Supplier change, market competition and enterprise innovation

PurposeThe purpose of this paper is to study enterprise innovation in the perspective of external supplier relationship. On this purpose, this paper examines the impact of supplier change on enterprise innovation with the moderating role of market competition.Design/methodology/approachUsing 2012–2020 empirical data of Chinese listed manufacturing enterprises, this paper investigates the relationship among supplier change, market competition and enterprise innovation through a two-way interaction model.FindingsThe results show that supplier change has a negative impact on enterprise innovation. And market competition intensifies the negative relationship between supplier change and enterprise innovation. Additional analyses indicate that the main effect and the moderating effect are more significant when the enterprise is non-state-owned or has lower ownership concentration.Originality/valueThis paper studies enterprise innovation from the perspective of external stakeholders. It focuses on supplier relationship in a dynamic variation view, instead of the traditional static ones. Moreover, this paper explores the contingency effect of market competition and gives practical implications for managers to adjust innovation strategy flexibly.

Criteria Clustering and Supplier Segmentation Based on Sustainable Shared Value Using BWM and PROMETHEE

With the advent of healthy visions, two of the trends that have become extremely important in the supply chain in recent decades are corporate social responsibility (CSR) and sustainability, which have affected the activities of buyers and suppliers. The next trend that is emerging is the vision of creating shared value (CSV), which wants to move the supply chain toward solving social problems in a completely strategic way. This research intends to develop a step-by-step framework for evaluating and segmenting suppliers based on CSV criteria in the supply chain. In the first stage, the criteria for creating sustainable shared value (CSSV) are obtained through existing activities in the field of CSR. The obtained criteria are then divided into two categories, strategic and critical, and then the weight of each criterion is obtained using the best–worst method (BWM). In the next step, based on the Kraljic model, the suppliers are divided into four clusters using the preference ranking organization method for enrichment evaluation (PROMETHEE) technique. This framework helps the buyer to conclude and select purchasing decisions and relationships with suppliers through the lenses of CSV and sustainability.

A data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare system

This research attempts to study the Supplier Selection and Order Allocation Problem (SSOAP) considering three crucial concepts, namely responsiveness, sustainability, and resilience. To do so, the current research develops a Multi-Stage Decision-Making Framework (MSDMF) to select potential suppliers and determine the quantity of orders. The first stage aims at computing the scores of the suppliers based on several indicators. To do this, a novel decision-making approach named the Stochastic Fuzzy Best–Worst Method (SFBWM) is developed. Then, in the second stage, a Multi-Objective Model (MOM) is suggested to deal with supplier selection and order allocation decisions. In the next step, a data-driven Fuzzy Robust Stochastic (FRS) optimization approach, based on the fuzzy robust stochastic method and the Seasonal Autoregressive Integrated Moving Average (SARIMA) methods, is employed to efficiently treat the hybrid uncertainty of the problem. Afterwards, a novel solution method named the developed Chebyshev Multi-Choice Goal Programming with Utility Function (CMCGP-UF) is developed to obtain the optimal solution. Moreover, given the crucial role of the Medical Equipment (ME) industry in society's health, especially during the recent Coronavirus disease, this important industry is taken into account. The outcomes of the first stage demonstrate that agility, cost, GHG emission, quality, robustness, and Waste Management (WM), respectively, are the most important criteria. The outcomes of the second stage determine the selected suppliers, utilized transportation systems, and established sites. It is also revealed that demand directly affects all the objective functions while increasing the rate of disruptions has a negative effect on the sustainability measures.

Integration AHP and MOORA for Sustainable Supplier Selection During the COVID-19 Pandemic Era: A Case Study

The global COVID-19 pandemic had a complex impact on the supply chain system. Manufacturing companies always strive to be able to face corporate competition and become superior with one of them through selecting the right supplier. Suppliers have the highest risk in a company, especially during the COVID-19 pandemic era, but with the correct selection of suppliers, the company can provide strength in global competition. The purpose of this research is to be able to solve the problem of sustainable supplier selection in a garment industry in Indonesia during the COVID-19 pandemic through the integration method between AHP and MOORA. AHP as a method that has been proven in many studies, in this study is used to determine the weight of each criterion. Furthermore, MOORA as a method that has good selectivity in choosing the best alternative will be used in the selection process. 12 criteria with 5 alternatives are used to determine the best supplier. The contribution of this research is the integration of the AHP and MOORA methods and the determination of important criteria in the era of the COVID-19 Pandemic. The results show that the criteria for the area with the level impact of COVID-19 (C12) have the greatest weight and supplier 3 becomes the first ranked supplier or the best supplier. The integration method between AHP and MOORA is easy to use and can choose the right sustainable supplier during the COVID-19 pandemic. © 2023 American Institute of Physics Inc.. All rights reserved.

Determinants of supplier payment times before and during the pandemic: Empirical evidence from UK firms

Timely, compliant payment to suppliers is a significant issue in purchasing and supply management (PSM) but research into its determinants is limited. Here we draw on social responsibility, fintech and digital purchasing literatures, and institutional and stakeholder theories to explain variations in supplier payment times. A sample of 509 firms spanning 2018–2021 is used for panel regression analysis. Contrary to expectations, the COVID-19 pandemic did not lead to longer payment times or reduced compliance with payment terms, which we attribute to state-funded business supports and corporate decisions to accelerate payment to vulnerable suppliers. Institutional pressure in the form of payment codes was associated with shorter payment times and improved compliance during the pandemic. Supply chain finance (SCF) was associated with longer payment times during the pandemic while e-invoicing had no effect. Overall, institutional pressure seems to contribute to faster payment but stakeholder-centred fintech and digitalisation does not.

A Markovian-based fuzzy decision-making approach for the customer-based sustainable-resilient supplier selection problem.

The supplier selection problem is one of the most important issues in supply chain management. So, many papers have investigated the mentioned problem. However, the related literature shows that researchers had less attention to the sustainability and resilience aspects based on the customer preferences in supplier selection problem. To cover this gap, this research tries to investigate the customer-based sustainable-resilient supplier selection problem. In this way, a Markovian-based fuzzy decision-making method is proposed. At the outset, the customer preferences are evaluated using a combination of the quality function deployment and the Markov transition matrix. Then, by combining the transition matrix and the fuzzy best-worst method, the weights of the indicators are calculated. Finally, the decision matrix is formed and the performance of suppliers is measured based on the multiplication of the decision matrix and vector of sub-criteria weights. Regarding the recent pandemic disruption (COVID-19), the importance of online marketplaces is highlighted more than the past. Hence, this study considers an online marketplace as a case study. Results show that in a pandemic situation, the preferences of customers when they cannot go shopping normally will change after a while. Based on the Markov steady state, these changes are from the priority of price, availability, and performance in initial time to serviceability, reliability, and availability in the future. Finally, based on the FBWM results, from the customer point of view, the top five sub-criteria for sustainable-resilient supplier selection include cost, quality, delivery, responsiveness, and service. So, based on these priorities, the case study potential suppliers are prioritized, respectively.

The role of supply chain diversification in mitigating the negative effects of supply chain disruptions in COVID-19

Purpose: This study examines the firm-level financial consequences caused by supply chain disruptions during COVID-19 and explores how firms' supply chain diversification strategies, including diversified suppliers, customers and products, moderate the negative effect on firm performance. Design/methodology/approach: Based on data drawn from 222 publicly traded firms in China, the authors use event study methodology to estimate the effects of supply chain disruptions on the financial performance of affected firms. Regression analyses are conducted to examine the moderating effects of supply chain diversification. Findings: Firms affected by supply chain disruptions during COVID-19 experienced a significant decline in shareholder value in two weeks and a subsequent decrease in operating performance in one year. Diversified suppliers, customers and products act as shock absorbers to alleviate the negative effects. Further regression shows a substitution effect between customer and product diversification. Cross-industry comparisons reveal that service firms experienced more loss than manufacturing firms. Customer diversification mitigates the adverse effects of supply chain disruptions for both manufacturing and service firms. Supplier diversification exerts a noteworthy role in manufacturing firms, while product diversification is beneficial for service firms. Originality/value: The study provides empirical evidence on the magnitude of financial consequences of supply chain disruptions during COVID-19 in both the short term and long term and enriches the current understanding of how to build resilience from the supply chain diversification perspective. © 2023, Emerald Publishing Limited.

A fuzzy multi-objective decision-making model for global green supplier selection and order allocation under quantity discounts

The selection of potential suppliers has recently become a big challenge for the manufacturing industries due to the rapid spread of covid-19 and the escalating frequency of natural calamities such as earthquakes and floods. When decision-makers (DMs) consider quantity discounts from multiple sources, things get much more complicated. Although previous studies have looked at selecting suitable suppliers from economic and environmental aspects, no one has considered foreign transportation risks while evaluating the textile industry's global green suppliers. In this regard, for the first time, this study combines economic and environmental factors with the foreign transportation risk criterion to develop a holistic model for global green supplier selection and order allocation (SS&OA) in the textile industry under all-unit quantity discounts. Initially, the fuzzy analytical hierarchy process (FAHP) method is used to calculate the relative weights of the criteria. Second, a multi-objective linear programming (MOLP) model is developed to reduce the total procurement cost, quality rejection rate, delivery lateness rate, greenhouse gas emissions from product procurement, and foreign transportation risks. Subsequently, the developed MOLP model is transformed into a fuzzy compromise programming (FCP) model to obtain order allocation quantities among selected suppliers with their offered quantity discount rates. A real-life case study of the Pakistani textile industry is presented to validate the proposed methodology's applicability by determining the optimal order allocation quantities among multiple suppliers based on two decision-making attitudes of DMs (neutral and risk-averse). Finally, sensitivity and comparative analyses are carried out to guarantee that the proposed technique produces accurate and optimal solutions. The final results of the proposed methodology show that it can effectively manage data uncertainties during SS&OA compared to other existing approaches. The suggested integrated methodology's outcomes can assist the supplier organization in overcoming its current shortcomings and developing a long-term relationship with the buyer organization. © 2023 Elsevier Ltd

Sustainable supplier selection and order allocation for multinational enterprises considering supply disruption in COVID-19 era

The unprecedented outbreak of COVID-19 has left many multinational enterprises facing extremely severe supply disruptions. Besides considering triple-bottom-line requirements, managers now also have to consider supply disruption due to the pandemic more seriously. However, existing research does not take these two key objectives into account simultaneously. To bridge this research gap, based on the characteristics of COVID-19 and similar global emergency events, this article proposes a model that aims to solve the problem of sustainable supplier selection and order allocation considering supply disruption in the COVID-19 era. It does so by using a multi-stage multi-objective optimization model applied to the different stages of development and spread of the pandemic. Then, a novel nRa-NSGA-II algorithm is proposed to solve the high-dimensional multi-objective optimization model. The applicability and effectiveness of the proposed model is illustrated in a well-known multinational producer of shortwave therapeutic instruments.JEL Classification: M11

Enhancing global supply chain resilience in the Indonesian medical device industry: a dynamic capability perspective

Purpose: This paper aims to understand how the global supply chain in the medical device industry embraces resilience by adopting agility approach following COVID-19. Design/methodology/approach: This study adopts an interpretative approach to examine the qualitative data drawn from interviews and observation under dynamic capability theory. The data collection concerned multiple stakeholders involved in purchasing and supply management in the medical device market: manufacturing suppliers, channel partners, hospital management and end-users. The coding analysis uses an application that helps the researchers categorise the nodes and extend the existing literature. Findings: The findings show that global supply chain leaders leverage the dynamic capability by centralising the business process decision to respond to the shifting demand from the local governments to the national health ministry, shaping the partnership style from the area- to the funnel-based agreement, even though it exposes a risk of product acceptability from the end-users, encouraging the distributor to convert just-in-time approach into holding safety stock to avoid penalty from missing the procurement target even and restructuring the local partners' debts to manage long-term performance. Originality/value: This study extends the emerging literature in international business by underpinning dynamic capability theory. © 2023, Emerald Publishing Limited.

Extended Multicriteria Group Decision Making with a Novel Aggregation Operator for Emergency Material Supplier Selection.

How to ensure the normal production of industries in an uncertain emergency environment has aroused a lot of concern in society. Selecting the best emergency material suppliers using the multicriteria group decision making (MCGDM) method will ensure the normal production of industries in this environment. However, there are few studies in emergency environments that consider the impact of the decision order of decision makers (DMs) on the decision results. Therefore, in order to fill the research gap, we propose an extended MCGDM method, whose main steps include the following: Firstly, the DMs give their assessment of all alternatives. Secondly, we take the AHP method and entropy weight method to weight the criteria and the DMs. Thirdly, we take the intuitionistic fuzzy hybrid priority weight average (IFHPWA) operator we proposed to aggregate evaluation information and take the TOPSIS method to rank all the alternatives. Finally, the proposed method is applied in a case to prove its practicability and effectiveness. The proposed method considers the influence of the decision order of the DMs on the decision results, which improves the accuracy and efficiency of decision-making results.

Data driven flexible supplier network of selfcare essentials during disruptions in supply chain.

During disruptive events, supply chains struggle to meet the demand due to limitations posed by logistics, transportation and supply side failures. In the present study, a flexible supplier network of personal protective equipment (PPEs), such as face masks, hand sanitizers, gloves, and face shields, has been modelled using an extensive risk enabled data driven decision making for addressing disruptions in the supply chain. This paper studies various risks which exists in PPE supply chain and evaluates the total supplier risk based on them. Furthermore, the paper proposes a Multi-objective Mixed Integer Linear Program (MOMILP) to optimally select suppliers and the sustainable allocation of orders under various risks, namely disruption, delay, receivables, inventory, and capacity. The proposed MOMILP model is also extended to promptly revise the orders to other suppliers under a disruption scenario enabling an effective response resulting in minimization of stockouts. The criteria-risk matrix is developed with the help of supply chain experts from industry and academia. Conclusively, the numerical case study and its computational analysis is conducted on the PPE data received from distributors to demonstrate the applicability of the proposed model. The findings suggest that the proposed flexible MOMILP can optimally revise allocations during disruptions to drastically reduce the stockouts and minimize overall cost of procurement in the PPE supply network.

Improving supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development

Purpose: The purpose of this study is to improve supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development. During the COVID-19 pandemic, the organizations have struggled a lot to maintain the supplier performance and strategic sourcing decisions in the organizational benefit. However, in this context, the organization's agile new product development (ANPD) process must be aligned with this requirement by maintaining the inventory and jobshop scheduling. As a result, identifying ANPD indicators, performance metrics and developing a structural framework to guide practitioners at various stages for smooth adoption is essential to improve the overall performance. Design/methodology/approach: A comprehensive literature review is conducted to identify jobshop scheduling, inventory management and ANPD indicators along with the performance metrics, and the hierarchical structure is developed with the help of expert opinion. The modified stepwise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assurance (WASPAS) techniques, along with expert judgement, are used in this study to calculate the weights of the indicators and the ranking of the performance metrics. Findings: As per the weight computation by SWARA method, the strategy indicators have the highest relative weight, followed by the product design indicators, management indicators, technical indicators, supply chain indicators and organization culture indicators. According to the ranking of performance metrics obtained through WASPAS, the "frequency of new product development is at the top”, followed by "advances in product design and development” and "estimated versus actual time to market”. Research limitations/implications: It is believed that the framework developed will help industrial practitioners to plan effectively to improve supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison. Practical implications: The outcomes of the present study will be extremely beneficial for the industry practitioners to improve the supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison. Originality/value: A unique combination of modified SWARA–WASPAS technique has been used in this study which would be beneficial for organizations willing to adopt the jobshop scheduling and inventory management and ANPD for improving supply chain performance. © 2023, Emerald Publishing Limited.

Structural transformation of fuzzy analytical hierarchy process: a relevant case for Covid-19

Covid-19 has posed difficult and challenging situations to the supply chains and companies are in fix how to choose the vendors under the uncertainty and complexity in recent years. Therefore, this research aims to incorporate structural transformation of the fuzzy analytical hierarchy process (FAHP) that is most appropriate for the uncertainty and disruption caused by Covid-19 like situation for ensuring supplies from vendors. The conventional approaches for vendor selection and evaluation use numerous multi-criteria decision-making tools that may not ensure reliability in a dynamic situation caused due to Covid-19. In this research, Fleiss' Kappa method ensures the reliability of responses from eight respondents by using pairwise comparisons and assigning weights as envisaged in FAHP. In addition to determine the reliability of responses, a step under FAHP has been altered. This alteration is demonstrated in the vendor selection case in the Covid-19 scenario. The research suggests a plausible system required to address the uncertainties associated with Covid-19 to select and evaluate vendors by modifying a FAHP. The proposed altered mechanism can be incorporated in a similar type of other decision-making circumstances such as Covid-19, where the decision-makers are more than one, and the situation is very dynamic. The study is likely to facilitate information management, algorithmic development in decision making, or machine-driven decisions in uncertain conditions. The study offers managerial implications to purchase managers to accommodate and combine multiple factors and responses concerning the vendor performances for their evaluation, thus making a process more reliable.

The role of cognitive capital in supply chain resilience: an investigation during the COVID-19 pandemic

PurposeResearch on the "black box” of cognitive capital remains limited in supply chain resilience (SCRES) literature. Drawing from an in-depth single case study of a major consumer electronics multinational facing the COVID-19 disruption, this paper aims to develop a clearer picture of cognitive capital's elements while contextualizing how they interact with SCRES temporal capabilities to prepare, respond, recover and learn.Design/methodology/approachConsisting of 40 in-depth interviews collected during a four-month period, this single case revolves around the buyer's view across 36 multiregional buyer–supplier dyads, spanning 17 product and service categories. Data were processed during the pandemic, while findings discuss pre- and intra-crisis events based on two scenarios: the impact of disruption on category demand, comparing sudden pandemic-driven product and service demand fluctuations (i.e. increase, decrease);and the geographical proximity of the supplier relative to the buying firm.FindingsThe case unveils different elements of cognitive capital (e.g. shared goals, assumptions, values, kinesics language, multilingualism, virtual negotiation, prior disruption experience, shared process capabilities) during a major global disruption, suggesting that different cognitive capital elements influence positively and differently SCRES' temporal capabilities. Overall, buying firms are urged to build on cognitive capital to improve SCRES preparation, response, recovery and learning.Originality/valueThis paper extends the understanding of cognitive capital in buyer–supplier relationships by identifying its elements and offering a theoretical articulation of how they enable episodically the four SCRES temporal capabilities under contingencies of increased and decreased demands, and suppliers' geographical proximity.

Inventory systems with uncertain supplier capacity: an application to covid-19 testing

The COVID-19 pandemic has forced governments to impose crippling restrictions on the day-to-day activities of citizens. To contain the virus and lift these restrictions safely, policymakers need to know quickly where the virus is spreading. This has been possible only through widespread testing. Not long after starting largescale testing in the early stages of the pandemic and more recently with a surge of new variants, countries hit a roadblock—the shortage of swabs used in the testing kits due to disruptions in the supply chain caused by COVID-19. This disruption translates to a variable production capacity of the swab suppliers. As a result, when countries order swabs from a swab supplier, their order might not be fully satisfied. Hence, adopting a proper swab inventory management model can help countries better manage COVID-19 testing and avoid widespread shortages of testing supplies. By considering two different swab demand patterns (i.e., stationary and stochastic) and two different production capacity scenarios for the swab supplier (i.e., ample and variable production capacity), we develop four analytical models, in which we consider all combinations of the above demand and capacity scenarios, to derive the optimal swab-procurement policy for a country. Given the rapid change of COVID-19 infection cases and the limited planning period, countries should aim for reactive scheduling. Through a comprehensive numerical study, we also provide guidelines on how countries should optimally react to these changes in the supply and demand of swabs. The research implications for managing inventory with stochastic supplier capacity and uncertain demand in a finite time horizon extend well beyond the application to COVID-19 testing.

Inventory planning under supplier uncertainty in a two-level supply chain

PurposeDue to uncertainty in supply chains caused by the coronavirus disease 2019 (COVID-19), organizations are adjusting their supply chain design to address challenges faced during the pandemic. To safeguard their operations against disruption in order quantities, supply chain members have been looking for alternate suppliers. This paper considers a two-level supply chain consisting of a manufacturer and two suppliers of a certain type of components required for the production of a finished product. The primary supplier (supplier A) is unreliable, in the sense that the quantity delivered is usually less than the ordered quantity. The proportion of the ordered quantity delivered by supplier A is a random variable with a known probability distribution. The secondary supplier (supplier B) always delivers the order in its entirety at a higher cost and can respond instantaneously. In order for supplier B to respond instantaneously, the manufacturer is required to reserve a certain quantity at an additional cost. Once the quantity received from the main supplier is observed, the manufacturer may place an order not exceeding the reserved quantity.Design/methodology/approachA mathematical model describing the production/inventory situation of the supply chain is formulated. The model allows the determination of the manufacturer's optimal ordering policy.FindingsAn expression for the expected total cost per unit time function is derived. The optimal solution is determined by solving a system of nonlinear equations obtained by minimizing the expected total cost function.Practical implicationsThe proposed model can be used by supply chain managers aiming at identifying various ways of handling the uncertainty in the flow of supplies across the chain.Originality/valueThis proposed model addresses a gap in the production/inventory literature.

Exploring factors of the sustainable supply chain in the post-COVID-19 pandemic: SWARA approach.

The present research aimed to provide a mechanism for enhancing sustainable supply chain survivability (SSCS) during and after the COVID-19 outbreak. Logistical and manufacturing operations have been restricted due to the severe lockdown, which significantly impacts the demand and supply of various items. COVID-19 has a massive effect on a multitude of units of various sectors. This research emphasized the important elements that must be considered to adapt the sustainable supply chain (SSC) practices. As a result, businesses are now attempting to improve the system for SSC. The stepwise weight assessment ratio analysis (SWARA) approach is used in this research to classify dynamic aspects for improving SSC survival in an epidemic condition. The results reveal that the viability of the supply chain network is the essential criteria for managing the relationships of suppliers and buyers and maximizing SSCS during and after COVID-19. This research will help businesses, manufacturers, and other stakeholders to concentrate on the described factors to achieve a brighter environment.

A model for green-resilient supplier selection: fuzzy best-worst multi-criteria decision-making method and its applications.

Supplier selection is regarded as the primary goal of supply chain management (SCM) because it affects its performance, productivity, pleasure, flexibility, and system speed in lockdown. A new method is proposed based on a multi-stage fuzzy sustainable supplier index (FSSI). Experts can use the triple bottom line (TBL) criteria to select the best supplier. In addition, the worst method is proposed based on trapezoidal membership and fuzzy membership functions, which can cover uncertainties and ambiguous environments. Because it collects the related criteria and sub-criteria and uses a direct fuzzy methodology, this research has impacted the SCM literature because it helps solve previous expert methods' computational difficulties. In addition, an ordered mean integration representation method has been implemented to prioritize the selection of the best supplier (SS) based on the sustainability performance of the best supplier, which improves the selection accuracy compared to the previous ranking method. This study can be used as a benchmark to determine which supplier is the best in sustainability. To provide the superiority and broad applicability of the proposed model, a practical case study was completed. On the other hand, the COVID-19 pandemic harms productivity, company performance, and selecting the best suppliers based on sustainability performance. The lockdown situation caused by the COVID-19 pandemic hurts company performance and management.