Bba 1584 planning n forecasting

Planning and Forecasting

What is Demand Management?

• Demand Management is one that takes a complete

view of a business

• It means discovering markets, planning products

and services for those markets and then fulfilling

these customer demands

• It is an integrative set of business processes,

across, not just the enterprise, but across all its

trading partner network ( both customers and

suppliers)

What does Demand Management

involve? • Discovering and understanding your market

• Establishing your customers needs and expectations and what draws them to your business

• Challenge of managing what, when, and how a product/service is designed, made, distributed, displayed , promoted and serviced

• Doing the pricing and inventory optimization at various levels of market and channels segmentation

• Satisfying customers on product, price, delivery and post-sales services

Managing Demand and Supply

• In any operating organization, it is important

to manage both demand and supply singly

or together by:

• Managing Demand thro various options

• Managing Supply thro various options

• All chosen options have their own

implications on customer service levels and

different costs incurred

Managing Demand

• Thro capacity reservation by shifting excess demand to a future period without losing it – by doing advance booking or appointments for future times

• Thro differential pricing to reduce peak demands( higher prices e.g. movie tickets) or build demand in off-season by lowering prices/special discounts)

• Thro advertising and sales promotions to even out demand patterns at different times( lower telecom rates for night use)

• Thro complementary products to even out seasonal demand products – e.g. woolen and cotton garments; winter creams and suntan lotions; lawn mowers and snow ploughs

Managing Demand and Supply

-cost implications Alternative Cost Implication

Managing demand Capacity reservation Planning & scheduling

costs

Influencing demand Marketing oriented

costs

Managing supply Build inventory Inventory holding costs

Backlog/backorder/

stock-out

Shortage/loss of

goodwill

Overtime/under-time Overtime costs, Loss of

productivity

Varying shifts Shift change costs

Hiring/layoff workers Training/hiring costs,

employee morale

Subcontract/outsource Transaction costs

Debottlenecking/adding

new capacity

Investment,

debottlenecking costs

What is Demand Planning?(1)

• This is a subset of Demand Management

• It is a business planning process that enables

sales teams(and customers) to develop demand

forecasts and inputs to feed various planning

processes, production, inventory planning,

procurement planning and revenue planning

• Based on estimated product demand, a firm can

plan for deployment of its resources to meet this

demand

• It is a bottom-up process as different from any top-

down management process


• It is also seen as a multistep operational SCM

process to create reliable demand forecasts

• Effective demand planning helps to improve

accuracy of revenue forecasts, align inventory

levels in line with demand changes and

enhance product-wise/channel-wise profitability

• Its purpose can be seen as to drive the supply

chain to meet customer demands thro effective

management of company resources


• For FMCG/retailing sectors, demand planning

takes a special meaning requiring integration of

point-of-sale information to flow back to the

manufacturer

• Besides getting such customer level demand data

thro distribution channel partners, key is to

leverage it by maximizing success in forecasting

efforts and accuracy( without normal distortions like

the bull-whip effect)

Benefits of Demand Planning

• Higher service levels and more responsive

to actual demand

• Reduced stock levels and inventory costs

• Improved purchase planning and

subsequent reduction in supply input costs

• Enhanced capacity utilization of production

facilities and logistics assets

• Focused promotion and product

planning/assortment/stocking levels at retail

level for FMCG products

Forecasting Factors

• Time required in future

• Availability of historical data

• Relevance of historical data into future

• Demand and sales variability patterns

• Required forecasting accuracy and likely errors

• Planning horizon/lead time for operational moves

Types of Forecasts

• Economic Forecasts- projections of economic growth, inflation rates, money supply based on economic and fiscal data trends along with policy interventions

• Demographic Forecasts- projections of population in aggregate and disaggregate form forecasts using birth and death rates in each case

• Technological Forecasts- predicting technological change e.g. in cloud computing or electronics sectors et al

• Other Forecasts- weather, earthquakes, tsunami et al

• Business Forecasts- involving demand and sales forecasts – our primary interest in this DPF course

What is Demand Forecasting?(1)

• Demand Forecasting is predicting the future

demand for products/services of an

organization

• To forecast is to estimate or calculate in

advance

• Since forecasts are estimates and involve

consideration of so many price and non-

price factors, no estimate is necessarily

100% accurate

What is Demand Forecasting/(2)

• Demand forecasting involves estimating future overall market demand for the proposed products/range

• This involves extensive market and marketing research into existing and new markets, end applications, current market size and future demand potential, market segmentation, customer profiling/attitudes/preferences et al

• Purpose is to finally help business decisions on how to cater to the overall market and plan its marketing mix and product-market positioning et al

• Demand forecasting is essentially an outward/external looking process

• Important as forms basis for sales forecasting operational planning and actions

Why Demand Forecasting?

• To help decide on facility capacity planning and

capital budgeting

• To help evaluate market opportunities worthy of

future investments

• To help assess its market share amongst other

competitors

• To serve as input to aggregate production planning

and materials requirement planning

• To plan for other organizational inputs ( like

manpower, funds and financing) and setting

policies and procedures

Key Functions of Forecasting

• Its use as an estimation tool

• Way to address the complex and uncertain

business environment issues

• A tool to predicting events related to operations

planning and control

• A vital prerequisite for the overall business

planning process

Forecasting Characteristics

• By its very nature, forecasting always has errors;

forecasts rarely match actual demand/sales; forecast

accuracy and errors are real issues

• Their chosen time horizon also determines

accuracy with shorter periods having higher accuracy;

the constant need to reduce lead times also puts focus

on shorter planning horizons( as in lean

manufacturing/JIT environments)

• Aggregate demand forecasts are more accurate

than market segmental forecasts( e.g. all Maruti 800

cars versus red Maruti 800s; all paints versus blue

color paints; all toothpastes versus herbal

toothpastes); these have implications at different

levels/stages of the supply chain

Forecasting Horizon-focus

• Short term forecasts – say for next 1-2 months for current production planning and scheduling; for specific products, machine capacities and deployment, labor skills and usage, cash inventories ; operational focus

• Medium term forecasts – say for next 3-12 months for plant level planning for product/volume changes requiring redeployment of resources; for product groups, departmental capacities, work force management, purchased materials and inventories; tactical focus

• Long term forecasts – 1 year to 3 years for planning a new plant or facility requiring major investments and other resources for both new and old product lines; strategic focus

Forecasting Horizon- methodology

• Short-term forecasting( 1 day to 3 months) for production planning needing disaggregated product forecasts with high accuracy levels; primarily uses time series data methods

• Medium-term forecasting( 3 months to 12-24 months) useful for aggregate sales and operational planning; also for seasonal business operations; uses both time series and causal forecasting models

• Long –term forecasting( beyond 24 months) useful for aggregate business planning for capacity and site/location decisions; uses judgment and causal models

Forecasting for Business

• Demand forecasting – to establish the current

total size for any product/service market and its

future growth potential and trends over time

• Sales forecasting- required for a firm to plan its

overall business operations within the overall

market size and potential for its range of products

• Product-life cycle forecasting- to assess the

likely demand development and trends as they

move from introduction -> growth-> maturity ->

decline phases

• All above forecasting types are to be looked at

Sales Forecasting

• Within overall demand, firm needs to establish its sales forecast to help operations

• Basis of sales forecasting is assessment of market share that firm can carve out of the total market given its past sales as also current marketing strategies

• Firming up of sales forecasts is a function of available capacity, plant performance, plant resources and stocks

• Sales forecasting is essentially an inward/internal process

• Forecasting from now is seen from operational context

Demand Forecast and Sales

Forecast(1)

• Demand forecasts relate to the total demand for a

product/service offered

• Demand forecasts consider various factors influencing

the overall demand for a product/service including

economic and demographic factors, customer needs and

expectations, market segmentation, disposable incomes

et al

• Sales forecasts are reflection of actual sales expected

and consequent share of the total market demand

• Sales forecasts also consider various supply-related

specific company factors like capability, product range and

capacity

Demand Forecast and Sales

Forecast(2) • It is important to understand separately the need

for demand and sales forecasts linked to their

purpose

• Demand forecasts are called for while doing

market entry exercises and planning long term

investments in new /added capacities

• Sales forecasts are needed to provide the input

basis for all production planning and supply chain

operations

• During this DPF course, demand and sales

forecasts terms may be used interchangeably, but

the clear distinction should be understood

Demand Forecasting Issues(1)

• Forecasting is the deliberate attempt to predict the

future- in all its dimensions !

• Crystal ball gazing or making astrological predictions are

also exercises in forecasting the future

• Is both an art and science as based on significant

behavioral and unstructured issues and an analytical

exercise using scientific principles

• Despite its limitations, essential for planned business

operations


• All decisions need information about future

circumstances

• Best we can do is to forecast these circumstances

• Since business decisions are driven by what the

market needs, it is necessary to forecast market

demand

• Since operational decisions are driven by what

their customers need, it is necessary to forecast

expected sales


• All factors influencing demand for a product or service have to

be first identified

• These factors could be both price and non-price determinants of

demand( including consideration of substitutes and

complementary products)

• Evolve a suitable methodology to assess these demand factors

and do quantitative and qualitative data analysis to arrive at

short term and long term demand estimates with identifiable

trends

• Prepare such forecasts to assist both long term and short term

decision-making needs of an organization

7-27

Forecasting Role in a Supply

Chain • Forms basis for all strategic and planning decisions in a

supply chain

• Used for both push and pull processes

• Examples:

– Production: scheduling, inventory, aggregate planning

– Marketing: sales force allocation, promotions, new production introduction

– Finance: plant/equipment investment, budgetary planning

– Personnel: workforce planning, hiring, layoffs

• All of these decisions are interrelated and part of aggregate production planning(APP)

Demand Patterns

Horizontal Trend

Seasonal Cyclical

Designing the

Forecast System

• Deciding what to forecast

– Level of aggregation.

– Units of measure.

• Choosing the type of forecasting method:

– Qualitative methods

• Judgment

– Quantitative methods

• Causal

• Time-series

Choosing the Type of

Forecasting Technique

• Judgment methods: A type of qualitative method that

translates the opinions of managers, expert opinions,

consumer surveys, and sales force estimates into quantitative

estimates.

• Causal methods: A type of quantitative method that uses

historical data on independent variables, such as promotional

campaigns, economic conditions, and competitors’ actions, to

predict demand.

• Time-series analysis: A statistical approach that relies heavily

on historical demand data to project the future size of demand

and recognizes trends and seasonal patterns.

• Collaborative planning, forecasting, and replenishment

(CPFR): A nine-step process for value-chain management that

allows a manufacturer and its customers to collaborate on

making the forecast by using the Internet.

Judgment Methods

• Sales force estimates: The forecasts that are compiled from

estimates of future demands made periodically by members of

a company’s sales force.

• Executive opinion: A forecasting method in which the

opinions, experience, and technical knowledge of one or more

managers are summarized to arrive at a single forecast.

– Executive opinion can also be used for technological

forecasting to keep abreast of the latest advances in

technology.

• Market research: A systematic approach to determine

external consumer interest in a service or product by creating

and testing hypotheses through data-gathering surveys.

• Delphi method: A process of gaining consensus from a group

of experts while maintaining their anonymity.

Guidelines for Using

Judgment Forecasts

• Judgment forecasting is clearly needed when no quantitative data are available to use quantitative forecasting approaches.

• Guidelines for the use of judgment to adjust quantitative forecasts to improve forecast quality are as follows:

1. Adjust quantitative forecasts when they tend to be

inaccurate and the decision maker has important

contextual knowledge.

2. Make adjustments to quantitative forecasts to compensate

for specific events, such as advertising campaigns, the

actions of competitors, or international developments.

Causal Methods

Linear Regression

• Causal methods are used when historical data are

available and the relationship between the factor to

be forecasted and other external or internal factors

can be identified.

• Linear regression: A causal method in which one

variable (the dependent variable) is related to one or

more independent variables by a linear equation.

• Dependent variable: The variable that one wants to

forecast.

• Independent variables: Variables that are

assumed to affect the dependent variable and

thereby ―cause‖ the results observed in the past.

• The production scheduler can use this forecast of

183,016 units to determine the quantity of brass

door hinges needed for month 6.

• If there are 62,500 units in stock, then the

requirement to be filled from production is 183,016 -

62,500 = 120,516 units.

Forecasting Demand for Example

Time Series Methods

• Naive forecast: A time-series method whereby the

forecast for the next period equals the demand for

the current period, or Forecast = Dt

• Simple moving average method: A time-series

method used to estimate the average of a demand

time series by averaging the demand for the n most

recent time periods.

– It removes the effects of random fluctuation and is most

useful when demand has no pronounced trend or seasonal

influences.

…

Forecasting Error

• For any forecasting method, it is important to

measure the accuracy of its forecasts.

• Forecast error is the difference found by

subtracting the forecast from actual demand for a

given period.

Et = Dt - Ft

where

Et = forecast error for period t

Dt = actual demand for period t

Ft = forecast for period t

Moving Average Method

a. Compute a three-week moving average forecast for

the arrival of medical clinic patients in week 4.

The numbers of arrivals for the past 3 weeks were:

Patient

Week Arrivals

1 400

2 380

3 411

b. If the actual number of patient arrivals in week

4 is 415, what is the forecast error for week 4?

c. What is the forecast for week 5?

Week

450 —

430 —

410 —

390 —

370 —

| | | | | |

0 5 10 15 20 25 30

Patient

arr

ivals

Actual patient

arrivals

Solution

The moving average method may involve the use of as many

periods of past demand as desired. The stability of the

demand series generally determines how many periods to

include.

Week Arrivals Average

1 400

2 380

3 411 397

4 415 402

5 ?

Solution continued

Forecast for week 5

is the average of

the arrivals for

weeks 2,3 and 4

Forecast error for week 4 is 18.

It is the difference between the

actual arrivals (415) for week 4

and the average of 397 that was

used as a forecast for week 4.

(415 – 397 = 18)

Forecast for week

4 is the average of

the arrivals for

weeks 1,2 and 3

F4 = 411 + 380 + 400

3

a.

c. b.

Comparison of

3- and 6-Week MA Forecasts

Week

Pati

en

t A

rriv

als

Actual patient arrivals

3-week moving

average forecast 6-week moving

average forecast

Application

• We will use the following customer-arrival data in

this moving average application:

© 2007 Pearson Education

Application Moving Average Method

F5 D4 D3 D2

3

790 810 740

3 780

780 customer arrivals

F6 D5 D4 D3

3

805 790 810

3 801.667


Weighted Moving Averages

• Weighted moving average method: A time-series

method in which each historical demand in the

average can have its own weight; the sum of the

weights equals 1.0.

Ft+1 = W1Dt + W2Dt-1 + …+ WnDt-n+1


Weighted Moving Average

F5 W1D4 W2D3 W3D2 0.50 790 0.30 810 0.20 740 786


F6 W1D5 W2D4 W3D3 0.50 805 0.30 790 0.20 810 801.5


Exponential Smoothing

Ft+1 = (Demand this period) + (1 – )(Forecast calculated last period)

= Dt + (1–)Ft

Or an equivalent equation: Ft+1 = Ft + (Dt – Ft )

Where alpha (is a smoothing parameter with a value between 0 and 1.0

Exponential smoothing is the most frequently used formal forecasting

method because of its simplicity and the small amount of data needed

to support it.

Exponential smoothing method: A sophisticated weighted

moving average method that calculates the average of a

time series by giving recent demands more weight than

earlier demands.

Reconsider the medical clinic patient arrival data. It is now the end of week 3. a. Using = 0.10, calculate the exponential smoothing forecast for week 4.

Ft+1 = Dt + (1-)Ft

F4 = 0.10(411) + 0.90(390) = 392.1

b. What is the forecast error for week 4 if the actual demand turned out to be 415?

E4 = 415 - 392 = 23

c. What is the forecast for week 5?

F5 = 0.10(415) + 0.90(392.1) = 394.4


Week Arrivals

1 400

2 380

3 411

4 415

5 ?



Ft1 Ft Dt Ft 783 0.20 790 783 784.4


Ft1 Ft Dt Ft 784.4 0.20 805 784.4 788.52


Trend-Adjusted


• A trend in a time series is a systematic increase or

decrease in the average of the series over time.

– Where a significant trend is present, exponential smoothing

approaches must be modified; otherwise, the forecasts tend

to be below or above the actual demand.

• Trend-adjusted exponential smoothing method:

The method for incorporating a trend in an

exponentially smoothed forecast.

– With this approach, the estimates for both the average and

the trend are smoothed, requiring two smoothing constants.

For each period, we calculate the average and the trend.

Ft+1 = At +Tt where At = Dt + (1 – )(At-1 + Tt-1)

Tt = (At – At-1) + (1 – )Tt-1

At = exponentially smoothed average of the series in period t

Tt = exponentially smoothed average of the trend in period t

= smoothing parameter for the average

= smoothing parameter for the trend

Dt = demand for period t

Ft+1 = forecast for period t + 1

Trend-Adjusted Exponential

Smoothing Formula

A0 = 28 patients and Tt = 3 patients

At = Dt + (1 – )(At-1 + Tt-1)

A1= 0.20(27) + 0.80(28 + 3) = 30.2

Tt = (At – At-1) + (1 – )Tt-1

T1 = 0.20(30.2 – 2.8) + 0.80(3) = 2.8

Ft+1 = At + Tt

F2 = 30.2 + 2.8 = 33 blood tests

Trend-Adjusted


Example Medanalysis ran an average of 28 blood tests

per week during the past four weeks. The trend over that

period was 3 additional patients per week. This week’s

demand was for 27 blood tests. We use = 0.20 and =

0.20 to calculate the forecast for next week.

| | | | | | | | | | | | | | |

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

80 —

70 —

60 —

50 —

40 —

30 —

Patient

arr

ivals

Week

Actual blood

test requests

Trend-adjusted

forecast

Medanalysis

Trend-Adjusted Exponential Smoothing


Forecast for Medanalysis Using the

Trend-Adjusted Exponential Smoothing Model

Discussion

The forecaster for Canine breath fresheners estimated

(in March) the sales average to be 300,000 cases

sold per month and the trend to be +8,000 per

month.

The actual sales for April were 330,000 cases.

What is the forecast for May,

assuming = 0.20 and = 0.10?


Solution

thousand

thousand

To make forecasts for periods beyond the next period, multiply the trend

estimate by the number of additional periods, and add the result to the

current average

Seasonal Patterns

• Seasonal patterns are regularly repeated upward or downward movements in demand measured in periods of less than one year. – An easy way to account for seasonal effects is to use one

of the techniques already described but to limit the data in the time series to those time periods in the same season.

• If the weighted moving average method is used, high weights are placed on prior periods belonging to the same season.

– Multiplicative seasonal method is a method whereby seasonal factors are multiplied by an estimate of average demand to arrive at a seasonal forecast.

– Additive seasonal method is a method whereby seasonal forecasts are generated by adding a constant to the estimate of the average demand per season.

Multiplicative Seasonal

Method • Step 1: For each year, calculate the average

demand for each season by dividing annual demand by the number of seasons per year.

• Step 2: For each year, divide the actual demand for each season by the average demand per season, resulting in a seasonal index for each season of the year, indicating the level of demand relative to the average demand.

• Step 3: Calculate the average seasonal index for each season using the results from Step 2. Add the seasonal indices for each season and divide by the number of years of data.

• Step 4: Calculate each season’s forecast for next year.

Quarter Year 1 Year 2 Year 3 Year 4

1 45 70 100 100

2 335 370 585 725

3 520 590 830 1160

4 100 170 285 215

Total 1000 1200 1800 2200

Using the Multiplicative

Seasonal Method Stanley Steemer, a carpet cleaning company needs a quarterly

forecast of the number of customers expected next year. The business is

seasonal, with a peak in the third quarter and a trough in the first quarter.

Forecast customer demand for each quarter of year 5, based on an

estimate of total year 5 demand of 2,600 customers.

Demand has been increasing by an average of 400 customers each year. The forecast

demand is found by extending that trend, and projecting an annual demand in year 5 of 2,200

+ 400 = 2,600 customers.


OM Explorer Solution

Measures of

Forecast Error • Cumulative sum of forecast errors (CFE): A

measurement of the total forecast error that

assesses the bias in a forecast.

• Mean squared error (MSE): A measurement of

the dispersion of forecast errors.

• Mean absolute deviation (MAD): A measurement

of the dispersion of forecast errors.

• Standard deviation (): A measurement

of the dispersion of forecast errors.

Et2

n MSE =

MAD = |Et |

n

= (Et – E )2

n – 1

CFE = Et

MAPE = [ |Et | / Dt ](100)

n

Measures of

Forecast Error

Mean absolute percent error (MAPE): A

measurement that relates the forecast error to the

level of demand and is useful for putting forecast

performance in the proper perspective.

Tracking signal: A measure that indicates

whether a method of forecasting is accurately

predicting actual changes in demand.

Tracking signal = CFE

MAD

Absolute

Error Absolute Percent

Month, Demand, Forecast, Error, Squared, Error, Error,

t Dt Ft Et Et2 |Et| (|Et|/Dt)(100)

1 200 225 -25 625 25 12.5%

2 240 220 20 400 20 8.3

3 300 285 15 225 15 5.0

4 270 290 –20 400 20 7.4

5 230 250 –20 400 20 8.7

6 260 240 20 400 20 7.7

7 210 250 –40 1600 40 19.0

8 275 240 35 1225 35 12.7

Total –15 5275 195 81.3%

Calculating Forecast Error

The following table shows the actual sales of

upholstered chairs for a furniture manufacturer and

the forecasts made for each of the last eight months. Calculate CFE, MSE, MAD, and MAPE for this product.


Forecast Error Measures

CFE = – 15 Cumulative forecast error (bias):

E = = – 1.875 – 15

8 Average forecast error (mean bias):

MSE = = 659.4 5275

8 Mean squared error:

= 27.4 Standard deviation:

MAD = = 24.4 195

8 Mean absolute deviation:

MAPE = = 10.2% 81.3%

8 Mean absolute percent error:

Tracking signal = = = -0.6148 CFE

MAD

-15

24.4

% of area of normal probability distribution within control limits of the tracking signal

Control Limit Spread Equivalent Percentage of Area

(number of MAD) Number of within Control Limits

57.62

76.98

89.04

95.44

98.36

99.48

99.86

± 0.80

± 1.20

± 1.60

± 2.00

± 2.40

± 2.80

± 3.20

± 1.0

± 1.5

± 2.0

± 2.5

± 3.0

± 3.5

± 4.0

Forecast Error Ranges

Forecasts stated as a single value can be less useful because they

do not indicate the range of likely errors. A better approach can be

to provide the manager with a forecasted value and an error range.

Tracking signal = CFE

MAD

+2.0 —

+1.5 —

+1.0 —

+0.5 —

0 —

–0.5 —

–1.0 —

–1.5 —

| | | | |

0 5 10 15 20 25

Observation number

Tra

ckin

g s

ignal

Control limit

Control limit

Out of control

Computer Support

Computer support, such as OM Explorer, makes error calculations

easy when evaluating how well forecasting models fit with past data.

Criteria for Selecting

Time-Series Methods

• Forecast error measures provide important information for

choosing the best forecasting method for a service or product.

• They also guide managers in selecting the best values for the

parameters needed for the method:

– n for the moving average method, the weights for the weighted

moving average method, and for exponential smoothing.

• The criteria to use in making forecast method and parameter

choices include

1. minimizing bias

2. minimizing MAPE, MAD, or MSE

3. meeting managerial expectations of changes in the

components of demand

4. minimizing the forecast error last period

Using Multiple Techniques

• Research during the last two decades suggests that combining

forecasts from multiple sources often produces more accurate

forecasts.

• Combination forecasts: Forecasts that are produced by

averaging independent forecasts based on different methods

or different data or both.

• Focus forecasting: A method of forecasting that selects the

best forecast from a group of forecasts generated by individual

techniques.

– The forecasts are compared to actual demand, and the

method that produces the forecast with the least error is

used to make the forecast for the next period. The method

used for each item may change from period to period.

Forecasting as a Process

The forecast process itself, typically done on a

monthly basis, consists of structured steps. They

often are facilitated by someone who might be called

a demand manager, forecast analyst, or

demand/supply planner.


Some Principles for the Forecasting Process • Better processes yield better forecasts.

• Demand forecasting is being done in virtually every company. The challenge is to do it better than the competition.

• Better forecasts result in better customer service and lower costs, as well as better relationships with suppliers and customers.

• The forecast can and must make sense based on the big picture, economic outlook, market share, and so on.

• The best way to improve forecast accuracy is to focus on reducing forecast error.

• Bias is the worst kind of forecast error; strive for zero bias.

• Whenever possible, forecast at higher, aggregate levels. Forecast in detail only where necessary.

• Far more can be gained by people collaborating and communicating well than by using the most advanced forecasting technique or model.

Need for Collaborative Supply Chains

• SCM integrates and optimizes the processes, but does not eliminate inherent conflict

• SCM mostly remains an in-corporate initiative

• SCM does not address the total business environment (different components of external value chain face different environments)

• Hence, need for collaborative supply chains

• Thus, born concept of Collaborative Planning and Forecast Replenishment( CPFR)

Forecasting Problems

• Lack of understanding of integrated market and supply realities by key decision makers within an organization

• Lack of trust and transparency amongst supply chain elements and partner organizations

• Lack of proper communication, coordination and collaboration amongst supply chain partners

• Lack of metrics for measuring total supply chain performance

• Lack of IT tools, processes, professional competencies to achieve accurate forecasts

Forecasting in Business Planning

Inputs Market Conditions Competitor Action Consumer Tastes

Products’ Life Cycle Season

Customers’ plans

Economic Outlook Business Cycle Status

Leading Indicators-Stock Prices, Bond Yields, Material

Prices, Business Failures, money Supply, Unemployment

Other Factors Legal, Political, Sociological,

Cultural

Forecasting Method(s)

Or Model(s)

Outputs Estimated Demands

for each Product in each Time Period

Other Outputs

Sales Forecast Forecast and Demand

for Each Product In Each Time Period

Processor

Production Capacity Available Resources

Risk Aversion Experience

Personal Values and Motives

Social and Cultural Values

Other Factors

Management Team

Forecast Errors

Feedback

Sales Forecast Forecast and Demand

for Each Product In Each Time Period

Procedure for Translating Sales

Forecast into Production

Resource Forecast

Business Strategy Marketing Plan-

Advertising Sales Effort, Price, Past

Sales Production Plans- Quality

Levels, Customer Service,

Capacity Costs Finance Plan—

Credit Policies, Billing Policies

Production Resource Forecasts Long Range

Factory capacities Capital Funds Facility Needs

Other

Medium Range Work Force

Department Capacities Purchased Material

Inventories Others

Short Range Labor by Skill Class Machine Capacities

Cash Inventories

Other

Processor

Implementing Integrated Sales &

Operational Planning

S&OP PROCESS STEPS

P1.1A – Identify, Prioritize, & Aggregate SC Requirements DAY 0 - 8 P1.4 – Establish &

Communicate SC Plans

DAY 13-15

P1.3A – Balance Supply Chain

Requirements with SC

Resources DAY 9 – 12

P1.2A – Identify, Prioritize, & Aggregate SC Resources DAY 0 - 8

Review Historical Sales

Data

Review Demand Metrics

Apply Historical Sales Data

Adjustments

Apply Future Demand Change

Notifications

Run Forecast Model

Agree & Communicate

Approved Plans

Communicate Implications to

Financial & Sales Plans

Review Supply Plan & Cost Projections

Develop/Modify

Supply Chain Plans

Review Supply Planning Measures

Adjust Supply Planning

Constraints

Load & Review Unconstrained Demand Plan

Submit Supply Plan with

Documented Options

Approve & Publish Supply

Plan

Approve & Publish

Unconstrained Demand Plan

Gather Data [DAY0]

Gather Data [DAY 0]

Define Supply Capability [DAY 1 – 8]

Develop Supply Plan Proposals

[DAY 9]

Finalize & Approve Supply

Plan [DAY 10 -12]

Aggregate All Sources of

Supply

Initiate Req Master Data

Changes

Review Inventory Available

Review Supply Capability

Create Demand Change

Summary

B

A

Partnership Meeting

[DAY 13]

Executive S&OP

[DAY 15]

Summarize Supply

Chain Plans

Gather Collaborative Input (Future Function)

Create Unconstrained Demand Plan [DAY 1 -8]

Develop Unconstrained

Revenue Projection

Apply New Characteristic

Combos

Adjust Statistical Parameters (if needed)

Review and Validate

Unconstrained Forecast

Input Source, Make, Deliver

Product & Capacity Plans

Create Supply Change

Summary

Develop Supply Plan Proposal (Optimization)

Review Alerts

Assess Impact & Develop Options

Review Excess Capacity, Supply Options, Demand

Exceptions

Issue Resolution

Agree to Supply Plan

Initiate any Master Data MOC

Review Supply Chain Plans

Review Revenue Projections

A

B

C

C

BI

Bba 1584 planning n forecasting

Business

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