lecturer's notes

1. Planning and Forecasting

2. 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)

3. 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

4. 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

5. 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

6. 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

7. 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

8. What is Demand Planning?(2)
•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

9. What is Demand Planning?(3)
•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)

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. Demand Forecasting Issues(2)
•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

26. Demand Forecasting Issues(3)
•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

27. 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)

28. Demand Patterns
Horizontal
Trend
Seasonal
Cyclical

29. 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

30. 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.

31. 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.

32. 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.

33. 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.

34. •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

35. 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.
…

36. 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

37. 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?

38. Week
450 —
430 —
410 —
390 —
370 —
| | | | | | 0 5 10 15 20 25 30
Patient arrivals
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.

39. 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.

40. Comparison of 3- and 6-Week MA Forecasts
Week
Patient Arrivals
Actual patient arrivals
3-week moving average forecast
6-week moving average forecast

41. Application
•We will use the following customer-arrival data in this moving average application:

42. © 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
802 customer arrivals

43. 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

44. © 2007 Pearson Education
Weighted Moving Average

F5  W1D4 W2D3 W3D2  0.50790 0.30810 0.20740  786
786 customer arrivals

F6  W1D5 W2D4 W3D3  0.50805 0.30790 0.20810  801.5
802 customer arrivals

45. 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.

46. 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
Exponential Smoothing
Week
Arrivals
1
400
2
380
3
411
4
415
5
?

47. © 2007 Pearson Education
Exponential Smoothing

Ft1  Ft  Dt  Ft    783 0.20790 783  784.4
784 customer arrivals

Ft1  Ft  Dt  Ft    784.4  0.20805 784.4  788.52
789 customer arrivals

48. Trend-Adjusted Exponential Smoothing
•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.

49. 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

50. 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 Exponential Smoothing
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.

51. | | | | | | | | | | | | | | |
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
80 —
70 —
60 —
50 —
40 —
30 —
Patient arrivals
Week
Actual blood test requests
Trend-adjusted forecast
Medanalysis Trend-Adjusted Exponential Smoothing

52. © 2007 Pearson Education
Forecast for Medanalysis Using the Trend-Adjusted Exponential Smoothing Model

53. 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?

54. © 2007 Pearson Education
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

55. 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.

56. 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.

57. 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.

58. © 2007 Pearson Education
OM Explorer Solution

59. 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

60. 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

61. 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.

62. © 2007 Pearson Education
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

63. % 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.

64. 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
Tracking signal
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.

65. 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

66. 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.

67. 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.

68. © 2007 Pearson Education
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.

69. 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)

70. 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

71. 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

72. 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

73. Implementing Integrated Sales & Operational Planning Page 73
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