Data Mining in SCM: Use of Bayesian Networks in Inventory Management

Combining Prior knowledge with data analysis

Effective inventory management is a crucial part of the supply chain of many organizations. Big Corporations such as Wal-Mart are always praised for maintaining a low inventory turnover while not facing shortage [1].

To maintain their inventory at an acceptable level, SCM executives use either forecast demand based on historical data or scheduled requirements demand based on known customers’ orders. There are many other techniques to manage inventory: renew inventory when if falls below certain threshold, holding a safety stock in case the demand cannot be precisely forecasted [2].

One would argue that all these techniques can give desirable results but present some limitations because new demand may be different from historical demand due to external factors and clients can cancel orders due to change in the economy or a shift in their own customers’ demand. When those changes can be predicted either by data, expertise or inquiry, a company would probably benefit from combining their data with such analysis so that they do not face disastrous situations.

A Bayesian network is the use of prior knowledge along with data to determine the outcome of the combination of certain factors. Even though partially based on beliefs or expertise knowledge, such networks have proven to be very reliable [3].

Two years ago, I was working for a major shipping line company and one of the challenges we were facing was the effective handling of our containers and stuffing products’ inventory. The inventory management was crucial because we did not want to have idle containers that would incur daily storage charges while they could be used in some places such as China where the demand was high. At the same time, we had 80% of the local market and would not want our customers to the competition just because we did not have enough containers or stuffing material.

This was challenging because we had customers that shipped seasonally (cashew nuts exporters or toys importers for Christmas and New Year) but their volume was closely dependent on external factors. For instance, the cashew nuts volume will be impacted by the local price that can be determined by the rainy season or the inflation and the global market demand that can be changed by a bad season in Brazil for example or a low demand in India. Thus when forecasting the volume, we would not only rely on the forecast demand based on historical volume and clients’ previsions but also on our knowledge of all external factors that can affect the overall volume.

_{Fig1: Partial representation of a BN, without probabilities}

 

We were informally using a Bayesian network to determine the probability of the volume being low or high based on our prior knowledge of the probabilities and conditional probabilities associated to every combination of events. For example, a political instability would lead to inflation on prices and we would know that the exporters could not afford to ship their forecasted volume.

 I believe that many companies use belief networks in their supply chain management but they fail to do it professionally by implementing an information system that would incorporate it into their SCM system. Such system would incorporate all the variables and can even get better by learning form constant data addition.

The Bayesian network is just an example of a data mining technique that can be added to traditional inventory management systems. With the development of analytics and BI, the companies are given new innovative ways to enhance their supply chain management and this might be the key differentiator between a good SCM and a great one.

 REFERENCES

1 - http://www.usanfranonline.com/wal-mart-successful-supply-chain-management/; accessed on September 11, 2013

2 - Managing Inventories—Reorder Point Systems (UVA-OM-0936) , (Freeland, Landel, and Weiss, Darden Business Publishing, 2000 and 2003)

3 - http://www.autonlab.org/tutorials/bayesnet09.pdf; accessed on September 11, 2013