Smart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Framework

Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments

Additive Manufacturing in the Supply Chain

Additive manufacturing (AM) is replacing traditional manufacturing approaches – such as subtractive and molding – in some industries. The product and supply chain impacts of AM continue to extend its industrial reach, improve engineer-to-order manufacturing, and pave the way to mass customization. This study explores the supply chain changes that may arise from a full or partial transition to AM-based production. Supply chain factors and dimensions that are greatly impacted are initially identified. Management and operational issues pertinent to each factor are discussed next. The interrelationships between these factors are then investigated considering the disruptive impact of AM on supply chain management. Next, the supply chain change matrix is presented for identifying the areas in that supply chains are expected to be impacted. Finally, the current literature and the future of AM-based supply chains are discussed. This chapter is concluded by providing a summary of the findings and insights into AM-based supply chain transition

Integrating sustainability into production scheduling in hybrid flow-shop environments

Global energy consumption is projected to grow by nearly 50% as of 2018, reaching a peak of 910.7 quadrillion BTU in 2050. The industrial sector accounts for the largest share of the energy consumed, making energy awareness on the shop foors imperative for promoting industrial sustainable development. Considering a growing awareness of the importance of sustainability, production planning and control require the incorporation of time-of-use electricity pricing models into scheduling problems for well-informed energy-saving decisions. Besides, modern manufacturing emphasizes the role of human factors in production processes. This study proposes a new approach for optimizing the hybrid fow-shop scheduling problems (HFSP) considering time-of-use electricity pricing, workers’ fexibility, and sequence-dependent setup time (SDST). Novelties of this study are twofold: to extend a new mathematical formulation and to develop an improved multi-objective optimization algorithm. Extensive numerical experiments are conducted to evaluate the performance of the developed solution method, the adjusted multi-objective genetic algorithm (AMOGA), comparing it with the state-of-the-art, i.e., strength Pareto evolutionary algorithm (SPEA2), and Pareto envelop-based selection algorithm (PESA2). It is shown that AMOGA performs better than the benchmarks considering the mean ideal distance, inverted generational distance, diversifcation, and quality metrics, providing more versatile and better solutions for production and energy efciency

Tailored Iterated Greedy metaheuristic for a scheduling problem in metal 3D printing

This article contributes to the additive manufacturing-based production planning literature by developing a Mixed-Integer Linear Programming (MILP) formulation for the Identical Parallel 3D-Printing Machines Scheduling Problem considering batching, multiple build platforms of restricted sizes, and sequence-independent setup times. Besides, a customized metaheuristic, named the Tailored Iterated Greedy (TIG) Algorithm is developed to solve the new optimization problem. TIG’s performance is evaluated through extensive numerical analysis and using a new testbed. It is shown that the customized computational mechanisms improve the optimization performance; statistical analysis is supportive of the significance of the resulting improvements. The developed mathematical model and optimization algorithm can be considered the basis for future developments in the optimization literature of additive manufacturing

N-list-enhanced heuristic for distributed three-stage assembly permutation flow shop scheduling

System-wide optimization of distributed manufacturing operations enables process improvement beyond the standalone and individual optimality norms. This study addresses the production planning of a distributed manufacturing system consisting of three stages: production of parts (subcomponents), assembly of components in Original Equipment Manufacturer (OEM) factories, and final assembly of products at the product manufacturer’s factory. Distributed Three Stage Assembly Permutation Flowshop Scheduling Problems (DTrSAPFSP) models this operational situation; it is the most recent development in the literature of distributed scheduling problems, which has seen very limited development for possible industrial applications. This research introduces a highly efficient constructive heuristic to contribute to the literature on DTrSAPFSP. Numerical experiments considering a comprehensive set of operational parameters are undertaken to evaluate the performance of the benchmark algorithms. It is shown that the N-list-enhanced Constructive Heuristic algorithm performs significantly better than the current best-performing algorithm and three new metaheuristics in terms of both solution quality and computational time. It can, therefore, be considered a competitive benchmark for future studies on distributed production scheduling and computing

Exploring the mutual influence among the social innovation factors amid the COVID-19 pandemic

From the triple bottom line, the social aspect has received relatively limited attention during the Corona Virus Disease (COVID-19) pandemic, particularly in the emerging economies. Social innovation factors help improve the sustainability performance of the companies. This study develops a social innovation decision framework and analyses the interrelationships among social innovation factors considering the COVID-19 situation. For this purpose, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) is extended by integrating the Z numbers and rough fuzzy set theory into its computational procedure. Z-numbers address the uncertainty of the decision and experts’ confidence in the evaluation and rough numbers are used for aggregating the experts’ opinions. On this basis, the mutual influence of social innovation factors and the influence weights of these factors are investigated. The results suggest that a quick response to market demand for sustainable products is the most influential factor in attaining social sustainability innovation during the pandemic. This article is concluded by providing insights for industrial experts and decision-makers to understand the underpinnings of social sustainability innovation during unforeseen situations

Delving Into the Interdependencies in the Network of Economic Sustainability Innovations

Legislative pressures and public awareness are urging companies to foster sustainability innovations that improve business operations. Limited studies explored the underpinnings of the economic dimension of sustainability innovations; studying economic innovation criteria in the manufacturing sector of emerging economies can inform other industries while recession fears loom the financial prospects. This article develops a decision analysis and evaluation framework for investigating the interdependencies in the network of economic sustainability innovation criteria using fuzzy Total Interpretive Structural Modeling (TISM). It is found that the ‘‘availability of financial resources for promoting innovation’’ is the criterion with the most network relations; this is what the managers should focus on to better pursue sustainability innovations in the supply chains and facilitate the shift towards sustainable industrial development. The study is concluded by providing practical insights into the economic dimension of sustainability innovations for industrial managers and academics

Production Management and Supply Chain Integration

Digitalization not only improves individual performance but also helps orchestrate supply chain partners to serve a bigger goal. To fully benefit from supply chain digitalization, a high level of integration across supply chain activities is required. This chapter introduces major interactions between production management decisions and the supply chain strategy, structure, and performance to explore supply chain integration (SCI). For this purpose, the design and development issues of production management are first investigated. On this basis, decisions with a strategic nature, including product design (what), material and technology selection (which), process design (how), and facility layout (where) optimization are analyzed. Next, the planning and control issues, including production scheduling (when), quality management, resource management and supervision (who), and planning for disruptions are explored, which have a rather tactical nature with short- to mid-range goals in response to the aggregate plan (why) of the supply chain. Every section is concluded by suggesting rooms for pursuing SCI

Destruction Decisions for Managing Excess Inventory in E-Commerce Logistics

The Internet has brought about new possibilities for innovation and radically changed business activities. Internet shopping is a prime example of increasing popularity, which is exacerbated due to the recent pandemic. It is expected that e-commerce will accommodate more than a quarter of the total retail sales worldwide in the next few years. Given the characteristics of e-commerce, inventory management is of paramount importance for an effective and timely response to the online customers’ demand. Despite its relevance, the issue of warehouse excess inventory is not sufficiently studied in the operations management literature. This study explores the factors, including sustainability and strategic considerations, that influence the inventory destruction decisions as one of the alternatives for managing excess inventory. Applying the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, the interrelationships between the decision factors are investigated and the decisive considerations are identified. Overall, the outcomes provide insights for the e-commerce practitioners and offer directions for modeling and managing inventory destruction decisions

Evaluating Resiliency of Supply Chain Network: A Data Envelopment Analysis Approach

Supply chains can be vulnerable to sudden disruptions, especially when it emphasizes efficient operation. In this regard, supply chain resilience (SCR) has received attention recently to cope with disruptions and improve competitiveness. This paper presents a novel methodology to measure resilience between different configurations of a supply chain network (SCN), based on a number of influential factors. For this reason, data envelopment analysis (DEA) is employed to identify the best-practice and less-performing SCN configurations among a group of alternatives. On this basis, the extent to which a current configuration can improve its resiliency is also measured. The methodology is applied to the case of E1, a liquefied petroleum gas (LPG) company in Korea. Topological and operational measures were used as variables to assess resilience. The results suggest that the LPG supply chain in the case study requires an addition in the number and capacity of supply nodes in its network