As competition in the retail industry heats up, businesses are increasingly resorting to kinds of artificial intelligence (AI) to differentiate themselves. E-commerce firms are combining technologies such as AI chatbots and augmented reality applications (ARA), which have established themselves as prominent customer service solutions in the practitioner area. However, little is known about consumers’ views and participation with developing technologies when they are implemented in a retail setting. A theory-based study model was developed to elucidate the motivational factors required for effective decision-making in this environment. The proposed model was supported by empirical testing conducted as a field study.
The purpose of this research is to determine whether strategic supplier selection based on supplier capability in new product development, supplier quality, and supplier cost directly or indirectly improves the buyer's competitive performance capabilities in the matched domains of buyer product innovation, buyer quality, and buyer competitive pricing. The resource-based view of the firm is used to frame the direct effects of strategic supplier selection, arguing that a buyer's ability to select a supplier with resources and expertise in a specified domain should improve the buyer's performance capability in the "matched" domain (but not necessarily in "unmatched" domains). Two supplier integration techniques are considered as potential mediators, assuming indirect pathways. The research hypotheses examine both direct and indirect impacts for each of the matched domains, but do not assume cross-domain interactions. For instance, supplier selection for new product development capability should have an effect on buyer product innovation (in matched domains), but not always on buyer quality capability (unmatched domains). While the direct impacts of strategic supplier selection on buyer performance are supported in each matched domain, the indirect effects via supplier integration are not substantial for the matched domains. Strategic supplier selection is identified as a viable source of competitive advantage in the resource-based view. By contrast, supplier development and supplier partnership do not provide additional performance benefits in a particular domain over and beyond those obtained from strategic supplier selection in that domain; rather, it is the type of the resources selected that determines competitive advantage.
This study examines characteristics that may influence buyers' desire to obtain goods and services from ethnic minority enterprises using data from 277 buyers employed at large buying organizations (LPOs) in the United States and the United Kingdom (EMBs). The literature on social capital is utilized to construct hypotheses about the cognitive, structural, and relational factors that may influence decisions to purchase from minority enterprises. Following that, current discrimination theory is used to deduce how buyers' views about supplier diversity affect the effects of social capital on their buying operations with EMBs. Multiple regression research indicates that in both the United States and the United Kingdom, buyers' perceived positive social capital has a direct, substantial association with their spending with EMBs. Additionally, the findings indicate that in both nations, purchasers' attitudes toward supplier diversity act as a moderator of the connection. Interestingly, despite the fact that the United States pioneered the concept of supplier variety, our study reveals that UK LPO buyers spend more with their EMBs. This research demonstrates how LPOs' strategic corporate social responsibility initiatives may be influenced by their buyers' social relationships with EMBs and their views about supplier diversity, based on these findings.
Recent research demonstrates the value of examining collaborations between established organizations and startups via the lens of the buyer–supplier relationship. However, enterprises must first find, analyze, and select potential startups as suppliers before they can exploit startups' resources and talents in a buyer–supplier relationship. Due to the fact that earlier research has focused exclusively on how purchasing firms select established firms as suppliers, it is unknown which processes, tools, or organizational approaches purchasing organizations employ when selecting startup firms as suppliers. These suppliers are qualitatively distinct in that they lack organizational structure, financial resources, and operational competencies, offering a substantial risk to purchasing organizations. This inductive, qualitative case study research elicits data from twenty established purchasing firms and examines how they choose startup suppliers. We begin by identifying five design motifs that differentiate purchasing firms' selection procedures. We create a typology of three supplier selection paradigms based on these themes. The findings suggest that enterprises who are ready and able to adjust their selection technique to startups should exhibit a higher level of selection performance, implying a greater likelihood of selecting acceptable startups as suppliers. The findings contribute to the literature on supplier selection and shed light on the burgeoning sector of new venture suppliers.
The abrogation of Multifiber Arrangement in the year 2005 pushed many developing nations into tough competition. Within the textile industry, despite having many advantages apparel manufacturing and exporting organizations (AMEOs) in developing nations are experiencing decline in their supply chain supply chain performance. Developing a comprehensive model to explore and classify factors, which affect the supply chain performance, is extremely significant. Owing to limited research in this area, an exploratory qualitative study involving a variety of organizations in apparel supply chain was carried out, in combination with a literature review, to determine the causes behind that decline. The outcome of preliminary exploratory study and literature review aided in the proposal of a conceptual framework. Employing that framework, a questionnaire survey was designed and piloted to support a quantitative study, which was conducted in the Karachi region in Pakistan. Collected data were analyzed by employing structural equation modeling. Results indicate that a number of factors have a strong influence on the supply chain performance of AMEOs. Apart from contributing to the literature, this study can also be of interest to managers and practitioners from the textile industry, as it clearly indicates areas on which AMEOs need to focus in order to improve their performance.
There are several critical measures in the project performance evaluation such as time, cost, and quality. In this paper, a new method for project performance evaluation is presented by combining the project Golden Triangle and the methods in decision science. At first, strategies and measures are introduced for the perspectives of the Balanced Scorecard (BSC) method according to their effect on time, cost, and quality. Next, the Analytic Hierarchy Process (AHP) method is used to weigh the measures by the judgments of experts after integrating AHP and BSC. Then, the weights of the five project phases — from the initiation to the closure — are calculated by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Our proposed TOPSIS is not only able to involve the judgments of experts, but also, in combination with AHP, can provide a comprehensive performance evaluation method for a project. Finally, using the judgments of experts of a real project-based organization, computational results show that financial measures are more important than other measures and the planning phase is the most important phase of the project. By the proposed method, a project-based organization can evaluate its performance and determine its competitiveness.
In recent times, the circular economy has gained much attention due to its emphasis on sustainability. Likewise, reverse logistics also plays a significant role in embracing and practical implications of the circular economy in the supply chain process. Disposition is among the critical factors that can strongly relate to reverse logistics in the light of sustainable practices. It also improves the overall operative productivity of reverse logistics. This study's primary goal is to investigate the concept of reverse logistics in the Pakistan textile manufacturing industry. Disposition decision effects using a triple bottom line that includes environmental, economic, and social performance under reverse logistics were also examined. Disposition decisions methodology and a triple bottom line approach were used for study hypothesis development. A survey-based approach was adopted using an online questionnaire technique by sending emails to 400 textile manufacturers in Pakistan. This study applied PLS (SEM) to test the hypothesis. Moreover, results also disclose positive linkage with triple bottom line performance. This research will be opening a new research paradigm for academicians, industries, and policymakers for the effective improvement in overall textile manufacturing reverse logistics.
This paper presents a mixed integer non-linear programming (MINLP) model for a bi- objective and multi-depot vehicle routing problem with time windows. The main goals of the paper are minimization of total cost and equitable distribution of commodities between vehicles. Two types of vehicle including delivery and installation vehicles are utilized in the network regarding customers’ needs. Satisfying all demands of the customers is not obligatory and unmet demands are permitted which leads to extra cost. A presented model is applied for real life case study in different provinces of Iran. To tackle the small-size problems, the augmented e-constraint method is utilized by linearization of the model. Because of the NP-hard nature of the problem, as the size of the problem increases, so does the complexity. As such, we develop multi-objective simulated annealing (MOSA meta-heuristic) algorithm for large scale problems. Then, several numerical experiments and sensitivity analyses are conducted to validate the presented model and the solution method, which indicate the efficiency of our proposed approach.
Proper storage conditions for pharmaceutical products and paramedical supplies are crucial to maintaining their quality, safety, and efficacy. Poor warehouse management practice may lead to wastage or blockade of financial resources, irrational utilization of drugs, shortage, or overage of essential medicines among many others. The current study aimed to assess the warehouse management practice of private pharmaceutical wholesalers in Gondar, Ethiopia. The study utilized an institution-based cross-sectional study method. The data was collected using a checklist adapted from the Logistics Indicators Assessment Tool (LIAT) developed by the USAID | DELIVER PROJECT and was analyzed using SPSS version 22. Descriptive analysis was conducted and results were presented using tables and figures. A total of five pharmaceutical wholesalers were surveyed. All of the surveyed entities were managing both medicines as well as other medical supplies, and they were supplying their products to both public and private health facilities. The majority of the facilities 4(80%) reported that they were using the Professional Electronic Data System (PEDS) for the management of pharmaceutical products in the warehouse. The overall put away/ storage performance of the facilities was 68.75%, which can be regarded as poor (<80%). All of the facilities lack sufficient product handling equipment for unloading and moving the incoming goods. In conclusion, the overall warehouse management practice at the private pharmaceutical wholesalers was found to be poor. Shortage of product handling equipment and inadequate layout of the warehouses were the two most common problem areas identified that need major improvement.
Urban delivery, especially the last-mile delivery, has become an increasingly important area in the global supply chain along with the boom of e-commerce. Delivery companies and merchants can introduce some innovative solutions such as the equipment of autonomous vehicles (AVs) to decrease their operating costs, environmental impact, and social risks during the delivery process. This paper mainly develops a mathematical model to get the best allocation of AVs among city logistics centers (CLCs) as a mixed delivery method. The advantage of the presented model stems from considering the equipment cost, the delivery cost, and the CO2 emission, which is measured through social carbon cost (SCC). In addition, this paper establishes a risk model considering the impact of seasonal variations to evaluate the infection risk of delivery during pandemic periods for four potential delivery scenarios: customers going to CLCs, ordering online and picking-up at CLCs, delivering by traditional vehicles (TVs), and delivering by the mixed method with the optimal allocation of AVs. The research finds the optimal allocation for a London case, reveals the relationship between the nominal service capacity (NCpa) of CLCs and the optimal number of CLCs equipped with AVs, concludes that the more CLCs are equipped with AVs, the fewer CO2 emissions and the fewer citizens will be infected, and provides some managerial insights that may help delivery companies and merchants make appropriate decisions about the allocation of AVs.
Sustainability and low-carbon manufacturing have been under the scrutiny of the academics and practitioners, along with the governmental and non-governmental organizations. Not to mention the growing awareness and concern of the consumers about the carbon footprint, leading the researchers to the joint problem of sustainable supply chain coordination and emission abatement. This study contributes to the current literature by developing a contract for a dyadic supply chain model under a manufacturer-led Stackelberg game where emission abatement is a key decision. Implementing a green-sensitive consumer preference and carbon abatement level into the demand function, a revenue-sharing contract is conducted to solve the channel conflict and double marginalization effect. The analytical results and numerical example prove the profit improvement of both channel members and validate the effectiveness and environmental-friendliness of the constructed supply chain structure.
Industry 4.0 technologies have capacities to improve competitiveness in the logistics chain by taking advantage of information flow through the logistics processes. This paper aims to determine the influence of effective and new information flow on logistics management in Nigeria. The methodology used in this study includes; the quantitative methodology, a mean item score, exploratory factor analysis, normality test, and Man-Whitney test. Findings revealed negotiating better contracts, better product tracking, better quality logistics information flow, expanded network, and enhanced information transfer as the top five effect of information flow on logistics management in Nigeria. It is recommended that Nigerian companies engaging in logistics activities need to adopt industry 4.0 technologies to aid effective and new information flow in their logistics management processes. Finally, the growth of logistics firm and its ability to compete depend on effective information flow.
The complexity of managing the port container terminals brings up many challenges in evaluating the global performance, so managers use the Multiple-criteria decision-making (MCDM) to assess the global performance but most of the methods are based in the experts jugements. In the case where we have a false judgment by experts, the final results will be wrong. This paper aims to develop the performance measurement system in port container terminals in order to assist decision-maker to evaluate the port container terminal. Furthermore, the paper proposes a novel framework which aims to detect and modify jugement in order to reduce inconsistancies. By reducing inconsitency, the performance results and analysis will be more stable and robust. The proposed framework combines two methods, the multiple criteria analysis using MACBETH method (Measuring Attractiveness by a Categorical Based Evaluation Technique) and DELPHI method. In order to give more legitimacy, the model is tested in four ports.
In this paper, an integer linear programming formulation is developed for a novel fuzzy multi-period multi-depot vehicle routing problem. The novelty belongs to both the model and the solution methodology. In the proposed model, vehicles are not forced to return to their starting depots. The fuzzy problem is transformed into a mixed-integer programming problem by applying credibility measure whose optimal solution is an (α,β)-credibility optimal solution to the fuzzy problem. To solve the problem, a hybrid genetic-simulated annealing-auction algorithm (HGSA), empowered by a modern simulated annealing cooling schedule function, is developed. Finally, the efficiency of the algorithm is illustrated by employing a variety of test problems and benchmark examples. The obtained results showed that the algorithm provides satisfactory results in terms of different performance criteria.
In this study, a two-stage mathematical model has been developed that facilitates tactical planning of supply of apples from the various location of farmers to the marketplaces and then to the cold storage facilities. The model enables efficient network planning by finding the most suitable and cost-effective dispatching method operating in multiple stages using a mixed fleet of jeeps and trucks. The model aims to optimize the cost and demand. The paper used a multi-stage model to examine the most effective and efficient route for the supply. Kullu, Himachal Pradesh has been taken as the case location, which is famous as key apple production location in India. The lack of infrastructure in the area poses challenges for the farmers to select a mix of both trucks and jeeps to make their goods available in the market. The present paper attempts to address these issues, facilitating an effective transportation planning of apple produce.
Sales territory design is an important research field because salesforce allocation within territories impacts sales organization effectiveness and customer service. This work presents a novel multi-objective model for re-designing sales territories with three main objectives: sales balancing, workload balancing, and geographic balancing. To measure sales and workload balancing, the variance among territories was calculated. The metric considered for geographic balancing was the sum of the distances from every salesperson to their assigned customers. A metaheuristic algorithm based on Tabu search was developed to solve a weighted aggregate function that integrates the three objectives. The algorithm is embedded in a procedure to systematically change the weights in the aggregate objective function to produce an approximate Pareto front of solutions. The algorithm was tested with instances based on data from a company in Mexico, providing salesperson-customer assignments that can be projected in territories in geographic information systems. The algorithm converges very fast for the instances studied and produces a Pareto front efficiently. Comparing the current situation of the company to a dominating solution obtained with the algorithm in the Pareto front, a significant improvement in the balance is achieved, in the order of 42.0 - 47.1% on average in the three objective functions. Another managerial benefit achieved by the company was a better understanding for the top managers of the salesforce, the customer preferences, and the challenge of serving a large and dispersed market.
The aim of this research is to investigate the effect of organizational culture (OC) on employee engagement (EE) and employee performance (EP) among academic staff of Malaysian Private Universities (MPU). However, studies of such have been given less attention within the context of MPU. Therefore, this paper highlights the issue through a systematic review of previous literature on the subject matter. A questionnaire was used to collect data from the respondents while Partial Least Square-Structural Equation Modeling (PLS-SEM) was used to test the study hypotheses. The results revealed that OC has a significant effect on EP while EE partially mediates the relationship between OC and EP. This study encourages university management to initiate sound OC and at the same time invest in OC in order to actualize EE and achieve a sustainable EP. This research has made a significant contribution to the operationalization of OC, EE, and EP literature which help to develop model, theory, research and practice in the fields of job performance.
Product customization is considered as the widespread strategy for the actual market trend oriented toward customer focus. In this field, mass customization sights mainly to emerge economy of scale and economy of scope in order to integrate mass production principles with customization abilities. This research views the collaborative management through an integrated procurement, production and distribution mixed integer linear programming (MILP) as a planning modeling approach for a multi-echelon and multi-site supply chain within tactical decision level. The model formulation is based on dyadic relationships according to leaders and followers tradeoffs where the supply chain’s stakeholders are depicted as follows, a) customers: Original Equipment Manufacturers (OEMs) identified as leaders and (b) first-tier suppliers: customized products manufacturers (c) second-tier suppliers: raw material suppliers, identified as followers. The feasibility of the proposed model has been provided through its resolution to optimality by an exact method, the decision-making process is focused on the first-tier suppliers’ operations in order to satisfy the customized demands taking into account realistic characteristics of mass customization environment for the internal and external constraints through the supply chain. The illustration of the model is performed with an example from the automotive industry, a sensitivity analysis has been conducted in order to provide the main decision points through key parameters, for instance, the capacities threshold according to a defined demand level and its customized structure which contribute to highlight a constructive managerial insights.
In Location-Arc Routing Problems (LARP), unlike the well-known locating-routing problems, demand is on the arc and using deadheading arcs is permitted. Few studies have focused on an arc-routing problem. In this research, a complex bi-objective linear mathematical model for the LARP with time windows under uncertainty is presented. Time windows in the arc-routing problem modeling have complexity since the required arc with time windows becomes a deadheading arc without time windows after service. Furthermore, modeling of the vehicle servicing to multiple required arc with the minimum deadheading arc in route is a feature of this work. The proposed LARP is used for modeling of transforming cash in the bank case study. In this case study, demand has uncertainty with unknown probability distributions and the Bertsimas and Sim’s approach is used for it. The case study problem is a node-basing problem with the closest node and time windows for servicing branches. For this purpose, the Multi-Objective Dragonfly Algorithm (MODA) and Non-dominated Sorting Genetic Algorithm (NSGA-II) are used for locating the cash supply centers of a bank in Tehran. Furthermore, comparing results of robust and deterministic LARP models show that the mean and standard deviation of objective function values in the robust model has better performance in realization.
Corporate social responsibility plays an important role in associating customers with socially responsible firms. Faithful consumers are willing to give extra money for commodities or services that incentive the firms to take corporate social responsibility (CSR). This article studies the coordination issue in a two-stage supply chain which is composed of a manufacturer and a retailer who sells a short shelf-life product in a single period. The manufacturer exhibits CSR and simultaneously determines its CSR investment and production quantity, as his production process is subject to random production yield. On the other hand, the retailer decides the selling price and order quantity simultaneously while facing price and CSR sensitive stochastic demand. We construct an agreement between the retailer and the manufacturer which comprises a revenue-sharing and a cost-sharing contract. We show that the supply chain can perfectly coordinate under this composite contract and allow arbitrary allocation of total channel profit to ensure that both the retailer and the manufacturer are benefited. We further analyze the impact of randomness in production as well as the effect of CSR investment on the performance of the entire supply chain. A numerical example is provided to explain the developed model and gain more insights.
