Agile Manufacturing Environment Research Articles

Improving Equipment Effectiveness through Visual Stream Mapping: Some Exploratory Research Findings in the Ready-Made Garment (RMG) Sector

Improving equipment effectiveness is crucial for flexible manufacturing, ensuring that machines and tools perform their functions efficiently and consistently. Our study aimed to enhance the Overall Equipment Effectiveness (OEE) in Bangladesh’s Ready-Made Garments (RMG) manufacturing system. We used the DMADV methodology incorporating Visual Stream Mapping (VSM) and OEE. By utilising VSM, we identified issues, eliminated them in the design phase, and evaluated performance in the verification phase. We automated the material handling system to reduce handling time, and the result was a significantly improved OEE in the automated manufacturing system compared to the manual one. This study has numerous benefits in flexible manufacturing and operations management, from immediate efficiency improvements to long-lasting organisational cultural transformations. Thus, it’s a noteworthy topic for practical applications and research. Enhancing Equipment Effectiveness through Visual Stream Mapping 4.0 has broad-reaching implications, including improved productivity, reduced waste, increased efficiency, better resource utilisation, and a more agile and responsive manufacturing environment. Although OEE and VSM are frequently used separately in different manufacturing systems, this study’s novelty lies in their combined application within garment manufacturing.

Understanding Powder Behavior in an Additive Manufacturing Process Using DEM

The handling of bulk solids in the form of powders is a fundamental process in a wide range of manufacturing industries, such as the automotive, aerospace, food, and healthcare sectors. All these sectors employ additive manufacturing (AM), as it enables the production of complex parts in a short amount of time. Thus, it is considered an established method for developing an agile manufacturing environment that can drastically reduce the lead time from conception to the production stage. At the same time, powder is a unique material sensitive to environmental and machine conditions; hence, establishing an optimal configuration is not straight-forward. This work presents a discrete element method (DEM) simulation of an experimental dosing system used in AM. We introduce a robust workflow that correlates suitable experimental data with simulation results, establishing models of real powders with different flowability. The results showed an excellent agreement between the experimental data and the simulation results and provided a better understanding of the material behavior. Furthermore, we employed a coarse-grained approach to extract continuum fields from the discrete data. The results showed that the cohesion level in the system was enough to create agglomerates that hindered the transport of the material and produced nonuniform distribution.

The ASSISTANT project: AI for high level decisions in manufacturing

This paper outlines the main idea and approach of the H2020 ASSISTANT (LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments) project. ASSISTANT is aimed at the investigation of AI-based tools for adaptive manufacturing environments, and focuses on the development of a set of digital twins for integration with, management of, and decision support for production planning and control. The ASSISTANT tools are based on the approach of extending generative design, an established methodology for product design, to a broader set of manufacturing decision making processes; and to make use of machine learning, optimisation, and simulation techniques to produce executable models capable of ethical reasoning and data-driven decision making for manufacturing systems. Combining human control and accountable AI, the ASSISTANT toolsets span a wide range of manufacturing processes and time scales, including process planning, production planning, scheduling, and real-time control. They are designed to be adaptable and applicable in a both general and specific manufacturing environments.

ASSISTANT: Learning and Robust Decision Support System for Agile Manufacturing Environments

Abstract The European project ASSISTANT will provide a set of AI-based digital twins that helps process engineers and production planners to operate collaborative mixed-model assembly lines based on the data collected from IoT devices and external data sources. Such a tool will help planners to design the assembly line, plan the production, operate the line, and improve process tuning. In addition, the system monitors the line in real-time, ensures that all required resources are available, and allows fast re-planning when necessary. ASSISTANT aims to make cost-effective decisions while ensuring product quality, safety and wellbeing of the workers, and managing the various sources of uncertainties. The resulting digital twin systems will be data-driven, agile, autonomous, collaborative and explainable, safe but reactive.

Design research on information coding system under the concept of agile manufacturing

Agile manufacturing can achieve the characteristics of high quality, low cost, short production cycle to meet the increasingly fierce market competition. Information coding system in agile manufacturing environment is particularly important, which directly determines the quality of information exchange in enterprise dynamic alliance of agile manufacturing. This paper analyzes the requirements of information coding system under agile manufacturing concept, and gives the design points of each module, providing some references for relevant researchers.Mathematics Subject Classification 2010: 74E99

Modeling Lean and Agile Approaches: A Western Canadian Forest Company Case Study

In the forest supply chain of the coast of British Columbia, the material flows are directed toward the push production of commodity products. This industry has not adopted lean and agile principles due to unclear economic impacts on the supply chain in changing market conditions. We tested the ability of lean and agile principles to improve performance in the coastal integrated forest industry. Mixed integer programming formulations were subject to over–under production capacity, and over–under demand fulfillment penalties to emulate agile, lean, and hybrid manufacturing environments, when solving the planning problem. Assuming that the coastal integrated forest industry performs as a hybrid environment, the profit results of each manufacturing environment were judged. The results show that, opportunities for profit improvement were 11% for adopting an agile environment when demand was stable with low variation and large batches of production. However, profit improvement was non-existent when the same demand attributes apply but with high variation. The opportunities for profit improvement were 12% when an agile environment or lean environment was adopted when demand was stable with low variation and small batches of production. However, opportunities for profit improvements of 15% existed for adopting an agile environment when demand was unstable with high variation and small batches of production.

Agility and production flow layouts: An analytical decision analysis

The term ‘agile manufacturing’ has referred to operational aspects of a manufacturing company concerning their ability to produce customized products at mass production prices and with short lead times. A core issue faced within agile manufacturing is the need for appropriate and supporting production and operations systems. Many design dimensions of agility and agile manufacturing exist. To help attain this goal for integrating the many design dimensions, operations infrastructure and capacity must be carefully planned to manage production flow, and thus production layout planning takes on an increasingly important role. Given the importance of these dimensions in response to agility, this paper seeks to make a contribution by providing insights into a decision aid for evaluating production flow layouts that support and enhance the agile manufacture of products. Layout design has a significant impact on the performance of a manufacturing or service industry system and has been an active research area for many decades. Strategic evaluation of production layouts requires consideration of both qualitative and quantitative factors (managerial, organizational, and technical). This paper makes use of the Analytical Network Process (ANP) which captures interdependencies among different criteria, sub-criteria and dimensions, an evident characteristic of production flow layouts in complex agile manufacturing environments. An application case study exemplifying the practical usefulness of this type of model describes how management, after implementation of the model, made a mid-course correction related to the production layout initially selected.

Research on Manufacturing Execution System Oriented to Agile Manufacturing

As a bridge between top-level transaction and low-level control system of facilities, MES (Manufacturing Execution System) is the key technology to implement the integration of all enterprise information systems. The research on MES oriented to agile manufacturing is of great importance in improving the level of management and automation. This paper is a brief introduction to the features of MES and its key technologies based on agile manufacturing environment. In view of the advanced technology and its practicability and feasibility, the methodology of software reuse is employed to study MES oriented to agile manufacturing. Meanwhile, the author analyses its key technologies, proposes an integration framework of MES oriented to agile manufacturing which can realize the horizontal integration and vertical integration for MES by adopting agent components, and presents a solution to MES’s implementation for the agile manufacturing company.

Agent-Based Control Framework for Mass Customization Manufacturing With UHF RFID Technology

Radio frequency identification (RFID) technology adoption in business environments has seen strong growth in recent years. Adopting an appropriate RFID-based information system has become increasingly important for enterprises making complex and highly customized products. However, most firms still use conventional barcode and run-card systems to manage their manufacturing processes. These systems often require human intervention during the production process. As a result, traditional systems are not able to fulfill the growing demand for managing dynamic process flows and are not able to obtain real-time work-in-process (WIP) views in mass customization manufacturing. This paper proposes an agent-based distributed production control framework with UHF RFID technology to help firms adapt to such a dynamic and agile manufacturing environment. This paper reports the design and development of the framework and the application of UHF RFID technology in manufacturing and logistic control applications. The framework's RFID event processing agent model is implemented in a smart end-point (SEP) device. A SEP can manage RFID readers, wirelessly communicate with shop-floor machines, make local decisions, and coordinate with other SEPs. A case study of a bicycle manufacturing company demonstrates how the proposed framework could improve a firm's mass customization operations. Results of experiments show the decentralized multiagent coordination scheme among SEPs outperformed the current practice of the firm in terms of reducing work-in-process and parts inventory.

Virtual company formation for agile manufacturing using ANP and Goal Programming

In the current competitive environment of further product customisation and agile response to customer requirements, stand-alone corporations are having difficulties in excelling without the aid of the specialised expertise and cooperation of other organisations. Temporary cooperation, achieved through virtual enterprises or corporations, may be valuable in satisfying these dynamic market and customer needs and expectations. In an agile manufacturing environment marked by rapid change and specialised activities and products, the success of the Virtual Corporation (VC) depends on the performance and hence appropriate selection of the various constituent organisations that form the VC which are termed as Virtual Constituent Companies (VCCs). This paper discusses one such comprehensive VCC selection methodology using Analytical Network Process (ANP) and Goal Programming (GP). The VCCs within this multi-phased methodology are evaluated on qualitative as well as quantitative factors.

Heuristic algorithm to optimize the manufacturing system design to get maximum profit

Agile manufacturing has become a topic of great interest over the past several years. The entire domain of modeling and analyzing different types of agile manufacturing environments and systems, however, remain largely unexplored. The objective of this paper is to provide fundamental insight into how manufacturing systems should be designed and reconfigured over time in order to cope with different agile manufacturing factors. To achieve this objective, three approaches are developed and integrated into one simulation-based model. The first approach is used to model different agile manufacturing environments. The second approach is used to define various ways in which manufacturing systems can be designed and reconfigured (i.e., design/reconfiguration strategies). The third comprises the cost and objective functions used to measure system performance when different design/reconfiguration strategies are used in different agile manufacturing environments. Based upon the assumptions adopted, the experimental work performed suggests that despite the fact that agility incurs high costs, agile manufacturing systems are indeed necessary for certain manufacturing environments in which product life cycles are short yet demand per product type is high. Therefore, it is important in certain manufacturing environments to focus on reconfiguration in short periods of time, even at the expense of higher reconfiguration costs.

Supplier selection in an agile manufacturing environment using Data Envelopment Analysis and Analytical Network Process

Supplier selection is one of the most important activities of organisations, where the short- and long term success of a buyer's organisation depends upon proper selection of its suppliers. The current competitive environment has placed increasing competitive pressures on suppliers to match the needs of buyer(s) in terms of quantity, quality, product mix, cost, time and place of delivery, to name a few performance measures. The focus on competitive supply chains and extended enterprises requires the adoption of agile manufacturing practices requiring their suppliers to have agile attributes. This study designs and implements a procedure for judging the suitability of suppliers for an organisation competing on agile manufacturing characteristics. Quantitative and qualitative factors are used to appraise and select appropriate suppliers to fit within an organisation's agility practices. In seeking to achieve this task, the synergistic integration of two techniques, the Analytical Network Process (ANP) and Data Envelopment Analysis (DEA) is applied in a multi-phased supplier selection approach. Initially, ANP is executed to appraise suppliers on their qualitative benefits, generating quantitative data from these qualitative dimensions. Secondly, DEA is used to synthesise the data to arrive at a ranking of the suppliers. The technique is validated in a small company case study.

Economic analysis for agility in manufacturing environment

This article is intended to model unpredictable change in an agile manufacturing environment. A model for unpredictable changes expressed by an agility index was developed and explained through numerical simulation. The model took into consideration the need for investing in agility depends upon the business circumstances and environmental turbulence. It shows that investing in agility increases the expected savings from changes and decreases the expected time a company needs to spend in dealing with change intelligently. The mathematical model is developed to evaluate the expected total cost through the four dimensions of change: cost, time, scope and robustness. This model supports the decision making process for justifying agility investment using change sensors analysis, and it aids in finding the optimal value of an agility index that minimises the expected total cost. It is found that there is an optimal value for an agility index that minimises the expected total cost for a certain production system.

AI-based project risk management process for a kind of manufacturing alliance

In this paper, an artificial intelligence-based project risk management process is proposed to implement quantitative risk management for a kind of manufacturing alliance in the agile manufacturing environment. After the literature review, a multiphase project risk management process is formed consisting of risk analysis, risk evaluation and risk control. Probability Risk Analysis is used as the main quantitative risk analysis technique. Some forms of mathematical programming models are formulated in risk evaluation and risk control phases. Because of the complexity, non-linearity, multiobjective stochastic constraints or interval or fuzzy coefficients of these models, they cannot be solved easily by conventional methods. Genetic algorithms are designed. A real life example is also given and the computational results illustrate the efficiency of the algorithms.

An agent-based intelligent control platform for industrial holonic manufacturing systems

New revolutionary manufacturing paradigms, which take advantage of the newest emerging mechatronics, information, and communication technologies, and integrate them in a highly dynamic and agile economic, technical, and organizational manufacturing environment, are being researched and developed since the last decade of the 20th century. This new generation of manufacturing systems is referenced as intelligent manufacturing systems (IMS), and both the "holonic manufacturing system" (HMS) and the "agent-oriented manufacturing system" paradigms have been presented in recent years as the basis for making such systems a reality. This paper reports on the design and realization of an agent-based intelligent control system for industrial manufacturing systems, which covers both conventional mechatronics equipment and embedded control agents. The agent-based control software components are implemented as Windows NT services and logic control programs and interfaced via distributed component object model (DCOM) and Ethernet. Results of the application in an industrial manufacturing environment are used to show the effectiveness of the proposed "Holonic" approach.

Design for quality in agile manufacturing environment through modified orthogonal array‐based experimentation

PurposeTo provide a modified orthogonal array‐based model for enabling the researchers and practitioners to exploit the technique, “design of experiments” in an agile manufacturing environment.Design/methodology/approachThe characteristics of Taguchi's off‐line models and agile manufacturing were studied. A theoretical model of modified orthogonal array‐based experimentation was designed. This model was subjected to implementation study in an Indian pump‐manufacturing company.FindingsThe model contributed in this paper has shown its feasibility in achieving quality in agile manufacturing environment.Research limitations/implicationsThe authors are residing in an Indian city where the majority of the companies have not adopted agile manufacturing criteria. Hence, it was not possible to carry out implementation study in an agile manufacturing company. Future researchers should examine the practical validity of the proposed model in agile manufacturing companies.Practical implicationsSince the manufacturing organizations are fast becoming agile, due to the customers' dynamic demands coupled with competition, the traditional quality improvement techniques are becoming obsolete. The model contributed in this paper is found to be useful in achieving continuous quality improvement in AM environment. Hence the model would be a useful technique for today's practitioners whose activities are increasingly focused towards achieving agility in manufacturing.Originality/valueThe literature survey covering articles on agile manufacturing indicates that no researcher or practitioner has contributed a model that would exploit the technique, “design of experiments” in an agile manufacturing environment. Hence the proposed model is expected to be of high value for researchers and practitioners to explore the way of achieving continuous quality improvement in agile manufacturing environment.

Complex assembly variant design in agile manufacturing. Part II: Assembly variant design methodology

In the first paper of this two-part series, the assembly variant design system architecture and complementary assembly methodology were presented. The general complementary assembly models, hierarchical assembly model and relational assembly model, are established which were further specified as the Assembly Variants Model (AVM) and the Assembly Mating Graphs (AMGs) respectively to cater for the needs for assembly variant design. This paper discusses the assembly variant design methodology which is based on these assembly models. The matching components are searched and retrieved from the AVM and then the constraint groups are identified by manipulating the AMGs. Then the assembly variant design process is formulated as a mixed-integer (linear or non-linear) programming problem which is solved using a standard solver or heuristic. This methodology provides a systematic approach to facilitate the variant design of complex assembly products in the agile manufacturing environment. Finally, a prototype system is developed and examples are presented.

The impact of alignment between virtual enterprise and information technology on business performance in an agile manufacturing environment

Manufacturing companies are facing rapid and unanticipated changes in their business environment. Agile manufacturing (AM) is a manufacturing paradigm that focuses on smaller scale, modular production facilities, and agile operations capable of dealing with turbulent and changing environments. From several enablers of AM, virtual enterprise (VE) and information technology (IT) were chosen. This empirical study explored the impact of the alignment between VE and IT on business performance in an AM setting. Extensive data collection strategy and several tests are used to establish the reliability and validity of the data collected. Extensive analyses of the data using structural equation molding were performed for five hypotheses. The results indicate that both VE and IT had positive influences on business performance. It was also established that the alignment between VE and IT had a positive impact on business performance. Further, it was shown that the impact of the alignment between VE and IT on business performance was more significant than the impact of VE and IT on business performance individually. In conclusion, the assessment of the results along with future research directions is provided.

An adaptive agile manufacturing control infrastructure based on TOPNs-CS modelling

In this paper, an infrastructure for adaptive production control in an agile manufacturing environment is proposed. With this infrastructure, Virtual Production Systems (VPSs), each of which takes care of the production of a specific customer ordered product, can be dynamically and flexibly constructed. This can be achieved logically by product workflow and physically by the resources in one or more manufacturing systems, e.g. job shops. To respond to changes and disturbances to a VPS, architecture for the adaptive controller of a VPS is designed based on adaptive control principles and Temporised Object-Oriented Petri Nets with Changeable Structure (OPNs-CS) modelling. A case study is used in this paper to illustrate how adaptive production control of VPS functions can be conducted to cope with changes and disturbances to the production system.

A branch and bound algorithm for sub-contractor selection in agile manufacturing environment

Partnership and partner selection play a key role for “Opportunity Driven” project contractors in agile manufacturing environment. In this paper, we present an investigation on the partner selection problems with engineering projects. Firstly, the problem is described by a 0–1 integer programming with non-analytical objective function. It is proven that the partner selection problem is a type of earliness and tardiness production planning problems. By introducing the concepts of inefficient and ideal candidates, we propose the theory of solution space reduction which can efficiently reduce the complexity of the problem. Then, a branch and bound algorithm embedded project scheduling is developed to obtain the solution. The approach was demonstrated by the use of an experimental example drawn from a construction project of coal fire power station. The computational results have been found to be satisfactory.