Wednesday, September 11, 2013

The Bullwhip Effect on Inventory Management

In 2002, ‘The Economist’ published an article on Professor Hau Lee’s curious and comic manner of teaching ‘The Bullwhip Effect’. Prof. Lee is a guru of Supply Chain at the Stanford Business School and explained the ‘bullwhip’ and it's effect on inventory management with a set of interesting examples in the interview. He and his colleagues at the Stanford Global Supply Chain Management Forum are in business to help firms run their supply chains more efficiently and effectively.

“The ‘bullwhip effect’ is named after the way the amplitude of a whip increases down its length — just as variations in orders tend to get amplified along the supply chain.” – Prof. Hau Lee, The Economist

He explained the concept with the following example: Procter & Gamble has to deal with widely fluctuating orders for its nappies, even when babies' consumption is generally quite steady. The reason is that each retailer bases his orders on his own, slightly exaggerated, forecast, thus increasingly distorting the information about real consumer demand. This is one of the most important causes of inefficiency in a supply chain.

Here is a video of Prof. Lee explaining the ‘bullwhip’.

Prof. Lee and his colleagues concentrated on supply-chain integration and ways of constantly monitoring and improving the whole system by using all the available data. The article discussed technologies being used by analysts and a few start-ups which are necessary to reduce the “bullwhip effect”. For example, software to speed up the information exchange with their partners and collaborate on planning. It is an interesting read on methods employed by people at different stages of the supply chain to use information to the fullest. They could be manual data mining techniques or long algorithm-based-software; they are used with the same objective of reducing uncertainty. As having too much or too less inventory stock is never good for a firm.

One such example of manual data mining can be seen in Seven Eleven Japan, a chain of convenience stores, where the cashiers have been instructed to record the sex and estimated age of each customer so it can set out its shelves in the most convenient way. That is why beer can now be found right next to ladies' stockings: the data showed that those stockings are bought mostly by men on their way home from work.

Similarly, Zara, a Spanish clothing giant, uses sales data to introduce new products all the time, about 12,000 each year. Its supply chain is so flexible that the lead time from designing a new piece of clothing to selling it in the shops is only two or three weeks.

What I look forward to learning from this week are the most effective technologies which firms have employed for data mining in the past? Because there is a wealth of information and techniques out there, but which tools have been tried, tested and are the best available in the market for data mining/inventory management?

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