|M.Sc Student||Korchatov Evgeni|
|Subject||The Value of Inventory Accuracy in Supply Chain Management:|
Correlation between Error Sources and Proactive
|Department||Department of Industrial Engineering and Management||Supervisors||Dr. Assaf Avrahami|
|Professor Yale Herer|
|Full Thesis text|
In supply chain management the idea of value of information is gaining momentum. Decision makers are aware of inaccuracies in inventory levels and therefore conduct inventory reviews to correct the discrepancies between IT records and actual inventory. Several studies have been conducted to investigate the sources of the errors and their cumulative effect on the holding costs, shortage costs, order-up-to levels and time between inventory counts. In most works the errors were independent of the demand which in reality is not accurate. In this work we use familiar inventory errors and information scenarios that were proposed by several previous papers. We propose a model that takes into consideration the correlation between inventory errors and the demand. The effect of relationship between the random variables is tested in context of several different scenarios. Each scenario contains a different level of information about the underlying demand and inventory errors. We then analyze the effect of changes of the covariance on the cost and time between inventory counts in each of the scenarios. Using these results we formulate the value of information and its dependence on the covariance. We use analytical methods to draw conclusions regarding single parameter set cases and a numerical full factorial study for average multi parameter cases. In both cases we show that the value of information reduces as the covariance increases. Moreover, the reduction is more significant when the information scenario makes less assumptions. The same behavior is observed in stock review frequency. As covariance increases, optimal number of periods between inventory reviews drops sharply. Finally, we propose several simple methods for proactive error correction. We show that without prior knowledge, these methods perform better than the basic information scenario. Using these results we are able to formulate recommendations to businesses with different profiles of correlation between demand and demand and errors. For example, automated warehouses with weak correlation compared to grocery store.