Managing Product Complexity and Working Capital Risk Using Buyer Behavior and Hedge Packaging
Authors: Mitchell Gary , Wikstrom Erik , Belcastro Joseph 
Published: November 2021

Abstract
A manufacturing firm creates share-holder value by consistently earning a return on invested capital (ROIC) that exceeds its cost of capital (COC) (Copeland et al, 1995). The amount of value created is impacted directly by the amount of capital invested, highlighting the importance of effective capital allocation. Firms can add significant value through creative ways of balancing customer requirements and working capital needs. This paper describes a generic methodology that uses customer-buying behavior to construct product offerings that minimize working capital risk without impacting service performance.
The methodology is specifically applied in an environment where product and option offerings can result in thousands of final product configurations. In these complex manufacturing environments, high-velocity product configurations and option packages are used to create hedge packages (a form of dynamic safety stock) of components with longer lead times than market requirements for finished product delivery. In many cases, the lead times of very complex and costly components can exceed customer delivery requirements by a factor of 10. For example, customers may expect delivery of final products within one week, yet key components of the finished product may require ten or more weeks of lead-time. Further exacerbating the situation are the realities that the longest lead items are by nature very complex and costly, and sales forecasting is very difficult. If inventory is managed too aggressively, part shortages, frequent un-planning or rescheduling messages to vendors, late deliveries, poor service reliability, and lost revenues are typical results.
This paper describes a fact-based and market-based methodology to hedge the forecast of key product components and achieve the correct balance between working capital and service requirements. Central to the process is the use of the “Affinity Analysis” tool. This tool is used to process large arrays of product configuration data with the objective of recognizing significant affinities between elements. In an automobile example, when a customer purchases a manual transmission how often do they also choose sport suspension? Knowledge of all of the high-correlation or high-affinity product selections or options can be exploited in the material requirements planning process without having to individually forecast the usage of all options. Forecasting at the individual option level is very difficult and rarely correct. The knowledge of key component affinities facilitates the creation of high-velocity product configurations and option packages that maximize revenue and minimize working capital and manufacturing complexity.
The paper includes a description of the affinity analysis tool, the input and output files, how the tool is used, and how the high-velocity and hedge packages are created. Furthermore, the reader is provided with a sample application of the methodology in a complex heavy-equipment manufacturing environment.