Shop Floor Operations Research Articles

Next Generation Industrial IoT Digitalization for Traceability in Metal Manufacturing Industry: A Case Study of Industry 4.0

This paper investigates digital traceability technologies taking careful consideration of the company’s needs to improve the traceability of products at the production of GPV Group as well as the efficiency and added value in their production cycles. GPV is primarily an electronics manufacturing service company (EMS) that manufactures electronic circuit boards, in addition to big metal products at their mechanics manufacturing sites. The company aims to embrace the next generation IoT technologies such as digital traceability in their internal supply chain at manufacturing sites in order to stay compatible with the Industry 4.0 requirements. In this paper, the capabilities of suitable digital traceability technologies are screened together with the actual GPV needs to determine if deployment of such technologies would benefit GPV shop floor operations and can solve the issues they face due to a lack of traceability. The traceability term refers to tracking the geolocation of products throughout the manufacturing steps and how that functionality can foster further optimization of the manufacturing processes. The paper focuses on comparing different IoT technologies and analyze their positive and negative attributes to identify a suitable technological solution for product traceability in the metal manufacturing industry. Finally, the paper proposes a suitable implementation road map for GPV, which can also be adopted from other metal manufacturing industries to deploy Industry 4.0 traceability at shop floor level.

Heuristics for cross-docking scheduling of truck arrivals, truck departures and shop-floor operations

This work studies the problem of cross-docking truck scheduling introduced by (Int J Prod Econ 194:102–112, Serrano et al. 2017), where internal operations including repacking and storing operations are considered. The objective is to minimize the number of inbound trucks which are assigned beyond their contracted time windows. For the special case where only inbound truck arrivals are considered while the outbound truck departures are not ensured, we present some properties of the model and propose a heuristic algorithm called Light Period and Less Options (LPLO) to solve it. For the general case of the problem, due to its NP-hardness, we design a non-trivial Three-phase Greedy (TG) algorithm and the Genetic algorithm (GA). Experimental results with comparison between exact solutions by the CPLEX solver and solutions by the two heuristic algorithms demonstrate that the both proposed heuristic algorithms are of high efficiency in generating optimal or nearly optimal solutions for the considered problem in this work.

The Industry’s Perspective of Suitable Tasks for Human-Robot Collaboration in Assembly Manufacturing

Human-robot collaboration (HRC) is the concept of combining a human and a robot into the same production cell and utilize the benefits of both. This concept has existed for more than a decade, but there are still quite few implementations of HRC within the manufacturing industry. One reason for this is the lack of knowledge when it comes to suitable tasks for HRC. Current research studies on the topic are mainly based on theoretical reasoning and/or research experiments, and little is known about what the industry perceive as suitable tasks for HRC. Therefore, this paper aims to investigate this and find out what industrial actors thinks are the most value-adding tasks for a human and a robot to carry out together. An in-depth interview study is undertaken with two companies and shop-floor operators, production engineers and automation engineers are interviewed. The result of the study pinpoints a number of tasks that the companies thinks are beneficial for HRC, which can serve as a guideline for other manufacturing companies considering to implement HRC.

Concepts for digitalisation of assembly instructions for short takt times

Operational complexity for shop-floor operators can be reduced with effective presentation of instructions, which in turn improves product quality. While this has been researched for longer takt times, final assembly with shorter takt times requires new approaches to properly support operators. Hence, this paper aims to present findings regarding concepts for digitalised assembly instructions to support shop-floor operators in a mixed model final assembly with shorter takt times. A bottom-up mixed-methods approach was applied in an iterative development process, resulting in concepts for short takt time instructions. The findings indicate how to cognitively support operators in two situations. First, work tasks are preferably taught beforehand, with educational instructions. Second, operators should be supported with simplified instructions, presenting key elements and deviations in real-time. Conclusively, these concepts will increase the likelihood of the instruction to be used by operators, which enables standardised work and contributes to enhanced product quality.

The changing role of shop-floor operators in zero defect manufacturing

In the journey towards zero defect manufacturing, digital technologies aiming to improve different aspects of production and quality control will be of high importance. This will not replace existing management approaches such as lean manufacturing, including continuous improvement at the shop-floor level. A single case study is performed, where we have examined various aspects that influence a successful continuous improvement for reducing scrap parts and prevent further propagation in interaction with new zero-defect solutions. The aim is to identify the changing roles of the shop-floor operators and we highlight that they still will remain a key part of the system.

Simulation-based decision support tool for in-house logistics: the basis for a digital twin

In-house logistics activities are an essential part of any organisation as they improve operations productivity and efficiency. In this context, agile decision-making raises the need for planning the overall shop-floor operations and effectively control them. This work tackles this need and proposes a simulation-based decision support tool for in-house logistics that analyses the activities that can occur in both a distribution facility and a production facility towards logistics 4.0. This is done by developing two simulation models using Simio – simulationmodelling framework based on intelligent objects. The resulted models are verified and validated based on two real-world operations. As a conclusion, the developed models are representative of the reality and can be applied in different in-house logistics settings, functioning as digital-twinning tools to operations improvement without real sets disruption.

Measuring the Turnaround in Business Performance due to WMS and MES Implementation in a Process Manufacturing Client

The Warehouse Management System (WMS) and the Manufacturing Execution Systems (MES) have proven to be of massive help in increasing the organization’s productivity and its ability to execute key logistic functions with great efficiency and effectiveness. The process manufacturing industry has a unique setup and requires good collaboration between the different elements of the supply chain to operate at the highest efficiency. At the same time, the shop floor operators must have a good understanding of the new WMS/MES systems to derive maximum benefit from them. This paper dwells into the business improvements witnessed by an alcoholic beverage client after successfully implementing the WMS and MES solutions. The paper includes a literature review of the benefits and risks in WMS/MES implementation. Also, the literature is supported by a case study wherein the client’s business and operational challenges are discussed, and the improvements resulting from the project implementation. The WMS and MES were found to have a positive impact on business performance and it was observed that a project of such scale needs diligent planning and effective implementation for maximum benefits.

The INCLUSIVE System: A General Framework for Adaptive Industrial Automation

While modern production systems are becoming increasingly technologically advanced, the presence of human operators remains fundamental in industrial workplaces. To complement and enhance the capabilities of human workers, approaches based on adaptive automation have been introduced. They consist of adapting the behavior of the system according to the user’s capabilities and effort. In this article, we present a general holistic framework for adaptive automation, called INCLUSIVE, that assists the operator during working tasks. The system consists of three modules. First, a thorough characterization of the operator’s constitutional and situational condition is provided; based on this, properly tailored adaptation is given, and if necessary, further training and support are provided. The framework has been implemented and tested considering three industrial use cases, selected as representative of a wide area of interest for the industry in Europe, in terms of both production requirements and involved operators. Tests have been carried out in real production environments, considering real production tasks carried out by 53 shop-floor workers. Results have shown that workers’ satisfaction when using the INCLUSIVE system and their performances was increased with respect to customary interaction systems currently used in industries. Moreover, the achieved results were used to formulate a set of recommendations for the design and implementation of an adaptive interaction system in relation to ensuring worker satisfaction and system usability in an industrial environment, as well as performance requirements. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the fact that, despite modern advanced automation, human operators are still central in the manufacturing process. However, technological progress often causes challenging interaction with complex industrial systems. The goal of this article is to introduce a complete framework for adaptive automation, with the ultimate goal of facilitating the interaction of human operators with complex industrial systems. The framework relies on three modules: measurement of human capabilities, the adaption of the interaction system, and additional teaching and support. The three modules are discussed at a high level, independently of the target application. Moreover, to facilitate their application in specific working contexts, examples are provided with respect to three different industrial applications. Results of tests carried out with shop-floor operators show that implementing the proposed framework allows better working performance and increases worker satisfaction with the use of automation.

Green lean operationalisation of the circular economy concept on production shop floor level

Addressing today’s general requirements on sustainability, as captured by for example the UN sustainability goals, is a necessity within production operations. It means that production managers need to find and manage new working procedures and methods on the shop floor to increase resource efficiency and overall sustainability. Utilizing a green lean environmental improvement tool called Green Performance Map in manufacturing and pharma industry has proven successful results in engaging shop floor managers and operators in green kaizen and demonstrated the value of integrating the waste hierarchy model, hence operationalising the concept of circular economy. This paper presents results of eight industrial cases of pilot trials of the Green Performance Map, demonstrating how the waste hierarchy model was used as an operationalisation mechanism for increasing the circularity on the shop floor. This was made by prioritizing and executing environmental improvements identified by the shop floor team that implied moving up one or more steps in the waste hierarchy. By this action, resource efficiency was improved as well as the overall environmental behaviour. The research presented contributes to the green lean theory and its integration with circular economy in a production context. On managerial level, the research demonstrates a concrete way of how the circularity could be improved on the shop floor.

A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective

This paper addresses the problems of data management and analytics for decision-aid by proposing a new vision of Digital Shadow (DS) which would be considered as the core component of a future Digital Twin. Knowledge generated by experts and artificial intelligence, is transformed into formal business rules and integrated into the DS to enable the characterization of the real behavior of the physical system throughout its operation stage. This behavior model is continuously enriched by direct or derived learning, in order to improve the digital twin. The proposed DS relies on data analytics (based on unsupervised learning) and on a knowledge inference engine. It enables the incidents to be detected and it is also able to decipher its operational context. An example of this application in the aeronautic machining industry is provided to stress both the feasibility of the proposition and its potential impact on shop floor performance.

Address business crisis caused by COVID‐19 with collaborative intelligent manufacturing technologies

While COVID-19 has affected the daily life of almost everyone around the world, it has also caused major disturbances to the global economy and to the operations of many businesses in the manufacturing industry. Reginal lockdowns resulted in supply chain breakages. Workforce shortages caused difficult shop floor operations. This study tries to discuss how collaborative intelligent manufacturing technologies can help to address these challenges through (i) optimal design of resilient collaborative supplier networks; (ii) collaborative planning of manufacturing operations among geographically distributed manufacturing plants; (iii) functional redundancy and dynamic reconfiguration of different shop floors; (iv) dynamic intelligent rescheduling of workforces for factory/shop floor operations; (v) remote testing and maintenance of manufacturing equipment with the support of digital twins technologies. These approaches are expected to be an effective hedge against the impact of COVID-19 or other natural disasters.

Online defect prognostic model for textile manufacturing

Defects in textile manufacturing process cause a significant waste of resources and affect the quality of product. This is challenging manufacturers to maintain growth and competitiveness in Industry 4.0. To solve the problem, models for predicting defects should be developed to assist shop floor operators. However, there is a lack of studies and models for solving the problem. Focusing on realistic needs, this study aims to develop online defect prognostic models for textile manufacturing. In particular, data from the manufacturing processes are collected in time series. Then, control charts are used to transform the collected data into region data of product. Based on these data, back-propagation neural networks are used for predicting defects at each stage. In addition, an experiment was conducted to validate the proposed approach. The results have shown the robustness and efficiency of the proposed model. This model can be implemented in practice to predict defects in advance that assists operators taking correct actions to prevent defect products and reduce waste.

3D bare-hand interactions enabling ubiquitous interactions with smart objects

Ubiquitous augmented reality (UAR) implementation can benefit smart shop floor operations significantly. UAR from a user’s first-person view can support and provide the user with suitable and comprehensive information without him/her being distracted from ongoing tasks. A natural hand-based interaction interface, namely, a mobile bare-hand interface (MBHI), is proposed to assist a user in exploring and navigating a large amount of information for a task in the user’s first-person view. The integration of a smart shop floor and UAR-based MBHI is particularly challenging. A real shop floor environment is composed of challenging conditions for the implementation of UAR, e.g., messy backgrounds and significant changes in illumination conditions. Meanwhile, the MBHI is required to provide precise and quick responses to minimize the difficulty of a user’s task. In this study, a wearable UAR system integrated with an MBHI is proposed to augment the shop floor environment with smart information. A case study is implemented to demonstrate the practicality and effectiveness of the proposed UAR and MBHI system.

Multi-objective flexibility-complexity trade-off problem in batch production systems using fuzzy goal programming

Several factors change the flexibility and the complexity of production systems. The flexibility of the production system means to meet the changing needs of the customers. As flexibility increases the complexity also increases. In this paper, a multi-objective linear programming is proposed to model the trade-off of the complexity and flexibility of a batch production system (BPS). Strategic, tactical and operational decision variables have been considered. Seven objective functions of the proposed model are assumed as flexibility and complexity. Sixteen tactical decision variables are defined to determine the level of the dimensions of flexibilities and complexities. Thirty-four operational decision variables are defined to tune the shop-floor operations. Several sets of constraints considering aspects of flexibility and complexity as well as the conditions of batch production systems have been considered. As the achievement to the objective functions is not possible simultaneously, and there is no unique and concise relation between these objective functions in a typical batch production system so, a fuzzy goal programming (FGP) approach is proposed to solve the model. Moreover, goal programming (GP), Fuzzy GP, multi-choice goal programming (MCGP) and fuzzy MCGP are proposed and used to compare the performance of solution procedures. The superior solution approach among GP, MCGP, FGP, and FMCGP is FMCGP which concurrently considers several aspiration levels for objective functions, maximization of the achievement level of objective functions, and satisfying uncertain preference of fuzzy objectives. An evolutionary algorithm, called non-dominated sorting genetic algorithm (NSGA-II) and a random weighted version of FMCGP are customized to regenerate several non-dominated designs for flexibility-complexity trade-off problem in BPS. The results are promising and the proposed model is capable to set the strategic, tactical and operational variables of a BPS.

A machine learning approach for improved shop-floor operator support using a two-level collaborative filtering and gamification features

The increasing gap in shopfloor operators’ skillset regarding advanced information and communication technologies along with workforce’s diversity require a cognitive system bridging such technical gaps in order to address evolving production demands and satisfy the human need for self-fulfillment and self-actualization at work. This study discusses on a two-level collaborative filtering approach to improve the distribution of information content provided to an operator for completing a manufacturing activity while considering his or her feedback. A prototype implementation is evaluated in a case study related to the operator’s job rotation on a shopfloor that involves multiple workstations and tasks.

Linking Scheduling Criteria to Shop Floor Performance in Permutation Flowshops

The goal of manufacturing scheduling is to allocate a set of jobs to the machines in the shop so these jobs are processed according to a given criterion (or set of criteria). Such criteria are based on properties of the jobs to be scheduled (e.g., their completion times, due dates); so it is not clear how these (short-term) criteria impact on (long-term) shop floor performance measures. In this paper, we analyse the connection between the usual scheduling criteria employed as objectives in flowshop scheduling (e.g., makespan or idle time), and customary shop floor performance measures (e.g., work-in-process and throughput). Two of these linkages can be theoretically predicted (i.e., makespan and throughput as well as completion time and average cycle time), and the other such relationships should be discovered on a numerical/empirical basis. In order to do so, we set up an experimental analysis consisting in finding optimal (or good) schedules under several scheduling criteria, and then computing how these schedules perform in terms of the different shop floor performance measures for several instance sizes and for different structures of processing times. Results indicate that makespan only performs well with respect to throughput, and that one formulation of idle times obtains nearly as good results as makespan, while outperforming it in terms of average cycle time and work in process. Similarly, minimisation of completion time seems to be quite balanced in terms of shop floor performance, although it does not aim exactly at work-in-process minimisation, as some literature suggests. Finally, the experiments show that some of the existing scheduling criteria are poorly related to the shop floor performance measures under consideration. These results may help to better understand the impact of scheduling on flowshop performance, so scheduling research may be more geared towards shop floor performance, which is sometimes suggested as a cause for the lack of applicability of some scheduling models in manufacturing.

Augmented reality in support of intelligent manufacturing – A systematic literature review

Industry increasingly moves towards digitally enabled ‘smart factories’ that utilise the internet of things (IoT) to realise intelligent manufacturing concepts like predictive maintenance or extensive machine to machine communication. A core technology to facilitate human integration in such a system is augmented reality (AR), which provides people with an interface to interact with the digital world of a smart factory. While AR is not ready yet for industrial deployment in some areas, it is already used in others. To provide an overview of research activities concerning AR in certain shop floor operations, a total of 96 relevant papers from 2011 to 2018 are reviewed. This paper presents the state of the art, the current challenges, and future directions of manufacturing related AR research through a systematic literature review and a citation network analysis. The results of this review indicate that the context of research concerning AR gets increasingly broader, especially by addressing challenges when implementing AR solutions.

Improvement of Operational Performance through Value Stream Mapping and Yamazumi Chart: A case of Bangladeshi RMG Industry

To secure a competitive position in the global market, the Ready Made Garment (RMG) sector in Bangladesh has been facing various challenges including the improvement of industrial operational performance. Among the various operational issues, balancing the cycle time along the production line is felt to be a common and effective way for enhanced performance. There are numerous exact and approximate methods which have already been proposed and are available in literature for repetitive batch production. Unfortunately such approaches are rarely applied in the real RMG shop floor presumably due to the enormous efforts needed to align with the frequent changes of product lines. Compared to the exact and approximate methods, adoption of lean tools like VSM and Yamazumi chart is highly acclaimed for their simpler and wider applicability with superior capability in addressing and solving the balancing problems. So for balancing purposes of the RMG shop floor an attempt is made to frame and apply an integrated model combining the VSM and Yamazumi chart as presented in this article. Depending on the theoretical framework, operational performance of an RMG shop floor is evaluated upon balancing the cycle time. In doing so, firstly a current state map (CSM) and the corresponding Yamazumi chart are plotted to comprehend the existing operational procedure and analyse the workstation cycle time with respect to the takt time. Using the Yamazumi chart, the workstations are reorganized to reduce or eliminate the non-value added activities, to introduce parallel workstations, and to merge workstations so that a balanced production line is attained. Performance is then assessed by means of Overall Effectiveness of Equipment (OEE), Capacity Utilization and Efficiency. The results as obtained through testing a real life case study of RMG industry, ensure the effectiveness of the integrated system of VSM and Yamazumi chart to identify the wastes, restore the line balance and improve operational performance.

Tempcore Process Simulator to Analyze Microstructural Evolution of Quenched and Tempered Rebar

Tempcore process simulator (TPS) has been developed in this study to analyze the microstructural evolution of quenched and tempered rebar. There has been an increasing need to relate the complex microstructures to the resulting properties of quenched and tempered rebar. However, information on such relationships typically requires precise thermal histories imposed on the workpiece. Therefore, TPS, capable of simulating the Tempcore process, has been developed to produce high-fidelity data. TPS mainly consists of a vacuum induction furnace, pilot rolling mill, box furnace, and cooling unit to simulate shop floor operations. A series of experimental tests were successfully carried out with various parameters, such as reheating temperature, water flow, water pressure, and cooling time. The effects of chemical compositions and cooling time on the microstructural evolution and mechanical properties of quenched and tempered rebar have been analyzed to validate the performance of TPS. The results show that TPS can simulate the Tempcore process with a high degree of fidelity and reliability.

Real-time shop floor scheduling method based on virtual queue adaptive control: Algorithm and experimental results

Shop floor scheduling requires consideration of the dynamic, time-varying and unpredictable nature of the manufacturing environment. A shop floor adaptive scheduling method is proposed given an abnormal event represented by machine breakdown. Because of the difficulty of predicting the duration of a machine breakdown when it occurs, an adaptive scheduling method based on queue control is proposed that not only guarantees production execution according to the initial schedule but also ensures the operational reliability of the shop floor when a disturbance occurs. A mechanism triggered by a threshold value is introduced to realize the adaptive rescheduling of the shop floor, which ensures real-time and stable shop floor operations. The proposed scheduling/rescheduling algorithm is based on Petri net and ant colony optimization (PN-ACO). The average relative error in the optimal solution for benchmark instances problems from BC data and BR date is 22.32 and 14.61 respectively, which illustrates the feasibility and efficiency of the proposed PN-ACO algorithm to solve flexible job shop scheduling problems (FJSPs).The experimental results confirm that the proposed adaptive scheduling method is effective for solving the dynamic flexible job shop scheduling problem with uncertain repair time after occurrence of machine breakdown.