Demand in Business Forecasting

1. Demand in Business Forecasting Application of the law of demand is not simple or we would not be here. In a highly competitive world, successful application of the law of demand can have a significant impact on profits.

2. Why Demand Is Not Easy to Measure • Changes in the design of products and entry of new products mean limited lifecycles. Changes make forecasting more difficult because you use data from previous products and time periods. 2008 study from Chicago and Columbia Business Schools showed: 40% of household expenditures are on goods created in the last 4 years and 20% of expenditures are on goods that disappear in the next four years. Rapid product entry and exit.

3. Bad forecasting process • The sales manager asks for the forecast. • The reps make a guess at what will close, then subtract 10%. • The sales manager takes the forecast and raises it 10% because he knows the reps are fudging. • The sales manager gives the forecast to top management which changes the numbers to match analyst expectations. • Manufacturing ignores the numbers and orders raw materials based on last year’s actual sales. • Actual sales turn out to bear no resemblance to any of the above. • Prior to earnings announcements, jigger the books so that they resemble the forecast.

4. Good forecasting process • Build a computerized forecasting model that predicts future buying behavior based upon previous years’ sales, seasonal changes in buying patterns, historical impact of marketing campaigns, overall state of the economy, fluctuations in currency exchange rates, and so forth. • Test the model against historical data to confirm that if it had been in place in the past if it would have predicted sales. • Goal sales and marketing on providing information that hones the accuracy of the model.

5. One Size May Not Fit All • Expanding business to new markets means new demographics (customer base), facing new competitors, different seasonal factors, packaging requirements, and distributional channels. Seller of a product is likely really in multiple mini-markets.

6. Accurate Measures Difficult • Difficulty in interpretation of historical data: Sales, orders, shipments and invoices are historical data that can cause confusion. • This may be innocent; but—may be evidence of theft; evidence of bad record keeping. • Besides trying to measure demand; an opportunity to try to understand company costs and operations better.

7. Measurement Difficulties, But Big Benefits • Sales today may not reflect future demand as substitutes or other factors that affect demand change. • Demand often underestimated. Were sales in a time period actual demand or did stock run out, thereby cutting sales? • Most data is old; look for more “real time” data. YRC Worldwide (transport) moves quickly to reduce its fleet and employee base when it sees shipments shrinking. Checks weight of shipments, frequency, etc. looking for changes in industry conditions. • H-P works with Wal-Mart to forecast PC demand. By getting earlier orders, H-P saves on manufacturing costs, which are lower if ordered far in advance.

8. Demand Forecasting • Forecasts are statistical estimates for the future. They can be improved by determining probability distributions for demand points by location and for specific times. • How much did actual demand deviates from prior demand forecasts? Improve estimates. Revision a good idea as time passes—were the estimates made six months ago for next year still the best estimate? • Based on experimenting with data, determine relevant time period. Example: Anheuser-Busch uses five-year historical data to better understand product lifecycles and seasonal demand.

9. One Company’s Application of Data Schwan Food—6,000 sales reps deliver frozen foods to 3 m. customers at home. They looked at 6 weeks of orders to decide what to suggest to customers. Sales flat for years. More sophisticated: match customers’ buying patterns; offer new products and discounts via hand held devices used by reps. Revenues up 3-4%

10. Improving Demand Measures • Detailed point-of-sale data (bar codes on products) allows better measures. These can be compared to vendor inventory or other data sources to check accuracy. • RFID chips provide better control of point-of-sale measures and inventory from production through distribution to the shelf level. Toyota requires these on all parts to track location of parts made around the world—what is where, when. Better inventory control means less waste.

11. Improving Demand Measures • More measures of possibly relevant factors: competitor prices, regional events, demographics, and weather. Some of this information is low cost. Computer time is low cost. Analysis is not. • The better we forecast demand for output, the better we control inputs (cost).

12. Post-Katrina: Drinking Water, Batteries, Cleaning Supplies, Ready-to-Eat Food

13. Responding to what consumers want Best Buy getss data on multiple offerings of products —who is buying and using electronics? What they learned: Many DVD players bought for young children. A store-brand with rubberized edges and spill resistant became good seller. GPS device with Google search feature (simple). Private label models do fine if have special features. Match.com—better algorithms for matching men and women.

14. Wide Range of Applications • Pricing restaurant meals an drinks; drinks have higher margins. Experiment with changing the mix of these services. • Inventory control—Walgreen cut number of products carried about 20%. Eliminate low value goods; focus on profitable goods. • Amazon runs many A-B experiments—two versions of websites appear to matched sets of customers to see reactions. • Google runs 100+ experiments a day.

15. Successful Practice One form of this is in “price-optimization software” that looks to past sales to determine where to set initial prices today and when to begin to discount. This helps avoid panic discounting if initial sales are weaker than expected. Nordstrom’s attributed much of its increase in profit margin from 5.2% in 2004 to 10.6% in 2006 to impact of the software.

16. Changing World Harrah’s casino was second tier. New CEO made it first tier as Caesar’s. Focus on data about customers from Total Rewards loyalty cards. Tested new promotions, price points, services, workflows, employee incentive plans and casino layouts. Let the customers tell you want they want. Roy Kohavi (Microsoft): Objective data are replacing HiPPOs (Highest Paid Person’s Opinions) as the basis for decision making—better cost control and better customer service.