Showing 27 results for Manufacturing
Rahul S Mor, Arvind Bhardwaj, Vishal Kharka, Manjeet Kharub,
Volume 32, Issue 2 (6-2021)
Abstract
Inventory management plays a vital role in attaining the desired service level and prevents excess capital from being tied up in the form of dead stock. This paper presents a framework to effectively determine the items subject to obsolescence in an automotive spare parts warehouse. The inventory management techniques are applied to minimize the costs and a framework is proposed based on ABC-XYZ and FSN analysis to prioritize the spare parts based on their criticality. Further, the importance of items in the warehouse is carried out to eliminate the dead stock. The ABC classification findings reveal that A-class items accounted for 10.39% and hold the highest inventory value grouping. XYZ classification concludes that much priority should be given to the management of 52.7% of items under the Z category as the demand trend of these items is highly fluctuating. The N category items have no demand in recent times and need immediate attention, thereby preventing further unnecessary procurement. Thus, based on the ABC-XYZ and FSN analysis, the non-critical items, i.e., the non-moving items having fluctuating demand, are sorted out.
Bhagwan Kumar Mishra, Anupam Das,
Volume 32, Issue 4 (12-2021)
Abstract
The article highlights the development of a Non-Gaussian Process Monitoring Strategy for a Steel Billet Manufacturing Unit (SBMU). The non-Gaussian monitoring strategy being proposed is based on Modified Independent Component Analysis (ICA) which is a variant of the widely employed conventional ICA. The Independent Components(IC) being extracted by modified ICA technique are ordered as per the variance explained akin to that of Principle Component Analysis (PCA). Whereas in conventional ICA the variance explained by the ICs are not known and thereby causes hindrance in the selection of influential ICs for eventual building of the nominal model for the ensuing monitoring strategy. Hotelling T2 control chart based on modified ICA scores was used for detection of fault(s) whose control limit was estimated via Bootstrap procedure owing to the non-Gaussian distribution of the underlying data. The Diagnosis of the Detected Fault(s) was carried out by employment of Fault Diagnostic Statistic. The Diagnosis of the Fault(s) involved determination of the contribution of the responsible Process and Feedstock characteristics. The non-Gaussian strategy thus devised was able to correctly detect and satisfactory diagnose the detected fault(s)
Vahid Razmjoei, Iraj Mahdavi, Nezam Mahdavi-Amiri, Mohammad Mahdi Paydar,
Volume 33, Issue 2 (6-2022)
Abstract
Companies and firms, nowadays, due to mounting competition and product diversity, seek to apply virtual cellular manufacturing systems to reduce production costs and improve quality of the products. In addition, as a result of rapid advancement of technology and the reduction of product life cycle, production systems have turned towards dynamic production environments. Dynamic cellular manufacturing environments examine multi-period planning horizon, with changing demands for the periods. A dynamic virtual cellular manufacturing system is a new production approach to help manufacturers for decision making. Here, due to variability of demand rates in different periods, which turns to flow variability, a mathematical model is presented for dynamic production planning. In this model, we consider virtual cell production conditions and worker flexibility, so that a proper relationship between capital and production parameters (part-machine-worker) is determined by the minimum lost sales of products to customers, a minimal inventory cost, along with a minimal material handling cost. The problems based on the proposed model are solved using LINGO, as well as an epsilon constraint algorithm.
Chaymae Bahtat, Abdellah El Barkany, Abdelouahhab Jabri,
Volume 34, Issue 2 (6-2023)
Abstract
The productivity and flexibility of current manufacturing systems (dedicated and flexible production systems) are no longer competitive as products are developed and brought to market in increasingly shorter cycles. As a result, a new generation of reconfigurable manufacturing systems (RMS) has emerged that should be responsive enough to cope with sudden market changes while maintaining excellent product quality at low prices. These systems could also leverage technologies at the heart of Industry 4.0, such as artificial intelligence and machine learning, the Internet of Things (IoT), and digital twins, to create a smart, dynamic, and most importantly, reconfigurable factory, dubbed the Reconfigurable Factory 4.0. This study provides an organized and up-to-date systematic review of the literature on reconfigurable manufacturing systems, from design to simulation, and from automation to the fourth industrial revolution (Industry 4.0) highlighting the application areas as well as the significant approaches and technologies that have contributed to the development of a Reconfigurable Factory 4.0.
Hamed Nozari, Maryam Rahmaty,
Volume 34, Issue 4 (12-2023)
Abstract
In this paper, the modeling of a make-to-order problem considering the order queue system under the robust fuzzy programming method is discussed. Considering the importance of timely delivery of ideal demand, a four-level model of suppliers, production centers, distribution centers, and customers has been designed to reduce total costs. Due to the uncertainty of transportation costs and ideal demand, the robust fuzzy programming method is used to control the model. The analysis of different sample problems with the League Championship Algorithm (LCA), Particle Swarm Optimization (PSO), and Salp Swarm Algorithm (SSA) methods shows that with the increase in the uncertainty rate, the amount of ideal demand has increased, and this has led to an increase in total costs. On the other hand, with the increase of the stability coefficients of the model, contrary to the reduction of the shortage costs, the total costs of the model have increased due to transportation. Also, the analysis showed that with the increase in the number of servers in the production and distribution centers, the average waiting time for customers' order queues has decreased. By reducing the waiting time, the total delivery time of customer demand decreases, and the amount of actual demand increases. On the other hand, due to the lack of significant difference between the Objective Function Value (OBF) averages among the solution methods, they were prioritized, and SSA was recognized as an efficient algorithm. By implementing the model in a real case study in Iran for electronic components, it was observed that 4 areas of the Tehran metropolis (8-18-16-22) were selected as actual distribution centers. Also, the costs of the whole model were investigated in the case study and the results show the high efficiency of the solution methods in solving the make-to-order supply chain problem.
Tenaw Tegbar Tsega, Thoben Klaus-Dieter, Rao D.k.nageswara, Bereket Haile Woldegiorgis,
Volume 35, Issue 2 (6-2024)
Abstract
Ethiopia has made enormous efforts in the leather industry to gain manufacturing capabilities that can be scaled up to other sectors. Those efforts have resulted in the industry shifting its role from raw material supplier to producer of value-added products for the global supply chain (GSC). However, the industry has faced severe challenges in generating the expected revenue, utilizing capacity, and finally coping with the global competitive environment. Studies reveal that manufacturing firms tackle similar challenges by improving their supply chain performance (SCP). The challenges that appeared in the leather industry of Ethiopia could also be solved by improving its SCP. Nonetheless, there is a lack of study on the basic characteristics and SCP of the industry after it has shifted its role. The main objective of this study is, therefore, to measure the SCP to know where it stands using a bench mark and identify the elements that contribute considerably to the low overall SCP in order to lay the foundation for subsequent improvement. To achieve the research objective, data was collected from primary and secondary sources through a questionnaire, survey, observation, and focus group discussion. The data is analyzed using the supply chain operations reference model (SCOR version 12.0). Accordingly, the overall SCP is found to be 67.33%, suggesting an average rating as per the set benchmark. The source process is identified as the most influential element for the overall low SCP, with a percentage gap of 17.23%. Taking corrective action on the identified elements could help the industry overcome the existing challenges by improving its SCP.
Atef Fakhfakh, Salaheldin Salaheldin, Amr Noureldin, Mohamed Aboueldahab, Neama Elwakeel,
Volume 36, Issue 1 (3-2025)
Abstract
This study investigates the interplay between manufacturing ambidexterity, Industry 4.0 readiness, and sustainable excellence in Egypt's food and beverage sector. It explores how Industry 4.0 readiness mediates and moderates the relationship between ambidexterity and sustainability outcomes. A quantitative research design was employed, utilizing a survey of 308 professionals in Egypt's food and beverage industry. Structural equation modeling (SEM) was used to analyze the relationships among manufacturing ambidexterity, Industry 4.0 readiness, and sustainable excellence. The results reveal that Industry 4.0 readiness fully mediates and significantly moderates the relationship between manufacturing ambidexterity and sustainable excellence. While manufacturing ambidexterity alone does not directly impact sustainable excellence, its effect becomes significant through Industry 4.0 readiness, highlighting the importance of digital transformation. This study focuses on a single sector in Egypt, limiting generalizability. Future research could explore other industries and regions or examine specific dimensions of Industry 4.0 readiness. The findings emphasize the need for organizations to invest in digital infrastructure and foster ambidextrous capabilities to achieve sustainability goals. Policymakers are encouraged to support Industry 4.0 adoption through incentives and training programs to enhance competitiveness and sustainability in emerging markets. This study contributes to the limited research on the application of manufacturing ambidexterity and Industry 4.0 technologies in developing economies, offering insights into achieving sustainable excellence through digital transformation.
