Component part standardization: An analysis of commonality sources and indices

Abstract

Abstract The importance of higher component part standardization has been recognized as an important area of empirical investigation since it has been hypothesized to reduce inventory levels by reducing safety requirements, to reduce planned load through larger lot sizes, and to reduce planning complexity through reducing number of items to be planned. Therefore, component part standardization offers considerable promise for managers wishing to improve their production capabilities. In order to achieve higher standardization, measures indicating the degree of standardization are necessary. The most traditional measure of component part standardization is the degree of commonality index (DCI), which indicates the average number of uses per component parts. Unfortunately, this measure has many theoretical limitations. First, it is a cardinal measure and, therefore, cannot measure the degree of uncommon part numbers that frequently cause production planning problems. Additionally, as a cardinal measure, it cannot be used to compare planning across organizations and is not useful for making summary comparisons of planning complexities across organizations. This study develops a relative index that has boundaries of standardization between 0 and 1 corresponding to the lay language usage‐each item being unique (no standardization) and one item used everywhere (complete standardization). A second major weakness of the DCI is that it does not recognize sources of standardization for decision making. There are at least three principal decisions on which component part standardization indices can provide information: 1) within‐product decisions, 2) between‐product decisions, and 3) make‐buy decisions. The within‐product decision refers to using each component as frequently as possible within each end item. This increased usage means fewer unique items within each end item and is expected to reduce that item's planning complexity. The between‐product index is used to examine the design of new end items. Its purpose is to give indications of additional planning complexity by the increased number of new component parts. Hence, between‐product indices give information for reducing planning complexity with the introduction of new products. This article develops these two types of indices to indicate the relative proliferation of new component parts. The make‐buy decision involves indices that adequately describe manufactured versus made components. For the manufactured components, the level index computes the commonality by each product level of the bill of material. This index gives information for the design of a new, more automated manufacturing system by analyzing each level's standardization for possible inclusion into a more automated system (group technology, cell manufacturing, flexible manufacturing system, or automated factory). For the buy decision, the indices developed here can be utilized for reduction of the number of vendors due to “uncommon” components. Both level and buy indices can be used for analyzing and reducing the planning complexity of the production system. A third fundamental problem with DCI is its lack of realistic dimensions of end‐item volume, quantity per assembly, and cost. The DCI weights each end item precisely equally regardless of its volume. Therefore, an item produced once every two years would be weighted exactly the same as the best selling item. Similarly, the DCI does not consider the quantity per assembly (Q/A) of each component. Consequently, an item that had very small (Q/A) inside of an item has exactly the same weight as a high (Q/A) inside an item. Last, the DCI ignored the price of the component, which further limits its usefulness by causing the DCI not to have cost dimensions. This study developed relative within‐product, between‐product, and total commonality indices that include end‐item volumes, quantities per assembly, and component price. These indices can provide valuable insights for reducing relative planning complexities that improve system performance through analyzing relative costs of component parts usages.

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