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The purpose of this paper is to investigate the impact of price adjustment speed on the stability of Bertrand–Nash equilibrium in the context of a dual-channel supply chain competition.

The paper considers a dual-channel supply chain comprising a manufacturer, a traditional retailer and an online retailer. A two-dimensional discrete dynamical system is used to examine the Bertrand competition between the retailers. The retailers are assumed to follow bounded rational expectations. Local stability of Bertrand–Nash equilibrium is investigated with respect to the price adjustment speed.

As the price adjustment speed increases, the stability of Bertrand–Nash equilibrium is lost, leading to complex chaotic dynamics. The results showed that chaotic dynamics deteriorates the profit of the retailers. The authors also found that the chaos can be controlled using an adaptive adjustment mechanism and the retailers enjoy higher profit when the chaos is controlled.

This study helps retail managers to choose an appropriate price adjustment speed to maximize profit.

The heterogeneity of the retailers is not considered in the studies involving dynamics of retailer competition. This paper contributes to the literature by considering the operational difference between a traditional retailer and an online retailer, i.e. price adjustment speed. In addition, the study establishes a link between price adjustment speed and profit.

In most economies, there are rules from the market regulators or government to sell at an equal wholesale price (EWP). But when one upstream channel is facing a negative demand disruption and another positive, EWP can create extra pressure on the disadvantageous supply chain partner, which faces negative disruption. The purpose of this study is to analyse the impact of EWP and the scope of the discriminatory wholesale price (DWP) during disruptions.

For the study, the authors used a dual-channel supply chain consisting of a manufacturer, online retailer (OR) and traditional brick-and-mortar (BM) retailer. Stackelberg game is used to model the interaction between the upstream and downstream channel partners, and the horizontal Nash game to analyse the interaction within downstream channel partners. For modelling asymmetric disruption, the authors took instances from the lock-down and post-lock-down periods of the COVID-19 pandemic, where consumers flow from BM retailer to OR store.

By analysing the disruption period, the authors found that this asymmetric disruption is detrimental to the BM channel, favourable to OR and has no impact on the manufacturer. But with DWP, the authors found that the profit of the BM channel and manufacturer can be increased during disruption. Though the profit of the OR decreased, it was found to be higher than in the pre-disruption period. Under DWP, the consumer surplus increased during disruption, making it favourable for the customers also. Thus, DWP can aid in creating a win-win strategy for all the supply chain partners during asymmetric disruption. Later as an extension to the study, the authors analysed the impact of the consumer transfer factor and found that it plays a crucial role in the optimal decisions of the channel partner during DWP.

Very scant literature analyses the intersection of DWP and disruptions. To the best of the authors’ knowledge, this study, for the first time uses DWP as a tool to help the disadvantageous supply chain partner during asymmetric disruptions. The study findings will assist the government, market regulators and manufacturers in revamping the wholesale pricing policies and strategies to help the disadvantageous supply chain partner during asymmetric disruption.

Increasing public consciousness and demand for sustainable environment make selection of a safe location for effective disposal of healthcare waste (HCW) a challenging issue. This problem becomes more complicated due to involvement of multiple decision makers having varying knowledge and interest, conflicting quantitative and qualitative evaluation criteria, and presence of several alternative locations.

To efficiently resolve the problem, the past researchers have already coupled different multi-criteria decision-making tools with uncertainty models and criteria weight measurement techniques, which are time-consuming and highly computationally complex. Based on involvement of a group of experts expressing their opinions with respect to relative importance of criteria and performance of alternative locations against each criterion, this paper proposes application of ordinal priority approach (OPA) integrated with grey numbers to solve an HCW disposal location selection problem.

The grey OPA can simultaneously estimate weights of the experts, criteria and locations relieving the decision makers from complicated computational steps. The potentiality of grey OPA in solving an HCW disposal location selection problem is demonstrated here using an illustrative example consisting of three experts, six criteria and four alternative locations.

The derived results show that it can be employed to deal with real-time HCW disposal location selection problems in uncertain environment providing acceptable and robust decisions. It relieves the experts from pair-wise comparisons of criteria, normalization of data, identification of ideal and anti-ideal solutions, aggregation of information and so on, while arriving at the most consistent decision with minimum computational effort.

Supplier selection along with continuous evaluation of their performance is a crucial activity in healthcare supply chain management for effective utilization of scarce resources while providing quality service at an affordable price, and minimizing chances of stock-out, avoiding serious consequences on the illness or fatality of the patients. Presence of both qualitative and quantitative evaluation criteria, set of potential suppliers and participation of different stakeholders with varying interest make healthcare supplier selection a challenging task which can be effectively solved using any of the multi-criteria decision making (MCDM) methods.

To deal with various qualitative criteria, like cost, quality, delivery performance, reliability, responsiveness and flexibility, this paper proposes integration of grey system theory with a newly developed MCDM tool, i.e. mixed aggregation by comprehensive normalization technique (MACONT) to identify the best performing supplier for pharmaceutical items in a healthcare unit from a pool of six competing alternatives based on the opinions of three healthcare professionals.

While assessing importance of the six evaluation criteria and performance of the alternative healthcare suppliers against those criteria using grey numbers, and exploring use of three normalization procedures and two aggregation operations of MACONT method, this integrated approach singles out S5 as the most compromised healthcare supplier for the considered problem. A sensitivity analysis of its ranking performance against varying values of both balance parameters and preference parameters also validates its solution accuracy and robustness.

This integrated approach can thus efficiently solve healthcare supplier selection problems based on qualitative evaluation criteria in uncertain group decision making environment. It can also be deployed to deal with other decision making problems in the healthcare sector, like supplier selection for healthcare devices, performance evaluation of healthcare units, ranking of physicians etc.

In the present-day highly customer-conscious service environment, supply chain management has become a critical component of health-care industry, helping in fulfilling patient expectation, optimizing inventory and automating departmental activities. Supplier selection is one of the crucial elements of health-care supplier chain, establishing mutually beneficial relationships with the reliable suppliers that provide the most value of money. Health-care supplier selection with feasible sets of alternatives and conflicting criteria can be treated as a multi-criteria decision making (MCDM) problem. Among the MCDM methods, grey relational analysis (GRA) appears as a potent tool due to its simple computational steps and ability to deal with imprecise data. The purpose of this paper is to explore the applicability of a newly developed MCDM tool for solving a health-care supplier selection problem.

In GRA, the distinguishing coefficient (ξ) plays a contributive role in final ranking of the alternative suppliers and almost all the past researchers have considered its value as 0.5. In this paper, a newly developed MCDM tool, i.e. dynamic GRA (DGRA), is adopted to evaluate the relative performance of 25 leading pharmaceutical suppliers for a health-care unit based on nine important financial metrics. Instead of static value of ξ, DGRA treats it as a dynamic variable dependent on grey relational variator and ranks the health-care suppliers using their computed rank product scores.

Based on rank product scores and developed exponential curve, DGRA classifies all the suppliers into reliable, moderately reliable and unreliable clusters, helping the health-care unit in identifying the best performing suppliers for subsequent order allocation. Among the reliable suppliers, alternatives A₂ and A₁₁ occupy the top two positions having almost the same performance with respect to the considered financial metrics.

Application of DGRA along with determination of the most reliable suppliers would help in effectively adopting multi-sourcing strategy to increase resilience while diversifying the supply portfolio, thereby enabling the health-care unit to minimize chances of sudden disruption in the supply chain. It can act as an intelligent decision-making framework aiding in solving health-care supplier selection problems considering perceived risks and dynamic input data.

The purpose of this paper is to investigate the impact of wholesale price discrimination by a manufacturer in a retailer–e-tailer dual-channel supply chain for different product categories based on their online channel preference.

This paper considers a dual-channel supply chain comprising of a retailer and an e-tailer engaged in competition. Game-theoretic models are developed to model the competition between the retailer and e-tailer and to derive their optimal price, optimal order quantity and optimal profit under (1) equal wholesale price strategy and (2) discriminatory wholesale price strategy. Further, a numerical example was employed to quantify the results and to capture the variation with respect to online channel preference of the product.

It is beneficial for the manufacturer to adopt a discriminatory wholesale price strategy for products having both high online channel preference and low online channel preference. However, equal wholesale price strategy is beneficial for the e-tailer and the retailer in the case of products having high online channel preference and in the case of products having low online channel preference, respectively.

The study helps the manufacturers to maximize their profit by adopting the right wholesale price strategy considering the online channel preference of the product when the manufacturers are supplying to heterogeneous retailers.

There is scant literature on the wholesale price strategy of the manufacturer considering the heterogeneous downstream retailers. This paper contributes the literature by bridging this gap. In addition, the study establishes a link between the wholesale price strategy and online channel preference of the product.

The purpose of this paper is to help online retailers who have an existing reselling channel to figure out the risk of introducing an additional marketplace channel and identify the introduction threshold with an overall consideration to the fulfilment cost and services.

In order to evaluate the risk of the marketplace channel strategy, this paper develops a Retailer–Stackelberg pricing model. Products are divided into two categories according to different fulfilment cost–value ratio to get a more targeted strategy.

The results show that the strategy of introducing the marketplace is not always satisfying. Retailers prefer this strategy when they are the prevailing parties in service output. The overall trend is that retailers have to encourage their marketplace partners to improve services for the product with a big fulfilment cost–value ratio. Otherwise, retailers should block the marketplace from entering.

For an intuitive conclusion, this paper assumes that the operating costs (except fulfilment cost) are equal in two channels. This suggests a need to further investigate the impact of other costs. Meanwhile, it would be interesting to examine the competition among suppliers and retailers.

This research provides the suggestions for online retailers who want to introduce and well manage the marketplace channel.

This research also helps both academia and industry become more intelligent about the significant influences of category management on channel strategy.

Most prior research is unaware of the risk of introducing a new channel, which also rarely considers how to manage it. This research points out that the effectiveness of this channel strategy differs in different categories. Moreover, retailers can benefit from managing the marketplace’s service output.

Businesses are increasingly striving to become sustainable in terms of economic, environmental and social aspects. However, in the fresh food retail supply chains (SCs), achieving environmental objectives can be challenging because of the unique characteristics of products such as perishability, bulkiness, short product lifecycle and the requirement for cold chain infrastructure. The retail industry is the face of a SC. Therefore, its role in achieving sustainable objectives is pivotal. This study examines the effect of green in-store operations on sustainability performance indicators of fresh food retail and examines the moderating role of organization size in this context.

Data are collected through surveys using self-administered questionnaires from 70 retail stores with 188 completed responses. Data are analyzed using structural equation modeling.

Results show a positive relationship between green in-store operation with environmental social and economic performance. Furthermore, these relationships are moderated by the organization size such that the positive green in-store operation and performance relationships are stronger in the case of environmental and social performance only and for larger retail stores. No moderation is seen for economic performance.

The study broadens the understanding of green SC management’s effect on sustainability performance in the retail industry. It shows how the positive implications of a green SC are contingent on organization size and have prominence for environmental and social performance.

This paper aims to analyze the potentials of dynamic, commitment and revenue-sharing contracts; that a nonrebate offering manufacturer can use to safeguard his profit while his competitor offers customer rebates in a supply chain consisting of two manufacturers and a common retailer.

We consider a two-period supply chain model to explore optimal decisions under eight possible scenarios based on the contract and rebate offering decisions. Because the manufacturers are selling substitutable products, therefore, a customer rebate on one of the products negatively impacts the selling quantity of other. Optimal price, rebate, and quantities are examined and compared to explore the strategic choice for both the rebate offering and non-rebate offering manufacturer. Comparative evaluation is conducted to pinpoint how the parameters such as contract parameters and its nature affect the members.

The results demonstrate that all these contracts instigate the rebate offering manufacturer to provide a higher rebate, but do not ensure a higher profit. If the revenue sharing contract is offered to the common retailer, the effectiveness of the rebate program might reduce significantly, and the rebate offering manufacturer might receives lower profits. A non-rebate offering manufacturer might use a commitment contract to ensure higher profits for all the members and make sure the common retailer continues the product.

The effect of customer rebate vs. supply chain contract under competition has not yet been explored comprehensively. Therefore, the study contributes to the literature regarding interplay among pricing decision, contract choice and rebate promotion in a two-period setting. The conceptual and managerial insights contribute to a better understanding of strategic decision-making for both competing manufacturers under consumer rebates.

This study proposes and demonstrates a novel approach to analyzing customer channel preferences and willingness-to-pay (WTP) in the dual sales channel (DSC) system involving direct online channels and conventional offline retailers, and to how the pricing decisions are made under specific game competition.

Questionnaire survey based on central composite experiment design was utilized to obtain primary data. The model for customer channel preferences and WTP was then built by using multinomial logistic regression. The propensity of a customer to make purchases in either channel estimated by using the logit model was inserted in the bilevel programming model to formulate and solve for the Stackelberg competition where the conventional retailer acted as a leader.

The study found that channel prices have nonlinear impacts on WTP and channel preference. The empirical results complement the mathematical formulation well where high-order own-price and cross-price effects on channel selection are generally not analytical tractable. Under the Stackelberg competition, the traditional retailer (as the leader) still achieves higher profits than the online facility.

The proposed framework provides an empirical approach that can easily address the competition model in the sales channel when complicated own-price or cross-price effects are present.

The present work provides a novel approach to analyze customer preference and WTP of the DSC systems. This alternative method simplifies the procedure for investigating and estimating price sensitivity, especially when the online and offline prices affect customer WTP and channel preferences nonlinearly. This model is also utilized in the game competition to facilitate data-driven price decision making to better formulate and understand real-world DSC problems.