A micro-model of internal supply forecasting for technical manpower planning

The manpower planning process includes forecasting the future demand for manpower and the future internal supply of manpower and then developing action plans which will balance supply and demand. Many of the models which exist for forecasting internal supply are for periods of one year or longer, which makes them inappropriate for many project planning and short term human resource management applications. This paper presents an easily implemented short-range (12-month horizon) model for forecasting internal supply. Time series analysis techniques are used to identify seasonal patterns and trends which exist in the determinants of internal supply. These are employed in the development of an internal supply forecast at both the aggregate firm level and at the individual skill group level. Feasibility of the model is demonstrated using empirical data. Output of the model is useful for further manpower planning.     

Toward the design of an intelligent DSS for forecasting

The emergence of decision support systems (DSS), artificial intelligence (AI), and microcomputers in the information systems arena provides managers with some new insights to the planning and control of a productive system.

This paper illustrates how an intelligent microcomputer-based decision support system (MDSS) was developed to support demand forecasting.     

Tourism demand forecasting using novel hybrid system

Accurate prediction of tourism demand is a crucial issue for the tourism and service industry because it can efficiently provide basic information for subsequent tourism planning and policy making. To successfully achieve an accurate prediction of tourism demand, this study develops a novel forecasting system for accurately forecasting tourism demand. The construction of the novel forecasting system combines fuzzy c-means (FCM) with logarithm least-squares support vector regression (LLS-SVR) technologies. Genetic algorithms (GA) were optimally used simultaneously to select the parameters of the LLS-SVR. Data on tourist arrivals to Taiwan and Hong Kong were used. Empirical results indicate that the proposed forecasting system demonstrates a superior performance to other methods in terms of forecasting accuracy.     

An Information Handling Method for Corporate Planning Decision Support

This paper, by analyzing actual corporate planning procedures, proposes a method to classify management information used for decision making into several groups according to their characteristics. In order to select appropriate data as well as keep the data updated and readjusted to fit the continuously changing corporate planning environment, we emphasize the needs of information management by utilizing the classification proposed in this paper. An outline of the design concept of the data management system through the analysis of such characteristics on the actual data for planning in the oil refining industry is illustrated.     

Economic value added of supply chain demand planning: A system dynamics simulation

The development of a successful demand plan is typically a joint effort between different functional units such as Logistics, Marketing, Sales and executive management at one hand and between different business units on the other. Starting a project to structurally improve the demand planning often requires convincing all parties involved in such an effort. The key is to quantify the bottom-line impact of an increased demand planning reliability in the supply chain. This paper proposes a system dynamics simulation modeling framework that allows different managers to examine how improvements in their demand reliability will impact the overall corporate bottom-line. For example, supply chain managers can investigate how proposed changes in the supply chain demand forecasting structure, different suppliers, different logistics routes, or alternative inventory methods, may increase the overall profitability. The simulation model has been tested, validated with a real-life case of LG. Philips Displays Europe.     

Developing and assessing an intelligent forex rolling forecasting and trading decision support system for online e‐service

An effective foreign exchange (forex) trading decision is usually dependent on effective forex forecasting. In this study, an intelligent system framework integrating forex forecasting and trading decision is first proposed. Based on this framework, an advanced intelligent decision support system (DSS) incorporating a back‐propagation neural network (BPNN)‐based forex forecasting subsystem and Web‐based forex trading decision support subsystem is developed, which has been used to predict the directional change of daily forex rates and provide intelligent online decision support for financial institutions and individual investors. This article describes the forex forecasting and trading decision method, the system architecture, main functions, and operation of the developed DSS system. A comparative study is conducted between our developed system and others commonly used in order to assess the overall performance of the developed system. The assessment results show that our developed DSS outperforms some commonly used forex forecasting and trading decision systems and can provide intelligent e‐service for forex traders to make useful trading decisions in the forex market. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 475–499, 2007.     

A DSS generator for district planning

This paper describes the experience of the authors in designing a decision support system (DSS) for district planning. The iterative nature of the planning process, the need for flexible analysis of large volume of detailed data, the need for frequent interaction with the database, and the potential use of interactive graphics make the context ideal for design of a DSS. The evolution of a DSS Generator through several stages of software designs is highlighted. A proposal is then made for a DSS Generator which will add enough power to existing software that already provides the flexibility needed to cover many application domains in district planning.     

An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank

Developing decision support system (DSS) can overcome the issues with personnel attributes and specifications. Personnel specifications have greatest impact on total efficiency. They can enhance total efficiency of critical personnel attributes. This study presents an intelligent integrated decision support system (DSS) for forecasting and optimization of complex personnel efficiency. DSS assesses the impact of personnel efficiency by data envelopment analysis (DEA), artificial neural network (ANN), rough set theory (RST), and K-Means clustering algorithm. DEA has two roles in this study. It provides data to ANN and finally it selects the best reduct through ANN results. Reduct is described as a minimum subset of features, completely discriminating all objects in a data set. The reduct selection is achieved by RST. ANN has two roles in the integrated algorithm. ANN results are basis for selecting the best reduct and it is used for forecasting total efficiency. Finally, K-Means algorithm is used to develop the DSS. A procedure is proposed to develop the DSS with stated tools and completed rule base. The DSS could help managers to forecast and optimize efficiencies by selected attributes and grouping inferred efficiency. Also, it is an ideal tool for careful forecasting and planning. The proposed DSS is applied to an actual banking system and its superiorities and advantages are discussed.     

A spiking neural network (SNN) forecast engine for short-term electrical load forecasting

Short-term load forecasting (STLF) is one of the planning strategies adopted in the daily power system operation and control. All though many forecasting models have been developed through the years, the uncertainties present in the load profile significantly degrade the performance of these models. The uncertainties are mainly due to the sensitivity of the load demand with varying weather conditions, consumption pattern during month and day of the year. Therefore, the effect of these weather variables on the load consumption pattern is discussed. Based on the literature survey, artificial neural networks (ANN) models are found to be an alternative to classical statistical methods in terms of accuracy of the forecasted results. However, handling of bulk volumes of historical data and forecasting accuracy is still a major challenge. The development of third generation neural networks such as spike train models which are closer to their biological counterparts is recently emerging as a robust model. So, this paper presents a load forecasting system known as the SNNSTLF (spiking neural network short-term load forecaster). The proposed model has been tested on the database obtained from the Australian Energy Market Operator (AEMO) website for Victoria State.     

多产品相互制约条件下的制造业产品需求预测研究

供应链环境中,产品需求预测对于制造业安排生产计划具有导向作用。对于大多数制造企业,通常生产的同一系列产品下有多种型号产品。由于受产能及市场容量限制,同系列下多型号产品之间对各自产品需求会产生相互影响作用。鉴于此,考虑历史需求数据在时间序列上的相邻关联性,研究在不同时间序列上各型号产品相互制约影响下产生的不同需求形态。同时考虑产品自身属性差异、供应链环境等影响因素,预测出某型号产品未来一段时间内的需求量。通过GRU-BP组合神经网络预测模型,对模型分析求解后证明预测结果的可行性。     

A new approach to water quality modelling and environmental decision support systems

The concepts and technology of environmental decision support systems (EDSS) have developed considerably over recent decades, although core concepts such as flexibility and adaptability within a changing decision environment remain paramount. Much recent EDSS theory has focussed on model integration and re-use in decision support system (DSS) tools and for design and construction of ‘DSS generators’. Many current specific DSS have architectures, tools, models and operational characteristics that are either fixed or difficult to change in the face of changing management needs. This paper reports on development and deployment of an EDSS that encompasses a new approach to DSS tools, generators and specific DSS applications. The system, named E2, is built upon a conceptualisation of terrestrial and aquatic environmental systems that has resulted in a robust and flexible system architecture. The architecture provides a set of base classes to represent fundamental concepts, and which can be instantiated and combined to form DSS generators of varying complexity. A DSS generator is described within which system users are able to select and link models, data, analysis tools and reporting tools to create specific DSS for particular problems, and for which new models and tools can be created and, through software reflection (introspection), discovered to provide expanded capability where required. This system offers a new approach within which environmental systems can be described in the form of specific DSS at a scale and level of complexity suited to the problems and needs of decision makers.     

A stochastic programming model for strategic capacity planning in thin film transistor-liquid crystal display (TFT-LCD) industry

This paper studies strategic capacity planning problems under demand uncertainties in thin film transistor-liquid crystal display (TFT-LCD) industry. Due to the following trends, capacity planning has become a critical strategic issue in TFT-LCD industry: (1) complex product hierarchy and product types caused by a wide range of product applications; (2) coexistence of multiple generation of manufacturing technologies in a multi-site production system; and (3) rapid growing and changing market demand derived by the needs for replacing traditional cathode ray tube (CRT) display. Furthermore, demand forecasts are usually inaccurate and vary rapidly over time.     

A decision support system for the technical sustainability assessment of water distribution systems

The purpose of this study is to develop a decision support system (DSS) for the technical sustainability assessment of water distribution systems (WDSs). The technical sustainability is assessed based on the sustainability index methodology using reliability, resiliency, and vulnerability as performance criteria. These performance criteria are visualized by the DSS combining several visualization techniques to improve the raw data readability and the effectiveness of the decision-making process. The technical sustainability of the existing WDS is assessed using the sustainability index methodology and two alternative scenarios are proposed to improve the sustainability. The “new pump” scenario is based on adding network components. The second scenario is based on using reclaimed water for non-potable water demand and fire flow. The results show that the DSS is effective to illustrate time-dependent variables in the WDS and that the sustainability index methodology is a credible approach to compare scenarios and to identify problematic locations.     

Forecasting models for Taiwanese tourism demand after allowance for Mainland China tourists visiting Taiwan

The tourism industry is an increasingly important national industry for Taiwan. Government policymakers and business managers pay close attention to the development of the tourism industry. In a rapidly changing environment that is influenced by numerous socioeconomic factors, the tourism industry must have an accurate method to forecast future tourism demand such that decision makers will be able to meet future challenges more effectively. Based on these concerns, this study proposes the SARIMA–GARCH model to analyze and forecast the tourism demand in Taiwan and compare the predictive power of this model and other forecasting models. The results provide a valuable reference for decision-makers in the tourism industry of Taiwan.     

Der Wertbeitrag von Prozessflexibilität

Promising to cope with increasing demand variety and uncertainty, flexibility in general and process flexibility in particular are becoming ever more desired corporate capabilities. During the last years, the business process management and the production/operations management communities have proposed numerous approaches that investigate how to valuate and determine an appropriate level of process flexibility. Most of these approaches are very restrictive regarding their application domain, neglect characteristics of the involved processes and outputs other than demand and capacity, and do not conduct a thorough economic analysis of process flexibility. Against this backdrop, the authors propose an optimization model that determines an appropriate level of process flexibility in line with the principles of value-based business process management. The model includes demand uncertainty, variability, criticality, and similarity as process characteristics. The paper also reports on the insights gained from applying the optimization model to the coverage switching processes of an insurance broker pool company.     

Time-series modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software

Forecasting, using historic time-series data, has become an important tool for fisheries management. ARIMA modeling, Modeling for Optimal Forecasting techniques and Decision Support Systems based on fuzzy mathematics may be used to predict the general trend of a given fish landings time-series with increased reliability and accuracy. The present paper applies these three modeling methods to forecast anchovy fish catches landed in a given port (Thessaloniki, Greece) during 1979–2000 and hake and bonito total fish catches during 1982–2000. The paper attempts to assess the model's accuracy by comparing model results to the actual monthly fish catches of the year 2000. According to the measures of forecasting accuracy established, the best forecasting performance for anchovy was shown by the DSS model (MAPE = 28.06%, RMSE = 76.56, U-statistic = 0.67 and R² = 0.69). The optimal forecasting technique of genetic modeling improved significantly the forecasting values obtained by the selected ARIMA model. Similarly, the DSS model showed a noteworthy forecasting efficiency for the prediction of hake landings, during the year 2000 (MAPE = 2.88%, RMSE = 13.75, U-statistic = 0.19 and R² = 0.98), as compared to the other two modeling techniques. Optimal forecasting produced by combined modeling scored better than application of the simple ARIMA model. Overall, DSS results showed that the Fuzzy Expected Intervals methodology could be used as a very reliable tool for short-term predictions of fishery landings.     

A production forecasting and planning system for dairy processing

The dairy processing industry is unique among the processing industries because of the perishability of products and the seasonality of the demand. This paper describes the design and implementation of a production forecasting and planning system for a high volume standardized product—the 1-gallon package of milk. Regression techniques were used to forecast the aggregate demand for the 1-gallon packages of milk and linear programming was used to plan the weekly production. The production planning model was then evaluated through comparison of costs with the existing system and a system with perfect information.     

An evolution-based approach with modularized evaluations to forecast financial distress

Due to the radical changing of the global economy, a more precise forecasting of corporate financial distress helps provide important judgment principles to decision-makers. Although financial statements reflect a firm's business activities, it is very challenging to discover critical information from these statements. Applying machine learning algorithms can be demonstrated to improve forecasting accuracy in predicting corporate bankruptcy. In this paper, we introduce an evolutionary approach with modularized evaluation functions to forecast financial distress, which allows using any evolutionary algorithm to extract the set of critical financial ratios and integrates more evaluation function modules to achieve a better forecasting accuracy by assigning distinct weights. To achieve a more precise predicting accuracy, the undesirable forecasting results from some modules are weeded out, if their predicting accuracies are out of the allowable tolerance range as learned from our mechanism.     

Directed forgetting in organisations: the positive effects of decision support systems on mental resources and well-being

Decision makers in organisations are often overtaxed by huge amounts of information in daily business processes. As a potential support strategy, this study examined ‘directed forgetting’ (Bjork, 1970) in a simulated sales planning scenario. We assumed that the availability of a computer-based decision support system (DSS) triggers the forgetting of decision-related background information. Such directed forgetting should not only release memory capacities for additional tasks but also enhance decision quality and decrease strain of decision makers. Assumptions were tested in an experimental study with N?=?90 participants. Consistent with our assumptions, results revealed a higher recall of decision-unrelated information, higher decision quality and higher well-being when participants could use a DSS as compared to two Control conditions without a DSS. Moreover, directed forgetting effects were qualified by participants’ trust in the DSS. This study provides the first evidence for directed forgetting effects cued by information systems in a business context.

Practitioner summary: Information overload is an increasing challenge in modern business organisations. Extending findings from basic memory research, this study shows that availability of a computer-based decision support system triggers forgetting of decision-related background information, which in turn increases users’ mental resources for additional tasks, decision quality, and well-being.     

A supply chain contract with flexibility as a risk-sharing mechanism for demand forecasting

Demand forecasting is one of the main causes of the bullwhip effect in a supply chain. As a countermeasure for demand uncertainty as well as a risk-sharing mechanism for demand forecasting in a supply chain, this article studies a bilateral contract with order quantity flexibility. Under the contract, the buyer places orders in advance for the predetermined horizons and makes minimum purchase commitments. The supplier, in return, provides the buyer with the flexibility to adjust the order quantities later, according to the most updated demand information. To conduct comparative simulations, four-echelon supply chain models, that employ the contracts and different forecasting techniques under dynamic market demands, are developed. The simulation outcomes show that demand fluctuation can be effectively absorbed by the contract scheme, which enables better inventory management and customer service. Furthermore, it has been verified that the contract scheme under study plays a role as an effective coordination mechanism in a decentralised supply chain.