1. P.Chandiran LIBA

2.  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.  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 returns is predicting the timing and quantity of returns within a given system based on past sales and return data.

5.  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.  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 v p or not at all.

7.  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.  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. Mt=pr n (1)n t-1 +pr n (2)n t-2 +…..+ pr n (t-1)n 1 +Et P-probability that a product will ever be returned R n (k)-probability that the product will be returned after k periods t-period Et ~ N(0,ơ 2 )

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

11.  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.  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.  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.  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.  Return policy  Type of product  Type of customer  Return process

16.  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.  Clear packaging  Follow up calls  Toll free help lines  Information sharing on reason for returns