A fuzzy multi-objective programming approach for determination of resilient supply portfolio under supply failure risks
Introduction
The purchasing function and associated decisions are a managerial priority. The cost of component parts in most companies constitutes up to 70% of the total cost (Holweg et al., 2011). In such circumstances, the purchasing department can play a key role in cost reduction. Supplier selection, especially in the area of assigning orders among appropriate suppliers, is one of the most important functions of purchasing management (Tempelmeier, 2002, Aissaoui et al., 2007). In order to optimally allocate the buyer's total demand among selected suppliers, different purchasing criteria are considered. Traditionally, studies on the supplier selection and order allocation (SS&OA) problem have expatiated on cost, quality and delivery time. However, modern supply chains are exposed to the increasing supply failure risks of unexpected natural or man-made disasters such as earthquakes, fires, floods, volcanic eruptions, hurricanes, transport accidents or equipment breakdowns, labour strikes, economic crisis or bankruptcy, deliberate sabotage or terrorist attack (Heckmann et al., 2015). Supply failure risk can be defined as ‘‘the probability that supply of an item will be affected because of problems at the supplier's end and the resulting costs as its impact’’ (Zsidisin, 2003, Sarkar and Mohapatra, 2009). Generally speaking, supply failure risk can be divided into two risk categories: operational and disruption (Tang, 2006, Torabi et al., 2015). Operational risks refer to those inherent uncertainties that inevitably exist in supply systems. These include, but are not limited to, supply uncertainty due to poor quality, environmental problems, operational inflexibility or difficulties (Torabi et al., 2015). Disruption risks refer to the major disruptions caused by unexpected natural or man-made disasters such as earthquakes, floods, volcanic eruptions, hurricanes, transport accidents, deliberate sabotage or terrorist attacks (Heckmann et al., 2015).
Most firms have reported that their supply chains are vulnerable to supply failures with large unanticipated consequences of seemingly contained incidents (Harland et al., 2003). For example, the recent earthquake and tsunami in Japan severely affected global electronics production and led to extended business disruptions for the automotive industry. In October 2011, the catastrophic floods in Thailand, where almost 1000 electronics factories were concentrated, caused business disruption in global supply chains and resulted in an estimated US$20 billion in losses (The World Bank, 2011). These disruptions are detrimental to businesses from the lost productivity and revenue standpoint. In a 2011 survey by The World Economic Forum (2011), more than 90% of respondents, almost 400 executives across 10 major industries, indicated that supply chain and transport risk management has become a greater priority in their organisations. Therefore, providing a resilient supply portfolio to protect the buyer from shortages and disruption in the supply chain is all the more critical. Resilience can be defined as “the adaptive capability of a firm to survive, adapt, and grow in the face of change and uncertainty” (Fiksel, 2006). Resilient supply portfolio, for purchasing and supply management, refers to a resilient portfolio of suppliers with flexible capability of supplying parts in the face of disruption events due to supply failure – this includes, for instance, the pre-positioned emergency output capacities at backup suppliers for crucial operations or business functions in the events (Sawik, 2013, Torabi et al., 2015).
The unanticipated consequences of supply failures and their impacts on companies’ performance have vividly demonstrated the recent need for changes regarding traditional strategies (Snyder et al., 2005). This motivated researchers and practitioners to increasingly explore how companies can overcome impacts arising from sudden and unforeseen events by means of resilient practices (Zsidisin and Wagner, 2010, Carvalho et al., 2012, Sawik, 2013, Pereira et al., 2014, Torabi et al., 2015). In this context, we review the most relevant published works addressing the SS&OA problem under supply failure risks and accounting for resilient supply chains, specifically as they use common mitigation strategies in the supply side of a supply chain for improving supply chain resilience. It is remarked that research on the SS&OA problem under supply failure risks has not been well-explored in the recent area of “resilience”. In particular, the research on quantitative approaches for building a resilient portfolio of suppliers with the consideration of proactive strategies is very limited in the current literature (Sawik, 2013). It addresses an important research gap in purchasing and supply management, especially those addressing the SS&OA problem under supply failure risk. Hence, the development of a managerial decision tool that helps procurement managers better select the supply base to cope with supply failures more effectively is considered the incentive and motivation of conducting this work.
This paper aims to develop a new decision model that considers contracting with backup suppliers a portion of allocation remaining capacity to build a resilient supply portfolio under supply failure risks. Contracting with backup suppliers is one of the important approaches aiming to ameliorate supply failure risk (Torabi et al., 2015). A portion of allocation remaining capacity can be pre-positioned at backup suppliers. The capacity of the supplier that does not fail remains unchanged under a disruption event, and the pre-positioned emergency capacity output can be used to replace non-delivered parts from failed suppliers hit by disruptions in the event.
The central question of this research is how to build a fortification (protection against supply failure) model, considering such resilient practices, that aids the decisions as to which supplier to select for parts delivery and how to allocate order quantities among the selected suppliers, and which of the selected suppliers to protect against disruptions and how to allocate emergency capacity among the backup suppliers. For a real-life supplier selection, decision makers need to specify multiple objectives with different weights and to deal with the problem of uncertainty related to the objectives. To this end, a weighted additive fuzzy multi-objective model, which has been widely used in multi-objective supplier selection problems (Amid et al., 2009, Yücel and Guneri, 2011, Shaw et al., 2012), is proposed to simultaneously consider the imprecision of information and the relative importance of objectives for determining a resilient supply portfolio against supply failures. The model accounts for the uncertainty of critical data, such as some costs being difficult to measure, and with net rejected items and net late deliveries being considered as vague goals.
To the best of our knowledge, this paper is the first in the literature to quantitatively account for protection decisions on contracting with backup suppliers’ emergency capacities against supply failure in the SS&OA problem. A number of theoretical and practical implications are concluded as a result of this study. Likewise, for these implications, the light shed on the issues underpinning the development of a resilient supply portfolio from a procurement perspective can also be considered a contribution to the purchasing and supply management literature as well as its practice. The remainder of this paper is organised as follows: Section 2 provides a review of the related literature. Section 3 includes the problem description and model development. A weighted additive fuzzy programming approach is developed in Section 4. An application case and sensitivity analysis is presented in Section 5. Finally, conclusions are made in the last section.
Section snippets
Literature review
A large number of studies are available in the literature on SS&OA problems. Here, a review of the relevant literature is presented below in two distinct but related research streams: supplier order allocation under supply failure risk and resilient supply chain.
Problem description
In this study, we analyse the problem of resilient SS&OA among the suppliers within the supply base of one single item, while minimising the total cost, minimising the net rejected items and minimising the net late deliveries. The suppliers have limited capacity of production output and offer different unit item prices with a multiple-breakpoint quantity discount. Under supply failure risks, the suppliers are subject to a unique event (denoted by ) associated with a particular supplier, and
A weighted additive fuzzy multi-objective programming approach
Applying mathematical programming models to solve real-world problems is a challenging task, because it is difficult for decision makers to accurately estimate the parameters used in these models and to specify goals and constraints exactly. For example, the total cost consists of item purchase costs, supplier maintenance cost, contracting cost, shortage loss and the emergency cost of purchasing in the event of supply failure. Some of these costs are not easily measured and hence a great deal
Application case
In this section, an application case with global and domestic suppliers is used to demonstrate the model's potential to obtain the resilient supply portfolio under global sourcing risks. We consider a manufacturer that wants to buy a component part from four available suppliers in the set of pre-qualified suppliers X, such that X={sA1, sA2, sB1, sB2}. Two local suppliers (sA1, sA2) in close proximity location A are relatively reliable but provide more expensive items, while two foreign
Conclusion
With the growing dependence on global sourcing in recent years, and the consequent increase of supply disruption, it is crucial to provide a reliable level of resilience to the supply base to protect from shortages and disruption in global supply chains. This study reviews supplier order allocation literature highlighting the significant need for quantitative approaches for the selection of a resilient supply portfolio against disruptions. Therefore, we present a new procurement decision model
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