandi NA NA NA NA 256.95 258.35 259.58 260.68


NAM | Chad Moutray NA NA NA NA 256.95 258.35 259.58 260.68




US Chamber | J.D. Foster NA NA NA NA 255.20 256.16 257.55 258.89

Wells Fargo | Jay Bryson NA NA NA NA 256.90 258.30 260.00 261.30

Consensus 14651.07 14810.88 14955.71 15110.10 256.54 257.79 259.16 260.42


PARTICIPANTS GROSS DOMESTIC PRODUCT (GDP)Bil. of Chained 2012 Dollars | Level

PERSONAL DISPOSABLE INCOMEBased on GDP Concept | Curr. Bil. of $, Level (SAAR)

QUARTER 19-3 19-4 20-1 20-2 19-3 19-4 20-1 20-2







NAM | Chad Moutray 19110.00 19215.00 19305.00 19385.00 16305.00 16500.00 16668.00 16835.00





Wells Fargo | Jay Bryson 19131.20 19248.30 19348.70 19451.20 NA NA NA NA

Consensus 19098.96 19203.42 19297.66 19394.53 16298.66 16479.14 16650.81 16826.44

PARTICIPANTS PERSONAL CONSUMPTION EXPENDITUREBased on GDP Concept | Curr. Bil. of $ | Level (SAAR)

CONSUMER PRICE INDEX 1982-1984=100 | LEVEL

QUARTER 19-3 19-4 20-1 20-2 19-3 19-4 20-1 20-2





Moody’s Economy | Mark Zandi NA NA NA NA 256.95 258.35 259.58 260.68


NAM | Chad Moutray NA NA NA NA 256.95 258.35 259.58 260.68





Wells Fargo | Jay Bryson NA NA NA NA 256.90 258.30 260.00 261.30

Consensus 14651.07 14810.88 14955.71 15110.10 256.54 257.79 259.16 260.42

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PARTICIPANTS TOTAL LIGHT VEHICLE SALESFOR & DOM. | Mil. of Units (SAAR)

CHAINED PRICE INDEX2000 | Level

QUARTER 19-3 19-4 20-1 20-2 19-3 19-4 20-1 20-2

Conf. Board | Gad Levanon 17.00 17.00 17.00 17.00 NA NA NA NA

Fannie Mae | Doug Duncan 17.00 17.10 17.10 17.20 NA NA NA NA


ICIMS | Josh Wright 16.88 16.83 16.80 16.73 NA NA NA NA



NAM | Chad Moutray 16.90 16.85 16.80 16.75 NA NA NA NA





Wells Fargo | Jay Bryson 16.70 16.60 16.60 16.50 112.70 113.20 113.80 114.40

Consensus16.83 16.80 16.75 16.78 112.65 113.20 113.79 114.33

PARTICIPANTS FEDERAL FUNDS RATE %

AAA CORPORATE BOND RATE %

QUARTER 19-3 19-4 20-1 20-2 19-3 19-4 20-1 20-2







NAM | Chad Moutray 2.13 1.88 1.88 2.13 3.82 4.19 4.51 4.76



S & P | Beth Ann Bovino 2.38 2.38 2.45 2.63 NA NA NA NA



Consensus 2.30 2.26 2.27 2.29 3.70 3.86 3.96 3.95


PARTICIPANTS TOTAL LIGHT VEHICLE SALESFOR & DOM. | Mil. of Units (SAAR)

CHAINED PRICE INDEX2000 | Level

QUARTER 19-3 19-4 20-1 20-2 19-3 19-4 20-1 20-2

Conf. Board | Gad Levanon 17.00 17.00 17.00 17.00 NA NA NA NA



ICIMS | Josh Wright 16.88 16.83 16.80 16.73 NA NA NA NA



NAM | Chad Moutray 16.90 16.85 16.80 16.75 NA NA NA NA






Consensus16.83 16.80 16.75 16.78 112.65 113.20 113.79 114.33

PARTICIPANTS FEDERAL FUNDS RATE %

AAA CORPORATE BOND RATE %

QUARTER 19-3 19-4 20-1 20-2 19-3 19-4 20-1 20-2







NAM | Chad Moutray 2.13 1.88 1.88 2.13 3.82 4.19 4.51 4.76



S & P | Beth Ann Bovino 2.38 2.38 2.45 2.63 NA NA NA NA



Consensus 2.30 2.26 2.27 2.29 3.70 3.86 3.96 3.95

UNEMPLOYMENT Civilian % (SAAR)

NON-RESIDENTIAL FIXED INVESTMENT(Bil. of Chained 2012 Dollars)

19-3 19-4 20-1 20-2 19-3 19-4 20-1 20-2

3.60 3.50 3.50 3.40 2846.82 2880.42 2914.86 2949.00

4.00 4.00 4.00 4.00 2824.00 2842.00 2867.00 2888.00

3.68 3.74 3.84 3.74 2824.41 2846.05 2870.71 2898.16

3.50 3.60 3.60 3.70 NA NA NA NA

3.47 3.36 3.30 3.21 2880.59 2920.30 2949.04 2965.89

NA NA NA NA NA NA NA NA

3.60 3.50 3.80 4.00 2835.00 2880.00 2920.00 2950.00

4.00 4.20 4.40 4.30 2864.30 2916.60 2942.30 2981.10

NA NA NA NA NA NA NA NA

3.51 3.50 3.47 3.52 2822.22 2837.07 2860.48 2885.81

3.80 3.90 3.90 4.00 2838.19 2871.08 2903.02 2934.48

3.70 3.60 3.50 3.50 2831.50 2855.00 2882.70 2908.10

3.69 3.69 3.73 3.74 2840.78 2872.06 2901.12 2928.95

INDUSTRIAL CAPACITY UTILIZATION(SAAR)

MONEY SUPPLY M2Bil. of $, Level (SAAR)

PRIVATE HOUSING START TOTALMil. of Units (SAAR)

19-3 19-4 20-1 20-2 19-3 19-4 20-1 20-2 19-3 19-4 20-1 20-2

NA NA NA NA NA NA NA NA 1.21 1.21 1.22 1.22


76.56 76.17 75.90 75.67 14655.54 14772.41 14910.62 15059.02 1.22 1.24 1.23 1.23

NA NA NA NA 14724.94 14872.19 15020.91 15171.12 1.20 1.17 1.16 1.18

77.34 77.50 77.46 77.33 14859.74 15143.37 15411.93 15669.15 1.37 1.40 1.40 1.39

NA NA NA NA NA NA NA NA NA NA NA NA

79.30 79.50 79.50 79.40 14660.00 14860.00 15145.00 15412.00 1.26 1.28 NA 1.30

78.50 78.80 78.20 78.10 14746.10 14906.60 15087.00 15257.70 1.30 1.28 1.32 1.37

NA NA NA NA NA NA NA NA NA NA NA NA

79.00 79.20 79.17 79.34 NA NA NA NA 1.30 1.30 1.31 1.31


NA NA NA NA 14800.00 14950.00 15125.00 15300.00 1.27 1.28 1.28 1.29

78.14 78.23 78.05 77.97 14741.05 14917.43 15116.74 15311.50 1.26 1.27 1.28 1.29

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making up the majority of the gain. This phenomenon points to further deepening of wealth distribution disparity with long-term implications in term of consumption, savings, education, social mobility, and productivity growth. Homeownership is rising only among those who can afford pricier homes. The Bureau of Economic Analysis reported the first quarter of GDP growth at 3.2 percent, well above the Dow Jones expected 2.5 percent. Our consensus puts the growth in the GDP at a conservative 1.55 percent between 2019: Q3 and 2020: Q2.

CONSUMERS: HOME OWNERSHIP AT ALL TIME LOW

Thanks to the Fed slowing down in reducing its balance sheet to tighten the credit market, mortgage rates have trended down. However, a mix of low stock and high priced new homes is making home purchases out of reach for many households. Homeownership hit another all-time low at 62.9 percent in 2016: Q2, matching the same rate in 1965: Q1. Homeownership has rebounded somewhat to 64.2 percent in 2019: Q1, well below the 69.2 percent we saw in 2004: Q2. Historically, homeownership has been the major source of wealth for most U. S. Households, with the smoothing effect of consumption through business and life cycles. The low homeownership rate will contribute to volatility in consumption spending both in the short-run and the long-run during the retirement age. Consensus puts the growth in personal disposable income at 3.24 percent by the middle of 2020, slightly above the personal consumption expenditure of 3.13 percent, leaving practically no surplus to contribute to household savings. Consensus expects unemployment to reverse course between 2019: Q3 and 2020: Q2 from 3.69 percent to 3.74 percent. The Conference Board Consumer Confidence Index dropped to 97.9 in June, down from 100 in May, due to the rising trade tensions and low new job creation. Jay Bryson of Wells Fargo points to the shift in the age distribution of U. S. households that is expected to impact their consumption spending mix. Due to the aging of the population, healthcare, education, and food are expected to gain, while transportation, housing, and apparel are expected to experience slippage. Some of the changes in consumer spending are due to the change in relative prices and not the consumption mix. For example, in the past thirty years, food prices have doubled while the price of healthcare has risen threefold. The Chained Price Index and the Consumer Price Index are expected to rise by 1.5 percent by mid-2020, consistently missing the Fed’s target of 2 percent. However, Sarah House, Wells Fargo’s senior economist, predicts a maximum 0.4 percent CPI inflation hike if the President’s 25

percent tariff on Chinese imports is put into effect. On the other hand, if importers do not pass the full tariffs to consumers, the inflationary effect on the CPI could be less and only a one-time transitory shift at best. A stronger dollar and devalued Renminbi is expected to help keep the price of imports in check.

FIRMS: CORPORATE PROFITS FALL AND NEW JOB CREATION DROPS

Growth in corporate profit has reversed course with a 3.5 percent decline in the first quarter of 2019 and zero change in the last quarter of 2018. Nonfarm business labor productivity jumped by 3.4 percent in the first quarter of 2019, with a 2.4 percent average for 2018: Q1–2019: Q1. Unit labor cost declined by 1.7 percent in the first quarter of 2019, mitigating the impact of hourly wage growth. The drop in corporate profit could be the culprit behind the surprisingly low new job creation in May of 75, 000. In the first five months of 2019, average new job creation stands at 164, 000, compared with the average of 223, 000 in 2018, signaling the winding down of the 10-year expansion. The St. Louis Fed predicts a 2.8 percent decline in productivity growth between 2020 and 2040 as baby boomers retire and take their human capital with them. Since the first quarter of 2018, new business formation has reversed course after eight years of steady growth. Consensus expects total light-vehicle sales to decline by 0.32 percent by mid-2020 to 16.78 million units. Non-residential fixed investment is expected to rise by 3.1 percent by 2020: Q2, up from the 2.84 percent we reported in the last issue. Industrial capacity utilization is expected to decline slightly to 78 percent. Consensus expects the triple-A corporate bond rate to rise from 3.7 percent in 2019: Q3 to 3.95 percent in 2020: Q2, increasing the cost of private corporate borrowing slightly. Private housing starts are expected to slowdown in growth relative to what we reported in the last issue, from 1.29 million to 1.26 million units, with a focus on more luxury homes. Growth in urbanization is making land more scarce, pushing up the value of homes constructed on limited land. This phenomenon supports the prediction that in the future, the less affluent will live in the suburbs with a longer commute.

INTEREST, CREDIT, AND THE FED: RATES EXPECTED TO HOLD

The market expects the Fed to keep the key interest rate steady in June but reduce rates in July amid restrained


inflation and declining new job creation. The Fed has dropped the word ‘patient’ from its announcements, signaling its willingness to react if the economy slows down too fast. The Fed already has slowed down offloading the bonds on its balance sheet to ease monetary policy. The consensus puts the Federal Fund Rate at 2.29 percent by mid-2020. Rajeev Dhawan, of Georgia State University, points to two shocks to the economy related to the Boeing 737 Max planes and the stress in the oil market due to geopolitical issues in Venezuela, Libya, and Iran. Dhawan credits the 2017 tax cuts and Job Acts for strong the 2018 growth that is now wearing off. Shocks to the equity market counter balanced growth in employment and income, leaving retail in a weak position overall. The closely watched Empire State Manufacturing Index, compiled by the New York Fed, tumbled to 8.6 from a 17.8 reading in May. The Institute for Supply Management and the Markit PMI point to either flat or contracting figures in Manufacturing, a sector that has created only 500, 000 new jobs since President Trump entered the White House. Just 13, 000 of those were created in 2019. Although many believe that the Fed should, and will, reduce the key interest rate, not everyone agrees that it will help if the economic slowdown is driven by tariffs and a tight labor market.

CONCLUSIONThe U. S. economy is doing well in terms of employment,

income, and stable prices. There are also some headwinds that are expected to impact the long-term welfare of the population. The decline in home ownership and rising equity wealth will contribute to a growing wealth distribution disparity that has been plaguing the U. S. economy for some time. If not

addressed, wealth disparity will have long-term consequences ranging from sluggish labor productivity, social mobility, the decline in physical and psychological health, and dependency on opioids and alcohol. The population with the most wealth are increasing their fertility, spending heavily on educating their children and are working longer hours while middle-class working hours have declined, making tens of millions underemployed with declining productivity. In years past, a loan officer job meant full-time employment with a middle-income lifestyle. This segment of the population is losing ground, and increasingly feels insecure about their current and retirement years. The data from the Federal Reserve point to the spread of the ‘left behind’ to all elements of American society except the top ten percent. Income stagnation of the middle and upper middle class that make up the engine of economic growth is worrisome. The prices of what the middle and upper middle class buys such as homes, education, and autos are rising faster than income, pushing this population segment to tap into costly debt. As my elders would say, many families are keeping their rosy cheeks by slapping themselves in the face. The Atlanta Fed points to rising property tax for the majority of single-family homeowners. Only 25 percent of the population is experiencing income growth faster than growth in property taxes. Forty-eight percent of American households over the age of 55 still have no retirement savings. The median 401(k) account value for an investor age 65 and older is a meager $58, 035 at Vanguard. With tens of millions retiring or near retirement with no savings, we expect tremendous pressure on their children and their families who are already struggling to put their children through college and pay off their mounting debt.


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