A Statistical Based Model to Manage Perishable Goods Within a Cold Supply Chain

"Cold chain" management, the management of a temperature sensitive supply chain, has its own particular set of logistical challenges and issues when compared to a non-temperature sensitive supply chain. Current supply chain management techniques and tools that are utilized today in industry do not take this unique set of challenges into account. This thesis will focus on how using a model that has been built upon the theories of statistical process control techniques that does take the challenges into account, when monitoring and evaluating in-transit shipment of perishable goods throughout the cold supply chain, can assist in continuously improving the cold supply chain, accomplishing the ultimate goal for every perishables retailer, delivering cheaper, better, and fast moving products, that are not only safe but top quality products to happy end customers world-wide.

The goal of this thesis is to educate individuals involved in cold supply chain management in the deficiencies of the current techniques used in industry to manage a supply chain and how a new statistically based process improvement model, which takes in account the perishable nature of cold supply chain products, can assist in the cold supply chain management and improvement process.

For this expanding market, optimizing the increasing volumes of "temperature-sensitive" material flow through the cold supply chain is not only needed but required due to the fact that the current state of the management of the cold supply chain is negatively affecting product quality and all financial bottom lines in the food industry. In addition, new and emerging pressures, stricter guidelines on quality control, improved methods and technologies for transporting perishable goods, plus tougher food safety and public health regulations, have compounded the need for cold supply chain management tools and models that aid in making cold supply chain improvements. The logistical handling of perishable food products and the quality of a cold supply chain has gained new attention since poor cold chain management can result in billions and billions of dollars of wasted perishable products for businesses.

Beef patties, for example, which have a quality standard of -10^oF, if delivered at -6^oF will be dehydrated, may now have a different cooking time than that recommended on the package, a deteriorated taste and also be subject to bacterial growth. Consequently, the consignee could refuse the shipment, implement charge backs, suffer losses due to deteriorated product, litigate or find a different carrier.

With more regulations being applied to a cold supply chain looming, businesses with a cold supply chain require tools and models that are currently unavailable to assist in managing the cold supply chain's logistic practices and processes to ensure optimal outcome of such temperature sensitive goods ultimately improving bottom lines, which is especially important since most businesses who are involved in a cold supply chain run on razor thin profit margins. This thesis has been designed to help such mentioned individuals in their cold supply chain management via a statistically based proven cold supply chain model for continuous improvement.