Productivity measurement of the manufacturing process for outsourcing decisions: the case of a Taiwanese printed circuit board manufacturer

Outsourcing is used to alleviate capacity shortages and increase production flexibility. Generally, the outsourcing decision of manufacturing processes is determined by the capacity utilization rate and focus on individual process. In this article, multicriteria are considered by applying the data envelopment analysis to evaluate the relative efficiencies of all the manufacturing processes to help to assess the allocation of a company's resources. The Malmquist productivity index (MPI) is further employed to measure the productivity changes of each process, on which the outsourcing decision can be based. The empirical case of a Taiwanese printed circuit board manufacturer is applied to illustrate the outsourcing decision process. According to the outsourcing decision matrix proposed in this article, the manufacturing process with low efficiency score and MPI value was identified as a high priority for outsourcing. The outcome shows that the manufacturing process with a higher investment of fixed assets does not guarantee better efficiency. If a company follows the outsourcing decision suggested by the proposed approach, the capacity utility rate and the production quantity per person can be increased. The results and analysis model can provide an alternative for managerial thinking and practical application for outsourcing decisions.     

Integrated model for supplier selection and negotiation in a make-to-order environment

The paper deals with the development of an integrated supplier selection and negotiation process for multiple parts/materials procurement. The main objective is to integrate decisions in the internal supply chain of a make-to-order manufacturer. Two main decisions during the negotiation process are considered: (1) the manufacturing planning decision responsible for determining the production schedule and fabrication lot size and (2) the supplier selection decision concerning which suppliers are selected for company business and the order volume allocated to each selected supplier. The model is designed to support the negotiation process by generating a set of effective alternatives in each negotiation period. Its structure is multi-objective and non-linear. The combination of the interactive weighted Tchebycheff method and Benders decomposition method is applied to generate a set of effective alternatives to support the decision-maker in each negotiation period.     

Bayesian framework for managing preferences in decision-making

A rational decision-making process does not exclude the possibility of decision makers expressing different preferences and disagreeing regarding the effects of consequences and optimal course of actions. This point of view is explored in depth in this paper. A framework is developed that includes several decision makers (instead of just one) and allows for the variability of preferences among these decision makers. The information provided by the varying opinions of decision makers can be used to optimize our own decision-making. To achieve this, likelihood functions are developed for stated preferences among both discrete and continuous alternatives, and stated preference rankings of alternatives. Two applications are pursued: the optimization of the lifecycle utility of a structural system subject to consequences of failure proportional to the intensity of hazards exceeding a variable threshold, and to follow-up consequences. Also, the problem of tight decisions or close calls is investigated in order to explore the efficiency of a Bayesian approach using stated preferences and stated rankings.     

Congruence of manufacturing decision areas in a production system: a research framework

A well designed production system secures environmental and internal fit. Environmental fit in a production system refers to alignment of manufacturing decisions to the external settings such as product and market. Internal fit implies that manufacturing decisions are mutually supportive. This paper develops a framework to analyse congruence of manufacturing decision areas in a production system. The framework considers six broad manufacturing decision areas. Based on the literature review, 54 decision types and alternative decision choices for each decision type are identified. The subjective and/or objective constructs to measure decision type are presented which should be useful in designing construct and in data gathering to conduct empirical research. Using the proposed framework, many research questions concerning the settings of several decision types for a specific type of production system can be generated and empirically tested.     

An integrated emergency ordering and production planning optimization model with demand and yield uncertainty

This paper proposes an integrated emergency ordering and production planning scheme for a multi-item, multi-product problem in which each product is composed of several ingredients. Each item can be supplied from both cheap unreliable suppliers prone to yield uncertainty and expensive reliable suppliers. A two-stage decision-making process is proposed in which orders are placed to the unreliable suppliers during the first stage and an emergency order can be placed in the second stage. In addition, a flexible backup ordering contract between the buyer and emergency supplier is proposed. A similar two-stage decision-making process is considered for production planning, where in the first stage, the main production plan is determined and in the second stage, the decision about a limited increase in the production plan is made as an emergency decision. An integrated ordering and production planning decision process is proposed for the problem. The value of emergency decisions, including the value of emergency ordering and the value of emergency production planning evaluates the effectiveness of the emergency decisions. Due to the staggering size of the problem, sample average approximation method is used to solve the problem.     

An interval-parameter two-stage stochastic integer programming model for environmental systems planning under uncertainty

An interval-parameter two-stage stochastic mixed integer programming (ITMILP) technique is developed for waste management under uncertainty. It is a hybrid of inexact two-stage stochastic programming and mixed integer linear programming methods. The ITMILP method can directly handle uncertainties expressed not only as probability density functions but also as discrete intervals. It can be used to analyse various policy scenarios that are associated with different levels of economic penalties when the promised policy targets are violated. More importantly, it can facilitate dynamic analysis of decisions on capacity expansion planning within a multi-region, multi-facility, multi-period, and multi-option context. The results will help to generate a range of decision alternatives under various system conditions, and thus offer insight into the trade-offs between environmental and economic objectives. The ITMILP method is applied to planning facility expansion and waste flow allocation within a waste management system. The results indicate that reasonable solutions have been generated for both binary and continuous variables. The binary-variable solutions represent the decisions of facility expansion, while the continuous-variable solutions are related to decisions on waste flow allocation.     

Choices in manufacturing strategy decision areas in batch production system – six case studies

The configuration of a production system can be described by the choices a firm makes in its manufacturing decision areas. Manufacturing strategy literature lacks empirical research in manufacturing decision areas. The current paper is an exploratory study using six case companies on alternative configurations that can exist in a batch production system. Choices made in decisions such as layout, shop floor control, etc., were found to be similar for all six companies that use batch process. However, there were a number of decisions that were found to be non-process specific and are influenced by product complexity, important competitive priorities, strategic orientation of manufacturing, top management decisions and the size of the company. The paper concludes with managerial implications and future research directions.     

疫情演变不确定情境下考虑多参考点的应急医疗用品生产决策模型

针对疫情演变不确定情境下考虑多种决策信息和企业社会责任的应急医疗用品生产决策问题，提出一种基于累积前景理论的考虑多个参考点的应急医疗用品生产决策模型。综合考虑企业经济目标和社会责任对生产决策的影响，基于决策者的风险规避、参考依赖和损失厌恶等心理特征，建立多个参考点以表征疫情演变方向不确定时决策者期望的不确定性。将含有多种评价信息的决策矩阵归一化处理，利用不同演变方向的参考点，建立益损决策矩阵。根据决策者面对收益和损失的态度，建立主观价值矩阵，依据主观概率函数得到扭曲后演变方向的概率。计算各个生产方案的累积前景值并对其进行优先排序。通过算例验证模型的有效性，进行对比分析和敏感性分析证明模型的优越性。     

基于三支决策的双激素人工胰脏模型预测控制算法研究

目前,关于双激素人工胰脏子系统切换机制的研究较少,并且现有研究在运行效率和群体控制质量上有待提高.以三支决策理论和模型预测控制算法为依托,提出一种基于三支决策的双激素人工胰脏模型预测控制算法.该算法利用三支决策理论设计3个子系统之间的切换规则,该规则通过设计包含经济代价和高低血糖风险的代价矩阵,计算切换到每个子系统的决...     

An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis

Selecting a proper machine tool is one of the important decisions a company has to make. Companies which fail to do so face many problems which negatively affect the firm's productivity, flexibility, precision and its responsiveness capabilities. Selection of a machine tool involves a lot of criteria to be simultaneously studied and so it requires a multi-criterion decision making (MCDM) method to solve it. Also the subjectivity involved in such decisions ask for the use of theories such as fuzzy and grey which are very effective in handling subjective inputs. This paper integrates the fuzzy analytic hierarchy process (AHP) and grey relational analysis approaches for the selection of a machine tool from a given set of alternatives. Fuzzy AHP is used to calculate the priority weights of the criteria. Subsequently grey relational analysis (GRA) is employed to rank the alternatives. A well known problem existing in literature has been picked up for the numerical illustration. The results obtained in this paper are better when compared with that existing in literature.     

Extensive comparison of biodiesel production alternatives with life cycle,PESTLE and multi-criteria decision analyses

Biodiesel production showed an immense increase worldwide in the past decade. Since the comprehensive analyses of biodiesel production processes and their comparative evaluation are both rare and not informative enough, e.g., for scientists and decision makers, in this work different, favored biodiesel production alternatives (rapeseed, soybean and palm) are analyzed from multiple viewpoints and compared. A complex examination is carried out with Political, Economic, Social, Technological, Legal and Environmental (PESTLE) analysis, where cradle-to-grave life cycle analysis is incorporated and performed within PESTLE factors. Life cycle inventory is set up based on Ecoinvent 3.3 database, while life cycle impact assessments are achieved by IPCC 2013, IMPACT 2002+, EPS 2000 and 2015dx methods. Monte Carlo analysis is also carried out in order to make certain about the robustness of input data. The investigated factors are weighted and ranked with multi-criteria decision analysis, wherein Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method is applied for the comparison of alternatives. Our work presents a clear methodology for the comprehensive evaluation of biodiesel production alternatives, but the guideline can be followed for the evaluation of other production alternatives. In spite that the life cycle analysis shows the palm oil as the best alternative, the results of our comprehensive analysis show that the highest overall TOPSIS score can be achieved with rapeseed-based biodiesel pathway, especially for the European region.     

Survey of methods to visualize alternatives in multiple criteria decision making problems

When solving decision problems where multiple conflicting criteria are to be considered simultaneously, decision makers must compare several different alternatives and select the most preferred one. The task of comparing multidimensional vectors is very demanding for the decision maker without any support. Different graphical visualization tools can be used to support and help the decision maker in understanding similarities and differences between the alternatives and graphical illustration is a very important part of decision support systems that are used in solving multiple criteria decision making problems. The visualization task is by no means trivial because, on the one hand, the graphics must be easy to comprehend and not too much information should be lost but, on the other hand, no extra unintentional information should be included. In this paper, we survey and analyze different ways of visualizing a small set of discrete alternatives graphically in the context of multiple criteria decision making. Some of the ways discussed are widely used and some others deserve to be brought into a wider awareness. This survey provides a starting point for all those who deal with multiple criteria decision making problems and need information of what kind of visualization techniques could be put to use in order to support the decision maker better.     

A rule-based,object-oriented framework for operating flexible manufacturing systems

A methodology for operating a flexible manufacturing system (FMS) is presented which consists of a decomposition of the problem, where heuristics, rules, and simulation are integrated to make the control decisions needed for random FMS's. Expert systems are used to make operating decisions, to choose heuristics, and make operating policies. Multiple passes of discrete-event simulation are employed to evaluate decision alternatives. Object-oriented programming provides a suitable environment to implement the integrative framework     

A method for decision making using sustainability indicators

Calculations aimed at representing the thought process of decision makers are common within multiobjective decision support tools. These calculations that mathematically describe preferences most often use weighting factors for each desire or objective to combine various utility scores onto a single scale to allow a ranking of alternatives. However, seldom are the tradeoffs implied in creating a single scale for multiple objectives described explicitly. This paper illustrates how choices for combining utility scores are in fact a statement of equivalence between the weighted utility scores of these objectives, even if the choice of weighting factors was intended to be value free or “equal weighting.” In addition, relationships between objectives, perhaps developed by stakeholders, can be rewritten as a series of equations (i.e., relationships) for the weighting factors, where it should be noted that seldom will stakeholders provide a set of relationships that exactly match the number of unknowns. Depending on the number of relationships specified, the weighting factors can be underdetermined, unique, or overdetermined. Calculations using the singular value decomposition method can be used as a general method to determine the weighting factors for each of these situations, allowing for explicit representations of the implied tradeoffs for decision makers. Finally, a simple but powerful method for calculating total utility using marginal rates of substitution between utility scores rather than weighting factors is presented. In addition to using marginal rates of substitution, the calculation of utility can be done with (process) attribute values or using EPA’s GREENSCOPE tool sustainability indicator scores. Utility calculations based on these more intuitive factors (marginal rates of substitution, attribute values, and/or GREENSCOPE indicator scores) can then be used to evaluate various alternatives. The decision maker can see the effects of changing the marginal rates of substitution (i.e., utility tradeoffs) and attribute (i.e., design or operating parameter) values or GREENSCOPE indicator scores for alternatives. While an example from chemical production for terephthalic acid is presented, the methods shown are generally applicable.     

A review of biodiesel production from microalgae

As the search for alternatives to fossil fuels continues, microalgae have emerged as a promising renewable feedstock for biodiesel. Many species contain high lipid concentrations and require simple cultivation—including reduced freshwater and land area needs—compared to traditional crops used for biofuels. Recently, technological advancements have brought microalgae biodiesel closer to becoming economically feasible through increased efficiency of the cultivation, harvesting, pretreatment, lipid extraction, and transesterification subsystems. The metabolism of microalgae can be favorably manipulated to increase lipid productivity through environmental stressors, and “green” techniques such as using flue gas as a carbon source and wastewater as a media replacement can lower the environmental impact of biodiesel production. Through life cycle assessment and the creation of process models, valuable insights have been made into the energy and material sinks of the manufacturing process, helping to identify methods to successfully scale up microalgae biodiesel production. Several companies are already exploring the microalgae industry, offsetting operating costs through isolation of co-products and careful unit operation selection. With numerous examples drawn from industry and the literature, this review provides a practical approach for creating a microalgae biodiesel facility.     

Maximization of System Reliability with a Choice of Redundancy Strategies

Optimal solutions to the redundancy allocation problem are determined when either active or cold-standby redundancy can be selectively chosen for individual subsystems. This problem involves the selection of components and redundancy levels to maximize system reliability. Previously, solutions to the problem could only be found if analysts were restricted to a predetermined redundancy strategy for the complete system. Generally, it had been assumed that active redundancy was to be used. However, in practice both active and cold-standby redundancy may be used within a particular system design and the choice of redundancy strategy becomes an additional decision variable. Available optimization algorithms are inadequate for these design problems and better alternatives are required. The methodology presented here is specifically developed to accommodate the case where there is a choice of redundancy strategy. The problem is formulated with imperfect sensing and switching of cold-standby redundant components and k -Erlang distributed time-to-failure. Optimal solutions to the problem are found by an equivalent problem formulation and integer programming. The methodology is demonstrated on a well-known test problem with interesting results. The optimal system design is distinctly different from the corresponding design obtained with only active redundancy. The availability of this tool can result in more reliable and cost-effective engineering designs.     

Strategic Thrust of Manufacturing Automation Decisions: A Conceptual Framework

American manufacturing firms increasingly consider automation as the means to increase productivity and improve their competitiveness. However, decisions to automate do not always produce the expected results and lead to further frustration. The so called “islands of automation” are often blamed for such failures and other technological alternatives such as computer integrated manufacturing (CIM) are adopted on a piece-meal fashion. This paper examines the adoption of new technology in general, and die decision to automate in particular, from a strategic perspective. In order to deal with the underlying complexities of automation, a conceptual framework is developed that can guide decision makers through a step-by-step process. Within an experimental analysis context, a decision support system is built that uses the “Expert Choice” cell to test the effectiveness of the introduced framework. The results indicate that the study's method can greatly enhance the decision making capabilities of firms contemplating automation     

Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand

The hierarchical structure of production planning has the advantage of assigning different decision variables to their respective time horizons and therefore ensures their manageability. However, the restrictive structure of this top-down approach implying that upper level decisions are the constraints for lower level decisions also has its shortcomings. One problem that occurs is that deterministic mixed integer decision problems are often used for long-term planning, but the real production system faces a set of stochastic influences. Therefore, a planned utilisation factor has to be included into this deterministic aggregate planning problem. In practice, this decision is often based on past data and not consciously taken. In this paper, the effect of long-term forecast error on the optimal planned utilisation factor is evaluated for a production system facing stochastic demand and the benefit of exploiting this decision’s potential is discussed. Overall costs including capacity, backorder and inventory costs, are determined with simulation for different multi-stage and multi-item production system structures. The results show that the planned utilisation factor used in the aggregate planning problem has a high influence on optimal costs. Additionally, the negative effect of forecast errors is evaluated and discussed in detail for different production system environments.     

A study of convergence in decentralized design processes

The decomposition and coordination of decisions in the design of complex engineering systems is a great challenge. Companies who design these systems routinely allocate design responsibility of the various subsystems and components to different people, teams or even suppliers. The mechanisms behind this network of decentralized design decisions create difficult management and coordination issues. However, developing efficient design processes is paramount, especially with market pressures and customer expectations. Standard techniques to modeling and solving decentralized design problems typically fail to understand the underlying dynamics of the decentralized processes and therefore result in suboptimal solutions. This paper aims to model and understand the mechanisms and dynamics behind a decentralized set of decisions within a complex design process. By using concepts from the fields of mathematics and economics, including Game Theory and the Cobweb model, we model a simple decentralized design problem and provide efficient solutions. This new approach uses matrix series and linear algebra as tools to determine conditions for convergence of such decentralized design problems. The goal of this paper is to establish the first steps towards understanding the mechanisms of decentralized decision processes. This includes two major steps: studying the convergence characteristics and finding the final equilibrium solution of a decentralized problem. Illustrations of the developments are provided in the form of two decentralized design problems with different underlying behavior.     

Decision support system for vendor managed inventory supply chain: a case study

Vendor-managed inventory (VMI) is a widely used collaborative inventory management policy in which manufacturers manages the inventory of retailers and takes responsibility for making decisions related to the timing and extent of inventory replenishment. VMI partnerships help organisations to reduce demand variability, inventory holding and distribution costs. This study provides empirical evidence that significant economic benefits can be achieved with the use of a genetic algorithm (GA)-based decision support system (DSS) in a VMI supply chain. A two-stage serial supply chain in which retailers and their supplier are operating VMI in an uncertain demand environment is studied. Performance was measured in terms of cost, profit, stockouts and service levels. The results generated from GA-based model were compared to traditional alternatives. The study found that the GA-based approach outperformed traditional methods and its use can be economically justified in small- and medium-sized enterprises (SMEs).