Manufacturing performance measurement and target setting: A data envelopment analysis approach

Manufacturing decision makers have to deal with a large number of reports and metrics for evaluating the performance of manufacturing systems. Since the metrics provide different and at times conflicting assessments, it is hard for the manufacturing decision makers to track and improve overall manufacturing system performance. This research presents a data envelopment analysis (DEA) based approach for performance measurement and target setting of manufacturing systems. The approach is applied to two different manufacturing environments. The performance peer groups identified using DEA are utilized to set performance targets and to guide performance improvement efforts. The DEA scores are checked against past process modifications that led to identified performance changes. Limitations of the DEA based approach are presented when considering measures that are influenced by factors outside of the control of the manufacturing decision makers. The potential of a DEA based generic performance measurement approach for manufacturing systems is provided.     

Better management of blood supply-chain with GIS-based analytics

This paper presents a novel application of operations research, data mining and geographic information-systems-based analytics to support decision making in blood supply chain management. This, blood reserve availability assessment, tracking, and management system (BRAMS), research project has been funded by the Office of the Secretary of Defense. (This DoD funded SBIR project is performed by the researchers at Knowledge Based Systems, Inc. (KBSI).) The rapidly increasing demand, criticality of the product, strict storage and handling requirements, and the vastness of the theater of operations, make blood supply-chain management a complex, yet vital problem for the department of defense. In order to address this problem a variety of contemporary analytic techniques are used to analyze inventory and consumption patterns, evaluate supply chain status, monitor performance metrics at different levels of granularity, and detect potential problems and opportunities for improvement. The current implementation of the system is being actively used by 130 mangers at different levels in the supply chain including facilities at Osan Air Force Base in South Korea and Incirlik Air Force Base in Turkey.     

Approximate analysis and optimization of batch ordering policies in capacitated supply chains

Devising manufacturing/distribution strategies for supply chains and determining their parameter values have been challenging problems. Linking production management to stock keeping processes improves the planning of the supply chain activities, including material management, culminating in improved customer service levels. In this study, we investigate a multi-echelon supply chain consisting of a supplier, a plant, a distribution center and a retailer. Material flow between stages is driven by reorder point/order quantity inventory control policies. We develop a model to analyze supply chain behavior using some key performance metrics such as the time averages of inventory and backorder levels, as well as customer service levels at each echelon. The model is validated against simulation, yielding good agreement of robust performance metrics. The metrics are then used within an optimization framework to design the supply chain so as to minimize expected total system costs. The outcome of the optimization framework specifies how to move inventory throughout the supply chain and how to set inventory control parameters, i.e., reorder levels and replenishment batch sizes.     

以费用为独立变量的随机机会约束规划模型研究

美军提出的"以费用为独立变量(CAIV)"方法从论证阶段开始,就强调需求、性能与费用三者之间的平衡,并采用费用-效能综合权衡优化模型进行系统费用和性能参数的最优决策.但是该模型没有考虑采办过程中的不确定因素,且由于决策者往往很关心权衡结果中费用与性能的置信水平问题,因此提出要将采办中的不确定性因素作为随机变量引入权衡模型,建立一种综合权衡优化的随机机会约束规划模型,并进行了示例验证.     

Nursing workload measurement as management information

Goal of the study presented in this paper was to balance the supply and demand of nursing care at nursing units within general hospitals. A ‘management control framework’ is developed, containing the relevant decision levels, the goal variable and the information needed to control the balance between supply and demand. A nursing workload measurement instrument is introduced, and an experiment is set up to test the performance of the framework and the measurement system in the daily practice of eight nursing units in two hospitals, during 20 consecutive weeks. Intervention has taken place in both the staffing and patient planning processes. The effects upon the goal variable has been measured. The variation coefficient of the work pressure is used as an indicator for the stability of the balance. The results of the experiment are presented.     

Resource-based strategies and problem structuring: using resource maps to manage resource systems

The resource-based view of strategy seeks to explain why some firms consistently outperform rivals in the same industry by acquiring a unique set of strategic assets (or resources). However, differences between dominant managerial mental models in management teams lead to disagreement at the moment of implementing distinct resource-building strategies. This managerial and cognitive view of strategic decision making and competition lends itself to investigation through problem structuring methods. We suggest that resource maps, as a problem structuring method, can be used to interpret managerial mental models for strategic decision making in terms of resource-building processes. Through resource maps, we represent the system of asset stocks believed to be most important for driving business performance. We illustrate the framework by comparing and contrasting maps of the system of resources (asset stocks) that best characterize two leading firms in the UK commercial radio broadcasting industry.     

A distance friction minimization approach in data envelopment analysis: A comparative study on airport efficiency

This paper aims to present a newly developed distance friction minimization (DFM) method in the context of data envelopment analysis (DEA) in order to generate an appropriate (non-radial) efficiency-improving projection model, for both input reduction and output increase. In this approach, a generalized distance function, based on a Euclidean distance metric in weighted spaces, is proposed to assist a decision making unit (DMU) to improve its performance by an appropriate movement towards the efficiency frontier surface. A suitable form of multidimensional projection function for efficiency improvement is given by a Multiple Objective Quadratic Programming (MOQP) model. The paper describes the various steps involved in a systematic manner.     

A case-based fuzzy multicriteria decision support model for tropical cyclone forecasting

This paper proposes a multi-stage framework for intelligent decision support. The proposed framework integrates case-based reasoning and fuzzy multicriteria decision making techniques. It potentially leads to more accurate, flexible and efficient retrieval of alternatives that are most similar and most useful to the current decision situation. Additionally, the framework provides intelligent assistance in articulating domain expert's preferences through outranking relations. We illustrated the proposed approach in the context of tropical cyclone prediction. Ten years of historical observation data about tropical cyclones was represented within fuzzy multicriteria decision-making problem. We describe a prototype intelligent decision support system, which helps the forecaster in retrieving best-fitted solutions in terms of both usefulness and similarity to the current observed case.     

Functionality defense through diversity: a design framework to multitier systems

Diversification is one of the most effective approaches to defend multitier systems against attacks, failure, and accidents. However, designing such a system with effective diversification is a challenging task because of stochastic user and attacker behaviors, combinatorial-explosive solution space, and multiple conflicting design objectives. In this study, we present a systematic framework for exploring the solution space, and consequently help the designer select a satisfactory system solution. A simulation model is employed to evaluate design solutions, and an artificial neural network is trained to approximate the behavior of the system based on simulation output. Guided by a trained neural network, a multi-objective evolutionary algorithm (MOEA) is proposed to search the solution space and identify potentially good solutions. Our MOEA incorporates the concept of Herbert Simon??s satisficing. It uses the decision maker??s aspiration levels for system performance metrics as its search direction to identity potentially good solutions. Such solutions are then evaluated via simulation. The newly-obtained simulation results are used to refine the neural network. The exploration process stops when the result converges or a satisfactory solution is found. We demonstrate and validate our framework using a design case of a three-tier web system.     

Solving multi-metric network problems: An interplay between idempotent semiring rules

We motivate computations in a multifunctional networked system as instances of algebraic path problems on labeled graphs. We illustrate, using examples, that composition operators used in many function computations in a networked system follow semiring axioms. We present an abstract framework, using a special idempotent semiring algebraic path problem, to handle multiple metrics for composition. We show that using different vector order relations in this abstract framework, we can obtain different rules of compositions such as Pareto, lexicographic and max-order efficiency. Under this framework, we identify a class of tractable composition rules that can be solved in different multi-criteria settings at affordable computational cost. We demonstrate using an example of trusted routing in which logical security rules of admission control can be combined with delay performance metrics in the multi-criteria optimization framework.     

Computer aided decision support system for water strategic planning in Jordan

The Nominal Group Technique and a multi-criteria decision aid software are utilised to develop a decision support system for strategic planning of water resources in Jordan. The system described is novel in that it integrates the various decision analytical management techniques in order to increase the flexibility and efficiency of the decision making process.     

Representation of decision making processes in an ecological evaluation system

This paper deals with the problems of knowledge representation for a decision support system (DSS) applicable in a dynamic environment. Some special principles concerning environment applications are considered in order to understand better human decision making and behavior. The approach of representing static and dynamic aspects of a system and reflecting them using deep knowledge representation is proposed. The formalization of multiple objective decision making mechanisms is considered. The results of modeling cognitive processes leading to decisions are demonstrated by an example developed during the design stages of an ecological evaluation system.     

The sugar production factory—A dynamic decision task

This article describes the Sugar Production Factor and its structural equivalent, the Personal Interaction task. These are two simple, individual dynamic decision-making tasks in which subjects make interdependent decisions to reach a goal, and receive feedback on the outcome of their efforts along the way. An important result from human learning experiments using these two tasks and their variants is that subjects reliably improve their ability to reach the goal over a moderate number of training trials (40–90) but do not show consistent improvement in other measures of task knowledge. These other measures focus on subjects' ability to accurately predict the task environment's response to their actions and subjects' ability to produce useful heuristics. This pattern of results runs counter to the idea that decision makers' performance in dynamic decision tasks depends critically on the predictive accuracy their internal models of the task environment. Variants of both tasks have been used to manipulate this pattern of results and explore more deeply the nature of the internal models that subjects form of the task environment. These variants are discussed in the context of other relevant findings in the dynamic decision making literature.     

Optimising a value-based performance indicator in mid-term sales and operations planning

Economic Value Added (EVA^®) and corresponding value driver trees are prevalent frameworks of value-based management to measure and analyse shareholder value creation. However, they are explanatory models from an operations research perspective and do not provide decision support for performance optimisation. In this paper, we develop a comprehensive value-based decision framework for mid-term sales and operations planning (S&OP) in the supply chain implementing EVA as the objective function. The pivotal element of our framework is a decision-oriented extension of EVA-based value driver trees bridging the gap to the decision variables of S&OP as the operational performance levers. We utilise a numerical example to highlight the significant improvement potential due to the value-based optimisation approach. Working capital management emerges as the major mid- to short-term value driver in the supply chain.     

A reliability-based manufacturing process planning method for the components of a complex mechatronic system

Uncertainties in the values of the parameters of a system can originate from the manufacturing tolerances of the system components, which can produce a degree of unreliability in the performance of the system. A systematic framework for realistic reliability assessment of an electro-hydraulic servo system has been presented in this paper with the objective of providing adequate information for the selection of the best manufacturing process for each of the servo valve components. Monte Carlo simulation has been employed to evaluate the effect of these uncertainties of the servo valve parameters on the statistical performance of the system. Possible manufacturing processes have been introduced for each component and the justifiability of using each one has been discussed based on the estimated reliability of the system.     

Optimal inspection and perfect repair

Models are developed for decision making about monitoring andmaintenance of systems whose performance through time is describedby a general stochastic process. The system is monitored andpreventive and corrective maintenance actions are carried outin response to the observed system state. The decision processis simplified by using the maximum process as a decision variable.The models developed generalize age replacement models and othersimple maintenance strategies. The approach can deal with failuresthat prevent the system functioning further, and also failuresthat are defined by regulation or economic considerations. Attentionis restricted to perfect repair and inspection, but the structureprovides the framework for further developments.     

Multi-objective evolutionary algorithm for donor–recipient decision system in liver transplants

This paper reports on a decision support system for assigning a liver from a donor to a recipient on a waiting-list that maximises the probability of belonging to the survival graft class after a year of transplant and/or minimises the probability of belonging to the non-survival graft class in a two objective framework. This is done with two models of neural networks for classification obtained from the Pareto front built by a multi-objective evolutionary algorithm – called MPENSGA2. This type of neural network is a new model of the generalised radial basis functions for obtaining optimal values in C (Correctly Classified Rate) and MS (Minimum Sensitivity) in the classifier, and is compared to other competitive classifiers. The decision support system has been proposed using, as simply as possible, those models which lead to making the correct decision about receptor choice based on efficient and impartial criteria.     

Investing in product development and production capabilities: The crucial linkage between time-to-market and ramp-up time

Shorter product life cycles, more rapid product obsolescence, and the increasing intensity of global competition have driven firms to strive for a more rapid introduction of new products to market. We introduce a normative model which yields insights concerning several key new product development (NPD) decisions. First, we examine investment strategies related to the timing and duration for investments in both design and process capacity over a given planning horizon. Second, the model offers guidance regarding the optimal time-to-market and ramp-up time necessary to meet peak demand for the new product. The model thus provides both theoretical and managerial insights into the crucial linkage between time-to-market and ramp-up time decisions. Finally, the implications of several specific NPD investment mechanisms on these NPD metrics are explored.     

The impact of controllable production rates on the performance of inventory systems: A systematic review of the literature

The lot sizing problem has attracted the attention of researchers for more than a century, and it still belongs to the most relevant decision problems in many manufacturing companies. During the evolution of research on lot sizing, the seminal economic order quantity (EOQ) model proposed by Harris [1913. How many parts to make at once. Factory, the Magazine of Management, 10 (2), 135-136.] has remained the most popular model, despite its limitations. To support lot sizing decisions in practice, researchers have frequently extended Harris’ basic EOQ model to better reflect the characteristics of real production processes. One of these extensions is the consideration of controllable (variable) production rates, which gives production planners more flexibility in managing the build-up and depletion of inventory and in controlling costs.The aim of this paper is to provide a comprehensive and systematic overview of EPQ-type lot sizing models that consider controllable production rates. First, the paper proposes a conceptual framework that captures the characteristics of controllable production rates including the planning horizon (short vs. long term), the number of potential interventions per production run (one vs. multiple), the effect of controllable production rates on the performance of the inventory system (e.g., unit production costs, energy consumption, product quality), and the type of lot sizing model considered (e.g., two-stage models, multi-stage models, multi-item models). Secondly, the paper presents the results of a systematic literature review and evaluates the state-of-research of lot sizing models with controllable production rates. Based on the analysis of the literature, key trends are summarized and promising research opportunities are discussed.     

Using simulation modelling to support decisions in hail suppression programmes with airborne means

This article presents the development and implementation of a stochastic discrete event simulation model. The project aims to analyse and improve the operational processes of a Hail Suppression Programme with airborne means. The main objective of the programme is the minimization of crop damages because of hailstorms. The project involves a simulation model as part of the decision support analysis, which investigates costs and effectiveness of various operational scenarios concerning the interception and neutralization of hailstorms, using airborne seeding. Apart from estimating responses and metrics under miscellaneous scenarios, the model facilitates what-if analysis to inspect and reveal relations between service (interception) times, number of aircrafts, and acceptable values of efficiency metrics. Subsequently, the model has been used to estimate a regression function that has been further implemented in the assessment process for new technologies and materials.