Abstract

The main content of this research is about the ATP (Available to promise) allocation problem based on Assemble-to-Order Supply Chain. In the case of Multiple-Suppliers, the synchronous planning method is selected to discuss the supply plan and demand plan and an ATP allocation decision model adapted to ATO supply chain production environment is established. Based on the equal-weight combination forecasting method, the forecast demand was input into the ATP distribution model. The mixed integer linear programming method was adopted to solve the calculation and analyze the cost and benefit. Finally, the optimal ATP distribution scheme was obtained.

Highlights

  • In today’s competitive market, the importance of customers is attracting more and more attention from companies

  • In the case of Multiple-Suppliers, the synchronous planning method is selected to discuss the supply plan and demand plan and an ATP allocation decision model adapted to ATO supply chain production environment is established

  • This paper expects to solve the problem of rational allocation of resources under the condition of short supply by studying the ATP allocation problem of ATO type production enterprises, so as to improve the response speed of supply chain and make reliable order fulfillment commitment

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Summary

Introduction

In today’s competitive market, the importance of customers is attracting more and more attention from companies. How to maintain the relationship with key customers has become an important part of enterprise development. As a principle in the supply chain, the accurate and timely commitment of production enterprises to orders has become one of the important indicators to measure their competitiveness. The factors that affect the timely delivery of enterprises mainly come from the uncertainty of the supply chain. In order to reduce the loss caused by the failure to fulfill orders on time caused by the uncertainty, it is necessary to allocation in advance the enterprise’s supply chain ATP (Available to Promise).

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