A closed loop outsourcing decision model for developing effective manufacturing strategy

The case study deals with aligning production capacity for a US manufacturer of industrial thermal transfer bench-top printer, by exploring various insourcing and outsourcing options based on production, cost and delivery capabilities. A modelling framework is devised which aids in selecting an effective manufacturing strategy that considers key enablers and barriers to successful outsourcing. The proposed model is closed loop, which stresses regular re-evaluation of the existing outsourcing strategy. It models the total product cost for the product currently being manufactured in the US and compares the total costs if it were manufactured offshore in countries such as, Malaysia, China, Slovakia and Mexico. The analysis from the model shows moving manufacturing of the product to Mexico results in the largest business benefit of about $400 savings per unit compared to other countries studied. A closer look at China in the future could be considered as the economic, social and security risks may change enough to warrant re-evaluation of this option. To get a clearer picture of how Mexico rates against Malaysia in the existing state, these risks may be reviewed as an extra data point to finalise the decision to move manufacturing of industrial thermal transfer bench-top printers to Mexico.     

Optimal decision of an economic production quantity model for imperfect manufacturing under hybrid maintenance policy with shortages and partial backlogging

Considering the characteristics of the stochastic shift of the machine state and the uncertainty of the product quality of production, in this paper, we develop an optimisation decision of economic production quantity model for an imperfect manufacturing system under hybrid maintenance policy with shortages and partial backlogging. We assume that the production process is imperfect stemming from the machine reliability and the probability of out-of-control, a hybrid maintenance policy combined of emergency maintenance and preventive maintenance is executed during each production run. Three decision models based on the scenarios of machine breakdown and repair time are developed. The optimal production quantity and maintenance inspection number during each production run are solved with minimising the expected average cost of the system. Numerical examples are used to demonstrate the effectiveness and feasibility of the model. Sensitivity analysis is conducted to analyse the impacts of key parameters on the optimal decision. Some implications related to the effective and economical execution of maintenance policy for practitioners are derived.     

A systematic approach to determine the optimal maintenance policy for an automated manufacturing system

We propose a systematic approach to determine the optimal maintenance policy for an automated manufacturing system which includes a flexible manufacturing cell (FMC) and several automated machine shops. The systematic approach combines simulation, fractional factorial design, noise or outer array of Taguchi design, regression metamodelling, and classical queueing analysis. A useful expression of the fractional utilization of the manufacturing system is derived and incorporated into formulating and solving the corresponding decision problem. The systematic approach provides an effective implementation procedure to handle practical maintenance problems found in a complex manufacturing environment. © 1997 John Wiley & Sons, Ltd.     

Investing in lean manufacturing practices: an environmental and operational perspective

Lean manufacturing practices (LMPs) and corporate environmental sustainability are becoming inextricably linked. Throughout the lean and green debate, many organisations have recognised that LMPs have implications for their sustainable development and competitive positioning. Not only LMPs are complex on their own, but when perceived from an environmental sustainability perspective, the decision to implement an LMP can become even more intricate. Although general tools exist, the lack of effective decision-making tools to help in the implementation of LMPs with an environmental sustainability dimension is palpable. Thus, this study tackles the aforementioned decision problem by incorporating environmental and operational performance outcome expectations as these expectations are viewed in light of the ease of implementation of various LMPs. A novel multi-criteria decision-making (MCDM) model for evaluation of LMPs is developed in this respect. The model integrates a three-parameter interval grey number with rough set theory and the TODIM method. The model is run using empirical data from six manufacturing organisations. The findings facilitate the identification of a ‘locus of investments’ for a better selection of LMPs. The robustness of the decision support model developed is assessed through sensitivity analysis.     

Joint production and quality control of unreliable batch manufacturing systems with rectifying inspection

Production control policy and economic sampling plan design problems have been studied separately in previous research. This paper considers a joint production control policy and economic single sampling plan design for an unreliable batch manufacturing system. The production is controlled by a modified hedging point policy which consists of building and maintaining a safety stock of finished product to avoid shortages during corrective maintenance. The main objective of this paper is to determine simultaneously the economic production quantity, the optimal safety stock level and the economic sampling plan design which minimise the expected overall cost. A stochastic mathematical model is developed and solved using a simulation optimisation approach based on the response surface methodology. Simulation is used to imitate the complex dynamic and stochastic behaviour of processes as in the real-life industrial systems. The obtained results show clearly strong interactions between production quantity, inventory state and sampling plan design which confirm the necessity of jointly considering production and quality control parameters in an integrated model. Moreover, it is shown a significant impact of production system reliability on the economic sampling plan design and therefore on the quality of finished product delivered to consumers. Numerical example and sensitivity analyses are presented for illustrative purposes.     

Improving the implementation effectiveness of cellular manufacturing: a comprehensive framework for practitioners

In today's competitive environment cellular manufacturing (CM) is a well-known strategy in improving manufacturing performance. To obtain the full benefits that CM has to offer successful implementation is a critical factor. Evidence indicates that firms converting to CM often struggle with implementation and achieve results that are less than anticipated. A comprehensive review of implementation literature was undertaken and a multi-phase model developed and evaluated through a case study. The framework recognizes the importance of both technical and human aspects of CM and provides practitioners with a better understanding of the various phases in the implementation process, including the many activities and issues which need to be considered for each step. In the case study company, implementation of CM not only provided many of the benefits associated with this form of manufacturing but also allowed operators to become a value-adding link in respect to process and product improvement and new product development.     

An ISM approach for modelling the enablers of flexible manufacturing system: the case for India

The volatile condition of today's market is forcing the manufacturing managers to adapt the flexible manufacturing systems (FMS) to meet the challenges imposed by international competition, ever changing customer demands, rapid delivery to market and advancement in technology. There are certain enablers, which help in the implementation of FMS or in the transition process from traditional manufacturing systems to FMS. The utmost need is to analyse the behaviour of these enablers for their effective utilization in the implementation of FMS. The main objective of this paper is to understand the mutual interaction of these enablers and identify the ‘driving enablers’ (i.e. which influence the other enablers) and the ‘dependent enablers’ (i.e. which are influenced by others). In the present work, these enablers have been identified through the literature, their ranking is done by a questionnaire-based survey and interpretive structural modelling (ISM) approach has been utilized in analysing their mutual interaction. An ISM model has been prepared to identify some key enablers and their managerial implications in the implementation of FMS.     

Optimal control of an unreliable machine using fuzzy-logic control: from design to implementation

In this paper, the development, implementation and validation of fuzzy logic to control an unreliable machine in manufacturing systems are presented. The fuzzy-logic controller developed in this context is based on the optimal control policy using hedging-point methodology. The fuzzy-logic controller has two-subsets of fuzzy-logic controls. The first provides a decision whether the system should produce part at make-to-stock or at make-to-order mode. The decision provided by the first fuzzy-logic control is then used by the second fuzzy-logic control to specify at what production rate the part should be produced. Simulation and implementation have been performed by controlling an unreliable machine using the developed fuzzy-logic controller. Then, the simulation results are compared with the simulation results given by the optimal control policy (hedging-point methodology). The results show that the performance of the developed fuzzy logic outperforms and is more precise than the hedging-point method under certain conditions.     

Optimal production control policy in unreliable batch processing manufacturing systems with transportation delay

This paper considers the problem of production planning of unreliable batch processing manufacturing systems. The finished goods are produced in lots, and are then transported to a storage area in order to continuously meet a constant demand rate. The main objective of this work is to jointly determine the optimal lot sizing and optimal production control policy that minimise the total expected cost of inventory/backlog and transportation, over an infinite time horizon. The decision variables are the lot sizing and the production rate. The problem is formulated with a stochastic dynamic programming model and the impulse control theory is applied to establish the Hamilton–Jacobi–Bellman (HJB) equations. Based on a numerical resolution of the HJB equations, it is shown that the optimal control policy is governed by a base stock policy for production rate control and economic lot size for batch processing. A thorough analysis and practical issues are addressed with a simulation-based approach. Thus, a combined discrete–continuous simulation model is developed to determine the optimal parameters of the proposed policy when the failure and repair times follow general distributions. The results are illustrated with numerical examples and confirmed through sensitivity analysis.     

Production and subcontracting control for an unreliable manufacturing system with setups

This paper deals with the problem of joint production, setup and subcontracting control of unreliable manufacturing systems producing two product types. The production requires setups each time it switches from one product type to another. Subcontracting is an integral part of the decision-making process due to limited production capacity in existing facility. The objective is to propose an effective control policy for the considered system which simultaneously manages production, setup and subcontracting activities. The complexity of the problem lies in the interaction between internal manufacturing decisions and subcontracting that outsource a part of the production, in a dynamic and stochastic environment. An experimental optimisation approach is adopted to determine the optimal control parameters which minimise the average total cost. Extensive sensitivity analyses are performed to illustrate the robustness and the usefulness of the adopted approach. An in-depth study comparing five control policies across a wide range of system parameters is also conducted. Extended cases closer to reality are also investigated considering elements such as the preventive maintenance and the production of non-conforming products. The best control policy in terms of economic performance is then obtained. Valuable insights providing a better understanding of interactions involving production, setup, and subcontracting are discussed.     

Research on sustainable evolution of China's cloud manufacturing policies

The emergence and development of China's cloud manufacturing benefit from the national government's policies drive. The measurement and analysis based on the policy text will help clarify the evolution mechanism of cloud manufacturing and provide guidance and suggestions for the landing and application of cloud manufacturing. By collecting and sorting out China's cloud manufacturing policies from 2010 to 2020, using social network analysis, content analysis and other methods, Ucinet, ROST and other software, the sustainable evolution law of China's cloud manufacturing policies from the perspectives of policy issuing departments, policy focus topics and policy tools is explored. The research shows that the State Council of the People's Republic of China and Ministry of Industry and Information Technology of the People's Republic of China are the core departments of policy issuing, and the topics of policies concerned mainly cover four aspects: “cloud manufacturing development goals, cloud manufacturing object-oriented, cloud manufacturing operation links and cloud manufacturing foundation”, and various policy tools are comprehensively used in various policies. The evolution of cloud manufacturing policies presents obvious stage characteristics, which mainly goes through three stages: “encouraging development-top-level design-implementation guidance”. China's cloud manufacturing industry is still in the development stage, and it is urgent to introduce policies that directly affect the industry. The government should continue to improve the policy system by strengthening the joint publication of departments and improving the structural imbalance of policy tools.     

An integrated DEMATEL-MMDE-ISM based approach for analysing the barriers of IoT implementation in the manufacturing industry

Incorporation of smart devices within the older framework has brought along significant challenges. This paper presents a detailed analysis of the barriers faced during the implementation of Internet of Things (IoT) within the manufacturing sector. In addition, the authors aim to obtain a hierarchical structure, which will help the policymakers to identify the most crucial barriers enabling them to make an informed decision. With the help of databases like Scopus, Web of Science, etc. a comprehensive list of 22 barriers was initially obtained. This list was further narrowed down to 10 critical barriers. The first step of the analysis involved the application of Decision Making Trial and Evaluation Laboratory (DEMATEL) technique, which quantifies the influence of the barriers amongst one another. Maximum Mean De-Entropy (MMDE) technique is then used to obtain a scientific threshold value, which is later used in the Interpretive Structural Modelling (ISM) technique from which a hierarchical structure of the barriers is obtained. The results of this study are expected to highlight the most crucial barriers wherein the researchers and practitioners can focus their strategic efforts. This will facilitate the addressal of implicit issues while implementing IoT Techniques in the manufacturing industry.     

A heuristic for designing manufacturing focus units with resource considerations

This paper presents a model of the plant-within-a-plant (PWP) design problem and demonstrates a heuristic for analysing the problem. Although the benefits of a manufacturing focus have been articulated in the literature, methods for implementation with consideration for resource requirements have not been developed previously. In this study, we discuss the importance of including resource considerations and propose a methodology that can help managers arrive at a facility design with a high degree of focus and minimum resource needs. A heuristic is developed that incorporates the concept of order-winning criteria and volume into the focus design. The heuristic not only recognises the effects of conflicting manufacturing tasks, but also considers resource costs and material flows between PWP units. Experimental results show that the proposed methodology offers managers the opportunity to generate and assess alternative PWP designs, which are otherwise unavailable. Overall, this research provides an analytical framework for further research in focused manufacturing.     

Structural Equation Modelling of lean manufacturing practices

The purpose of this research study is to analyse lean manufacturing practices in different industries and to identify the critical factors for its success implementation. Despite the fact that lean manufacturing has been frequently promoted as a means of improving business competitiveness, little empirical evidence exists in the literature validating its positive link with organisational performance. Lean manufacturing practices not only help in reducing the number of defects but also reduces the cost of production. For this purpose, empirical data is collected to measure the lean manufacturing practices prevailing in different industries located in Tamil Nadu, India. A Structural Equation Modelling (SEM) technique is used to build the measurement and structural models. Later, statistical estimates are used to validate the model that has been built. The data analysis helps to determine whether to accept or reject the hypothesis that has been stated based on the structural model. The result shows how lean manufacturing practices are correlated and help in improving the organisational performance among the industries being surveyed.     

Availability consideration in the optimal selection of multiple-aspect RMS configurations

Machine availability has a profound influence on the performance of manufacturing systems. This paper extends a model for optimizing reconfigurable manufacturing systems (RMS) configurations with multiple-aspects to incorporate the effect of machine availability using the universal generating function (UGF). Two powerful meta-heuristic optimization techniques, namely genetic algorithms (GAs) and tabu search (TS), are used for optimizing the capital cost and system availability of the RMS configurations. The optimized configurations can handle multiple-parts and their structure is that of flow lines allowing paralleling of identical machines in each production stage. The various aspects considered in the RMS configurations include arrangement of machines, equipment selection and assignment of operations. A case study is presented and implementation of the optimization model is carried out using MATLAB software. The results of using both GAs and TS to solve the problem are then reported and compared for validation. Analysis of different cases of availability consideration including infinite and no buffer capacity is performed and results are compared to those obtained when machine availability is not considered. It has been shown that considering availability affects the optimal configuration selection and increases the required equipment. This increases the costs of the near-optimal configurations obtained especially in the case without buffers. The presented model can support the manufacturing systems configuration selection decisions at both the initial design and reconfiguration stages.     

模糊模拟在先进制造技术投资评估中的应用

建立一个先进制造技术投资评估模型,它包涵三方面的度量:经济指标、战略指标和风险指标.用模糊净现金流表示经济指标,用模糊变量代替战略指标和风险指标中的不确定性变量.在构建模型时运用层次分析法和模糊理论方法,利用模糊模拟技术处理模型,得到的三个数值用有效面方法来表示.这样可解决评估过程中存在的不确定性变量,使评估结果更加趋于合理.利用模型对三个先进制造技术投资项目评估.     

A genetic scheduling methodology for virtual cellular manufacturing systems: an industrial application

Effective solutions to the cell formation and the production scheduling problems are vital in the design of virtual cellular manufacturing systems (VCMSs). This paper presents a new mathematical model and a scheduling algorithm based on the techniques of genetic algorithms for solving such problems. The objectives are: (1) to minimize the total materials and components travelling distance incurred in manufacturing the products, and (2) to minimize the sum of the tardiness of all products. The proposed algorithm differs from the canonical genetic algorithms in that the populations of candidate solutions consist of individuals of different age groups, and that each individual's birth and survival rates are governed by predefined aging patterns. The condition governing the birth and survival rates is developed to ensure a stable search process. In addition, Markov Chain analysis is used to investigate the convergence properties of the genetic search process theoretically. The results obtained indicate that if the individual representing the best candidate solution obtained is maintained throughout the search process, the genetic search process converges to the global optimal solution exponentially.

The proposed methodology is applied to design the manufacturing system of a company in China producing component parts for internal combustion engines. The performance of the proposed age-based genetic algorithm is compared with that of the conventional genetic algorithm based on this industrial case. The results show that the methodology proposed in this paper provides a simple, effective and efficient method for solving the manufacturing cell formation and production scheduling problems for VCMSs.     

Conceptualising complementarities in manufacturing flexibility: a comprehensive view

In this paper, we develop an extended view of the manufacturing flexibility construct. Instead of a sequential view as emphasised with the competence-capability perspective, the model presented in this study suggests a holistic view of manufacturing flexibility comprised of six complementary dimensions. We contend that the complementary view of manufacturing flexibility (MFLX) consisting of six dimensions – product-mix flexibility (PMX), routing flexibility (RTG), equipment flexibility (EQP), volume flexibility (VOL), labour flexibility (LBR), and supply management flexibility (SPM) – provides a path to implementation of manufacturing flexibility. Manufacturing flexibility is hypothesised as a second order construct comprised of the six complementary dimensions. We test our proposed model using data collected from US manufacturing organisations. Structural equation modelling was used to analyse the data. The results of confirmatory factor analysis support our hypothesis of a second order multi-dimensional construct structure. This comprehensive conceptualisation of manufacturing flexibility should help researchers evaluate the effect of manufacturing flexibility on operational and financial performance.