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Efforts to implement supplier selection and order allocation (SSOA) approaches in small and medium-sized enterprises (SMEs) are quite restricted due to the lack of affordable and simple-to-use strategies. Although there is a huge amount of literature on SSOA techniques, very few studies have attempted to address the issues faced by SMEs and develop strategies from their point of view. The purpose of this study is to provide an effective, practical, and time-tested integrated SSOA framework for evaluating the performance of suppliers and allocating orders to them that can improve the efficiency and competitiveness of SMEs.

This study was conducted in two stages. First, an integrated supplier selection approach was designed, which consists of the analytic hierarchy process and newly developed measurement alternatives and ranking using compromise solution to evaluate supplier performance and rank them. Second, the Wagner-Whitin algorithm is used to determine optimal order quantities and optimize inventory carrying and ordering costs. The joint impact of quantity discounts is also evaluated at the end.

Insights derived from the case study proved that the proposed approach is capable of assisting purchase managers in the SSOA decision-making process. In addition, this case study resulted in 10.89% total cost savings and fewer stock-out situations.

Criteria selected in this study are based on the advice of the managers in the selected manufacturing organizations. So the methods applied are limited to manufacturing SMEs. There were some aspects of the supplier selection process that this study could not explore. The development of an effective, reliable supplier selection procedure is a continuous process and it is indeed certainly possible that there are other aspects of supplier selection that are more crucial but are not considered in the proposed approach.

Purchase managers working in SMEs will be the primary beneficiaries of the developed approach. The suggested integrated approach can make a strategic difference in the working of SMEs.

A practical SSOA framework is developed for professionals working in SMEs. This approach will help SMEs to manage their operations effectively.

Product discounts have been widely applied in digital commerce as a method to attract and retain customers to purchase in China. Given that digital channels differ in their attributes, customers may behave differently when they respond to the same discount across channels. However, little attention has been paid to explore the heterogeneity of customer responses to discounts across channels. This study aims to fill in the gap by exploring how customers’ purchase responses to price discounts differ across digital channels.

This study applies a Poisson regression to an unbalanced data set of purchasing history from Chinese footwear brands with 3,510 customers in a two-year time window across the three digital channels (i.e. personal computer [PC], app and mobile website), with a correction for endogeneity by using the Gaussian copula method.

This paper finds that price discounts have the strongest positive effect on consumers’ purchase volumes on the PC channel, followed by the app channel, while discounts show the weakest impact in the mobile website channel. By so, this paper demonstrates that customers respond differently to online and mobile channels, and they also respond differently within mobile channels when they purchase products with price discounts.

This study is original in analyzing the difference in customers’ discount responses across digital channels, offering valuable contributions to existing research on multichannel marketing as well as mobile marketing and providing helpful insights for multichannel merchants to design digital discount strategies.

As an important form of the e-commerce industry, online group buying is under the spotlight from with two sides: cheaper price but longer waiting time. The purpose of this paper is to adequately investigate the interaction between saving and waiting time of group buying comprehensively.

To fill the research gap, the authors elaborate a dual-channel supply chain (SC) with regular retail (individual buying) and group-buying channel, and formulate the demand based on the consumer utility with the positive effect of saving money and the negative effect of wasting time.

The authors find that power structure only changes the optimal prices, instead of the waiting time. The selling price mainly influences consumer demands, instead of the price discount of group buying. The SC profits are only positive to the channel preference, and it is the decisive parameter of consumers' choice. The price sensitivity lays a more remarkable impact on the SC compared to the time sensitivity. Above all, the price is the main factor of group buying, instead of time.

These results underscore the improvement for the dual-channel SC of group buying, providing managerial insights for the group-buying industry.

The purpose of this paper is to show that forgotten classics, such as Melvin T. Copeland’s (1924) Principles of Merchandising, can still teach lessons to students of the history of marketing thought.

The method involved using various key words on several internet search engines. The extensive internet search produced more than a dozen contemporaneous reviews and commentaries. Additionally, there was an intensive search through the histories of marketing thought literature. The extensive and intensive searches allowed a meta-analysis reexamining Copeland’s principles in light of future historical developments from the mid-1920s to the 21st century.

Historically, Copeland’s principles established the commodity school of marketing thought. (One of the three traditional approaches to understanding marketing taught to generations of students from the mid-1920s until the mid-1960s.) Although the traditional approaches/schools have long gone out of favor, Copeland’s classification of consumer and industrial (business) goods (products and services) have stood the test of time and are still in use 100 years later. Long overlooked, Copeland’s (1924) Principles of Merchandising also anticipated the marketing management/strategy as well as the consumer/buyer behavior schools of marketing thought, dominant in the discipline since the 1960s, for which he has seldom – if ever – been acknowledged.

Historical research is limited because some relevant source material may no longer exist or may have been overlooked.

There have been no reviews of Copeland’s principles in almost a century, and no published meta-analysis of this forgotten classic exists. New discoveries reveal the value in studying marketing history and the history of marketing thought. For marketing as a social science to progress, it is invaluable to understand how ideas originated, were improved and integrated into larger conceptualizations, classification schema and theories over time.

Ready-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with various formulations and solving methods. This problem can range from a simple scenario involving one source, one material and one destination to a more challenging and complex case involving multiple sources, multiple materials and multiple destinations. This paper presents an Internet of Things (IoT)-supported active building information modeling (BIM) system for optimized multi-project ready-mix concrete (RMC) delivery.

The presented system is BIM-based, IoT supported, dynamic and automatic input/output exchange to provide an optimal delivery program for multi-project ready-mix-concrete problem. The input parameters are extracted as real-time map-supported IoT data and transferred to the system via an application programming interface (API) into a mixed-integer linear programming (MILP) optimization model developed to perform the optimization. The obtained optimization results are further integrated into BIM by conventional project management tools. To demonstrate the features of the suggested system, an RMCDP example was applied to solve that included four building sites, seven eligible concrete plants and three necessary RMC mixtures.

The system provides the optimum delivery schedule for multiple RMCs to multiple construction sites, as well as the optimum RMC quantities to be delivered, the quantities from each concrete plant that must be supplied, the best delivery routes, the optimum execution times for each construction site, and the total minimal costs, while also assuring the dynamic transfer of the optimized results back into the portfolio of multiple BIM projects. The system can generate as many solutions as needed by updating the real-time input parameters in terms of change of the routes, unit prices and availability of concrete plants.

The suggested system allows dynamic adjustments during the optimization process, andis adaptable to changes in input data also considering the real-time input data. The system is based on spreadsheets, which are widely used and common tool that most stakeholders already utilize daily, while also providing the possibility to apply a more specialized tool. Based on this, the RMCDP can be solved using both conventional and advanced optimization software, enabling the system to handle even large-scale tasks as necessary.

The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA.

The design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected.

The results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased.

(1) Design a closed-loop reverse logistics network for after-sales services; (2) Introduce a multi-objective multi-echelon mixed integer linear programming model; (3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units; (4) Use the GSC factors and DEA method in reverse logistics network.

Food manufacturing supply chain systems are the most relevant wheels of the world economy since they provide essential products supporting daily life. Nevertheless, various supply inefficiencies have been reported to compromise food safety in different regions. Sustainable supplier management and digitalization practices have become cornerstone activities in addressing these shortcomings. Therefore, this paper proposes an integrated method for sustainability management in digital manufacturing supply chain systems (DMSCS) from the food industry.

The Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) was used to weigh the criteria and subcriteria under uncertainty. Second, the Intuitionistic Fuzzy Decision-Making Trial and Evaluation Laboratory (IF-DEMATEL) was applied to determine the main DMSCS sustainability drivers whilst incorporating the expert's hesitancy. Finally, the Combined Compromise Solution (CoCoSo) was implemented to pinpoint the weaknesses hindering DMSCS sustainability. A case study from the pork supply chain was presented to validate this method.

The most important criterion for DMSCS sustainability management is “location” while “manufacturing capacity” is the most significant dispatcher.

This paper presents a novel approach integrating IF-AHP, IF-DEMATEL, and CoCoSo methods for sustainability management of DMSCS pillaring the food industry.

The importance of service firms cooperating with digital platforms is widely acknowledged. The authors study three contractual relationships (fixed-cost, cost-sharing, and profit-sharing) between service firms (specifically hotels) and digital platforms in a highly fragmented service supply chain to examine which of these contract types optimizes profits.

The authors extend prior models analyzing the optimal expected total profit from the travel service firm (hotel)–digital platform relationship, providing new insights into each contract type’s ability to coordinate decentralized systems and optimize profits for both parties.

This study finds that fixed cost contracts cannot coordinate the decentralized system. Cost-sharing contracts can coordinate the decentralized system but only allow one channel profit split. In contrast, profit-sharing contracts may not always perfectly coordinate the decentralized system but support alternative profit allocations. Practically, both profit-sharing and cost-sharing contracts are preferable to fixed-cost contracts.

The paper includes implications for travel service firm managers to consider when structuring contracts with digital platforms to focus on profit optimization. Profit-sharing contracts are most preferable when cost and revenue data are fully shared between parties, while cost-sharing contracts are preferable over fixed-cost contracts.

This study extends prior investigations into the utility of different contract types on the optimal profit of a travel service firm (hotel)-digital platform provider relationship. The research fills a gap in the literature concerning the contracts used in these relationship types.

The existence of the advertising delay effect and its impact on supply chain operations have been demonstrated in the current study. Therefore, this study develops a timely inventory control strategy for the fresh produce supply chain to address the advertising delay effect in the fresh produce supply chain.

This study proposes a game model based on the Nerlove-Arrow time delay differential equation and Pontryagin's maximum principle. Through comparative analyses of the optimal equilibrium strategies, the authors compare the optimal equilibrium strategies, product goodwill and optimal inventory trajectories for suppliers and retailers under secondary replenishment decisions and decentralized decisions.

The authors find that (1) Only when the sales cycle meets certain conditions can the overall profit of the supply chain under the secondary replenishment decision be greater than that under the decentralized decision. As the price markup coefficient increases, the total profit of the supply chain first increases and then decreases. (2) With the increase in the delay time, the replenishment quantity during the initial period gradually decreases. After the delay time elapses, the inventory depletion rate under secondary replenishment decisions is faster than that under decentralized decision-making. (3) Although there is a continuously increasing maximum value of product goodwill with the increase in delay time, it becomes difficult to achieve this value for longer delays.

The authors’ findings provide a theoretical basis for supply chain members of fresh agricultural products to select replenishment and inventory control strategies when adopting different levels of delay in advertising marketing.

Firstly, this paper explains the impact of advertising delay effect on fresh produce supply chain from a dynamic perspective, and secondly, it provides guidance on advertising formulation and inventory replenishment for fresh produce retailers under the influence of advertising delay effect.