Forecasting Returns

1. Forecasting Returns P.Chandiran LIBA

2. Importance of Forecasting EOL returns • Major input for PPC for remanufacturing • Plan for procurement decisions w.r.t. new components or products. • Plan for capacity for processing returns and disposal • Planning routing and scheduling in reverse logistics

3. Importance of Forecasting for commercial returns • To decide planning of disposal • To plan repackaging • To plan reverse logistics • Committing resources for reverse logistics • To plan how to reduce, reuse and recycle returns

4. Forecasting -Definition • Forecasting returns is predicting the timing and quantity of returns within a given system based on past sales and return data.

5. Proportions of product returns depend on • The design of the product • Collection system • The customer interface • The mean life of the product • Innovation in the market • Consumer awareness about returns, recycling and environmental issues • Reverse channel system

6. Approaches • Key to forecasting EOL returns is to observe that returns in one period are generated by sales in the preceding periods. • A sale in the current period will generate a return for ‘p’ periods from now with probability vp or not at all.

7. Data required for forecasting • Period-level information in terms of total sales and return volume in each period (eg. Beverage containers, toner cartridges) • Item level information-sales and return dates of each product are known (Copiers, PCs)

8. How to calculate return probability? • Can be calculated as a ratio of Cumulative returns to cumulative sales over a period of time. • It gives only probability and no return delay is inferred from this.

9. A model for forecasting using Period-level information Mt=prn(1)nt-1+prn(2)nt-2+…..+ prn(t-1)n1+Et P-probability that a product will ever be returned Rn(k)-probability that the product will be returned after k periods t-period Et ~ N(0,ơ2)

10. Model • In this model, if a camera was sold in period t, the probability it comes back in period t+k is prn(k). • Mt-return quantity in period t • Nt=sales in period t

11. Item level information usuage • When items are tracked on an individual basis, it is possible to determine the exact return delay of returned items • If the item is not returned yet, it is known that the delay is longer than the elapsed time or possibly infinite • Expectation maximization algorithm used to compute maximum likelihood estimates given information

12. Item level tracking • Companies use Sensors • Some can use RFID tags • GPS and other satellite based systems can be used • Computerization of product data is important • Service centres may play an important role here

13. Forecasting based on PLC • If a product is early PLC, the return rate will be less. • If a product is in mature stage of PLC, the return rate will increase • 100% returns is not possible

14. Other ideas • Make it compulsory to return the old product if they want to buy new one • Give discount to increase returns while selling new products • Returns can be formulated as a function of different factors like Price, incentive for old product return, product condition, awareness etc.,(Regression Model)

15. Commercial Returns-Major factors • Return policy • Type of product • Type of customer • Return process

16. Issues • Lenient return policy may increase demand for a product but it may also increase returns • A lenient policy acts as a signal of quality much like a warranty • Return policies allow customers to test the product • Full return policy maximize profit only if customers are sufficiently risk averse.

17. Reducing returns • Clear packaging • Follow up calls • Toll free help lines • Information sharing on reason for returns