Manufacturing Supply Chain Management Research Articles

An RFID-Powered Multisensing Fusion Industrial IoT System for Food Quality Assessment and Sensing

The development in Internet of Things (IoT) have empowered revolution in almost all walks of life. Although substantial effort has been made to bring IoT in manufacturing, there is still technical challenges of providing solutions of real-time pervasive multi-sensing, quality evaluation and boundary-less information sharing, which put people at risk from deteriorated food products and food adulteration. This investigation presents an Industrial IoT-based system for food product quality assessment and prediction. This research completes the real-time food quality assessment via radio frequency identification-based multi-sensor fusion for the first time. Also, a novel concept of shelf-life prediction is proposed. The RFID-powered sensors provide a new idea for nondestructive food product and environment sensing. A five-layer architecture considering sensing, controlling, communication, interfaces, and data analysis is highlighted. A novel RFID metadata structure is first proposed to achieve multi-dimensional information traceability and global data sharing. Machine learning-based multi-sensor fusion is proposed to provide accurate quality assessment and prediction. The proposed system is implemented as a smart shelf system to demonstrate its feasibility and advantages. The system is of great significance in improving food safety, reducing food waste and providing powerful information support for food manufacturing line and supply chain management.

NFT based Digital Twins for Tracing Value Added Creation in Manufacturing Supply Chains

The globalization of products and markets increases the distance between the origin of products and consumers. This leads to a condition where customers don't have information about the origins of their products. Thus, traceability has become an essential sub-system of manufacturing supply chain management. However, due to globalization and complexity of supply chain interactions among the suppliers and manufacturing enterprises, it is hard to pinpoint the exact contributions of different actors in a supply chain. Integrated supply network structure with suitable visibility and usage of real time data transfer is another area of great importance. This paper focuses on how NFT (Non-Fungible Token) coupled with smart contracts could utilize blockchain to make it easier to track the products in a supply chain. Explaining how NFT's could help in tracking the contributions of different stakeholders in a supply chain by tracking the product throughout the entire process of sourcing, production, and sale by using a digital twin. In a manufacturing supply chain enabled by NFT Technology, whenever raw materials are transferred and processed through the supply chain, an NFT would be attached to its digital twin which will capture the created values. Each NFT can easily and uniquely be known by its data stored. Data would be updated based on real time information and will enable the stakeholders to trace the product information about how much each company has contributed to the produced products. The data stored in the form of a smart contract in the blockchain prevents the data being entered from being destroyed, eliminated, or changed without permission. Thus, there is a secure data flow among different stakeholders.

Special Issue on “Green Manufacturing and Sustainable Supply Chain Management”

Manufacturing plays a vital role in the global economy, as it drives economic growth and development [...]

Productivity and Quality Improvement by SCM in Textile and Apparel Industry - An Overview

Retailing, marketing, and merchandising in fibre production, yarn spinning, fabric manufacturing, wet processing, product distribution and disposal are coming under the textile manufacturing process. Any supply chain management consists of many deals such as money, goods, information, and services both inside and among different entities like suppliers, information processing, warehousing and customer dealing. India is one of the world’s few countries which works across the entire value chain process in the textile and apparel industry. Supply chain management in modern textile manufacturing is a crucial part. In the analysis of supply chain management, statistical techniques like factor analysis, structural equations, etc. are used. The three essential variants in the supply chain are such as push-pull chain, pull supply chains, and push/pull supply chains. Information is a major component of supply chain.

Kesan Orientasi Pengurusan Strategik Terhadap Kecekapan Firma Pembuatan Halal

High demand for halal products globally has contributed the rapid development of halal small and medium enterprises (SMEs). Halal Manufacturing Supply Chain Management (HSCM) has been adopted by SME as a dynamic action in improving firm performance (FP). However, the benefit of HSCM can not be fully achieved without effective strategic manufacturing management (SMM). Based on this concept, the relationships between SMM, HSCM, and FP have been debated. Hence, rigorous research is necessary to identify the factors and the implications of SMM towards HSCM and also FP as a whole. The primary objective of this research is to recognize the SMM practises applied in the halal manufacturing SME sectors. This research uses purposive sampling. A Structural Equation Modeling (SEM) by Partial Least Square (PLS) technique has been employed in analysing the collected data. Market orientations were found to have a significant impact on business performance when applied to halal-certified SMEs. On the other hand, despite having statistically significant effects on business performance, supply chain orientation exerted a reduced influence. This study establishes a framework for halal practitioners to improve their business operations through the application of strategic orientation principles.

Differences in manufacturing and healthcare supply chain management: an overview

Differences in manufacturing and healthcare supply chain management: an overview

The Era of Industry 4.0 Technologies and Sustainable Organizational Performance of Pakistan's Garment Industry: The Role of Lean Manufacturing and Green Supply Chain Management Practices with the Moderating Effect of Sustainability Culture

The Era of Industry 4.0 Technologies and Sustainable Organizational Performance of Pakistan's Garment Industry: The Role of Lean Manufacturing and Green Supply Chain Management Practices with the Moderating Effect of Sustainability Culture

Differences in manufacturing and healthcare supply chain management: an overview

Differences in manufacturing and healthcare supply chain management: an overview

Effect of Uncertainty, Supplier Involvement, Supplier Performance and Partnership Quality on Buyer-Supplier Relationship

The research is undertaken to evaluate the aspects of Uncertainty, Earlier Supplier Involvement, Supplier Performance and Partnership quality on Buyer dependence on the Supplier. In this explanatory research, a model is developed to validate the premise that by drafting value creating supplier relationship in order to help and implement strategies in supply chain management in manufacturing and non-manufacturing companies which will increase the buyer’s dependency of the suppliers. The study used data from atleast 228 procurement personnel from various industries. The results show that early supplier involvement, partnership quality, supplier performance have a significant and positive impact on buyer’s dependence. Whereas, uncertainty has also a positive but insignificant relationship with buyer dependence. To increase buyer dependency suppliers should be involved in early stage of the design and production of the product. Partnership quality and supplier performance also have significant impact on buyer dependence so management should consider the factors.

A mixed-method study on the barriers of industry 4.0 adoption in the Indonesian SMEs manufacturing supply chains

PurposeThis paper aims to investigate the adoption barriers of Industry 4.0 in the Indonesian manufacturing supply chains.Design/methodology/approachThe mixed method was deployed to validate the findings. First, the qualitative study was conducted based on the interviews. Then, the companies were approached using filter questions on the involvement in adopting industry 4.0 and its impact on the supply chain.FindingsBased on the qualitative study, nine main barriers were found in the thematic analysis. Thus, to get a consensus on the barriers in the industry, the barrier indicators were tested using a structural equation model retrieved from 173 small and medium Indonesian manufacturing firms. Results indicate that five main barriers (e.g. unclear Industry 4.0 policy, higher-risk investment, insecure data sharing, lack of expertise and lack of incentive) are confirmed as the adoption barriers.Practical implicationsThe successful adoption of supply chain integration with Industry 4.0 technology can strengthen the manufacturing sector and competitiveness. Therefore, this study can be a complimentary assessment to evaluate the Indonesia Industry 4.0 Readiness Index (INDI 4.0) and the effectiveness of the government support program.Originality/valueThe results can be used as the framework to foresee the successful implementation of smart manufacturing supply chain management and its integration. Therefore, the authors proposed the framework to foresee the successful implementation of smart manufacturing, supply chain management and integration.

Dynamic Modeling and Simulation of How Information Sharing Influences Supply Chain Performance

With the growing intensity of cooperation, the partners in the manufacturing supply chain (SC) raise stricter requirements for information sharing (IS) within the SC, which has been an effective capability to improve the performance of manufacturing SC. To reveal the influence of IS on SC performance, this paper firstly carries out a theoretical analysis on the influencing factors of IS, SC integration and SC performance, and builds a theoretical model of the IS’s impacting on SC performance. Next, valid index data were obtained by investigating typical manufacturing enterprises in Yangtze River Delta region of China. Then, according to the dynamics system flow of IS influence on SC performance, the proposed theoretical model would be modeled from system dynamics and simulated by Vensim PLE software. The results show that IS among manufacturing SC nodes enhances the SC performance via SC integration; when SC integration is suitable for IS, SC performance will be improved as long as the IS amount is greater than zero; however, excessive IS would reduce the quality and weaken the amount of IS; overall, the degree of IS and SC integration are the key to better SC performance. This implies that how to improve IS among SC partners is a very important thing in manufacturing SC management. And, it also enlightens that enterprise managers should pay attention to establish a high-level trust relationship with SC partners, achieve high-quality IS with a wider range, and then promote the SC system more integrated to improve its operational performance.

System Dynamics Modeling in Additive Manufacturing Supply Chain Management

A system dynamics model was developed with the primary purpose of visualizing the behavior of a supply chain (SC) when it adopts a disruptive technology such as additive manufacturing (AM). The model proposed a dynamic hypothesis that defines the following issue: what is the impact of the AM characteristics and processes in the SC? The model was represented through a causal diagram in thirteen variables related to the SC, organized in two feedback cycles and a data flow diagram, based mainly on the three-essential links of the SC and the order display traceability: supplier–focal manufacturer–distribution Network. Once proposed, the model was validated through the evaluation of extreme conditions and sensitivity analysis. As a result, the dynamic behavior of the variables that condition the chain management was analyzed, evidencing reduction times in production, especially in products that require greater complexity and detail, as well as reductions in inventories and the amount of raw material due to production and storing practices from AM. This model is the starting point for alternative supply chain scenarios through structural operating policies and operating policies in terms of process management.

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.

Versatile edge gateway for improving manufacturing supply chain management via collaborative networks

ABSTRACT As a natural consequence of Industry 4.0 demands related to industrial processes control digitization, the smart manufacturing (SM) concept was enriched by integrating advanced technologies of digital information processing. This paper presents an original solution for improving the most representative instance of SM, namely, supply chain management (SCM), by using the most recent approach in hierarchical networked control systems (HNCS), that is Edge Computing. The paper presents both the software and hardware architecture of an Edge Gateway device which offers, on one hand, dual communication both on vertical (between the network layers from the local, inferior, represented by IIoT, to the central, superior, represented by the Cloud) and on horizontal (between similar devices located on the same Edge level) and, on the other hand, can locally process data to take fast decisions which facilitate real-time process control. The validity of the proposed solution is supported by a case study performed on a flexible assembly/disassembly line (laboratory model).

Properties of Metal Extrusion Additive Manufacturing and Its Application in Digital Supply Chain Management

Abstract The Fourth Industrial Revolution with a global demand for digital product life-cycle and the digital economy. The pervasiveness of internet of things, sensors, artificial intelligence, machine learning, and particularly digital manufacturing and digital supply chain management led to the formation and development of the digital economy. A digital device alters from an auxiliary tool to an essential production element when the cost of production is low and the needs for inventory is minimized, so that it can be used for different financial levels. In fact, the development of digital production systems and supply chain will not only affect how the supply chain is managed, but also will define the concurrency of the production with the other activities in product life-cycle. In this paper, the role of Material Extrusion Additive Manufacturing (MEAM) on Digital Production Systems and supply chain management is investigated. It is discussed how MEAM is comparable to the other metal 3D printing methods, in terms of machine cost, printing and post-processing. In addition, it is discussed that considering overall mechanical properties of the final products, production requirement, and cost what can be expected as the role of MEAM in digital production’s economy.

Product Quality Tracing in Manufacturing Supply Chain Based on Big Data Technology

Background: Big data technology has been widely used in manufacturing supply chain management. However, traditional big data technology has some limitations, and it cannot achieve the continuous improvement of whole-process product quality tracing. Objective : The purpose of this study is to overcome the limitations by patents analysis and provide new big data technology and technical modes to make the continuous improvements of whole-process product quality tracing for achieving effective product lifecycle management based on big data technology. Methods: The research method, patent analysis, and comparative analysis are employed in this study to analyze product quality tracing in the manufacturing supply chain based on big data technology. Moreover, the procedure and steps of the new big data technology - Product Digital Twin (PDT), and its technical modes are designed by process design methods. Its key technologies are also analyzed and compared with traditional big data technology by the comparative study. Results: The research achieves the continuous improvements of whole-process product quality tracing based on new big data technology - PDT by patent analysis. The formation process and behavior of manufactured products in the realistic environment are simulated, monitored, diagnosed, predicted, and controlled. In this way, the high-efficient coordination in various stages of the product lifecycle is propelled fundamentally and the continuous improvements of the whole-process product quality tracing based on big data technology is analyzed. Conclusion: Three new technical modes based on big data technology are predicted for future researches and patents, namely, the immersive development mode integrating big data and the virtual reality technology, the knowledge-based multivariant coordinated development mode, and the lifecycle extended development model based on multi-domain interoperability.

Review of green supply chain management in manufacturing: A case study

AbstrakSupply Chain Management (SCM) is a series of activities related to the flow of goods, information, and finance from suppliers, manufacturers, warehouses, distributors, and retailers to end customers (users). Whereas Green Supply Chain (GSCM) is an environmental innovation that integrates environmental issues and SCM to improve the organization’s sustainable performance. Some literature uses qualitative, quantitative, and a combination of the two methods in analyzing the positive impact of GSCM. Literature study was conducted to determine the impact felt by the company if implementing GSCM in its organization. So that it can be a reference for the manager in deciding the application of GSCM processes in the organization. There are four green processes in GSCM, namely: green design, green manufacturing, green logistics, reconditioning and core disposal. Some positive impacts that can be felt by the company, such as increasing organizational productivity (financially and efficiency), improving environmental performance, and to identify trends in organizational development. This practice can be implemented well if it is supported by pressures from various interests and pressures of the organization (management). Thus forcing companies to perfect the organization’s business model.

A model for evaluating green credit rating and its impact on sustainability performance

The development of economic activities and social progress index leads to the governmental considerations for the environmental challenge’s issues. The Green Credit Policy (GCP) in China for manufacturing, as a part of a sustainable finance package, initiatives restrictions with suppliers to reduce harmful pollution for the environment. The study mainly validates the impact of GCP on manufacturing for diminishing the emerged pollution to the environment. The study develops Neutrosophic Multiple-Criteria Decision-Making Framework (N-MCDMF) according to neutrosophic theory and various MCDM methods of grey relational analysis (GRA), analytic network process (ANP), the Decision-Making Trial and Evaluation Laboratory technique (DEMATEL), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to support the decision-makers with highly systematic procedures in the uncertain and inconsistent environmental conditions. The N-MCDMF evaluates the conditions of GCP and recommends the optimal Supply Chain Management (SCM) in manufacturing alternatives. A case study is presented for the validation of the issues of applicability and flexibility for the proposed N-MCDMF. The results obtained from the implementation of the N-MCDMF indicates the applicability and flexibility of the proposed approach. In addition, results show that SCM in manufacturing can provide more cooperation for the environment to reduce harmful pollution and to attain sustainability for achieving motivations under the restrictions of GCP.

Reliable Identification Schemes for Asset and Production Tracking in Industry 4.0.

Revolutionizing logistics and supply chain management in smart manufacturing is one of the main goals of the Industry 4.0 movement. Emerging technologies such as autonomous vehicles, Cyber-Physical Systems and digital twins enable highly automated and optimized solutions in these fields to achieve full traceability of individual products. Tracking various assets within shop-floors and the warehouse is a focal point of asset management; its aim is to enhance the efficiency of logistical tasks. Global players implement their own solutions based on the state of the art technologies. Small and medium companies, however, are still skeptic toward identification based tracking methods, because of the lack of low-cost and reliable solutions. This paper presents a novel, working, reliable, low-cost, scalable solution for asset tracking, supporting global asset management for Industry4.0. The solution uses high accuracy indoor positioning—based on Ultra-Wideband (UWB) radio technology—combined with RFID-based tracking features. Identifying assets is one of the most challenging parts of this work, so this paper focuses on how different identification approaches can be combined to facilitate an efficient and reliable identification scheme.

Manufacturing 4.0: Discussion on Application in the Extractive Industry of Essential Oils

Yield uncertainties characterize the industrial extraction of essential oils. The variable plant contents, undetected and uncorrected operational issues such as production in small batches, with a significant variation in the process cycles, productivity, and quality, compromise process performance, and the value of the finished product. The technological advances of industry 4.0 provide an opportunity to achieve economic, environmental, and productivity gains, even with the wide variety of products manufactured. This study aimed at improving the performance of such processes using the pillars of industry 4.0 as a method, highlighting the techniques of data acquisition and processing for intelligent analysis, based on automated and systemic learning, generating improvements for the overall performance of the operation as well as enhancing good manufacturing practices (process safety, quality, repeatability and traceability) and supply chain management. Our findings showed the adherence of 4.0 vision to this industry.