Machine scheduling with outsourcing. Coping with supply chain uncertainty with a second supplying source

• Published date: 06 May 2014
• Pages: 133-159
• Date: 06 May 2014
• DOI: https://doi.org/10.1108/IJLM-12-2012-0142
• Author: Feng Liu,Jian-Jun Wang,Haozhe Chen,De-Li Yang
• Subject Matter: Management science & operations,Logistics

Machine scheduling with

outsourcing

Coping with supply chain uncertainty with

a second supplying source

Feng Liu

School of Management Science and Engineering,

Dalian University of Technology, Dalian, P.R. China and Department of Supply

Chain Management & Marketing Sciences, Rutgers Business School,

Rutgers University, Newark, New Jersey, USA

Jian-Jun Wang

School of Management Science and Engineering,

Dalian University of Technology, Dalian, P.R. China

Haozhe Chen

College of Business, East Carolina University, Greenville,

North Carolina, USA, and

De-Li Yang

School of Management Science and Engineering,

Dalian University of Technology, Dalian, P.R. China

Abstract

Purpose – The purpose of this paper is to study the use of outsourcing as a mechanism to cope

with supply chain uncertainty, more specifically, how to deal with sudden arrival of higher priority

jobs that require immediate processing, in an in-house manufacturer’s facility from the perspective

of outsourcing. An operational level schedule of production and distribution of outsourced jobs

to the manufacturer’s facility should be determined for the subcontractor in order to achieve overall

optimality.

Design/methodology/approach – The problem is of bi-criteria in that both the transportation

cost measured by number of delivery vehicles and schedule performance measured by jobs’

delivery times. In order to obtain the problem’s Pareto front, we propose dynamic programming (DP)

heuristic solution procedure based on integrated decision making, and population-heuristic solution

procedures using different encoding schemes based on sequential decision making. Computational

studies are designed and car ried out by randomly generating comparative variations of numerical

problem instances.

Findings – By comparing several existing performance metrics for the obtained Pareto fronts, it is

found that DP heuristic outperforms population-heuristic in both solutions diversity and proximity to

optimal Pareto front. Also in population-heuristic, sub-range keys representation appears to be abetter

encoding scheme for the problem than random keys representation.

Originality/value – This study contributes to the limited yet important knowledge body on

using outsourcing approach to coping with possible supply chain disruptions in production

scheduling due to sudden customer orders. More specifically, we used modeling methodology

to confirm the importance of co llaboration with subcontractors to effective supply chain risk

management.

Keywords Uncertainty, Pareto front, Outsourcing, Dynamic programming, Machine scheduling,

Population-based heuristics

Paper type Research p aper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0957-4093.htm

Received 10 December 2012

Revised 28 May 2013

Accepted 30 September 2013

The International Journal of Logistics

Management

Vol.25 No.1, 2014

pp. 133-159

rEmeraldGroup Publishing Limited

0957-4093

DOI 10.1108/IJLM-12-2012-0142

133

Machine

scheduling with

outsourcing

1. Introduction

Modern supply chains are more vulnerable than ever (Wagner and Bode, 2008),

because companies are now exposed to many types of risks (Rao and Goldsby, 2009;

Svensson, 2000; Tang and Musa, 2011). Supply chain risks come in different forms,

ranging from drastic events such as natural disasters or terrorist attacks to other

uncertainties that companies face on a more frequent basis. Among these uncertaintie s

Christopher and Lee (2004) identified “chaos risk,” which is the result of supply chain

complexity and uncertainties, and market risks, and market risk, which refers to missing

market opportunities. The chaos effects may result from unnecessary interventions,

second guessing, mistrust, and distorted information throughout a supply chain

(Childerhouse et al., 2003). Market risks exist when a supply chain cannot be responsive to

changing market needs, and examples include companies’ inability to change production

or supplies to meet demand or failure to meet customer orders with short lead times

(Christopher and Lee, 2004). These risks can cause significant consequences such as

supply chain disruption and loss of sales or customers, which in turn will reduce

operational performance, profitability, and shareholder value over the long term (Ellis

et al., 2011; Hendricks and Singhal, 2003). Unfortunately,most companies are still struggling

to adjust their upstream functions to respondtofluctuationsintheirdownstream

demand ( Jack and Raturi, 2002). Therefore, it is crucial for companies to develop effective

mechanisms to cope with these uncertainties. The current study is thus undertaken to

contribute to the knowledge body of supply chain uncertainty coping mechanism.

In extant literature, researchers often relate outsou rcing of manufacturing to

increased supply chain risks because such arrangement will increase the fragility and

vulnerability of supply chains (Craighead et al., 2007; Tang and Musa, 2011; Wagner

and Bode, 2006, 2008; Zsidisin et al., 2005). This is true when considering that

outsourcing activities usually get more parties involved and make the processes

(particularly inter-firm) more complex. However, in today’s production industries, with

the rapid application of IT technologies and widespread of globalization, outsourcing

is playing an ever-increasing role. Generally speaking, companies outsou rce non-core

operations to other firms to save costs, focus on its core competencies, and maintain

competition advantages (Cachon and Harker, 2002; Wu et al., 2012). Therefore, we

cannot expect companies to give up outsourcing just because of the potential risks

involved. Instead, we believe it is important fo r companies to better utilize outsourcing

as a helpful means to cope with uncertainties and mitigate su pply chain risks. As Qi

(2008) suggested, the advantages brought by outsourcing could not be fully realized

unless an effective production schedule is produced. In the current study, we focus on

using outsourcing to mitigate one specific type of supply chain uncertainty – customer

order volume uncertainty. Jack and Ratu ri (2002) conducted a comprehensive empirical

study in order to identify the sources of volume flexibility: short-term sources include

overtime, inventory buffers, and capacity buffers; and long-term sources include planning

and control system improvements and workforce level adjustments. While their study

resultshave important implications, outsourcing was not mentioned. Therefore,we study

the use of outsourcing as a second supplying source can significantly enhance a

company’s capability in handling volume uncertainties.

In line with Jenkins and Wright’s (1998) argument that it feasible to enhance supply

chain flexibility through structural changes suc h as the manufacturing processes,

past researchers have studied the practice of utilizing outsourcing as a second supply

source. For example, Kim et al. (2010) pointed out firms that rely on functioning

mission-critical equipment for their businesses often outsource equipment’s restoration

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