Multi-stage hybrid model for supplier selection and order allocation considering disruption risks and disruptive technologies

https://doi.org/10.1016/j.ijpe.2020.107830Get rights and content

Abstract

In recent times, Supply chains are required to undergo the structural changes in order to adapt to the positive events such as Industry 4.0 and negative events such as natural and man-made disasters. Both positive and negative events tend to cause disruptions and affect business operations continuity. Supplier selection, being the critical and foremost activity must ensure that selected suppliers are capable of supporting the organizations against disruptions caused by these events. Hence, supplier selection and order allocation must be restructured considering the dynamics of Industry 4.0 and disaster events to ensure undisrupted flow of materials across supply chain. The paper proposes a multi stage hybrid model for integrated supplier segmentation, selection and order allocation considering risks and disruptions. The suppliers are then evaluated based on set of criteria suitable in Industry 4.0 environment using Data Envelopment Analysis (DEA) and are further prioritized using Fuzzy Analytical Hierarchical Process and Technique for Order of Preference by Similarity to Ideal Solution (FAHP-TOPSIS). The risk associated with each supplier is computed. The paper also proposes a Mixed Integer Program (MIP) as to optimize multi-period, multi item order allocation to suppliers in such a way that overall cost and risk of disruption is simultaneously minimized. In event of any disruption either from supply or demand side, the multi-stage hybrid model tends to reduce its economic impact by allocating emergency orders, thus, ensuring business operations continuity. The proposed multi-stage hybrid model is illustrated using a case of an automobile company.

Introduction

The fourth industrial revolution-Industry 4.0 has led to a significant change in every business function not just manufacturing. The business organizations are in the process of re-structuring their supply chains using the three main components of Industry 4.0: cyber-physical systems, Internet of things and smart factories to meet highly volatile and uncertain demand (Ghadimi et al., 2019). The disruptive technologies must be embedded into the entire supply chain structural design (Dolgui et al., 2018; Tjahjono et al., 2017) because an organization cannot truly benefit from disruptive technologies if its other supply chain partners are still functioning in conventional ways. In addition to disruptive technologies, ensuring business continuity in the event of disaster is also an equally important concern for the global and complex supply chains. The natural and manmade disasters lead to supply shortages and disruptions across supply chains (Sheffi, 2001, 2015) and supply chains must be re-structured in order to avoid the potential risks of disruptions as a part of their disaster preparedness (Wunnava, 2011; Sahebjamnia et al., 2015). Both the industrial revolution (positive events) and natural and manmade disasters (negative events) can cause disruptions if supply chains are not continuously adapted or aligned towards them. The disruption is the temporary or permanent loss in business functionality due to any unprecedented event (Tierney, 2007). Therefore, there is a need of supply chain structural design changes to protect against the risk of technological advancements as well as disruptions caused by disasters.

Suppliers plays a vital role in efficient functioning of entire supply chain. The organizations evolving into Industry 4.0 may not successfully realize the benefits of Industry 4.0 (such as transparency, visualization and automation) if the suppliers are still functioning in the conventional manner. The supplier base much be technically as well as technologically competent to match the requirements of an interconnected value chain (Müller, 2019). The process of selecting the supplier base and allocation of the final orders must be re-structured to incorporate the potential challenges caused by disruptive technologies as well as the disruption risks caused by disasters (Bhutta and Huq, 2002; Ivanov and Sokolov, 2012). In addition, the suppliers must be selected based upon their potential ability to support the organization during any crisis or disaster (Parmar et al., 2010). Hence, the supplier selection process must also consider the supplier's resilience or ability to mitigate risks. The research frameworks for resilient supplier selection proposed in literature are mostly proactive in nature (Torabi et al., 2015; Haldar et al., 2014). However, the occurrence of disaster is inevitable and it can disturb the entire supply chain dynamics if not handled efficiently. Therefore, process of supplier selection and order allocation must also be adaptive in nature and be able to minimize the impact of disruptions from propagating downstream.

In view of this, the paper proposes a multi-stage hybrid model for structural design of supplier selection and order allocation in presence of disruptive technologies and disruption risks. The entire structure of supplier selection and order allocation process is redesigned starting from criteria definition to final order allocations. The technological criteria for supplier selection in identified and evaluated using Fuzzy AHP in order to match the dynamics of Industry 4.0 as well as the growing need of business resilience. The suppliers are segmented using the Data Envelopment Analysis (DEA) as efficient and inefficient suppliers based on their performance on the set of criteria. The inefficient suppliers are not considered for further evaluation and order allocations. The efficient set of suppliers is further evaluated and prioritized using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The paper proposes the method to compute the average risk percentage for each supplier which is associated with each supplier. Finally, the paper proposes a Mixed Integer Program (MIP) considering disruption risk in Industry 4.0 environment and allocates the orders to the suppliers for multiple items over a multi period planning horizon such that total cost of procurement and risk associated can be minimized. The proposed MIP is further extended to model the demand and supply side disruption scenarios and emergency order allocation to optimize the overall cost and risk. The proposed multi-stage hybrid model is demonstrated using a case illustration of an automobile company where supplier selection process in being upgraded to address the dynamics of disruptive technologies and disaster caused disruptions. The findings of the study suggest that proactive segmentation and selection of suppliers to reduce the risk of disruption can actually support an organization to handle actual disruption scenarios to minimize the cost impact of disruptions and ensure the continuity of business operations. However, it has also been observed that in case of disaster, when demand unexpectedly fluctuates or supplier capacity is disrupted, the regular orders may not meet the demand. Therefore, the extended MIP proposed in paper attempts to ensure business continuity by allocating emergency orders in cost efficient manner. The research attempts to highlight the role of supplier's technological capabilities in the business resilience and ensuring business operations continuity.

The rest of the paper as structured as follows. Section 2 presents the review of literature. Section 3 discusses the problem statement and the proposed multi-stage hybrid model. Section 4 demonstrates the proposed model with the help of a case illustration. Section 5 discuss the managerial implications and theoretical contributions of the paper. Section 6 presents the conclusion and future scope of the study.

Section snippets

Literature review

This section reviews the literature on supplier selection problem. Section 2.1 reviews the supply chain models considering disruption risks, Section 2.2 reviews the supply chain models from Industry 4.0 perspective and Section 2.3 studies the supplier selection models under disruptive technologies and disruption risks. Section 2.4 identifies research gaps and research objectives are framed.

Problem statement

The paper addresses the restructuring of the supplier selection and order allocation process of a global manufacturing company which is in process of transition into an Industry 4.0 and the company also intend to minimize the risk of disruptions due to any disaster event. Therefore, the suppliers must be selected in such a manner that they are technologically competent so that the company can realize the actual benefits of Industry 4.0 and ensure the undisrupted flow of materials to its

Case illustration: XYZ automobiles private limited

In this section, the proposed multi stage hybrid model for supplier selection and order allocation is illustrated using the case of XYZ Automobiles Pvt. Ltd. XYZ has been a market leader in automobiles for several years despite encountering some disruptions from time to time. In recent years, the company has been investing heavily in disruptive technologies and hence bringing in major structural changes in its overall functioning. XYZ has realized that it has not been able to gain any

Managerial implications

The outcomes of the proposed research can help supply chain managers to reconfigure their conventional supplier selection and order allocation process to match the changing dynamics of the industry. The proposed framework can be used as a decision making tool by the business organization which can assist supply chain managers in evaluation of supplier alternatives on their technological capabilities as well as their ability to ensure business continuity against disruptions from disasters. The

Conclusions and future scope of work

The fourth industrial revolution has led to the need of technologically upgrading the supply chains and for companies to identify the technologically competent partners. In addition to this, global supply chains also have to select the supply chain partners considering the risk of disruptions caused by natural and man-made disasters. Therefore, the paper addresses the reconfiguration of supplier selection and order allocation process broadly from two perspectives i.e. disruptive technologies

References (72)

  • C.K. Lovell et al.

    Radial DEA models without inputs or without outputs

    Eur. J. Oper. Res.

    (1999)
  • S. Opricovic et al.

    Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS

    Eur. J. Oper. Res.

    (2004)
  • H. Panetto et al.

    Challenges for the cyber-physical manufacturing enterprises of the future

    Annu. Rev. Contr.

    (2019)
  • A. Parreño-Marchante et al.

    Advanced traceability system in aquaculture supply chain

    J. Food Eng.

    (2014)
  • N. Sahebjamnia et al.

    Integrated business continuity and disaster recovery planning: towards organizational resilience

    Eur. J. Oper. Res.

    (2015)
  • T. Sawik

    Selection of resilient supply portfolio under disruption risks

    Omega

    (2013)
  • A.J. Schmitt et al.

    A quantitative analysis of disruption risk in a multi-echelon supply chain

    Int. J. Prod. Econ.

    (2012)
  • B. Tjahjono et al.

    What does industry 4.0 mean to supply chain?

    Procedia Manufacturing

    (2017)
  • S.A. Torabi et al.

    Resilient supplier selection and order allocation under operational and disruption risks

    Transport. Res. E Logist. Transport. Rev.

    (2015)
  • H. Yu et al.

    Single or dual sourcing: decision-making in the presence of supply chain disruption risks

    Omega

    (2009)
  • S.X. Zhu

    Dynamic replenishment, production, and pricing decisions, in the face of supply disruption and random price-sensitive demand

    Int. J. Prod. Econ.

    (2013)
  • N. Altay et al.

    Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within the humanitarian setting: a dynamic capability view

    Prod. Plann. Contr.

    (2018)
  • L. Ardito et al.

    Towards industry 4.0: mapping digital technologies for supply chain management-marketing integration

    Bus. Process Manag. J.

    (2019)
  • J. Barata et al.

    Mobile supply chain management in the industry 4.0 era: an annotated bibliography and guide for future research

    J. Enterprise Inf. Manag.

    (2018)
  • A.P. Barroso et al.

    Toward a resilient supply chain with supply disturbances

  • K.S. Bhutta et al.

    Supplier selection problem: a comparison of the total cost of ownership and analytic hierarchy process approaches

    Supply Chain Manag.: Int. J.

    (2002)
  • M. Brettel et al.

    How virtualization, decentralization and network building change the manufacturing landscape: an industry 4.0 perspective

    International journal of mechanical, industrial science and engineering

    (2014)
  • I.M. Cavalcantea et al.

    A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing

    Int. J. Inf. Manag.

    (2019)
  • S. Chopra et al.

    Supply-chain breakdown

    MIT Sloan Manag. Rev.

    (2004)
  • C. Colicchia et al.

    Increasing supply chain resilience in a global sourcing context

    Prod. Plann. Contr.

    (2010)
  • A. Dolgui et al.

    Ripple effect in the supply chain: an analysis and recent literature

    Int. J. Prod. Res.

    (2018)
  • S. Duarte et al.

    An investigation of lean and green supply chain in the industry 4.0

  • R. Dubey et al.

    The design of a responsive sustainable supply chain network under uncertainty

    Int. J. Adv. Manuf. Technol.

    (2015)
  • R. Dubey et al.

    Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience

    Int. J. Prod. Res.

    (2019)
  • R. Dubey et al.

    Antecedents of resilient supply chains: an empirical study

    IEEE Trans. Eng. Manag.

    (2017)
  • S.E. Fawcett et al.

    Information technology as an enabler of supply chain collaboration: a dynamic‐capabilities perspective

    J. Supply Chain Manag.

    (2011)
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