USING ARTIFICIAL INTELLIGENCE TECHNIQUES TO ENHANCE THE CAPABILITIES OF MODEL MANAGEMENT SYSTEMS

Decision Support Systems (DSSs) originally were proposed as interactive problem-solving vehicles through which models and analytical techniques could be made available to decision makers. Model management represents a line of research within the DSS field that focuses on the design and implementation of software environments to support the use of DSSs for this purpose. This paper describes the tasks that must be supported within these environments and illustrates how existing prototype Model Management Systems (MMSs) implementations provide this support. The use of artificial intelligence techniques in such implementations are reveiwed, and three scenarios are presented to show how future MMSs could be constructed using these techniques.     

THE ECONOMICS OF CAPACITY AND MARKETING MEASURES IN A SIMPLE MANUFACTURING ENVIRONMENT

As manufacturing firms push to achieve shorter lead times and higher levels of customer service, the basic capability of underlying manufacturing processes must be reexamined. The capacity and operational variability of a process dictate a certain set of realistic performance goals. In this paper, we examine this fundamental relationship from an economic perspective using two levels of analysis. At the aggregate level, we model the manufacturing process as a single server queue and compare the traditional roles of marketing and manufacturing in setting performance and process design parameters. Insights gained at this level are incorporated into the analysis of a realistic multiserver, multistation manufacturing line. We develop an interaction decision tool to guide the selection of process and performance parameters in this more complex environment.     

Linking IT Applications with Manufacturing Strategy: An Intelligent Decision Support System Approach

Research has indicated the importance of matching Information Technology (IT) applications or manufacturing systems with the competitive strategy of a company. Selection of the right type of IT application is, however, a challenging task. When a company with a given dominant process structure emphasizes two or more competitive priorities, such as quality, product flexibility, etc., an unaided manager faces a complex decision problem in choosing from alternative IT applications available in the areas of product design through distribution. In this paper, we develop an Intelligent Decision Support System (IDSS) to assist managers with assessment of the relative importance of competitive priorities in their organization, evaluation of the fit between the competitive priorities and their dominant process structure, and identification of the IT applications that are consistent with both the competitive priorities and the process structure. The IDSS is comprised of an interactive user interface, a knowledge database, a decision model, and a Knowledge-Based System (KBS) that was developed using the 1st Class KBS shell. Validation of the system illustrates that its performance is as good as the human expert, and it has the potential to facilitate effective and swift decision making in the selection of appropriate IT applications that best match an organization's manufacturing strategy. The choice and use of the right type of IT application should provide a company with a competitive edge.     

Adaptive versus Proactive Behavior in Service Recovery: The Role of Self‐Managing Teams

In this article, we develop a conceptual model of adaptive versus proactive recovery behavior by self‐managing teams (SMTs) in service recovery operations. To empirically test the conceptual model a combination of bank employee, customer, and archival data is collected. The results demonstrate support for independent group‐level effects of intrateam support on adaptive and proactive recovery behavior, indicating that perceptual consensus within service teams has incremental value in explaining service recovery performance. In addition, we provide evidence that adaptive and proactive recovery behavior have differential effects on external performance measures. More specifically, higher levels of adaptive performance positively influence customer‐based parameters (i.e., service recovery satisfaction and loyalty intentions), while employee proactive recovery behavior contributes to higher share of customer rates.     

THROUGHPUT TIME REDUCTION: TAKING ONE'S MEDICINE

Managing throughput time and its relation to work-in-process (wip) inventory and customer service is the focus of this paper. This research combines theory, simulation results, and the analysis of corporate data in an effort to address the issues associated with how one company (Eli Lilly) managed a reduction in their throughput times and an improvement in their delivery reliability. The results for this company suggest that production control decisions—expediting and de-expediting—can lead to a vicious circle of decisions, which in turn can lead to increased levels of WIP inventory and higher and more unpredictable throughput times.     

The Influences of User Characteristics on Performance in a Logistics DSS Application

Decision Support Systems (DSS) are widely used in logistics decision applications, and a large number and variety of systems are commercially available. We investigate the contributions of user characteristics including experiences, data preferences, intuition, and effort to decision performance in a logistics DSS context. The study includes a laboratory experiment in which decision makers with varied experiences used a DSS to make facility network design decisions for problems of varying complexity. Two variants of the DSS are utilized in order to examine the interactions of a DSS decision aid with user characteristics. We find that intuition and effort are associated with decision-making performance. High analytic ability is not related to intuition, however. Education and previous experience are associated with performance. Yet these characteristics are also unrelated to intuition. Decision makers who highly value disaggregated data provided by the DSS tend to perform poorly. Also, the results suggest that the effects of users' experiences and preferences on performance are influenced by an analytical decision aid.     

Using Data Envelopment Analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system

The competitive nature of the business environment requires the productivity-driven organization to be aware of its relative level of effectiveness and efficiency vis-à-vis its competitors. This suggests the need, first, for an effective mechanism that allows for discovering appropriate productivity models for improving overall organizational performance, and, second for a feedback-type mechanism that allows for evaluating multiple productivity models in order to select the most suitable one. In this paper our focus is on organizations that consider the states of their internal (e.g., possibly exemplified by resource-based view) and external (e.g., possibly exemplified by positioning) organizational environment in the formulation of their strategies. We propose and test a DEA-centric Decision Support System (DSS) that aims to assess and manage the relative performance of such organizations.     

Performance-Net: A Decision Support System for Reconfiguring a Bank's Branch Network

This paper presents a Decision Support System (DSS) that enables the management of a retail bank to evaluate and reconfigure its branch network. The DSS uses computational methods and knowledge that arises from information about the bank's operational and fixed costs, as well as demographic characteristics from the geographical area where branches are located. The DSS that we call Performance-Net estimates the performance of a branch network and determines the optimum number of branches and the optimum mix of services that each one should provide in order to maximize the bank's revenue- generating measures. Its computational engine is based on a linear programming optimization model and its implementation is developed using the standard MS Excel program. Performance-Net provides efficient solutions, is particularly user-friendly and can reach excellent answers for a wide variety of “what if” parametric scenarios.     

The Efficacy of a Decision Support System in Enhancing Risk Assessment Performance

A pervasive challenge for decision‐makers is evaluating data of varying form (e.g., quantitative vs. qualitative) and credibility in arriving at an overall risk assessment judgment. The current study tests the efficacy of a Decision Support System (DSS) for facilitating auditors’ evaluation and assimilation of financial and nonfinancial information in accurately assessing the risk of material misstatements (RMM) in financial information. Utilizing the proximity compatibility principle, the DSS manipulates the display of cues either in an integral (where pieces of information are displayed on one computer screen) or separable (where pieces of information are displayed on different computer screens) format. Based on cognitive fit theory, we expect that the integral (separable) display best supports financial (nonfinancial) information processing, leading to enhanced risk assessment performance. In addition, we predict that consistent DSS display of financial and nonfinancial information facilitates risk assessment performance. Further, this study accentuates the importance of auditors’ preference for presentation of financial and nonfinancial information and consistent presentation of all the information in strengthening the effect of DSS display format on risk assessment performance. We design a case which includes a seeded high fraud risk. A total of 112 audit seniors participated in the experiment where the DSS display format was manipulated and the auditors’ RMM assessments and display preferences were measured. The results support the hypotheses and highlight the value of the DSS in enhancing risk assessment performance.     

Distributed manufacturing support systems: the integration of distributed group support systems with manufacturing support systems

Current Manufacturing Support Systems (MSS), such as Computer-aided Manufacturing (CAM) Systems, Computer-aided Design (CAD) Systems, Computer Integrated Manufacturing (CIM) Systems, Material Resource Planning (MRP) Systems, and Manufacturing Accounting Control (MAC) Systems, are mostly independent systems that are operated in limited decision spaces, provide mainly formal and quantitative information, and thus pursue a goal of local optimization. To assist modern manufacturing in meeting the needs for integration, communication, collaboration, and decision making, we introduce the concept of integrating MSS with Distributed Group Support System (DGSS) into a Distributed Manufacturing Support System (DMSS). A rigorous system design approach is taken to model the manufacturing information requirements from a global perspective and pattern decision making processes within the structural (organizational design) and infrastructural (information system design) elements of manufacturing. The result is a conceptual DMSS design that provides an intelligent interface, accommodates incremental manufacturing integration, offers controllable message exchange facilities, and allows configurable communication networks.     

On Inventory Abatement via Manufacturing Randomness Reductions*

Under a continuous improvement framework, the policy of abating inventory via reductions in manufacturing randomness is considered. To explore this policy, a model of a real-world production-inventory system is developed, tested, and studied. The results suggest that manufacturing randomness reductions, even substantial ones, may not necessarily lead to inventory abatement, and paradoxically may lead sometimes to an inventory increase. In these cases, however, manufacturing randomness reductions will translate into higher customer service levels.     

A model-driven DSS architecture for delivery management in collaborative supply chains with lack of homogeneity in products

Uniform product deliveries are required in the ceramic, horticulture and leather sectors because customers require product homogeneity to use, present or consume them together. Some industries cannot prevent the lack of homogeneity in products in their manufacturing processes; hence, they cannot avoid non-uniform finished products arriving at their warehouses and, consequently, fragmentation of their stocks. Therefore, final uniform product amounts do not match planned production ones, which frequently makes serving previous committed orders with homogeneous quantities impossible. This paper proposes a model-driven decision support system (DSS) to help the person in charge of delivery management to reallocate the available real inventory to orders to satisfy homogenous customer requirements in a collaborative supply chain (SC). The DSS has been validated in a ceramic tile collaborative SC.     

Decision support systems: The reality that seems hard to accept?

The authors have identified 350 articles written and published on Decision Support Systems (DSS) since 1970. 77.3% of those articles were published between January 1980 and November 1983. Many critics see this sudden surge in the DSS literature as no more than a fad and the term itself as no more than a label. This paper examines the reasons behind the emergence of DSS in the light of this sudden surge in the DSS literature and seeks to determine whether DSSs are unique or merely a repackaging of Management Science/Operational Research and Management Information systems.     

Planned Lead Times in Multistage Systems

This study investigates the impact of planned lead times on performance in multistage manufacturing where material requirements planning is used in a make-to-stock environment. We simulate a variety of different operating environments and find: (1) planned lead times are important to customer service levels under all operating environments examined, but have a smaller impact on inventory investment; (2) tight due dates introduced by short planned lead times hurt customer service without saving much inventory; (3) small increases to tight planned lead times improve customer service substantially with small inventory increases; (4) co-component inventories change with planned lead times, and disparity between such inventories is a sign of poor timing coordination; (5) the fixed order quantity rule performs better than the periodic order quantity rule; and (6) tall product structure and large lot sizes require particular attention to planned lead times. The findings also extend the current understanding of planned lead times by including uncertainties such as forecast error, yield loss, and equipment reliability. The study concludes with a way to diagnose and improve poorly set planned lead times.     

The Role of Automation and Labor in Determining Customer Satisfaction in a Telephone Repair Service Process

Telephone repair process and performance relationships are modeled using a structural equation approach. The basic structure of the causal model is a function of process flow. The data analysis and modeling approach advocated here can be applied to any manufacturing or service process that has a recurrent process flow and good internal and external performance data. Our primary objectives are to define quantitatively (1) how the performance of repair persons dispatched to the customers' premises is related to automatic testing and repair, and (2) how internal process performance is linked to external customer satisfaction. A secondary objective of this article is to explain briefly some of the practical problems of modeling process-level performance relationships. Results indicate that the structural equation models are statistically significant and have a meaningful interpretation. Examples of “what if” scenarios using these structural equation models are presented to help managers predict customer satisfaction levels given certain process performance improvements, set standards of performance, and better understand the interdependencies between labor and automation resources in this telephone repair process.     

Simulation Analysis of a Manufacturing Supply Chain*

A supply chain is a series of manufacturing plants that transform raw material into finished product. A pipeline within a supply chain refers to the stream of information, material, components, and assemblies that are associated with a particular product. It is typical for manufacturing plants to put considerable effort to optimize the performance of a horizontal slice of a supply chain (such as coordination among parts that share a common resource). The need to optimize the performance of the vertical slice (the supply chain connecting raw material to finished product) by controlling the transmission of schedule instability and the resulting inventory fluctuation is often overlooked. A schedule is stable if actual production requirements for a given period do not change from the forecast production requirements. Stable production schedules are important when managing supply chains as they help control inventory fluctuation and inventory accumulation. Failure to control schedule instability results in high average inventory levels in the system. In this paper a simulation analysis of supply chain instability and inventory is conducted, and it is shown how supply chains can be analyzed for continuous improvement opportunities using simulation. The focus is on a stamping pipeline in an automobile supply chain based on operating data from General Motors (GM). It is shown that the techniques used in this paper are a useful tool for supply chain analysis.     

Implementation of just-in-time methodology in a small company

This paper describes the implementation of JIT in a small manufacturing company and the benefits that resulted for the company's operations. Preliminary analysis identified various problems in the existing manufacturing operations. The pre-implementation and post-implementation conditions of the company arc detailed. The achievements of the JIT implementation included a reduction in material traversing, reduced lead limes and inventories leading to an overall reduction in the cost of manufacturing. A smooth flow of material from the raw material stage to the finished product stage was established. Three separate product lines were combined into a flexible manufacturing assembly line. With the adoption of a holistic approach to JIT implementation, it was found that even a small company can make significant strides towards world class manufacturing status. The experience gained by the company can encourage and benefit other small companies to embrace the JIT approach.     

基于成本/服务权衡的逆向物流网络设计问题研究

逆向物流网络设计是企业获得经济利益和提高竞争力的战略决策之一。本文针对售后服务逆向物流网络设计问题,考虑了同时优化物流网络运营成本和服务水平,提出了一个多目标整数规划模型来确定服务中心的个数、位置、容量等级以及客户区指派到服务中心的策略,并得到成本和服务之间的权衡关系。设计了一种混合多目标进化算法求解模型。通过实例分析,找出了影响物流网络设计的关键因素。通过算例测试,对混合多目标进化算法和ε约束法进行了比较,结果表明混合多目标进化算法对求解中大规模的问题是一种良好的方法。     

Decision model of customer order decoupling point on multiple customer demands in logistics service supply chain

The key factor in realising competitiveness of a logistics service supply chain (LSSC) is its ability to meet customised requirements with the cost of mass service. This paper introduces the concepts of mass customisation and customer order decoupling point (CODP) into the field of logistics service to solve the problem of CODP when a logistics service integrator (LSI) in an LSSC operates under mass customisation. Two models of CODP decision, respectively, on single and multiple customer demands, are developed. Both process constraints and lead time constraints from the customised logistics service orders are considered. Unlike CODP positioning models within the manufacturing supply chain, the objective of this paper is to exclusively consider the cost of order transferring and order waiting at CODP to minimise total cost of the LSI. The applications of the two models are also discussed by two real cases, and three major conclusions are drawn. First, introducing mass customisation into an LSSC and minimising total cost of order processing of the LSI by selecting an optimised CODP is feasible. Second, total cost will decrease monotonously along with the movement of CODP toward the last service procedure under the precondition of satisfying lead time constraints. Third, parameters about the customers’ logistics service orders will bring about great influence on the selection of CODP. For instance, the lead time of orders will influence the number of feasible CODPs and the position of optimal CODP. Also, decreasing cost of order transferring and order waiting will not cause the change of optimal CODP, while increasing cost of order transferring and order waiting will lead to the switch of optimal CODP.