Supply Chain Management (SCM) Forecasting 3

1. Supply Chain Management (SCM) Forecasting 3 Dr. Husam Arman

2. Today’s Outline • Qualitative methods • Economic indicators • Market research • Historical analogy • Delphi method • Sales force composites • Scenario writing and analysis • Contemplations and conclusions

3. Qualitative forecasting techniques • Often use data and models but with human interpretation/ judgment to form a view on the future

4. Qualitative forecasting techniques Qualitative Economic indicators Scenario writing Sales force composites Market research More human judgment More models and data Delphi Methods Historical analogy

5. Economic indicators 1 • Originated in the US following the depression • Monthly, quarterly and annual series on prices, employment, production etc • Closely relates to observed economic activity and business cycles • Useful for interpretative, judgmental forecasting by many organizations

6. Economic indicators 2 • Economic indicator: an economic series from which a forecast is based • Leading indicators: advance warning of probable change in economic activity • Coincident indicators: reflect current performance of economy • Lagging indicators: confirm changes previously signaled • Interpretation/impact depends on nature of the forecast, sector, type of organization, location etc

7. Market research 1 • Extracts information form a sample of a target market and infers something about the population • Useful for information on product preferences • e.g. opinions on existing products, opinions on new products, opinions on competitors products and more general preferences • May provide sophisticated accurate forecasts on market potential

8. Market research 2 • Needs to be designed, executed and analyzed with care • Decisions on sample size and sample type • Decisions on medium and method for information gathering • Prior selection methods for statistical inference • Many sources of expertise • May be costly and time-consuming • How do we do it? 

9. Historical analogy 1 • Forecasting relation to new products, take up of new technologies where little or no previous market experience • Link the new products with an assumed analogous occurrence in the past • Forecast for the demand for a product in a new market might be made by analogy with the known demand for the same product in a mature market

10. Historical analogy 2 • Forecast demand for a new product by analogy with known demand for a related product • Analogy of mail order as a basis for predicting the development of e-shopping • If Ad-hoc method, many potential dangers • May aid understanding with qualitative information on the shape of the demand curve

11. Delphi methods • DELPHI method attempts to systematically evaluate expert judgment on the likelihood of future events without expert or analyst interaction

12. Delphi steps • Establish panel of expert • Establish a questionnaire • Evaluate responses by producing numerical summary • - Modal values and extreme values are highlighted • Controlled feedback • - Make the extremists justify their position and decide whether to include or exclude extreme values. • Repeat (3) and (4) until a clear, not necessarily unanimous, forecast emerges. Extremes may persist • Summaries the result

13. Delphi • Difficulties • How many experts to use, how many rounds are appropriate, when should extremes be eliminated? • Time consuming and may be costly • Successful in broad studies of issues that affect demand in many businesses in the longer term. e.g. • future of the Common Agricultural • growth in different tourist destinations

14. Sales force composites • Utilizes knowledge and experience of sales-force to produce a forecast • Useful when • complex product mix, few customers • where sales force have close contact with customers, technical expertise, closely involved in negotiation, pricing and specification • but there are many problems / sources of error, • like what ?

15. Scenario writing and analysis 1 • A scenario is a narrative description of future conditions and how a business and its competitors may react to those conditions • Identifies the principal factors that affect the future and explores a number of different future scenarios with some indications of the likelihood of each scenario occurring • Closely linked with corporate strategy and planning

16. Scenario writing and analysis 2 • Attempts to understand and plan for the future rather than producing ’blind’ forecasts • Acknowledges that different scenarios may be plausible from a given starting point • No generally accepted way of constructing scenarios • Simulation approaches may be useful particularly System Dynamics

17. Contemplation and Conclusions • Many ‘advanced’ time series extrapolation methods – little evidence that complex methods significantly outperform simpler approaches • Errors made consistently in one direction imply bias, important to track errors and bias over time • Automation of forecasting techniques for large scale inventory systems is difficult - challenging in ERP

18. How much should we invest in forecasting? Naive models Sophisticated models Increasing costs Cost of operating a forecasting process Cost of forecasting error Decreasing forecast errors

19. Forecasting in SCM • Whatever techniques are employed, forecasts need to be embedded in the decision making processes • Failure to forecast or act on forecasts may • lead to implicit acceptance of a previous outdated forecasts • may be an assumption that present conditions will persist in the future • result in lack of preparation for change

20. Longer term/higher level forecasting • In operations we typically need longer term forecasts for: • Strategy – decide if demand is sufficient to entre a market • e.g. 3-10 years • Longer term capacity needs for facility design • e.g. exceeding 2 years • Medium term capacity and resource ‘flexing’ • recruiting/shedding labor, balancing production across multiple sites • supply chain ‘ramp’ up and down • e.g. 6 months to 2 years

21. Selecting the appropriate forecasting techniques • What is the purpose of the forecast? How is it to be used? • What are the dynamics of the system for which the forecast will be made? • How important is the past in estimating the future? • What about the different stages of the product life cycle? • Can we use more than one technique?