International Journal of Supply and Operations Management
Volume:10 Issue: 3, Summer 2023

Recently, developing strategies for sustainable development (SD) in the hotel industry has been seen globally as a crucial issue. Numerous management systems can assist the hotel industry in creating sustainable performance, such as Total Quality Management (TQM) which is well-reputed in the industry. As such, selecting TQM under an evolving hotel industry environment is seen as an important decision from a strategic perspective given it constitutes contradictory practices, thereby making it a multi-criteria decision-making (MCDM) issue. In achieving this aim, a Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach was adopted in determining the inter-relationships between the primary practices and sub-practices in addition to applying the Analytic Network Process (ANP) for examining the weights of primary practices and sub-practices. In other words, this study aims to provide innovative insight to researchers and practitioners to examine the TQM optimal practices to be implemented incrementally in phases within the hotel industry environment.

After a natural disaster, medical supplies will be in high demand in the disaster-affected communities. Providing prompt and high-quality rescue resources is critical to the emergency relief network's overall quality. This study presents a mathematical optimization model for constructing a multi-period emergency relief system that minimizes the system's overall expected costs. The model considers location, allocation, and distribution decisions as well as flow of medical supplies and injured people. Medical supply distribution centers and roads are vulnerable to failure in the suggested model. Since certain parameters in the real world are unknown, the model parameters' uncertainty is explored. There are four sources of uncertainty regarding the number of injured people, demand, costs, and the probability of failure. To cope these uncertainties, a robust-stochastic optimization approach is used. Also, a case study focused on an earthquake in southern and western cities of Fars province is discussed to assess the efficacy of the suggested model. The findings demonstrate that the robust-stochastic approach is capable of effectively controlling cost and demand uncertainty, and that failing to account for uncertainty when planning relief logistics would be extremely deceptive. The planned relief system has the highest cost at the highest level of uncertainty, but it will offer a better protected solution to uncertainty with a greater level of robustness. The stochastic model has the lowest cost, but it is unable to produce the most conservative solution with the best uncertainty protection when there is a great deal of uncertainty in the system.

An efficient logistic system has become more important in today’s business process. Milk run system is being introduced to encourage efficient logistic system in manufacturing which has indirectly resulted in a reduction of transportation cost, travelling path, as well as fuel consumption. However, the poor optimal state of the original delivery route and low vehicle loading rate has a huge impact on the production effectiveness. The objective of this study is to evaluate the current milk run route, optimize the transportation volume capacity and propose a transportation route for the milk run logistics system. The milk run concept is introduced to deliver components to the production line from multiple suppliers. This approach is based on the Just-In-Time concept promoted by Toyota Production System where the small batch is delivered to the production line to reduce the side inventory. A high frequency of delivery is required. Therefore, the load for each of the delivery needs to be calculated to achieve maximum load with minimum inventory. The Saving Matrix Method based on Tabu Search model and Ant Colony Optimization model is used to evaluate the current milk run route. The result of the analysis showed an unutilized capacity of 49% that can be reduced to 3% with a distance deviation between 0% for direct milk run route and 2.0% to 6.8% for indirect milk run route. The managerial suggestions that can increase the logistics efficiency of the milk run are provided to benefit the organization by reducing the total logistics cost.

Nowadays, in production environments where the production system is parallel machines, the reliability of the machines is important and the uncertainty of scheduling parameters is common. In this paper, unrelated parallel machine scheduling problem using a fuzzy approach with machines maintenance activities and process constraints is of concern. An important application of this problem is in the production of products that the due dates are defined as a time window and the best due date is close to the middle of the time window and the jobs processing times depend on other factors such as operator and their value is not specified and are announced as interval under uncertainty. In this study, first, a fuzzy mathematical model is proposed in which changing between a fuzzy approach and a deterministic model is described. Then, since the problem is NP-hard, a fuzzy-based genetic algorithm to solve large instances is developed. In this algorithm, a greedy decoding approach according to fuzzy parameters is developed. Numerical experiments are used to evaluate the performance of the developed algorithm. It is concluded that the proposed algorithm shows great performance in large instances and is superior to the proposed mathematical model in small instances too.

Value chain operations have significantly improved due to rapid technological advancements, leading to a positive impact on the lifestyle of end users. The advent and widespread availability of the internet have had a profound influence on numerous industries, compelling them to shift from manual to automated operations and from offline to online activities. The digitalization of the supply chain plays a crucial role in enhancing business planning and execution, thereby facilitating the achievement of various organizational objectives in the future. This review focuses on assessing the significance of supply chain digitalization by extensively analysing literature published between 2018 and 2022. The study investigates the impact of digitalization on manufacturing operations and identifies new avenues for future research through a thorough content analysis. To evaluate the level and nature of supply chain digitalization in the industrial sector, the authors conducted a comprehensive review of 115 research articles. Various techniques, including bibliometric analysis, network data analysis, cluster analysis, and content analysis, were employed by the authors. Through this study, the authors successfully identified the changes and advancements associated with the implementation of digital supply chains (DSC) in the manufacturing industry. Moreover, by examining the literature review, the authors pinpointed emerging issues that require attention and investigation in the future. These insights will serve as valuable guidance in determining the appropriate research directions moving forward.

This work focuses on best practice processes and methods of onboarding in the supply chain management (SCM) of industrial companies by means of a qualitative investigation. The findings from other industrial companies are compared with each other as well as with the processes and methods given in the literature. This study highlight similarities, differences and examples of best practices. The key findings of this study show that structured onboarding is successful above all through the interplay of professional and social onboarding. The study also identified that social familiarization is given special consideration in the literature as well as in practice through the topics of appreciation and trust as well as the development of a godparent, mentor or buddy program. None of the industrial companies makes use of holistic concepts of onboarding, which are presented in the literature. This is due to the fact that various interfaces common in the SCM-industry exhibit highly individual requirements.
The key findings of this study show that structured onboarding is successful above all through the interplay of professional and social onboarding. The preparation of these processes takes place before the start of the employment relationship using checklists in order to enable new employees to become productive as quickly as possible. The study also identified that social familiarization is given special consideration in the literature as well as in practice through the topics of appreciation and trust as well as the development of a godparent, mentor or buddy program.

Integrating the lot sizing and scheduling problems for improving capacity utilization in process industries is crucial. In order to deal with this problem realistically and to obtain applicable schedules, it is a prerequisite to consider the typical characteristics of the industry under consideration. From this point of view, in this study, the lot sizing and scheduling problem in cement grinding, a multi-product, multi-period optimization problem with non-identical parallel machines, is addressed by considering the unique and industry-specific characteristics of the process. Besides applicability, it is aimed to create schedules that minimize total costs, including inventory holding, production, electricity, and lost sales. A lot sizing and scheduling model (LSM) based on the General Lot Sizing Problem (GLSP) and a capacity control model (CCM) derived from LSM has been developed for the considered problem with these objectives. The proposed approach based on the cyclical running of LSM and CCM has been applied for one year using the real data of a firm operating in the cement industry. The performance of this approach has been evaluated by comparing it with the firm's realized performance during that year. As a result, the proposed approach has significantly reduced inventory holding costs by 47.51%, production during setups by 62.54%, production after setups by 1.49%, and electrical energy by 8.65%.