Forecasting ReturnsP.Chandiran

LIBA

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

Importance of Forecasting EOL 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

Importance of Forecasting for commercial returns

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

Forecasting -Definition

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

Proportions of product returns depend on

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.

Approaches

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)

Data required for forecasting

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.

How to calculate return probability?

Mt=prn(1)nt-1+prn(2)nt-2+…..+ prn(t-1)n1+Et

P-probability that a product will ever be returnedRn(k)-probability that the product will be returned after k periodst-periodEt ~ N(0,ơ2)

A model for forecasting using Period-level information

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

Model

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

Item level information usuage

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

Item level tracking

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

Forecasting based on PLC

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)

Other ideas

Return policy Type of product Type of customer Return process

Commercial Returns-Major factors

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.

Issues

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

Reducing returns