scholarly journals Adaptive decision support for suggesting a machine tool maintenance strategy

2018 ◽  
Vol 24 (3) ◽  
pp. 376-399 ◽  
Author(s):  
Abubaker Shagluf ◽  
Simon Parkinson ◽  
Andrew Peter Longstaff ◽  
Simon Fletcher
Keyword(s):  
Decision Support ◽  
Machine Tool ◽  
Cost Estimation ◽  
Expert Knowledge ◽  
Production Costs ◽  
Maintenance Cost ◽  
Maintenance Strategy ◽  
Content Type ◽  
Machine Tool Accuracy

Purpose The purpose of this paper is to produce a decision support aid for machine tool owners to utilise while deciding upon a maintenance strategy. Furthermore, the decision support tool is adaptive and capable of suggesting different strategies by monitoring for any change in machine tool manufacturing accuracy. Design/methodology/approach A maintenance cost estimation model is utilised within the research and development of this decision support system (DSS). An empirical-based methodology is pursued and validated through case study analysis. Findings A case study is provided where a schedule of preventative maintenance actions is produced to reduce the need for the future occurrences of reactive maintenance actions based on historical machine tool accuracy information. In the case study, a 28 per cent reduction in predicted accuracy-related expenditure is presented, equating to a saving of £14k per machine over a five year period. Research limitations/implications The emphasis on improving machine tool accuracy and reducing production costs is increasing. The presented research is pioneering in the development of a software-based tool to help reduce the requirement on domain-specific expert knowledge. Originality/value The paper presents an adaptive DSS to assist with maintenance strategy selection. This is the first of its kind and is able to suggest a preventative strategy for those undertaking only reactive maintenance. This is of value for both manufacturers and researchers alike. Manufacturers will benefit from reducing maintenance costs, and researchers will benefit from the development and application of a novel decision support technique.

An empirical investigation into intelligent cost analysis in purchasing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Frank Bodendorf ◽  
Manuel Lutz ◽  
Stefan Michelberger ◽  
Joerg Franke
Keyword(s):  
Cost Analysis ◽  
Cost Estimation ◽  
Expert Knowledge ◽  
Added Value ◽  
Cost Calculation ◽  
Content Type ◽  
Intelligent Decision Support Systems ◽  
The One ◽  
Management Recommendations ◽  
Intelligent Decision Support

Purpose Cost transparency is of central importance to reach a consensus between supply chain partners. The purpose of this paper is to contribute to the instrument of cost analysis which supports the link between buyers and suppliers. Design/methodology/approach Based on a detailed literature review in the area of cost analysis and purchasing, intelligent decision support systems for cost estimation are identified. Subsequently, expert interviews are conducted to determine the application possibilities for managers. The application potential is derived from the synthesis of motivation, identified applications and challenges in the industry. Management recommendations are to be derived by bringing together scientific and practical approaches in the industry. Findings On the one hand, the results of this study show that machine learning (ML) is a complex technology that poses many challenges for cost and purchasing managers. On the other hand, ML methods, especially in combination with expert knowledge and other analytical methods, offer immense added value for cost analysis in purchasing. Originality/value Digital transformation allows to facilitate the cost calculation process in purchasing decisions. In this context, the application of ML approaches has gained increased attention. While such approaches can lead to high cost reductions on the side of both suppliers and buyers, an intelligent cost analysis is very demanding.

Dynamic safety prewarning mechanism of human–machine–environment using computer vision

2020 ◽  
Vol 27 (8) ◽  
pp. 1813-1833 ◽  
Author(s):  
Wenpei Xu ◽  
Ting-Kwei Wang
Keyword(s):  
Risk Assessment ◽  
Computer Vision ◽  
Expert Knowledge ◽  
Safety Information ◽  
Assessment Model ◽  
Risk Indicators ◽  
Risk Assessment Model ◽  
Content Type ◽  
Risk Levels

PurposeThis study provides a safety prewarning mechanism, which includes a comprehensive risk assessment model and a safety prewarning system. The comprehensive risk assessment model is capable of assessing nine safety indicators, which can be categorised into workers’ behaviour, environment and machine-related safety indicators, and the model is embedded in the safety prewarning system. The safety prewarning system can automatically extract safety information from surveillance cameras based on computer vision, assess risks based on the embedded comprehensive risk assessment model, categorise risks into five levels and provide timely suggestions.Design/methodology/approachFirstly, the comprehensive risk assessment model is constructed by adopting grey multihierarchical analysis method. The method combines the Analytic Hierarchy Process (AHP) and the grey clustering evaluation in the grey theory. Expert knowledge, obtained through the questionnaire approach, contributes to set weights of risk indicators and evaluate risks. Secondly, a safety prewarning system is developed, including data acquisition layer, data processing layer and prewarning layer. Computer vision is applied in the system to automatically extract real-time safety information from the surveillance cameras. The safety information is then processed through the comprehensive risk assessment model and categorized into five risk levels. A case study is presented to verify the proposed mechanism.FindingsThrough a case study, the result shows that the proposed mechanism is capable of analyzing integrated human-machine-environment risk, timely categorising risks into five risk levels and providing potential suggestions.Originality/valueThe comprehensive risk assessment model is capable of assessing nine risk indicators, identifying three types of entities, workers, environment and machine on the construction site, presenting the integrated risk based on nine indicators. The proposed mechanism, which adopts expert knowledge through Building Information Modeling (BIM) safety simulation and extracts safety information based on computer vision, can perform a dynamic real-time risk analysis, categorize risks into five risk levels and provide potential suggestions to corresponding risk owners. The proposed mechanism can allow the project manager to take timely actions.

scholarly journals Value of project management in university–industry R&D collaborations

2020 ◽  
Vol 13 (4) ◽  
pp. 819-843
Author(s):  
Gabriela Fernandes ◽  
David O' Sullivan ◽  
Eduardo B. Pinto ◽  
Madalena Araújo ◽  
Ricardo J. Machado
Keyword(s):  
Decision Support ◽  
Project Management ◽  
Empirical Research ◽  
Research Literature ◽  
Competing Values ◽  
Content Type ◽  
Key Stakeholders ◽  
Multi Stakeholder ◽  
University Industry

PurposeUniversity–industry projects provide special challenges in understanding and expressing the values required of project management (PM) in delivering stakeholder benefits. This paper presents a framework for understanding, identifying and managing the values of PM in major university–industry R&D projects.Design/methodology/approachThe value framework identifies for each of the key stakeholders, the key PM values that may require to be managed and are largely derived from research literature. Empirical research then explores, prioritises and selects key PM values that need to be managed for a specific project. A large case study is used involving one university and one industry collaborating on a multi-million Euro initiative over six years. Empirical research was conducted by researchers who observed at close quarters, the challenges and successes of managing the competing values of key stakeholders.FindingsThe value framework takes a stakeholders' perspective by identifying the respective PM values for each of six stakeholders: university–industry consortium, university, industry, R&D external entities, funding entity and society.Research limitations/implicationsThe research was performed using only one case study which limits the generalisability of its findings; however, the findings are presented as a decision support aid for project consortia in developing values for their own collaboration.Practical implicationsGuidance and decision support are provided to multi-stakeholder research consortia when selecting values that need to be managed for achieving tangible and intangible project benefits.Originality/valueThe paper demonstrates a proposed framework for designing and managing the value of PM in large multi-stakeholder university–industry R&D projects.

Factors influencing maintenance-related investments in industry: a multiple-case study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Camilla Lundgren ◽  
Jon Bokrantz ◽  
Anders Skoogh
Keyword(s):  
Decision Support ◽  
Manufacturing Industry ◽  
Production Systems ◽  
Multiple Case Study ◽  
Sustainable Production ◽  
Content Type ◽  
Factors Influencing ◽  
Multiple Case ◽  
Investment Process

PurposeThe purpose of this study is to ensure productive, robust and sustainable production systems by enabling future investments in maintenance. This study aims to provide a deeper understanding of the investment process and thereby facilitate future maintenance-related investments. The objectives are to describe the investment process, map the decision support and roles involved and identify factors influencing the process.Design/methodology/approachThe study was designed as a multiple-case study, with three industrial cases of maintenance-related investments. A structured coding procedure was used to analyse the empirical data from the cases.FindingsThis paper provides a deeper understanding of the process of maintenance-related investments. Eleven factors influencing the investment process could be identified, three of which were seen in all three cases. These three factors are: fact-based decision-support, internal integration and foresight.Practical implicationsInvestments in modern maintenance are needed to ensure productive, robust and sustainable production in the future. However, it is a challenge in manufacturing industry to justify maintenance-related investments. This challenge may be solved by developing a decision-support system, or a structured work procedure, that considers the findings of this study.Originality/valueFrom this study, an extended view of the relation between quantifying effects of maintenance and maintenance-related investment is proposed, including surrounding factors influencing the investment process. The factors were identified using a structured and transparent coding procedure which is rarely used in maintenance research.

Barriers to implementing waste-to-energy projects in Sri Lanka: a PESTEL analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Binashi Kumarasiri ◽  
Piumi Dissanayake
Keyword(s):  
Sri Lanka ◽  
Expert Knowledge ◽  
Research Strategy ◽  
Waste To Energy ◽  
Successful Implementation ◽  
Structured Interviews ◽  
Content Type ◽  
The Government ◽  
Under Construction

PurposeIt is no surprise that garbage is not garbage for some. It is money. This is why garbage has been overestimated to a point that money allocated for waste-to-energy (WtE) projects feed individual pockets. Many countries have already adapted WtE as a successful solution for both energy and waste crisis. Although in Sri Lanka six WtE projects were promised, the government abruptly decided that it would not have any more projects other than the two plants that were under construction. The purpose of this paper is to analyse barriers to the implementation of WtE projects in Sri Lanka.Design/methodology/approachAn exploratory case study was selected as the research strategy to achieve the research aim. In total, two WtE megaprojects, which have been initiated implementation in Sri Lanka, were used as cases. A total of 12 semi-structured interviews with four personnel from each case and four government officials were used as the data collection technique. Data analysis was carried out using code-based content analysis. The barriers were extracted through analysis of case findings using an abductive analysis. The strategies to mitigate identified barriers were formulated based on attributes highlighted through case study findings and further validated through the opinions of three experts.FindingsBarriers were analysed using the PESTEL framework to get ample insight into barriers that impact on the implementation of WtE projects in Sri Lanka. Less support from the government due to their less awareness on WtE, high investment and operational cost, lack of expert knowledge on WtE technologies in Sri Lanka, absence of a proper regulatory framework for implementation WtE technologies, lengthy process and delay in getting approvals from government process, poor attitudes of public and their protests due to the less awareness on WtE are the foremost barriers identified in this study. Further, strategies were proposed based on the empirical research findings to overcome barriers to facilitate the successful implementation of WtE projects in Sri Lanka.Research limitations/implicationsSo far only two WtE megaprojects have been initiated the implementation in Sri Lanka. Therefore, the scope of the study was limited only to those projects. Moreover, the type of waste considered in this study is municipal solid waste (MSW), which has become a bigger problem in Sri Lanka.Originality/valueThe current study unveils an analysis of barriers for implementation of WtE projects in Sri Lanka, including strategies for mitigating identified barriers. The findings would enable relevant stakeholders, i.e. policymakers, industry practitioners, investors, government bodies and researchers to make informed decisions on implementation of WtE projects and thereby promote successful implementation of WtE projects in Sri Lanka.

A revised method for allocating the optimum number of similar machines to operators

2016 ◽  
Vol 65 (2) ◽  
pp. 223-244 ◽  
Author(s):  
Yossi Hadad ◽  
Baruch Keren
Keyword(s):  
Binomial Distribution ◽  
Manufacturing Systems ◽  
Real Life ◽  
Optimal Number ◽  
Production Costs ◽  
Optimum Number ◽  
Content Type ◽  
Manufacturing Operations ◽  
Machine Interference

Purpose – The purpose of this paper is to propose a method to determine the optimal number of operators to be assigned to a given number of machines, as well as the number of machines that will be run by each operator (a numerical partition). This determination should be made with the objective of minimizing production costs or maximizing profits. Design/methodology/approach – The method calculates the machines interference rate via the binomial distribution function. The optimal assignment is calculated by transformation of a partition problem into a problem of finding the shortest path on a directed acyclic graph. Findings – The method enables the authors to calculate the adjusted cycle time, the workload of the operators, and the utility of the machines, as well as the production yield, the total cost per unit, and the hourly profit for each potential assignment of operators to machines. In a case study, the deviation of the output per hour of the proposed method from the actual value was about 2 percent. Practical implications – The paper provides formulas and tables that give machine interference rates through the application of binomial distribution. The practicability of the proposed method is demonstrated by a real-life case study. Originality/value – The method can be applied in a wide variety of manufacturing systems that use many identical machines. This includes tire presses in tire manufacturing operations, ovens in pastry manufacturing systems, textile machines, and so on.

Sensor-based ambient intelligence for optimal energy efficiency

Sensor Review ◽  
2014 ◽  
Vol 34 (2) ◽  
pp. 170-181 ◽  
Author(s):  
David Robinson ◽  
David Adrian Sanders ◽  
Ebrahim Mazharsolook
Keyword(s):  
Energy Efficiency ◽  
Decision Support ◽  
Energy Consumption ◽  
Energy Management ◽  
Research Work ◽  
Management Systems ◽  
Manufacturing Companies ◽  
Content Type ◽  
Energy Management Systems

Purpose – This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation. Design/methodology/approach – A novel approach is taken to energy consumption monitoring by using ambient intelligence (AmI), extended data sets and knowledge management (KM) technologies. These are combined to create a decision support system as an innovative add-on to currently used energy management systems. Standard energy consumption data are complemented by information from AmI systems from both environment-ambient and process ambient sources and processed within a service-oriented-architecture-based platform. The new platform allows for building of different energy efficiency software services using measured and processed data. Four were selected for the system prototypes: condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase, and continuous improvement/optimisation of energy efficiency. Findings – An innovative and intelligent solution for energy efficiency optimisation is demonstrated in two typical manufacturing companies, within one case study. Energy efficiency is improved and the novel approach using AmI with KM technologies is shown to work well as an add-on to currently used energy management systems. Research limitations/implications – The decision support systems are only at the prototype stage. These systems improved on existing energy management systems. The system functionalities have only been trialled in two manufacturing companies (the one case study is described). Practical implications – A decision support system has been created as an innovative add-on to currently used energy management systems and energy efficiency software services are developed as the front end of the system. Energy efficiency is improved. Originality/value – For the first time, research work has moved into industry to optimise energy efficiency using AmI, extended data sets and KM technologies. An AmI monitoring system for energy consumption is presented that is intended for use in manufacturing companies to provide comprehensive information about energy use, and knowledge-based support for improvements in energy efficiency. The services interactively provide suggestions for appropriate actions for energy problem elimination and energy efficiency increase. The system functionalities were trialled in two typical manufacturing companies, within one case study described in the paper.

A developed case-based reasoning system for machine tool selection

2017 ◽  
Vol 24 (5) ◽  
pp. 1364-1385 ◽  
Author(s):  
Shankar Chakraborty ◽  
Soumava Boral
Keyword(s):  
Machine Tool ◽  
Expert Knowledge ◽  
Fine Tuning ◽  
Selection Problem ◽  
Case Based Reasoning ◽  
Tool Selection ◽  
Diverse Range ◽  
Content Type ◽  
Machine Tool Selection ◽  
Case Based

Purpose Subtractive manufacturing process is the controlled removal of unwanted material from the parent workpiece for having the desired shape and size of the product. Several types of available machine tools are utilized to carry out this manufacturing operation. Selection of the most appropriate machine tool is thus one of the most crucial factors in deciding the success of a manufacturing organization. Ill-suited machine tool may often lead to reduced productivity, flexibility, precision and poor responsiveness. Choosing the best suited machine tool for a specific machining operation becomes more complex, as the process engineers have to consider a diverse range of available alternatives based on a set of conflicting criteria. The paper aims to discuss these issues. Design/methodology/approach Case-based reasoning (CBR), an amalgamated domain of artificial intelligence and human cognitive process, has already been proven to be an effective tool for ill-defined and unstructured problems. It imitates human reasoning process, using specific knowledge accumulated from the previously encountered situations to solve new problems. This paper elucidates development and application of a CBR system for machine tool selection while fulfilling varying user defined requirements. Here, based on some specified process characteristic values, past similar cases are retrieved and reused to solve a current machine tool selection problem. Findings A software prototype is also developed in Visual BASIC 6.0 and three real time examples are illustrated to validate the application potentiality of CBR system for the said purpose. Originality/value The developed CBR system for machine tool selection retrieves a set of similar cases and selects the best matched case nearest to the given query set. It can successfully provide a reasonable solution to a given machine tool selection problem where there is a paucity of expert knowledge. It can also guide the process engineers in setting various parametric combinations for achieving maximum machining performance from the selected machine tool, although fine-tuning of those settings may often be required.

Creating leaders for successful change management

2014 ◽  
Vol 13 (3) ◽  
pp. 127-129 ◽  
Author(s):  
Eddie Kilkelly
Keyword(s):  
Change Management ◽  
Best Practice ◽  
Expert Knowledge ◽  
Development Program ◽  
Change Initiatives ◽  
Content Type ◽  
Big Picture ◽  
Change Leaders ◽  
Successful Change

Purpose – The purpose of this paper is to explain why change programs fail in spite of best practice processes and procedures and to examine the improvements that can be made by developing effective change leaders. Design/methodology/approach – The paper is based upon the author's expert knowledge and includes a case study of an organization that is an exemplar for successful change management, having been censured for its lack of success only a few years ago. The paper identifies the actions that helped this organization improve its capability for change. Findings – Change initiatives are more likely to be successful when change leaders are developed and mentored through an organization-wide, structured, aspirational career development program, which encourages change leaders to focus on the big picture, to use their network, to engage with stakeholders and to develop their own emotional intelligence and resilience. Practical implications – The paper explains that organizations need to change their thinking and practices around change management to do more to address the skills, attitudes, capabilities and relationships of the people involved – particularly change leaders. Originality/value – This paper examines the often-overlooked topic of developing, coaching and mentoring change leaders and includes a previously unpublished case study. It provides a blueprint for action for other organizations struggling to deliver successful change programs.

Mathematical model for deadlock resolution in multiple AGV scheduling and routing network: a case study

2015 ◽  
Vol 42 (3) ◽  
pp. 252-263 ◽  
Author(s):  
Hamed Fazlollahtabar ◽  
Mohammad Saidi-Mehrabad ◽  
Ellips Masehian
Keyword(s):  
Mathematical Model ◽  
Decision Support ◽  
Material Handling ◽  
Mathematical Program ◽  
Solution Space ◽  
Automated Guided Vehicles ◽  
Content Type ◽  
Earliness And Tardiness ◽  
Deadlock Resolution

Purpose This paper aims to propose and formulate a complicated routing/scheduling problem for multiple automated guided vehicles (AGVs) in a manufacturing system. Design/methodology/approach Considering the due date of AGVs requiring for material handling among shops in a jobshop layout, their earliness and tardiness are significant in satisfying the expected cycle time and from an economic view point. Therefore, the authors propose a mathematical program to minimize the penalized earliness and tardiness for a conflict-free and just-in-time production. Findings The model considers a new concept of turning point for deadlock resolution. As the mathematical program is difficult to solve with a conventional method, an optimization method in two stages, namely, searching the solution space and finding optimal solutions are proposed. The performance of the proposed mathematical model is tested in a numerical example. Practical implications A case study in real industrial environment is conducted. The findings lead the decision-makers to develop a user interface decision support as a simulator to plan the AGVs’ movement through the manufacturing network and help AGVs to prevent deadlock trap or conflicts. The proposed decision support can easily be commercialized. Originality/value The benefits of such commercialization are increase in the quality of material handling, improve the delivery time and prevent delays, decrease the cost of traditional handling, capability of computerized planning and control, intelligent tracking and validation experiments in simulation environment.

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