Abstract

Abstract Paper aims Mixing the Make-To-Stock (MTS) and Make-To-Order (MTO) strategies to benefit from the both manufacturing systems in an environment with impatient customers. Originality This is the first research article which uses the queuing theory to find the best place of the Order Penetration Point (OPP) in a production line with impatient customers. Research method Two scenarios are studied in this paper. 1- Semi-finished products are produced and stored in a buffer. No semi-finished product will be completed until a specific order comes to the system. This strategy leads to idle cost, but there is savings obtained in terms of eliminating investment in finished goods inventory and its holding cost. We can calculate the total cost of the system and find the optimal machine for the buffer of semi-finished products. 2- We use both MTS and MTO for completing the semi-finished products. When there is no customer in the system, semi-finished products are completed based on the MTS strategy and finished products are sent to a warehouse. But, when an order comes in for customization, a semi-finished product get assigned to that order and after finishing the current MTS job on each machine, this MTO job starts to complete the semi-finished product. To calculate system performance indexes, we use the Matrix Geometric Method (MGM) after modeling the systems with queuing theory concepts. Main findings Numerical examples show the convexity of total cost in terms of product completion percentage and number of customization lines after the OPP. Also, increasing the production rate leads to higher expected number of semi-finished products in the buffer. Implications for theory and practice Positioning OPP in manufacturing systems to compare different production strategies (MTS/MTO) has not been widely studied yet. This paper shows how manufacturing companies can apply the OPP to obtain benefits from both MTS and MTO strategies according to various cost parameters of the production lines. Using queuing theory concepts to model the problem under study helps to consider the external factors such as impatient customers and demand arrival uncertainty that can affect the performance measures of the system besides the internal factors such as production rate and inventory related costs. The idea of expanding the production line after a specific station and have more customization lines to improve customer satisfaction is studied in this paper as well.

Highlights

  • Balance of supply and demand has been one of the most important issues in manufacturing companies in recent decades

  • The motivation of this paper is to show how manufacturing companies can apply the Order Penetration Point (OPP) to obtain benefits from both MTS and MTO strategies according to various cost parameters of the production lines

  • In this article a model is developed to show the application of Make to Stock (MTS)/Make to Order (MTO) manufacturing in a production line to derive the benefits of both MTS and MTO strategies

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Summary

Introduction

Balance of supply and demand has been one of the most important issues in manufacturing companies in recent decades. Manufacturing companies look for an inventory and supply management approach which can obtain the most benefit out of the investment in raw materials, work in process, and finished products. The system under study tries to mix the Make-To-Stock (MTS) and Make-To-Order (MTO) strategies to benefit from the both manufacturing systems This idea is developed by applying the Order Penetration Point (OPP) in production lines. The positioning of OPP is widely studied as a challenging issue in supply chain management in recent decades Considering this concept in manufacturing systems to compare different production strategies (MTS/MTO) has not been widely studied yet. The motivation of this paper is to show how manufacturing companies can apply the OPP to obtain benefits from both MTS and MTO strategies according to various cost parameters of the production lines. A numerical example with a vast sensitivity analysis is presented in Section 5, and Section 6 concludes the study and discusses the future study paths

Literature review
Objective
Problem description
Scenario 1
Scenario 2
Problem formulation and system performance measures
State transition diagrams and balance equations
Performance evaluation measures
Model of the first Scenario
Model of the second Scenario
Numerical example
Total cost variations against λ fluctuations
System performance measures versus μ fluctuations
Findings
Conclusions
Full Text
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