A Spreadsheet Model That Estimates the Impact of Reduced Distribution Time on Inventory Investment Savings: What is a Day Taken Out of the Pipeline Worth in Inventory?

Abstract

In most of the literature dealing with inventory problems, either with a deterministic or probabilistic model, lead time is viewed as a prescribed constant or a stochastic variable that is not subject to control. But in many practical situations, lead time can be reduced by an extra crashing cost; in other words, it is controllable. This study proposes a repeatable spreadsheet optimization model that estimates the impact of reduced replenishment lead time on inventory investment savings at forward and strategic locations to motivate decision makers to support enterprise-wide distribution process improvement. The study provides users with a means of automatically calculating inventory control parameters such as safety stocks and reorder points, and automatically estimating the savings caused by lead time mean or variability reduction. A trade-off analysis can be done to determine whether reducing lead time would override the lead time crashing cost. First, the model finds the optimal safety factor of an item based on a fill rate goal using Excel Solver. Then, Excel's VBA automates the process of finding safety factors for other items before and after lead time reduction. Finally, the model is applied to three different supply support activities to illustrate its superior features, which include allowing the user to change and upgrade it for future research.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2012
Accession Number
ADA557282

Entities

People

  • Serhat Saylam

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Business Administration
  • Computer Programming
  • Engineering
  • Inventory Control
  • Lead Time
  • Logistics
  • Mathematical Models
  • Operations Management
  • Operations Research
  • Probabilistic Models
  • Probability
  • Random Variables
  • Safety Factor
  • Supply Chain
  • Supply Chain Management
  • United States
  • United States Transportation Command

Fields of Study

  • Business

Readers

  • Computational Modeling and Simulation
  • Logistics and Supply Chain Management.