Manufacturing Management Research Articles

Improving food allergen management in food manufacturing: An incentive-based approach

The improvement of food allergen management among food manufacturers has been encumbered by a lack of behavioural incentives to adopt good practices (e.g. multiple verifications and adoption of advanced technologies etc.). This study aims to tackle this challenge by proposing an incentive-based approach. We develop this approach by compiling, through a scoping literature review, a comprehensive list of operational errors in food allergen management for food manufacturers; identifying incentives with a behavioural cost‒benefit approach; and integrating machine learning and human learning into a coherent framework for sustaining behavioural incentives. Such a synthesised approach could help to develop advanced food traceability technologies, more effective regulations and better food safety culture for improving food allergen management. • Incentivising the improvement of food allergen management. • A systems approach to improving food allergen management. • Integrating machine learning and human learning for food allergen management. • A behavioural cost-benefit model for food allergen management proposed. • Operational errors in food allergen management identified.

An Automated Tomato Maturity Grading System Using Transfer Learning Based AlexNet

Tomato maturity classification is the process that classifies the tomatoes based on their maturity by its life cycle. It is green in color when it starts to grow; at its pre-ripening stage, it is Yellow, and when it is ripened, its color is Red. Thus, a tomato maturity classification task can be performed based on the color of tomatoes. Conventional skill-based methods cannot fulfill modern manufacturing management's precise selection criteria in the agriculture sector since they are time-consuming and have poor accuracy. The automatic feature extraction behavior of deep learning networks is most efficient in image classification and recognition tasks. Hence, this paper outlines an automated grading system for tomato maturity classification in terms of colors (Red, Green, Yellow) using the pre-trained network, namely 'AlexNet,' based on Transfer Learning. This study aims to formulate a low-cost solution with the best performance and accuracy for Tomato Maturity Grading. The results are gathered in terms of Accuracy, Loss curves, and confusion matrix. The results showed that the proposed model outperforms the other deep learning and the machine learning (ML) techniques used by researchers for tomato classification tasks in the last few years, obtaining 100% accuracy.

스포츠웨어 제조업체 관리자의 서번트 리더십이 사원의 상사신뢰 및 조직몰입에 미치는 영향

Purpose: This study identifies the effect of servant leadership of sportswear manufacturer managers on managers’ trust and organizational commitment.<BR> Method: The subject of this study was selected as a population of employees who worked in the sportswear footwear manufacturing industry in Saha-gu, Busan Metropolitan City, and 330 copies were collected using the convenience sampling method. Among them, 293 samples were used excluding 37 copies which were considered unfaithful. The data were analyzed with frequency analysis, exploratory factor analysis, Cronbach’ αcoefficient calculation, correlation analysis, simple and multiple regression analysis using IBM SPSS 25.0. All statistical significance levels were set to α = .05.<BR> Results: The results of this study are as follows. First, the manager’s servant leadership was found to have a positive effect on boss trust. Second, the manager’s servant leadership was found to have a positive effect on organizational commitment. Third, trust in the bosses had a positive effect on organizational commitment.<BR> Results: The results of this study are as follows. First, the manager’s servant leadership was found to have a positive effect on boss trust. Second, the manager’s servant leadership was found to have a positive effect on organizational commitment. Third, trust in the bosses had a positive effect on organizational commitment.<BR> Conclusion: In summary, the results of this study confirm that the provision of positive servant leadership of sportswear manufacturer managers can affect employees’ boss trust and organizational commitment. Therefore, managers should steadily manage and develop servant leadership to build boss trust and organizational commitment.

Information as a key dimension to develop industrial asset management in manufacturing

PurposeThe purpose of this work is to investigate industrial asset management (AM) in manufacturing. After depicting gaps for AM in this sector, the role of information as a key dimension is considered to realise a summary of challenges and advices for future development.Design/methodology/approachThe work is grounded on an extensive systematic literature review. Considering the eligible documents, descriptive statistics are provided and a content analysis is performed, both based on a sector-independent normative-based framework of analysis.FindingsAM principles, organisation and information are the dimensions defined to group ten areas of interest for AM in manufacturing. Information is the major concern for an effective AM implementation. Moreover, Internet of Things and big data management and analytics, as well as data modelling and ontology engineering, are the major technologies envisioned to advance the implementation of AM in manufacturing.Research limitations/implicationsThe identified challenges and advices for future development may serve to stimulate further research on AM in manufacturing, with special focus on information and data management. The sector-independent normative-based framework may also enable to analyse AM in different contexts of application, thus favouring cross-sectorial comparisons.Originality/valueIndustries with higher operational risk, like Oil&Gas and infrastructure, are advanced in AM, while others, like some in manufacturing, are laggard in this respect. This literature review is the first of a kind addressing AM in manufacturing and depicts the state-of-the-art to pave the way for future research and development.

Pro-environmental behavior model creating circular economy in steel recycling market, empirical study in Thailand

The purpose of this study is to examine the variables that influence both the Circular Economy (CE) and pro-environmental behavior in steel recycling manufacturing companies in Thailand. To develop and examine a model using structural equation modeling (SEM), we used a set of CE indicators proposed in previous studies. The variables identified in this study were determined through responses from stakeholders in the steel manufacturing and scrap management industries. A questionnaire-based survey was used to analyze the study target's (foundries, castings, and steel recycling manufacturers) behavior. Respondents contended that they value long-term relationships with suppliers. Quality control has a sizable impact on lean manufacturing techniques. Recycling costs and environmental concerns both have an effect on how behavioral control is perceived. It is critical for society to be educated about the circular economy and its practices. Environmental commitment is the most critical factor in the circular economy's success. The findings indicate that the most significant influence on the Circular Economy is the social willingness to recycle (CE). The study investigates the relationship between social willingness to recycle (SWR) and lean manufacturing techniques (LMT) and the success of circular economy practices. Environmentally responsible behavioral practices both promote and contribute to high levels of productivity. The CE's promotion is critical in light of the current trend toward globalization. We propose that steel manufacturers must continue to innovate in technical or non-technical ways, most notably through the use of efficient equipment such as pre-shredders, shredders, and power plants that accelerate long-term mineral resource savings.

A Study on The Safety Measures Against Fire Through An ESS Fire Analysis

Due to the increase in the frequency of fires due to the mass distribution of energy storage devices, following the introduction and operation of eco-friendly power generation facilities around the world, there is an urgent need to analyze the causes of such fires and to devise countermeasures against them. By analyzing the occurrence of ESS fires and reviewing the definition of ESS terminology, we intend to suggest some safety measures against ESS fires by understanding the related facilities and analyzing the causes. We would also like to propose new measures to prevent the spread of ESS fires. ESS fires occur most frequently during the discharge standby period after charging is completed in connected solar and wind power facilities. In addition, when we investigated the problems with the batteries, PCS manufacturing, ESS design, installation and construction, and the factors related to their usage and operation, we found such causes of fire as battery system defects, unsatisfactory protection systems against electrical shock, improper operations and environmental management, careless installations, and a lack of ESS integrated management systems. Therefore, a new approach is needed through the application of new technologies and safety measures including manufacturing standards, installation standards, operation and management standards, and firefighting standards for the purpose of ensuring the safety of these facilities and their operations, in order to prevent ESS fire accidents and the spread of fire.

Exploration of the safety management and maintenance of machinery manufacturing and processing equipment

With the rapid development of the economy, various industries are also rapidly growing and progressing, and the machinery manufacturing industry has also made greater breakthroughs. If various industries can achieve the goal of automatic production of machinery, mechanical equipment will be a crucial factor. The machinery manufacturing industry has a lot of room for development, and machinery manufacturing technology has also made tremendous progress. If the machinery and equipment produced by the machinery manufacturing industry become more and more excellent, it needs to try to meet the production needs of all walks of life, and improve the quality of manufacturing equipment. The current problems in the process must be solved, such as equipment failure, safety management.

Statement of the Manufacturing &amp; Service Operations Management Journal

Statement of the <i>Manufacturing &amp; Service Operations Management</i> Journal

Main approaches to the development of mechanical engineering as an echelon of economic dynamics

In the present article the specificity and basic directions of development of domestic mechanical engineering are considered. The optimality of a choice of progressive system of maintenance including procedures of fast management of manufacture of production, allowing raising efficiency of administrative and financial work at the enterprise, and also competitiveness of let out production is proved. This allows improving the organizational and economic model of operational production management, focused on improving the competitiveness of the machine-building enterprise products.

Optimization of Ultra-Dense Wireless Powered Networks.

The internet-of-things (IoT) is expected to have a transformative impact in several different domains, including energy management in smart grids, manufacturing, transportation, smart cities and communities, smart food and farming, and healthcare. To this direction, the maintenance cost of IoT deployments has been identified as one of the main challenges, which is directly related to energy efficiency and autonomy of IoT solutions. In order to increase the energy sustainability of next-generation IoT, wireless power transfer (WPT) emerged as a promising technology; however, its effectiveness is hindered as the distance between the base station and the wireless powered IoT devices increases. To counter this effect, decentralized approaches based on the use of distributed densely deployed remote radio heads (RRHs) can be utilized to diminish the distance between the transmitting and the receiving nodes. A trade-off ensues from the use of RRHs as power beacons (PBs) or access points (APs) that enable either energy transfer during downlink or information reception during uplink, respectively. To balance this trade-off, in this work, the maximization of the ergodic rate in ultra-dense wireless powered networks is investigated. In more detail, three different protocols are introduced, optimized, and compared to each other: density splitting, time splitting, and hybrid time and density splitting, which are based on the optimization of the portion of the number of RRHs that are employed as PBs or APs at a specific time instance. Additionally, two different policies are taken into account regarding the PBs’ power constraint. The formulated problems that correspond to the combination of the proposed protocols with each of the two considered power constraint policies are optimally solved by using convex optimization tools and closed-form solutions are derived that result to useful insights. Finally, numerical results are provided, which illustrate the ergodic rate achieved by each of the proposed protocols and offer interesting conclusions regarding their comparison, which are directly linked to design guidelines and the required capital and operational expenses.

Automated Design and Integration of Asset Administration Shells in Components of Industry 4.0.

One of the central concepts in the principles of Industry 4.0 relates to the methodology for designing and implementing the digital shell of the manufacturing process components. This concept, the Asset Administration Shell (AAS), embodies a systematically formed, standardized data envelope of a concrete component within Industry 4.0. The paper discusses the AAS in terms of its structure, its components, the sub-models that form a substantial part of the shell’s content, and its communication protocols (Open Platform Communication—Unified Architecture (OPC UA) and MQTT) or SW interfaces enabling vertical and horizontal communication to involve other components and levels of management systems. Using a case study of a virtual assembly line that integrates AASs into the technological process, the authors present a comprehensive analysis centered on forming AASs for individual components. In the given context, the manual AAS creation mode exploiting framework-based automated generation, which forms the AAS via a configuration wizard, is assessed. Another outcome consists of the activation of a virtual assembly line connected to real AASs, a step that allows us verify the properties of the distributed manufacturing management. Moreover, a discrete event system was modeled for the case study, enabling the effective application of the Industry 4.0 solution.

Energy-oriented joint optimization of machine maintenance and tool replacement in sustainable manufacturing

With the increasing attention on sustainable manufacturing, operation and maintenance (O&M) management focuses on not only budget limit, but also energy saving. For modern CNC systems, besides the energy consumption to operate and maintain the machine, a majority of energy consumption generated from tool wear should be considered. It means both machine degradation and tool wear are required to be modelled for the global saving energy. Thus, this paper proposes an energy-oriented joint optimization of machine maintenance and tool replacement (EJMR) policy by integrating energy consumption mechanisms and joint maintenance opportunities in a machine-tool system. The key issue is to combine the preventive maintenance (PM) scheduling of the machine and the polish/preventive replacement (PR) optimization of sequential tools to form energy-effective schemes. Therefore, joint maintenance opportunities of PM actions are utilized to perform tool polish/PR based on energy consumption mechanisms. Four successive procedures (energy consumption analysis, energy-oriented PM scheduling, machine-tool PR model and integrated decision-making process) are developed. Thereby optimal intervals of machine PM and tool polish/PR are obtained to save energy. The case study illustrates that compared with conventional maintenance policies, this proposed EJMR policy can significantly reduce the total non-value-added energy consumption (TNVE) in sustainable manufacturing.

Design and Realisation of Reusable Artefacts for Internal Supply Chain Management in Manufacturing Company

Maintaining many similar software products with huge similarities and some variations have been studied is Software Product Line Engineering (SPLE). Traditional software methodology lacks the notions to model and maintain those variability and commonality in all phases of standard known software methodology (e.g. from requirement phase up to the testing phase). This paper shows a proposed software methodology based on SPLE to ease the development and maintainability of similar software product. The paper focuses on the design and realizes the reusable artefacts to generate a family of software products systematically with case study example of Supply Chain Management in Manufacturing. This methodology utilized the commonly known UML diagram with the use of UML Profile of Delta Programming and some tools of code generator.

Continuous improvement and adaptation of predictive models in smart manufacturing and model management

Predictive models are increasingly deployed within smart manufacturing for the control of industrial plants. With this arises, the need for long-term monitoring of model performance and adaptation of models if surrounding conditions change and the desired prediction accuracy is no longer met. The heterogeneous landscape of application scenarios, machine learning frameworks, hardware-restricted IIoT platforms, and the diversity of enterprise systems require flexible, yet stable and error resilient solutions that allow the automated adaptation of prediction models. Recommendations are provided for the application and management of predictive models in smart manufacturing. Typical causes for concept drift in real-world smart manufacturing applications are analysed, and essential steps in data and prediction model management are highlighted, to ensure reliability and efficiency in such applications. For this purpose, recommendations and a reference architecture for model management are provided. In addition, experimental results of two model adaptation strategies on an artificial dataset are shown.

Using vendor management inventory system for goods inventory management in IoT manufacturing

ABSTRACT The revolution in the digital economy is forcing the manufacturing industry to develop new business models to achieve operational excellence. Vendor Management Inventory (VMI) is a key measure, aimed at increasing supply chain efficiency and mitigating the bullwhip effects on the Internet of Things (IoT). The problems encountered by the manufacturing supplier are the difficulty of obtaining inventory information at the hub, and being unable to easily integrate relevant inventory information in the IoT. The proposed method is to implement an integrated Hub VMI system to solve the inventory problem. This case study aims to implement an all-in-one integrated Hub VMI in an electronic manufacturing business. This proposal has two models: one comprises an Stock Transfer Order (STO)/Sales Order (SO) structure in which the purchase order is accepted and the product is manufactured onsite; the other consists of a Purchase Order (PO)/SO structure, whereby the PO is accepted onsite but the product is manufactured at another site. This case study designs an intelligent Radio Frequency Identified (RFID) system to extract product information from an inventory, and to provide a product, as recommended by customer services. The result and the contributions of this research include inventory management operation enhancements, a reduction in the cost and the total execution time, a reduction in the response time for the benefit of the customer and an increase in system performance efficiency.

Investigating the Dimensional and Geometric Accuracy of Laser-Based Powder Bed Fusion of PA2200 (PA12): Experiment Design and Execution

Variation management in additive manufacturing (AM) is progressively more important as technologies are implemented in industrial manufacturing systems; hence massive research efforts are focused on the modeling and optimization of process parameters and the effect on final part quality. These efforts are, however, hampered by the very problem they are seeking to solve, as conclusions are weakened by poor validity, reliability, and repeatability. This paper details an elaborate experiment design and the subsequent execution with the aim of making the research data available without loss of validity. Test artifacts were designed and allocated to fixed positions and orientations in a grid pattern within the build chamber to facilitate rigid analysis between different builds and positions in the build chamber. A total of 507 specimens were produced over three builds by laser sintering PA12 before inspection with a coordinate measuring machine. This research demonstrates the inherent variations of laser-based powder bed fusion of polymers (LB-PBF/P) that must be considered in experiment designs to account for noise factors. In particular, the results indicate that the position in the xy-plane has a major influence on the geometric accuracy, while the position in the z-direction appears to be less influential.

Research on a Traceability Process of Sand Core Information by Printing QR Code on Sand Core Surface in the Casting Production Process

The quality of the sand core directly affects the quality of the casting during the casting process. The establishment of an information link between the sand core and the casting can realize the traceability of sand core production and process data in the casting production. The technology can be used when casting defects related to the sand core occur and quickly respond to the causes and optimize the manufacturing process of the sand core making process. This article describes the necessity of establishing a traceability process for sand core making information and introduces a traceability process of sand core information in casting production to ensure that each sand core has its own production process information, which is for product quality management and process optimization. It provides information supervision and promotes the development of intelligent casting manufacturing technology and management technology.

A multi-criteria optimization study for locating industrial warehouses with the integration of BIM and GIS data

ABSTRACT One of the newest advancements in manufacturing and project management is Building Information Modeling (BIM) which uses creative processes for production, analysis, and management of building information. On the other hand, the output of the Geographic Information System (GIS) model includes one or more actual locations, which satisfy all needs. It provides points related to the BIM tool and it creates a 3D model for visualizing the optimum location. Integration of GIS and BIM can bring about several advantages including optimum locating. The aim of the current study is to propose a useful model for locating warehouses in large manufacturing factories using a BIM-GIS integrated model. To this end, BIM was used for simulation and locating of product warehouses in the casting unit of Esfahan’s Mobarakeh Steel Company (EMSC) in Iran. In order to meet these aims, several locations were selected in the EMSC compound, and decision making regarding three superior alternatives which were carried out by creating a 3D model of the warehouse and fitting of the model in three suggested locations. Using the extant literature, interviews with experts and managers, and survey questionnaires, essential criteria affecting the optimum locating of industrial warehouses were extracted based on the Building information modeling data. Then, the Analytic Hierarchy Process (AHP) method was used for data analysis. The results indicated that among various criteria, economic–social and crisis management criteria had the highest weights. These results were then used for selecting the optimum locating for the construction of warehouses.

Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

In cloud manufacturing, customers register customized requirements, and manufacturers provide appropriate services to complete the task. A cloud manufacturing manager establishes manufacturing schedules that determine the service provision time in a real-time manner as the requirements are registered in real time. In addition, customer satisfaction is affected by various measures such as cost, quality, tardiness, and reliability. Thus, multiobjective and real-time scheduling of tasks is important to operate cloud manufacturing effectively. In this paper, we establish a mathematical model to minimize tardiness, cost, quality, and reliability. Additionally, we propose an approach to solve the mathematical model in a real-time manner using a multiobjective genetic algorithm that includes chromosome representation, fitness function, and genetic operators. From the experimental results, we verify whether the proposed approach is effective and efficient.

Revenue management in manufacturing: systematic review of literature

Revenue management (RM) is selling the right product, at the right price, at the right time, to the right customer. While this technique is originally a tool of service operations, it shows the potential for application in manufacturing. To provide a comprehensive understanding of research in this field, this study adopted a systematic process of reviewing the literature. The classification of the articles into core activities of RM in manufacturing serves as a useful guide for practitioners and academics to trace aspects of this concept and to notice the gaps in the existing literature as well as directions for future research.