Multi-stage hybrid model for supplier selection and order allocation considering disruption risks and disruptive technologies
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)
- et al.
Industry 4.0 implications in logistics: an overview
Procedia Manufacturing
(2017) - et al.
Intelligent sustainable supplier selection using multi-agent technology: theory and application for industry 4.0 supply chains
Comput. Ind. Eng.
(2019) - et al.
Using Internet of Things technologies for a collaborative supply chain: application to tracking of pallets and containers
Procedia Computer Science
(2015) - et al.
Industry 4.0 and the current status as well as future prospects on logistics
Comput. Ind.
(2017) - et al.
Review of quantitative methods for supply chain resilience analysis
Transport. Res. Part E
(2019) - et al.
A Bayesian network model for resilience-based supplier selection
Int. J. Prod. Econ.
(2016) - et al.
Resilient supplier selection and optimal order allocation under disruption risks
Int. J. Prod. Econ.
(2019) - et al.
Additive manufacturing in the spare parts supply chain
Comput. Ind.
(2014) - et al.
Supply network disruption and resilience: a network structural perspective
J. Oper. Manag.
(2015) A fuzzy multi-objective programming approach for determination of resilient supply portfolio under supply failure risks
J. Purch. Supply Manag.
(2017)
Radial DEA models without inputs or without outputs
Eur. J. Oper. Res.
Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS
Eur. J. Oper. Res.
Challenges for the cyber-physical manufacturing enterprises of the future
Annu. Rev. Contr.
Advanced traceability system in aquaculture supply chain
J. Food Eng.
Integrated business continuity and disaster recovery planning: towards organizational resilience
Eur. J. Oper. Res.
Selection of resilient supply portfolio under disruption risks
Omega
A quantitative analysis of disruption risk in a multi-echelon supply chain
Int. J. Prod. Econ.
What does industry 4.0 mean to supply chain?
Procedia Manufacturing
Resilient supplier selection and order allocation under operational and disruption risks
Transport. Res. E Logist. Transport. Rev.
Single or dual sourcing: decision-making in the presence of supply chain disruption risks
Omega
Dynamic replenishment, production, and pricing decisions, in the face of supply disruption and random price-sensitive demand
Int. J. Prod. Econ.
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.
Towards industry 4.0: mapping digital technologies for supply chain management-marketing integration
Bus. Process Manag. J.
Mobile supply chain management in the industry 4.0 era: an annotated bibliography and guide for future research
J. Enterprise Inf. Manag.
Toward a resilient supply chain with supply disturbances
Supplier selection problem: a comparison of the total cost of ownership and analytic hierarchy process approaches
Supply Chain Manag.: Int. J.
How virtualization, decentralization and network building change the manufacturing landscape: an industry 4.0 perspective
International journal of mechanical, industrial science and engineering
A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing
Int. J. Inf. Manag.
Supply-chain breakdown
MIT Sloan Manag. Rev.
Increasing supply chain resilience in a global sourcing context
Prod. Plann. Contr.
Ripple effect in the supply chain: an analysis and recent literature
Int. J. Prod. Res.
An investigation of lean and green supply chain in the industry 4.0
The design of a responsive sustainable supply chain network under uncertainty
Int. J. Adv. Manuf. Technol.
Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience
Int. J. Prod. Res.
Antecedents of resilient supply chains: an empirical study
IEEE Trans. Eng. Manag.
Information technology as an enabler of supply chain collaboration: a dynamic‐capabilities perspective
J. Supply Chain Manag.
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