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Enabling the Consumer Driven Value Chain

Consumer goods companies face extreme pressure to simultaneously grow revenue and

margins in a challenging environment of value-conscious consumers and rising commoditycosts.

To achieve this effectively, companies need to get the right mix of:

Consumer centric sales marketing and supply chains capability

Best-in-class sales marketing and supply chain programs

Rapid product innovation

These requirements need to be supported by robust insight and planning processes such as

S&OP or use of Demand Signal Repositories to be more consumer/customer centric.

And while most organisations obtain the information necessary to support these objectives,

efficiently integrating insight and planning has proved to be difficult .

Transforming data into actionable insight for sales, supply, marketing and finance requires

deep industry knowledge and experience.

Consumer Goods companies such as Heineken, Twinings, Revlon, Gu Chocolate Puds,

CP Foods and Tayto use Exceedra to drive out complexity to achieve:

Increased sales Better returns from promotional investment

Increases in distribution and availability

Reduced working capital Reduced supply chain cost

Improved asset utilisation and supply chain cost

Improved productivity

All of which needs to be supported by robust demand and supply plans.

Particularly making the information easily understood and acted upon by the right people

in the business, so as to enable the support of efficient organisational and departmental

planning.

Whether its handling challenges around branded or own-label businesses, or if youre alarge or small industry player, the importance of insight driven planning is critical to all

companies that wish to work well in todays consumer good market.

Commercial Insight and Planning

o Gain deeper and more real-time insight into sales information.Saves days of

time in data analysis and planning around Trade promotional management,

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Joint business planning, budgeting forecasting and S&OP. Leading to

optimal sales performance.

Supply Chain Insights and Planningo Provides a single view of forward demand forecasts and constrained supply

and production requirements. Improves speed of corrective actions, as well

as improved planning capabilities. Category Insight & Planning

o Deeper insight into how categories are performing supporting insight

discovery across the multiple data sets within the organisation. Saving daysof effort to get to insights faster and better.

New Product Development and Recipe Management

o Shorten development times in NPD process and ensure organisational

alignment though streamlined processes. Enables full control of this key

asset of any consumer good company.

EPOS Demand Signal Repository

o Improve services to retailers via real-time demand data distilled into useful,

actionable information. Leads to maximised availability andresponsiveness. Single location for EPOS data and other market data.

Overview

Demand forecasting is the area of predictive analytics dedicated to understanding consumer

demand for goods or services. That understanding is harnessed and used to forecast

consumer demand. Knowledge of how demand will fluctuate enables the supplier to keepthe right amount of stock on hand. If demand is underestimated, sales can be lost due to the

lack of supply of goods. If demand is overestimated, the supplier is left with a surplus that

can also be a financial drain. Understanding demand makes a company more competitive in

the marketplace. Understanding demand and the ability to accurately predict it isimperative for efficient manufacturers, suppliers, and retailers. To be able to meet

consumers needs, appropriate forecasting models are vital. Although no forecasting model

is flawless, unnecessary costs stemming from too much or too little supply can often beavoided usingdata mining methods. Using these techniques, a business is better prepared

to meet the actual demands of its customers.

Understanding Consumer Demand

Demand Anomalies

In demand forecasting, as with most analysis endeavors, data preparation efforts are

critical. Data is the main resource in data mining; therefore it should be properly prepared

before applying data mining and forecasting tools. Without proper data preparation, the oldadage of "garbage in, garbage out" may apply: useless data results in meaningless forecast

models. Major strategic decisions are made based on the demand forecast results. Errors

and anomalies in the data used to create forecast models may impact the models ability to

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forecast. These errors give rise to the potential for bad forecasts, resulting in losses. With

properly prepared data, the best possible decisions can be made.

There are several sources for problems with data. Data entry errors are one possible sourceof error that can adversely affect the demand forecasting efforts. Basicstatistical

summaries and graphing procedures can often make these types of error apparent. Artificialdemand shifts are another error source. For example, consumer response to a promotional

offer may temporarily boost sales of an item. Without a similar promotion, the sameincrease cannot be expected in the future. Some uncontrollable factors have the ability to

influence consumer demand as well. A factor such as economic conditions may tend to

impact demand. An unusually mild winter will likely cause lower energy demand.Accounting for these influences of demand can help fine tune forecast modeling.

Seasonal Fluctuations

Every business sees seasonal fluctuations. Holidays and weather changes influence

products and services that consumers want. While it is extremely important to account forhow seasonal changes affect demand, it may be possible to benefit further from this.

Understanding how seasonal factors affect consumers helps businesses position themselves

to take advantage.

Forecasting Consumer Demand

Analysis Tools

A wide variety of analysis tools can be used to model consumer demand - from traditional

statistical approaches toneural networks anddata mining. Using these demand models

enables estimation of future demand: forecasting. Possibly, a combination of multiple typesof modeling tools may lead to the best forecasts.

Time series analysis is a statistical approach applicable for demand forecasting. This

technique aims to detect patterns in the data and extend those patterns as predictions. TheARIMA model, or autoregressive integrated moving average, in particular is used both to

gain understanding of the patterns in data and to predict in the series. Different parameters

are used to detect linear, quadratic, and constant trends.

Other approaches for building forecast models areNeural Networks and Data Mining,which are capable of modeling even very complex relationships in data. Demand

forecasting is a very complex issue for which these methods are well suited.MultilayerPerceptrons andRadial Basis Functionneural networks,Multivariate Adaptive RegressionSplines,Machine Learning, and Tree algorithms can all generate predictive models for this

application.

StatSoft has a 35 partvideo series on data mining that demonstrates many of these

approaches for model building. While the video series mainly uses credit risk data, theseries can help with learning the concepts.

http://statsoft.com/textbook/basic-statistics/http://statsoft.com/textbook/basic-statistics/http://statsoft.com/textbook/basic-statistics/http://statsoft.com/textbook/graphical-analytic-techniques/http://statsoft.com/textbook/neural-networks/http://statsoft.com/textbook/neural-networks/http://statsoft.com/textbook/data-mining-techniques/http://statsoft.com/textbook/data-mining-techniques/http://statsoft.com/textbook/time-series-analysis/http://statsoft.com/textbook/neural-networks/http://statsoft.com/textbook/data-mining-techniques/http://statsoft.com/textbook/neural-networks/#multilayerhttp://statsoft.com/textbook/neural-networks/#multilayerhttp://statsoft.com/textbook/neural-networks/#multilayerhttp://statsoft.com/textbook/neural-networks/#radialhttp://statsoft.com/textbook/neural-networks/#radialhttp://statsoft.com/textbook/neural-networks/#radialhttp://statsoft.com/textbook/multivariate-adaptive-regression-splines/http://statsoft.com/textbook/multivariate-adaptive-regression-splines/http://statsoft.com/textbook/multivariate-adaptive-regression-splines/http://statsoft.com/textbook/machine-learning/http://statsoft.com/textbook/machine-learning/http://statsoft.com/textbook/classification-and-regression-trees/http://www.youtube.com/statsoft#p/c/B804A810436AFB03/0/1VQ1qzgdRSshttp://www.youtube.com/statsoft#p/c/B804A810436AFB03/0/1VQ1qzgdRSshttp://statsoft.com/textbook/basic-statistics/http://statsoft.com/textbook/basic-statistics/http://statsoft.com/textbook/graphical-analytic-techniques/http://statsoft.com/textbook/neural-networks/http://statsoft.com/textbook/data-mining-techniques/http://statsoft.com/textbook/time-series-analysis/http://statsoft.com/textbook/neural-networks/http://statsoft.com/textbook/data-mining-techniques/http://statsoft.com/textbook/neural-networks/#multilayerhttp://statsoft.com/textbook/neural-networks/#multilayerhttp://statsoft.com/textbook/neural-networks/#radialhttp://statsoft.com/textbook/multivariate-adaptive-regression-splines/http://statsoft.com/textbook/multivariate-adaptive-regression-splines/http://statsoft.com/textbook/machine-learning/http://statsoft.com/textbook/classification-and-regression-trees/http://www.youtube.com/statsoft#p/c/B804A810436AFB03/0/1VQ1qzgdRSs

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Systematic Patterns vs. Trends

Generally, demand patterns consist of some basic classes of components, seasonality, and

trend. Seasonality refers to the portion of demand fluctuation accounted for by areoccurring pattern. The pattern repeats systematically over time. Trend is the portion of

behavior that does not repeat. For example, a trend may show a period of growth followedby a leveling off. In retail sales, seasonality will likely find patterns that repeat every year.With sufficient data, other seasonality trends may manifest across multiple years.

Forecasting Techniques

Once adequate predictive models are found, these models can then be used to forecast

demand. A demand forecast model may actually be an ensemble of multiple modelsworking together. This technique of combining models often results in better predictive

accuracy. When one model gets off track, the ensemble as a whole counteracts.

As more data accumulate about consumer behavior, demand forecast models should beupdated. This will be a continual effort monitoring and modeling demand in order to beconstantly aware of changes. Failing to update forecast models and take advantage of all

the information available will likely prove to be a costly mistake.

Inventory Management

Using up-to-date demand forecast models, inventory management becomes a much simplertask. The forecast models offer insight into when shifts will occur, but more importantly,

how big the shift will be. Using demand forecast models; inventory and human resources

can be properly planned and managed well in advance and with fewer surprises.