Manufacturing Operations Management for Smart Manufacturing – A Case Study

2020 ◽  
pp. 91-98
Author(s):  
Michael Meyer-Hentschel ◽  
Oliver Lohse ◽  
Subba Rao ◽  
Raffaello Lepratti
Keyword(s):  
Operations Management ◽  
Smart Manufacturing ◽  
Manufacturing Operations

scholarly journals Synthesis of Manufacturing and Facility Data for Sustainability Analysis

2012 ◽  
Author(s):  
John Michaloski ◽  
Goudong Shao ◽  
Frank Riddick ◽  
Swee Leong ◽  
Jonatan Berglund ◽  
...  
Keyword(s):  
Operations Management ◽  
Facility Management ◽  
Modeling Tools ◽  
Data Synthesis ◽  
Holistic View ◽  
Manufacturing Operations ◽  
Sustainability Analysis ◽  
Activity Models ◽  
Standard Framework

This paper discusses data synthesis of production and facility knowledge for sustainability analysis by applying the ISA–95 “Activity Models of Manufacturing Operations Management” (MOM) model. Presently, production and facility management basically function independently of each other. This paper presents the addition of facility activities to the MOM model, in accordance with the needs for attaining a holistic view of sustainability analysis. Historically, production and facility data are represented in various forms, e.g., data bases, CAD, and spread-sheets, without a common unifying representation. Based on this combination of incompatible modeling tools, the use of Core Manufacturing Simulation Data (CMSD) is proposed as a standard framework for integrating the broad range of technology. A case study of the data synthesis for a precision sand casting production facility is explored.

scholarly journals The HORSE Project: The Application of Business Process Management for Flexibility in Smart Manufacturing

2020 ◽  
Vol 10 (12) ◽  
pp. 4145 ◽  
Author(s):  
Jonnro Erasmus ◽  
Irene Vanderfeesten ◽  
Konstantinos Traganos ◽  
Ruud Keulen ◽  
Paul Grefen
Keyword(s):  
Business Process ◽  
Operations Management ◽  
Business Process Management ◽  
Process Management ◽  
New Technologies ◽  
Smart Manufacturing ◽  
Manufacturing Processes ◽  
Reference Architecture ◽  
Manufacturing Operations ◽  
Management Techniques

Several high-tech manufacturing technologies are emerging to meet the demand for mass customized products. These technologies include configurable robots, augmented reality and the Internet-of-Things. Manufacturing enterprises can leverage these new technologies to pursue increased flexibility, i.e., the ability to perform a larger variety of activities within a shorter time. However, the flexibility offered by these new technologies is not fully exploited, because current operations management techniques are not dynamic enough to support high variability and frequent change. The HORSE Project investigated several of the new technologies to find novel ways to improve flexibility, as part of the Horizon 2020 research and innovation program. The purpose of the project was to develop a system, integrating these new technologies, to support efficient and flexible manufacturing. This article presents the core result of the project: a reference architecture for a manufacturing operations management system. It is based on the application and extension of business process management (BPM) to manage dynamic manufacturing processes. It is argued that BPM can complement current operations management techniques by acting as an orchestrator in manufacturing processes augmented by smart technologies. Building on well-known information systems’ architecting frameworks, design science research is performed to determine how BPM can be applied and adapted in smart manufacturing operations. The resulting reference architecture is realized in a concrete HORSE system and deployed and evaluated in ten practical cases, of which one is discussed in detail. It is shown that the developed system can flexibly orchestrate the manufacturing process through vertical control of all agents, and dynamic allocation of agents in the manufacturing process. Based on that, we conclude that BPM can be applied to overcome some of the obstacles toward increased flexibility and smart manufacturing.

scholarly journals An Integrated Key Performance Measurement for Manufacturing Operations Management

2020 ◽  
Vol 12 (13) ◽  
pp. 5260
Author(s):  
Gyusun Hwang ◽  
Jun-Hee Han ◽  
Tai-Woo Chang
Keyword(s):  
Performance Measurement ◽  
Operations Management ◽  
Multiple Criteria Decision Making ◽  
Performance Model ◽  
Production Performance ◽  
Manufacturing Operations ◽  
Measurement Framework ◽  
Business Excellence ◽  
Result Analysis

This paper proposes a comprehensive production performance measurement framework and illustrates the method to evaluate the performance and guide practitioners to make further improvement. The development comprises four steps. (1) Performance indicators derived from business excellence models are enumerated to provide the performance model: 74 indicators, which can be classified in terms of their characteristics, are identified in six criteria. (2) A multiple criteria decision-making approach based on the analytic hierarchical and network processes, which determine the weights of the criteria and indicators, is applied. In addition, this study introduced additional formulas to derive the final performance values. (3) A performance measurement framework that integrates the measurement and result analysis processes is implemented. (4) The proposed framework is verified through a case study. The results of the case study show that the proposed framework identifies the gaps and discrepancies among the management levels, enabling the determination of means for continuous improvement.

scholarly journals Queue Length Forecasting in Complex Manufacturing Job Shops

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 322-338
Author(s):  
Marvin Carl May ◽  
Alexander Albers ◽  
Marc David Fischer ◽  
Florian Mayerhofer ◽  
Louis Schäfer ◽  
...  
Keyword(s):  
Operations Management ◽  
Queue Length ◽  
Manufacturing Systems ◽  
Production Control ◽  
Data Sets ◽  
Job Shops ◽  
Static Data ◽  
Queue Lengths ◽  
Operational Excellence

Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex and intelligent production control methods are developed in order to remain competitive and achieve operational excellence. Operations management described early on the influence among target metrics, such as queuing times, queue length, and production speed. However, accurate predictions of queue lengths have long been overlooked as a means to better understanding manufacturing systems. In order to provide queue length forecasts, this paper introduced a methodology to identify queue lengths in retrospect based on transitional data, as well as a comparison of easy-to-deploy machine learning-based queue forecasting models. Forecasting, based on static data sets, as well as time series models can be shown to be successfully applied in an exemplary semiconductor case study. The main findings concluded that accurate queue length prediction, even with minimal available data, is feasible by applying a variety of techniques, which can enable further research and predictions.

Does Water Scarcity Affect Environmental Performance? Evidence from Manufacturing Facilities in Texas

2021 ◽  
Author(s):  
Suresh Muthulingam ◽  
Suvrat Dhanorkar ◽  
Charles J. Corbett
Keyword(s):  
Water Scarcity ◽  
Operations Management ◽  
Environmental Performance ◽  
Data Set ◽  
Full Sample ◽  
Toxic Releases ◽  
Manufacturing Operations ◽  
Toxic Emissions ◽  
Manufacturing Facilities ◽  
The Impact

It is well known that manufacturing operations can affect the environment, but hardly any research explores whether the natural environment shapes manufacturing operations. Specifically, we investigate whether water scarcity, which results from environmental conditions, influences manufacturing firms to lower their toxic releases to the environment. We created a data set that spans 2000–2016 and includes details on the toxic emissions of 3,092 manufacturing facilities in Texas. Additionally, our data set includes measures of the water scarcity experienced by these facilities. Our econometric analysis shows that manufacturing facilities reduce their toxic releases into the environment when they have experienced drought conditions in the previous year. We examine facilities that release toxics to water as well as facilities with no toxic releases to water. We find that the reduction in total releases (to all media) is driven mainly by those facilities that release toxic chemicals to water. Further investigation at a more granular level indicates that water scarcity compels manufacturing facilities to lower their toxic releases into media other than water (i.e., land or air). The impact of water scarcity on toxic releases to water is more nuanced. A full-sample analysis fails to link water scarcity to lower toxic releases to water, but a further breakdown shows that manufacturing facilities in counties with a higher incidence of drought do lower their toxic releases to water. We also find that facilities that release toxics to water undertake more technical and input modifications to their manufacturing processes when they face water scarcity. This paper was accepted by David Simchi-Levi, operations management.

scholarly journals Toward Knowledge Management for Smart Manufacturing

2017 ◽  
Vol 17 (3) ◽  
Author(s):  
Shaw C. Feng ◽  
William Z. Bernstein ◽  
Thomas Hedberg ◽  
Allison Barnard Feeney
Keyword(s):  
Knowledge Management ◽  
Design Process ◽  
Process Planning ◽  
Smart Manufacturing ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Knowledge Objects ◽  
Planning Production

The need for capturing knowledge in the digital form in design, process planning, production, and inspection has increasingly become an issue in manufacturing industries as the variety and complexity of product lifecycle applications increase. Both knowledge and data need to be well managed for quality assurance, lifecycle impact assessment, and design improvement. Some technical barriers exist today that inhibit industry from fully utilizing design, planning, processing, and inspection knowledge. The primary barrier is a lack of a well-accepted mechanism that enables users to integrate data and knowledge. This paper prescribes knowledge management to address a lack of mechanisms for integrating, sharing, and updating domain-specific knowledge in smart manufacturing (SM). Aspects of the knowledge constructs include conceptual design, detailed design, process planning, material property, production, and inspection. The main contribution of this paper is to provide a methodology on what knowledge manufacturing organizations access, update, and archive in the context of SM. The case study in this paper provides some example knowledge objects to enable SM.

Towards Chinese smart manufacturing ecosystem in the context of the one belt one road initiative

2018 ◽  
Vol 11 (3) ◽  
pp. 291-310 ◽  
Author(s):  
Rafif Al-Sayed ◽  
Jianhua Yang
Keyword(s):  
Industrial Revolution ◽  
Structured Interview ◽  
Industrial Clusters ◽  
Added Value ◽  
Smart Manufacturing ◽  
Content Type ◽  
Factors Affecting ◽  
Industrial Sectors ◽  
Regional Collaboration

Purpose The purpose of this paper is to examine empirically China’s determined thrust to attain a high level of technological innovation and the factors affecting moving towards a smart and sophisticated manufacturing ecosystem in conjunction with the Belt and Road Initiative (OBOR). Design/methodology/approach This research provides empirical determination of the factors affecting moving towards smart manufacturing ecosystems in China. The method is based on combining two approaches: semi-structured interview and questionnaire-based with academics, experts and managers in various Chinese industrial sectors. The results are based on the multivariate analysis of the collected data. A case study of the current manufacturing ecosystem was also analyzed, in order to understand the present state as well as the potential for China’s competitive edge in the developed OBOR countries. Findings The results illustrate the importance of the infrastructure dimension comprising variables related to ecosystems, industrial clusters and Internet of Things IoT and other advanced technologies. A case study of the city of Shenzhen’s transformation into a smart cluster for innovative manufacturing points out how China’s OBOR initiative for regional collaboration will further transform the regional smart clusters into an ultra-large innovation based smart ecosystem. Originality/value This research is the first to study China’ policies towards playing a prominent role in the Fourth Industrial Revolution 4IR in the context of the OBOR initiative, through empirically defining the factors affecting moving towards a knowledge-intensive smart manufacturing ecosystem where the added value is mostly innovation based.

scholarly journals A proposal for a course of Operations Management for the Degree in Electronics and Automatic

2017 ◽  
Vol 8 ◽  
pp. 177
Author(s):  
Pilar I. Vidal-Carreras ◽  
Julio J. Garcia-Sabater ◽  
Lourdes Canos-Daros
Keyword(s):  
Group Work ◽  
Operations Management ◽  
Problem Based Learning ◽  
Project Based Learning ◽  
Project Learning ◽  
Large Groups

At this work a methodology is proposed for a course of the discipline of Operations Management with a focus on active methodologies in the degree of Electronics and Automatic. For the course is combined: lecture, group work, problem-based learning, project-based learning and presentation of group work. Previous experiences in the same course allow us to conclude the importance of the lecture in this environment in what is the only course of the discipline in all the degree. The importance of feedback in project learning is not easy for large groups such as the case study, suggesting the presentation of group work as a good solution to the problem

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