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Carbon emission is a significant issue for the current business market and global warming. Nowadays, most countries have focused to reduce the environmental impact of business with durable financial benefits. The purpose of this study is to optimize the entire cost functions with carbon emission and to find the sustainable optimal ordering quantity for retailers.

This paper illustrates a sustainable inventory model having a set of two non-instantaneous substitutable deteriorating items under joint replenishment with carbon emission. In this model demand and deterioration rate are considered as deterministic, constant and triangular fuzzy numbers. The objective is to find the optimal ordering quantity for retailers and to minimize the total cost function per unit time with carbon emission. The model is then solved with the help of Maple software.

This paper presents a solution method and also develop an algorithm to determine the order quantities which optimize the total cost function. A numerical experiment illustrates the improvement in optimal total cost of the inventory model with substitution over without substitution. The graphical results show the convexity of the cost function. Finally, sensitivity analysis is given to get the impact of parameters and validity of the model.

This study considers a set of two non-instantaneous substitutable deteriorating items under joint replenishment with carbon emission. From the literature review, in the authors’ knowledge no researcher has undergone this kind of study.

Inventory control models for perishable products have primarily used a FIFO issuing policy with the objective of minimizing the number of outdated units. This paper aims to develop a model to evaluate an issuing policy for a single product with a fixed shelf life in single echelon inventory system. The issuing policy considers the remaining shelf life of the in‐stock inventory and the expected time that the product will spend in inventory as the decision driver.

The model developed has an objective of maximizing expected revenue over time with a budget constraint. A heuristic algorithm is proposed to iteratively arrive at the best solution to the formulation. The heuristic is tested by employing a simulation model of the system.

The proposed heuristic is tested against both the FIFO and the random allocation approaches and found to be superior for all the in‐stock with remaining shelf life distribution means of above 40 percent. No significant performance differences were found for the three approaches for remaining shelf life distribution.

The research is focused on a single product with multiple expiration dates and further research is necessary to determine the best policies for the multi‐product multi‐expiration date environment where the items are substitutable..

Retailers stock items with multiple expiration dates. The customer, for obvious reasons, is more likely to choose the item with the longer remaining shelf life. Therefore, the supply to the retailer's shelves and issuing policies for making available the particular items to the customers affect product outdating and related costs. Revenues will be affected by the extent to which more can be charged for items with a longer remaining shelf life or by the impact of the remaining shelf life on demand. This paper provides for a practical approach to that end.

The proposed issuing policy has not been tested before and thus makes a contribution to the body of knowledge. The flexibility of using different values for acquisition costs, selling prices, salvage value and penalty functions is a particular strength of the proposed model. Moreover, its potential application to inventory control problems for a wide range of perishable products is substantial.

The purpose of this article is to develop a new model called strategy segmentation methodology (SSM) by combining the Kraljic portfolio matrix (KPM) and the supplier relationship model (SRM) so that the buyer company can effectively conduct its relations with its suppliers.

The importance weights of the criteria defining the dimensions of each model are calculated with the single-valued neutrosophic analytical hierarchy process (SVN-AHP) method. Subsequently, the derived importance weights are employed in the single-valued neutrosophic technique for order preference by similarity to ideal solution (SVN-TOPSIS) method to obtain the scores of the suppliers and their supplied items. In order to illustrate the feasibility of the proposed methodology, a case study in the machinery industry is performed with the related comparative analysis.

The implementation of SSM enables to formulate various strategies to manage suppliers taking into account the items they procure, their capabilities and performance and the supplier–buyer relationship strength. Based on the proposed strategies, it is concluded that the firm in the case study should terminate its relationship with six of its suppliers.

Although KPM has become the basis of purchasing strategies for various businesses, it neglects the characteristics of suppliers and the buyer–supplier relationship. In this study, KPM is integrated with the SRM approach presented by Olsen and Ellram (1997) to overcome these disadvantages of KPM. The novel integration of the two approaches enables the realization of a robust and reliable supplier classification model.

This paper aimed to validate the sustainable competitive advantage (SCA) measures in Malaysian electronics manufacturing organizations' context after the post coronavirus disease 2019 (COVID-19) outbreak. Post pandemic sustainability in competitive advantages have become a buzzword for Malaysian electronics industries in facilitating the value chain generation to consumers, besides enhancing profitability to the organization. SCAs are important when the manufacturer can provide something valuable to the market, and it differs from the competitors, especially during the crisis.

This study adopted the quantitative research approach in validating the SCA variables. The questionnaires were self-administered and randomly distributed among the electronics manufacturing organizations across Malaysia, with a total of 207 responses. Concisely, this research theoretically conceptualized SCAs as a multidimensional construct containing two dimensions: lower cost advantage (LCA) and differentiation advantage (DFA).

Besides, the findings ascertained the strategies to sustain the competitive advantages within an organization, which is underpinned by resource-based theory. In short, findings of this research would be an imperative implication for academicians and organization's policymakers to move forward towards advanced economy and Industrial Revolution 4.0 in the current global competitive environment.

There is no prescription of attaining competitive advantages that suits all conditions in Malaysian electronics industries; this implies that the literature gaps existed, and further research shall be conducted on SCAs.

Prioritising items for management attention has been advocated in operations management for a long time, normally using ABC analysis (inventory control). This focuses attention on the “A” category items to maximise managerial effectiveness. Empirical evidence shows that this is a reasonable rule for allocating scarce resource‐management time but presents difficulties when the manager has to take more than one important dimension of a situation into account. A joint criteria matrix is put forward within the ABC framework and an industrial application given. The joint criteria matrix has practical utility provided ranking on some scale of measurement is realistic. The appropriate number of categories must be defined by the user. Combining criteria will probably require different analytical approaches, e.g. goal programming or heuristic approaches. Utilisation of the matrix by managers can provide an explicit method for taking a range of criteria into account in the development of inventory policies.

This paper aims to develop a bi-objective mixed integer non-linear programing model to optimize the supply chain networks consisting of raw material providers, final product manufacturers and distribution centers. Raw material substitution caused by varying raw material supply amounts, prices and carbon emissions and final product substitution due to different product prices and carbon emissions are considered.

The proposed model aims to achieve total profit maximization and total carbon emission minimization. The objective function of carbon emissions is converted into a maximization problem by changing minimum to maximum. The composite objective function is the weighted sum of the bias value of each objective function. The model is then solved using software Lingo12.

Numerical analysis results show that an increase in the number of alternate raw materials for original raw material helps improve supply chain network performance, and variation in that number causes detectable but not significant changes in downstream final product substitution results.

The major differences between this work and existing research are as follows: first, although previous research considered carbon emissions in supply chain network optimization, it has not considered the substitution effects of products or raw materials. This paper considers the substitution of both raw material and productions. Second, the item substitution considered by previous research is derived from inventory shortage or price difference of original items. However, the substitution considered in the present paper is a response to differences in purchase price, production cost and carbon emissions for items.

ABC analysis is used to sort inventory into categories which determine how much attention they will be given. But it is of wider management application and can be extended to cope with more than one classification criterion.

The purpose of this paper is to propose a model for relief inventory ordering considering declining demand and realized shortage (RS) costs. This study is motivated by the overflow of unsolicited items in affected areas, which hinder the urgent relief flow.

This study proposes a dynamic programing model reflecting the change of demand and urgency during relief operations. A solution algorithm for finding a replenishing point of relief is also introduced here.

Relief ordering policy based on the RS cost increases the service level (in terms of time). The service level is dependent on the deterioration rate.

Deterioration is not applicable for all relief items. Therefore, the proposed model is applicable for certain relief items. Additionally, computation of temporal urgency is not included.

The study outcomes can be invaluable for resource planning. The model shows a gradual decrease of ordering quantity and a gradual increase of cycle length. This observation can be utilized for resource (e.g. truck) planning and fewer truck can be used in later stages of relief operations.

This study introduces temporal urgency for disaster logistics (DL) and proposes a relief inventory policy by incorporating temporal urgency. This model highlights the importance of considering declining demand for DL.

This study aims to understand client food preferences and how pantry offerings can be optimized by those preferences.

This study develops and administers customized surveys to study three food pantries within the Second Harvest Food Bank of Northwestern North Carolina network. This study then categorizes food items by client preferences, identifies the key predictors of those preferences and obtains preference scores by fitting the data to a predictive model. The preference scores are subsequently used in an optimization model that suggests an ideal mix of food items to stock based upon client preferences and the item and weight limits imposed by the pantry.

This study found that food pantry clients prefer fresh and frozen foods over shelf-friendly options and that gender, age and religion were the primary predictors. The optimization model incorporates these preferences, yielding an optimal stocking strategy for the pantry.

This research is based on a specific food bank network, and therefore, the client preferences may not be generalizable to other food banks. However, the framework and corresponding optimization model is generalizable to other food aid supply chains.

This study provides insights for food pantry managers to make informed decisions about stocking the pantry shelves based on the client’s preferences.

An emerging topic within the humanitarian food aid community is better matching of food availability with food that is desired in a way that minimizes food waste. This is achieved by providing more choice to food pantry users. This work shows how pantries can incorporate client preferences in inventory stocking decisions.

This study contributes to the literature on food pantry operations by providing a novel decision support system for pantry managers to aid in stocking their shelves according to client preferences.

Recommender systems (RS) are designed to communicate with users and drive consumers' engagement with the platform. However, little is known about the strength of this relationship and how RS can create stronger consumer engagement (CE) with the platform brand. Addressing this gap, this paper examines the role of RS in converting consumers' short-term engagement with the RS to their longer-term platform engagement.

To explore these issues, the authors review key literature in the areas of CE and RS, from which they develop a conceptual framework.

The proposed framework suggests RS design as an important precursor to consumers' RS use, which is expected to affect their platform engagement/disengagement, in turn impacting the firm's long-term outcomes. The authors also identify key managerial tactics, strategies and challenges to aid the conversion of consumers' RS to CE.

This research raises pertinent implications for research on the RS/CE interface, as synthesized in a proposed research agenda.

Based on the attained insight, authors outline implications for managing, facilitating and leveraging the proposed RS to CE conversion process. Correspondingly, authors argue that, to optimize RS effectiveness, RS designers should understand the nature of CE.

By exploring the effect of consumers' RS on their longer-term CE with the platform, the analyses offer pioneering managerial insight into RS effectiveness from a CE perspective.