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Demand Management

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  1. Demand Management Processing, Influencing, & Anticipating Demand

  2. Supplier Customer Supply-Demand Management Relationship Relationship "Make, Move, Store" Management Management Plant "Buy" "Sell" Plant Warehouse Customers Suppliers Plant Managing the sell side of a business

  3. Key questions • What is the scope of demand management? • What does order processing involve; why is it an important area for management attention? • What is customer profit potential, & how is it relevant for influencing demand? • What are 5 alternatives for improving forecast accuracy, what do they mean, & how can they be applied? • How do the tactics of part standardization & postponement of form or place help improve forecast accuracy? • What is the difference between long term & short term forecasting? • What are 4 long term forecasting methods; what are the risks of salesperson/customer input? • What are the components of demand, & which component is not forecasted? • How do the moving average, Winters, & focus forecasting methods work? • What is the role of the number of periods in the moving average method, & the smoothing parameters in the Winters method? • What is the purpose of filtering, & why is it important for computer-based forecasting? • What do the following principles of nature mean & how are they relevant for demand management? (1) law of large numbers, (2) trumpet of doom, (3) recency effect, (4) hockey stick effect, (5) Pareto phenomenon • What are the managerial insights from the chapter?

  4. Road map • Processing Demand • Influencing Demand • How to Improve Forecast Accuracy • Long Term Forecasting • Short Term Forecasting • Summary

  5. Scope of demand management • So what is demand management? • Concerned with processing, influencing, and anticipating demand • We’ll begin with processing demand or, in more common terms, order processing or order fulfillment

  6. Processing Demand Order processing • Order processing is usually viewed to span order booking to order shipment • Example steps? • Customer validation, order entry, credit checking, pricing, design changes, availability checks, delivery time estimation, notification of shipment, notification of delays

  7. CUSTOMER ORDER ENTRY AND CHECKING Customer Validation Credit Control Operations… ERP ORDER INTERRUPTION RETURNS ORDER PICKING AND ASSEMBLY CUSTOMER SERVICE SHIPPING INVOICING Processing Demand

  8. Processing Demand Characteristics • Can be a complex & time consuming process dealing largely with information flow • Susceptible to ad hoc modifications over time in response to problems (e.g., extra credit check added due to expensive nonpaying customer a few years ago) • A major customer contact point with organization  Can significantly impact customer perceptions • IT advances & high customer impact  A potential profitable target for improvement

  9. Processing Demand Example 1 Benetton • Electronic loop linking sales agent, factory, & warehouse • If not available, measurements transferred to knitting machine for production • Benetton uses a single warehouse • Staffed by 8 people & about 230,000 pieces shipped daily

  10. Processing Demand Example 2 K-Mart and MasterLock • Policy for mistake in shipment or invoice • Strike 1: $10,000, Strike 2: $50,000, Strike 3: lose business • MasterLock revamped their order processing function

  11. Processing Demand Example 3 – customer tools • Amazon online order tracking

  12. Processing Demand Example 4 – customer tools • UPS online order tracking

  13. Processing Demand Example 4 – continued • UPS online tools

  14. Road map • Processing Demand • Influencing Demand • How to Improve Forecast Accuracy • Long Term Forecasting • Short Term Forecasting • Summary

  15. Influencing Demand Measure customer profit potential A simple idea • Some customers are more profitable than others • Advancing technologies  more practical to estimate profit potential of individual customers • Can guide efforts/investments for customer retention & acquisition . . . investments to influence demand • E.g., • Electronics manufacturer: reviews historical customer profit before sending service contract renewal • Wireless phone firm: churn scores & lifetime value estimates influence # of customer contacts & attractiveness of offerings • Ongoing development of data mining methods

  16. Road map • Processing Demand • Influencing Demand • How to Improve Forecast Accuracy • Long Term Forecasting • Short Term Forecasting • Summary

  17. Forecasting Alternatives Motivating example 1 Sunbeam Improved forecasting led to 45% reduction in inventory • Included estimates from top 200 customers

  18. Forecasting Alternatives Motivating example 2 Apple A history of problems forecasting demand Many components sourced from 1 supplier - accurate forecasts are critical Over $1 billion in unfilled orders during the crucial holiday season. The CEO (Spindler) ousted a few months later

  19. Forecasting Alternatives Motivating example 3 IBM Badly misjudged demand in PC business in 1996 – went from being profitable in 1995 to a $200 million loss through 1st half of 1996

  20. Forecasting Alternatives Motivating example 4 Christmas 1999 & e-commerce takes off Large unanticipated increase in Internet orders –didn’t ship on time E.g., Many Toys ‘R Us Christmas orders not delivered until March – “I will never buy online again”

  21. Forecasting Alternatives Improvement alternatives • Change the forecasting method • Collect more or different data • Analyze the information differently • E.g., involve more people, new forecasting software, spend more time manually reviewing, focus groups etc. • Change operations or operating policies • Introduce early warning mechanisms • Take advantage of the law of large numbers • Reduce information delays & leadtimes (trumpet of doom) • Reduce demand volatility

  22. Forecasting Alternatives Early warning • Change policies so that some (or more) customers provide earlier commitment of future demand, e.g., • Early bird program for builder markets – discount for 60-day advance order • Invite large buyers to Aspen in February to view next year’s skiwear line, & encourage orders • “Commitment”  asking customers how much they are likely to buy next quarter

  23. Forecasting Alternatives Law of large numbers Principle of Nature • As volume increases, relative variability decreases • Postponement in form or place, e.g., • Dell – configure your own PC • From full product line at 12 regional DCs to full product line at a single super DC, with 10% of product line stocked at 11 regional DCs (i.e., fast movers that account for 70% of sales) • Part standardization, e.g., • Arby’s sandwich wrappers; plastic lids with push down drink indicator • Intel Pentium processors all the same size • 2.8 GHz tests out below 2.8 spec can be sold as a 2.66 GHz chip (“down-binning”)

  24. As forecast horizon increases, accuracy decreases, e.g., Reduce production & delivery leadtimes Dell pick-to-light system for assembly Reduce information delays EDI transmission of daily consumer demand up through multiple levels in the supply chain Forecasting Alternatives Trumpet of doom Principle of Nature

  25. Forecasting Alternatives Reduce demand volatility 2 Principles of Nature • Beware of product proliferation • Pareto analysis – separating the important few from the trivial many • Periodic length of line analysis to critically assess whether to continually offer “slow movers” • Principle of Nature: Pareto phenomenon – the lion’s share of an aggregate measure is determined by relatively few factors • E.g., “the 80-20 rule” – 80% of demand is due to 20% of product line • Beware of perverse cycle of promotions – customers wait for sale before buying, thereby forcing a sale • A step further – dynamic pricing to stabilize demand & align with supply • Reduce the hockey stick effect…

  26. Jan Feb Forecasting Alternatives Hockey stick effect Principle of Nature • Volume tends to pick up towards the end of a reporting period . . . why? • Look for ways to lessen the effect – contributes to demand volatility, inefficiency, poor service

  27. Forecasting Alternatives Channel stuffing One contributor to the hockey stick effect Lots of sales booked near the end of a quarter, then sales drop off at the start of the next quarter E.g., • A large brewer offered a vacation to the salesperson in each region who sold the most beer to stores over a 3 month period • One winner was able to convince a few stores to free up backroom space and fill it entirely with beer

  28. Forecasting Alternatives Improvement alternatives • We’re about to focus on methods for predicting demand short pork bellies • But, important to remember . . . many creative ways to improve forecast accuracy that have nothing to do with method • E.g., early warning incentives, law of large numbers, trumpet of doom, reduce demand volatility

  29. Road map • Processing Demand • Influencing Demand • How to Improve Forecast Accuracy • Long Term Forecasting • Short Term Forecasting • Summary

  30. Long Term Forecasting Characteristics of long term forecasts • Single or multi-year horizon • Monthly or annual time bucket • Aggregate units • Input to “long term” decisions • Accuracy generally more important than short term forecasts . . . why? • Tend to use expensive & time consuming methods . . . due to the preceding point & due to a PON . . . which is?

  31. Long Term Forecasting Recency effect Principle of Nature Humans tend to overreact to (or be overly influenced by) recent events E.g., Hughes Electronics Corp. developed an artificial intelligence based financial trading system. The developers did this by encoding the wisdom of Christine Downton, a successful portfolio manager. One motivation for creating the system is that it is immune to the recency effect, i.e., humans tend to get overly fixated on the most recent information.

  32. Long Term Forecasting Some alternative methods • Judgment • Salesperson & customer input • Great information source, but beware of bias potential & recency effect = humans tend to be overly influenced by recent events • Outside services • Causal methods . . . examples?

  33. Road map • Processing Demand • Influencing Demand • How to Improve Forecast Accuracy • Long Term Forecasting • Short Term Forecasting • Characteristics • Components of demand • Moving average • Winters method • Focus forecasting • Filtering • Summary

  34. Long term forecasts Single or multi-year horizon Monthly or annual time bucket Aggregate units (e.g., product/ service categories) Input to “long term” decisions Expensive & time consuming methods Accuracy importance Trumpet of doom Short term forecasts Weekly or monthly horizon Daily & weekly time bucket Detailed units (e.g., SKU) Input to “short term” decisions Inexpensive & quick methods Accuracy importance Trumpet of doom Short Term Forecasting Long term/short term characteristics Could argue using 2 different principles of nature that it’s [easier?/harder?] to be accurate with short term forecasting than with long term forecasting

  35. Definition of the Forecasting Process • The Art and Science of Predicting Future Events • Forecasting vs. Predicting • Based on Past Data • Economic vs. Demand Forecasting

  36. Elements of Demand Forecasting • Dynamic in Nature • Consider Uncertainty (Stochastic) • Rely on Information contained in Past Data • Applied to various time horizons • short term • medium term forecasts • long term forecasts

  37. Steps in the Forecasting Process • Determine the Use of the Forecast • Select the Items to be Forecasted • Determine a Suitable Time Horizon • Select an appropriate Set of Forecasting Models • Gather Relevant Data • Conduct the Analysis • Validate the Model - Assess its Accuracy • Make the Forecast • Implement the Results

  38. Independent Demand: What a firm can do to manage it? • Can take an active role to influence demandFORECASTING • Can take a passive role and simply respond to demand

  39. Types of Forecasts • Qualitative (Judgmental) • Quantitative • Time Series Analysis • Causal Relationships • Simulation

  40. Qualitative Methods Executive Judgment Grass Roots Qualitative Methods Market Research Historical analogy Delphi Method Panel Consensus

  41. Delphi Method • Choose the experts to participate representing a variety of knowledgeable people in different areas • Through a questionnaire (or E-mail), obtain forecasts (and any premises or qualifications for the forecasts) from all participants • Summarize the results and redistribute them to the participants along with appropriate new questions • Summarize again, refining forecasts and conditions, and again develop new questions • Repeat Step 4 as necessary and distribute the final results to all participants

  42. Quantitative Forecasting Models • Both Pattern Based and Correlational Models rest on the assumption that the relationships of the past will continue into the Future • Both can Mathematically Characterize the Probabilistic Nature of the Forecast • Both Use Information from Relevant Time Frames

  43. Road map • Processing Demand • Influencing Demand • How to Improve Forecast Accuracy • Long Term Forecasting • Short Term Forecasting • Characteristics • Components of demand • Moving average • Winters method • Focus forecasting • Filtering • Summary

  44. Components of Demand • Average demand for a period of time • Trend • Seasonal element • Cyclical elements • Random variation • Autocorrelation

  45. Pattern Based Analyses • Definition • Identifying an underlying pattern in historical data, describe it in mathematical terms, and then extrapolate it into the future • Uses a “Time Series” of Past Data

  46. Time Series Variation • Time Series of Demand Data Typically Contain Four Components of Variation About the Mean or Average • Pattern Based Forecasting Needs to Mathematically Characterize Each of these

  47. Seasonal variation Finding Components of Demand x x x Linear Trend x x x x x x x x x x x Sales x x x x x x x Average x x x x x x x x x x x x x x x x x x x x x x x x x x 1 2 3 4 Year

  48. Time Series Analysis • Time series forecasting models try to predict the future based on past data • You can pick models based on: 1. Time horizon to forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel

  49. Simple Moving Average Formula • The simple moving average model assumes an average is a good estimator of future behavior • The formula for the simple moving average is: Ft = Forecast for the coming period n = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods

  50. Simple Moving Average Problem (1) Question: What are the 3-week and 6-week moving average forecasts for demand? Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts