Shop Floor Operations Research Articles

Exploration of weak-PGML Method for Efficient Stability Control During Machining Operations

Stable control of the machining process can be difficult to maintain in production environments due to many factors, but especially uncertainty about selection of optimal machining parameters. The true underlying stability model for production machines on the shop floor is generally unknown, so parameters are typically selected by operators based on manufacturer recommendations or, in most cases, their accumulated shop floor experience. A class of methods referred to as physics-guided machine learning (PGML) offers a new approach for optimal parameter selection that incorporates theoretical knowledge about machining dynamics into the data-driven machine learning workflow. The focus of this paper is a version of PGML appropriate to shop floor operations in small and medium-sized machine shops (SMMs)—referred to as weak-PGML—that does not require a priori theoretical knowledge of machining dynamics to approximate the true underlying stability model for a particular machining scenario. Rather, the weak-PGML leverages the operator’s accumulated domain knowledge to uncover the true stability model and informs parameter selection. Weak-PGML reflects the limited resources and lack of technical expertise typical of most SMMs with legacy machines and a new generation of workforce without the accumulated experience of operators who have spent many years on the shop floor.

The transition to electrified vehicles: Evaluating the labor demand of manufacturing conventional versus battery electric vehicle powertrains

The shift from traditional internal combustion engine vehicles (ICEVs) to electric vehicles (EVs) has raised concerns about displacement of automotive manufacturing labor. Prior studies have mixed findings, and have been hindered by a lack of shop floor-level data on the labor hours required for ICEV and EV manufacturing. We collect detailed data from leading automotive manufacturers on the production process steps and labor inputs required to build key ICEV and battery electric vehicle (BEV) powertrain components. Our novel data set covers 252 process steps, with data on a further 78 process steps from the existing literature. We build a production process model that estimates the labor hours required to produce ICEV and BEV powertrain components at different production volumes and labor efficiency levels. We find that, accounting for production and assembly of battery packs, the labor intensity required for the manufacturing of BEV powertrain components is larger than for ICEV powertrain components. This difference in labor intensity holds even when comparing the highest-efficiency estimates for BEV production with the lowest efficiency-estimates for ICEVs. This finding depends on our shop-floor operations modeling approach and could not be derived from recent approaches focusing on part counts. Our results imply that vehicle electrification could lead to more automotive manufacturing jobs, at least in the short- to medium-term. While there are potential jobs to support transitions for incumbent automotive workers, the feasibility of these transitions will depend on geographic co-location of new battery production capacity with current ICEV production sites, and on matching between the skills of the automotive workforce and the demands of new EV jobs.

Shop floor performance feedback, employee's motivation and engagement: study of a beverage distribution company

PurposeThis paper aims at examining how different types of shop floor performance feedback affect employee motivation and engagement. Based on this dataset, the authors analyzed the relationship between five types of performance feedback (safety, quality, productivity, improvement and individual performance) and three types of human-related outputs (motivation, role clarity and engagement) in light of socio-technical systems theory.Design/methodology/approachThe authors designed a survey instrument and collected data from 492 employees of a large beverage distribution company. The authors used robust construct validity tests and multiple regression analysis to test the hypotheses.FindingsThe findings indicated significant positive effects of feedback on overall production, improvements and individual performance on all human-related outputs. In turn, safety and quality performance feedback had no statistically significant impact in motivation or engagement. These findings highlight the mixed nature of the impact of performance feedback on human-related outputs that have been neglected in the literature.Originality/valueThe joint analysis of the social and technical portion of performance feedback is unusual despite its clear relevance, which characterizes an original contribution of the authors’ work. Although previous literature supports the effect of performance feedback on motivation and engagement, previous studies have not tested how different types of feedback affect those social constructs.

Using an incentive-compatibility due-date quotation to improve the performance for one-of-a-kind production shop floors controlled by load-based pull systems

ABSTRACT For one-of-a-kind production (OKP) manufacturers adopting an engineer-to-order or make-to-order marketing strategy, it is very important to set an appropriate due date. We design an incentive-compatibility due-date quotation procedure to help OKP manufacturers obtain the rational due-date quotation from OKP customers. Based on the rational due-date quotation decision-making, the performance of an OKP shop floor controlled by load-based pull systems is evaluated in simulation. Our findings show that if the collaboration of due-date quotations and load-based pull control is effective, the order profit of OKP manufacturers will not be reduced when the delivery delay occurs. This study can provide more insights on order quotation decision-making and pull production control for OKP manufacturers.

ZPasteurAIzer: An AI-Enabled Solution for Product Quality Monitoring in Tunnel Pasteurization Machines

In the food and beverage industry, many foods, beers, and soft drinks need to be pasteurized in order to minimize the effect of micro-organisms on the physical stability, quality, and flavour of the product. Although modern tunnel pasteurizers provide integrated solutions for precise process monitoring and control, a great number of packaging plants continue to operate with legacy pasteurizers that require irregular manual measurements to be performed by shop floor operators in order to monitor the process. In this context, the present paper presents zPasteurAIzer, an end-to-end system that provides real-time quality monitoring for legacy tunnel pasteurization machines and constitutes a low-cost alternative to replacement or the upgrading of installed equipment by leveraging IoT technologies and AI-enabled virtual sensing techniques. We share details on the design and implementation of the system, which is based on a microservice-oriented architecture and includes functionalities such as configuration of the pasteurizer machine, data acquisition, and preprocessing methodology as well as machine learning-based estimation and live dashboard monitoring of the process parameters. Experimental work has been conducted in a real-world use case at a large brewing manufacturing plant in Greece, and the results indicate the value and potential of the proposed system.

Operator-Based Adaptive Tracking Capacity Control in Complex Manufacturing Processes

Nowadays, quickly changing customer demands are a big challenge in the manufacturing industry, especially for job shops, which are typical coupling and nonlinear multi-input–multi-output (MIMO) systems. In order to achieve good shop floor performance in the presence of short-term demand fluctuations, a key performance indicator—work in process (WIP)—is required to be effectively controlled in the vicinity of the desired levels. For this purpose, a machinery-oriented capacity adjustment approach via a reconfigurable machine tool (RMT) is employed to flexibly balance capacity and load in the case of a bottleneck. A mathematical model concerning the RMT and WIP was first established in the presence of uncertainty and delays. The operator-based robust right coprime factorization (RRCF) method was adopted to stabilize the uncertain system, and adaptive integral separated proportional–integral (ISPI) tracking controllers were further designed to improve the transient and robustness performance. The performance of the proposed ISPI-RRCF was analyzed and compared with that of a state-of-the-art method in a simulation. The results showed that both control systems could ensure that the WIP was within an allowed bound, while the former had lower overshoots, shorter setting times, and more concentrated distributions facing stochastic demands. This further indicated the effectiveness of the proposed algorithm in the avoidance of serious bottlenecks and unbalanced capacity distributions.

NSLNet: An improved deep learning model for steel surface defect classification utilizing small training datasets

Manufacturing industries contemplate integrating computer vision and artificial intelligence into shop floor operations, such as steel surface defect identification, to realize smart manufacturing goals. However, inadequate annotated training datasets and reduced prediction abilities with image perturbations restrict the practical implementation. This paper introduces NSLNet framework utilizing ImageNet as a feature-extractor combined with adversarial training in the extracted feature space through Neural Structure Learning to address these barriers. The experiments on public (NEU) and synthetically generated datasets (ENEU) showed that the NSLNet could learn with few training samples maintaining resilience against image perturbations outperforming conventional models significantly and nearest deep learning competitors marginally.

Graphical design based on digital twin and interaction generation

In the process of product design transformation, some problems arise, such as inefficient management, lack of accuracy in decision analysis of various operation and maintenance events, and the degree of intelligence to be improved, so this paper designs a graphical design based on digital twin and interaction generation. Digital twin is an important tool to achieve deep integration of graphic design, and the physical integration of complex product design and manufacturing information is the most important part of it, which focuses on shop floor operations and product O&M, but there is less research on the application of digital twin technology to complex product design and manufacturing integration. We analyze the problems of equipment monitoring and video monitoring, and systematically explain the feasibility, design ideas, technical routes and key technologies of applying digital twin technology to intelligent production process monitoring.

Using Tacit Expert Knowledge to Support Shop-floor Operators Through a Knowledge-based Assistance System

The increasing complexity of industrial production systems is challenging employees on the shop-floor in their daily work. Specific knowledge about manufacturing processes is often not available in explicit form but mainly as tacit knowledge of experienced shop-floor workers. A systematic approach to knowledge externalization and reuse is required to make this operational knowledge available. This paper proposes a method to systematically capture and structure expert knowledge while incorporating knowledge management and social research methods. The proposed method's application and evaluation occur in a continuous manufacturing scenario, externalizing tacit knowledge about coping with manufacturing anomalies. A digital assistance system is designed and prototypically implemented to manage and reuse the externalized knowledge. The early involvement of shop-floor workers in the development phase of the prototype ensures usability and user acceptance of the assistance system. The assistance system is developed as a collaboration supporting artifact in the shop-floor's common information space. To observe the resulting productivity performance improvements in the manufacturing scenario, a KPI-based evaluation of the assistance system is presented. Finally, a discussion about the major contributions of this paper, namely the development of an approach for knowledge externalization and a human-centered design of an assistance system, takes place. To assess the novelty of these approaches, they are contrasted with the state of the art identified in the literature before a final summary of the results is presented.

On-Machine Tool Condition Monitoring System Using Image Processing

An automation system of machine tools can free operators from simple and hard labor by the automatic loading and unloading of workpieces, and allowance of unmanned operation during nights and holidays. To achieve complete shop floor operations, various works that are manually performed by operators need to be automated as well. It is also crucial to accurately monitor tools to detect tool wear and chip winding during machining. In this study, we propose an on-machine tool condition monitoring system that measures tool length and diameter using images to detect tool breakage and winding chips.

Digital-twin-based decision support of dynamic maintenance task prioritization using simulation-based optimization and genetic programming

Modern decision support systems need to be connected online to equipment so that the large amount of data available can be used to guide the decisions of shop floor operators, making full use of the potential of industrial manufacturing systems. This paper investigates a novel optimization and data analytic method to implement such a decision support system, based on heuristic generation using genetic programming and simulation-based optimization running on a digital twin. Such a digital-twin-based decision support system allows the proactively searching of the best attribute combinations to be used in a data-driven composite dispatching rule for the short-term corrective maintenance task prioritization. Both the job (e.g., bottlenecks) and operator priorities use multiple criteria, including competence, utilization, operator walking distances on the shop floor, bottlenecks, work-in-process, and parallel resource availability. The data-driven composite dispatching rules are evaluated using a digital twin, built for a real-world machining line, which simulates the effects of decisions regarding disruptions. Experimental results show improved productivity because of using the composite dispatching rules generated by such heuristic generation method compared to the priority dispatching rules based on similar attributes and methods. The improvement is more pronounced when the number of operators is reduced. This paper thus offers new insights about how shop floor data can be transformed into useful knowledge with a digital-twin-based decision support system to enhance resource efficiency.

Exploration of digitalized presentation of information for Operator 4.0: Five industrial cases

In the digital transformation of manufacturing companies towards Industry 4.0, shop-floor operators of the future, Operator 4.0, will require digitalized presentation of information as cognitive support for their work. This paper explores five industrial cases where Information Support Technology have been conceptualized and developed. These cases have exemplified how digitalized presentation of information can be approached with considerations of operators with varying cognitive work situations and production characteristics. Furthermore, these new technical capabilities have increased the level of cognitive automation to support operators’ individual abilities to perform their work in an increasingly more complex production environment. In conclusion, Information Support Technology in the service of Operator 4.0 is intimately linked with digitalization strategies for transformation towards Industry 4.0.

A data-driven scheduling knowledge management method for smart shop floor

ABSTRACT The data-driven method has been widely used to mine knowledge from the smart shop floors’ production data to guide dynamic scheduling. However, the mined knowledge may be invalid when the production scene changes. In order to address this problem and ensure the validity of the knowledge, this paper studies a data-driven scheduling knowledge life-cycle management (SKLM) method for the smart shop floor. The proposed method includes four phases: knowledge generation, knowledge application, online knowledge evaluation, and knowledge update. Specifically, the extreme learning machine (ELM) is applied to learn knowledge based on the composite scheduling rules. The quality control theory is used to evaluate the quality of scheduling knowledge. And the online sequential ELM (OS-ELM) is adopted to update the knowledge. Knowledge life-cycle management is implemented through the iterative knowledge update. The proposed method is validated on the MIMAC6, which is a simulation model of the semiconductor production line. Experimental results show that the proposed method could improve the effectiveness of scheduling knowledge and further optimize the performance of the smart shop floor.

Performance Improvement in Manufacturing Shop Floor Operations of Developing Countries Based on Three Characteristics of Information Flow

The management of information flow for production improvement has always been a target in the research. In this paper, the focus is on the analysis model of the characteristics of information flow in shop floor operations based on the influence that dimension (support or medium), direction and the quality information flow have on the value of information flow using machine learning classification algorithms. The obtained results of classification algorithms used to analyze the value of information flow are Decision Trees (DT) and Random Forest (RF) with a score of 0.99% and the mean absolute error of 0.005. The results also show that the management of information flow using DT or RF shows that, the dimension of information such as digital information has the greatest value of information flow in shop floor operations when the shop floor is totally digitalized. Direction of information flow does not have any great influence on shop floor operations processes when the operations processes are digitalized or done by operators as machines.

Interventions Risk Evaluation and Management in Aseptic Manufacturing.

Interventions performed by personnel during an aseptic process can be a key source of microbiological contamination of sterile biopharmaceutical products, irrespective of the type of manufacturing system used. Understanding the relative risk of this source of contamination provides valuable information to help make decisions for the design, qualification, validation, operation, monitoring, and evaluation of the aseptic process. These decisions can be used to improve the aseptic process and provide assurance of the sterility of the products. To achieve these goals, an assessment of the contamination risk is needed. This risk assessment should be objective, accurate, and useful. This article presents an Intervention Risk Evaluation Model (IREM) philosophy and an objective, accurate, and useful method for intervention risk determination. The IREM uses a key word approach to identify, obtain, measure, and evaluate intervention risk factors. This article presents a general discussion of the method with the help of a case study to illustrate the development of the model, whereas subsequent parts would focus on application of this model with practical examples. This not only attempts to create objectivity of the entire process, but it develops awareness of the associated risks among shop floor operators, which can lead to a reduction of the overall risk level of the process and an improvement in the sterility assurance level.

An innovative approach to plant and process supervision, Danieli Intelligent Plant

Under the push of the European Green Deal [1], a transformation to “make things” is taking place. The Danieli Intelligent Plant DIP has the ambition to enable the Sustainability of metals manufacturing.DIP is a new fully immersive control and supervision model based on augmented, mixed and virtual reality sensing and processing technologies. Processes, machines and equipment are holistically integrated with operators/decision makers using data-driven approaches and AI and ML.This allows to extensively control, supervise processes and machines characterized by large degree of autonomy to optimize processes, quality and maintenance operations. Firstly, a new concept of HCI (Human Computer Machine Interface) has been realized based on the evolution of 3Q philosophy allowing local and remote monitoring and control of processes through the same approach customized for different technological scenarios. Remote cooperation, assistance and maintenance is also part of the environment for having no-man-on-floor increasing safety and putting the focus on control and human centrality. Not less important, the extensive use of mobile solutions (wireless Local Control Station) have an important role to reduce the human factor whenever a shop floor operation is mandatory.

Smart Wearable and Collaborative Technologies for the Operator 4.0 in the Present and Post-COVID Digital Manufacturing Worlds

Abstract This paper addresses the potential of smart wearable and collaborative technologies in support of healthier, safer, and more productive shop floor environments during the present and post– coronavirus 2019 pandemic emerging digital manufacturing worlds. It highlights the urgent need to “digitally transform” many high-touch shop floor operations into low-touch or no-touch ones, aiming not only at a safer but also more productive return to work as well as a healthier continuity of production operations in more socially sustainable working environments. Furthermore, it discusses the interrelated roles of people, data, and technology to develop smart and sustainable shop floor environments. Lastly, it provides relevant recommendations to the key business units in a manufacturing enterprise in regard to the adoption and leverage of smart wearable and collaborative technologies on the shop floor in order to ensure the short- and long-term operation of a factory amid the coronavirus 2019 pandemic and the future of production and work in the Industry 4.0 era.

A lean view of lean

A lean view of lean

Worker satisfaction with adaptive automation and working conditions: a theoretical model and questionnaire as an assessment tool

This article focuses on methods and tools to measure worker satisfaction with reference to industrial automation. Despite technological advances in automation, the role of human workers on industrial shop floors remains crucial. To promote humans’ roles, production systems should be designed and organized so workers are valued and get satisfactory jobs. The article presents a novel holistic model of worker satisfaction with adaptive automation and working conditions. The model takes into account psychosocial and physical working conditions and the characteristics of the automation system the worker interacts with and its user interface. We propose a questionnaire to be used as a practical tool to assess worker satisfaction with industrial automation, considering also the case of adaptive automation. The proposed version of the questionnaire is the result of pilot testing carried out among shop floor operators and takes into account adjustments derived from end-user feedback.

Cross-dock distribution and operation planning for overseas delivery consolidation: A case study in the automotive industry

One of the strategic objectives of supply chains in the automotive industry is the development of international sourcing. Renault works with a global network of cross-docking platforms to link distant assembly plants with first-tier suppliers. These platforms act both as information and physical consolidation points. At shop-floor, inland deliveries are received, sorted, repacked (if needed) and loaded onto containers for overseas transportation. Distribution and operation planning are key activities in such a cross-dock platform but despite its interdependency, current research considers them separately. This paper presents a detailed analysis of Renault cross-dock platforms called ILN (International Logistics Networks), their planning process and physical flows. An integer linear programming model to plan jointly the distribution and shop-floor operations in a cross-dock platform of Renault is developed. The objective is to minimize total cost composed of transportation cost (inbound and outbound), cost of internal resources and storage cost. Numerical offline tests of the model with CPLEX, based on real data for a number of past periods, have showed a 13% reduction of total cost. These encouraging tests confirm both the ability of the proposed approach to deal with real-size instances and the potential gains which can be obtained by considering decision related to distribution and planning together. Taking into account these results, the part of model concerning inbound logistics was integrated in the planning system used in a Renault ILN. The results of the first exploitation period have shown a reduction of 20% of inbound transportation cost comparing with the previous period without increasing the other costs.