Next Generation of Supply-Chain Cost-Evaluation Systems
Accurate evaluation of cost in a supply chain (SC) is a key component in decision-making. The SC decision-making process selects the best possible actions based on the available cost information-: therefore, the role of an effective cost-estimation system is vital in the success of SC decisions. However, currently many SC cost-related figures are evaluated based on aggregate numbers and averages over the same type of SC entities. For example, cost per mile for a truck is estimated using standardized or average numbers. Such averages are calculated by dividing the total travel cost of a set of trucks by the number of trucks in that set. Such averaging may fail to determine the efficiency and effectiveness of individual trucks if they have major differences with their standard model assumptions or their average numbers-: therefore, the decision-making process based on the current cost information may be flawed and ineffective.
At present, we are producing a set of abstract models that will serve to identify and capture the cost-related data at an entity level. By the word “entity” we mean the individual components of an SC that have predefined missions. For example, a truck is an entity and its mission is to transfer materials between different geographical locations. We are specifically interested in entities whose operations data can be automatically captured, recorded, and transferred to other SC components. In the truck example, truck operations such as fuel consumption, load weight, location, and origin or destination can be captured automatically using the current tools and technologies. Our goal is to generate more accurate cost information by bringing the automatic data collection and monitoring to the entity level of an SC.
In the last two decades, the advances in automation and information technologies have revolutionized the SC operations and information systems, but the cost-evaluation systems of SC have not improved accordingly. Today, most cost-evaluation systems are based on very old methods. These methods in principle use averaging processes frequently. The reason that these methods used averaging in the first place was because they could not capture individual entity information. In fact no technology was available for this cost-related information capture until recently. Advances in information technology have made it possible to capture the data even at the entity level-: therefore, the question remains if averaging is feasible financially and operationally. This project will address question by proposing the required automation models at the entity level and evaluating their feasibility.
Various Levels of Aggregation in SC
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