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This chapter is an up-to-date survey of the state-of-the art in consumer demand analysis. We review (and evaluate) advances in a number of related areas, in the spirit of the recent survey paper by Barnett and Serletis (2008). In doing so, we only deal with consumer choice in a static framework, ignoring a number of important issues, such as, the effects of demographic or other variables that affect demand, welfare comparisons across households (equivalence scales), and the many issues concerning aggregation across consumers.

Demand forecasting in the clothing industry is very complex due to the existence of a wide range of product references and the lack of historical sales data. To the authors' knowledge, there is an inadequate number of literature studies to forecast the demand with the adaptive network based fuzzy inference system for the clothing industry. The purpose of this paper is to construct a decision support system for demand forecasting in the clothing industry.

The adaptive‐network‐based fuzzy inference system (ANFIS) is used for forecasting demand in the clothing industry.

The results of the proposed study showed that an ANFIS‐based demand forecasting system can help clothing manufacturers to forecast demand more accurately, effectively and simply.

In this study, the demand is forecast in terms of clothing manufacturers by using ANFIS. ANFIS is a new technique for demand forecasting, it combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. The input and output criteria are determined based on clothing manufacturers' requirements and via literature research, and the forecasting horizon is about one month. The study includes the real life application of the proposed system and the proposed system is tested by using real demand values for clothing manufacturers.

According to literature research and conversations with apparel manufacturers' specialists, there is not any common analytic method for demand forecasting in apparel industry and to the authors' knowledge, there is not adequate number of study in literature to forecast the demand with adaptive network-based fuzzy inference system (ANFIS) for apparel manufacturers. The purpose of this paper is constructing an effective demand forecasting system for apparel manufacturers.

The ANFIS is used forecasting the demand for apparel manufacturers.

The results of the proposed study showed that an ANFIS-based demand forecasting system can help apparel manufacturers to forecast demand accurately, effectively and simply.

ANFIS is a new technique for demand forecasting, combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. In this study, the demand is forecasted in terms of apparel manufacturers by using ANFIS. The input and output criteria are determined based on apparel manufacturers' requirements and via literature research and the forecasting horizon is about one month. The study includes the real-life application of the proposed system, and the proposed system is tested by using real demand values for apparel manufacturers.

Key components of the logistics mix are described in an effort to create an understanding of the total logistics concept. Chapters include an introduction to logistics; the strategic role of logistics, customer service levels, channel relationships, facilities location, transport, inventory management, materials handling, interface with production, purchasing and materials management, estimating demand, order processing, systems performance, leadership and team building, business resource management.

The purpose of this paper is to assess the long‐term ability of the Integrated Southern Ghor Project (ISGP) to meet the required water demands, assess the resulting energy requirements, pumping costs, water transfers, benefits of the current system with respect to predevelopment conditions and effect of projected water demands increase on the resulting water deficits.

A surface water resources management model is developed using dynamic programming. The model inputs are the hydrological inflows from the different wadis in the project area, reservoirs characteristics and evaporation rates, system water demands. The model outputs are water deficits at the different demand areas, reservoirs storage and release sequences, water transfers and energy requirements and the associated costs. The average annual values of different performance criteria with the annual frequency curves are used to evaluate the implications of different water scenarios on the ISGP.

The results show the efficiency of the ISGP model in reducing the water deficits in the demand areas as compared to predevelopment conditions. Increased demand scenario showed the importance of finding new water projects to supplement the Southern Ghor Area in the future in order to meet the increasing water demands. The proposed water transfer will reduce the resulting deficits at the agricultural areas without the expenses of increasing the water deficits at other demand areas. The application of this model is expected to enhance decision making regarding water policies in Jordan.

This paper provides critical quantitative information to decision makers in Jordan about the potential of the different storage facilities and proposed transfers in meeting the required water demands in the Southern Ghor Project and assesses the required energy for that. This can help decision makers to have a holistic view about the expected water deficits in the area and therefore assist them in determining the areas impacted most and what alternative solution to use. The paper also shows the importance of using optimal control/management models to support water resources decision making in Jordan.

This paper aims to investigate the problem of searching for the equilibrium in the housing market, the mortgage lending market and the insurance market in the process of selling the residential property. Three classes of markets are established in three modes, which reflect the interdependence of the firms’ interests in these markets through the parameters of their integration. The paper aims to determine the prices in these markets on the basis of the compromises among the conflicting interests of the related firms, and, in addition, to assess the rationality of integration for firms, which are participants in the process of selling the residential property.

On the basis of the revenue sharing contracts and the supply chain coordination methods, the optimization models of the housing realtor, the mortgage bank and the insurance company are developed. The models consider the interdependence of the firms’ interests, the monopolistic competition in these markets and the conditions of the firms’ individual rationality in the interaction process.

The results of the study are as follows. First, as a consequence of a decrease in the demand curves in monopolistic competition, the housing market, the mortgage market and the insurance market are interconnected, therefore, the optimization models of the firms in these markets are interdependent through the revenue sharing parameters. Second, in these markets the individual firms’ sales optimums are not identical, therefore, the interests of the firms are contradictory. Third, in the realtor-bank-insurer system, the equilibrium satisfies the condition of zero revenue sharing payments between the agents; additionally, the equilibrium prices in these markets are mutually independent. Fourth, in the disequilibrium, the prices in these markets are interrelated, i.e. the price in one market increases with the price in another market, if the payment is directed from the former to the latter, and vice versa.

The results of the study are applicable in practice, if the markets demonstrate the decreasing demand curves and if the needs of buyers in related markets are interconnected.

The interaction between the realtor and the mortgage bank enables the realtor to raise its sales and the bank to increase in the number of loans, i.e. it leads to growth of their profits. The interaction between the insurer and the mortgage bank enables the insurer to increase in the number of policies and the bank to reduce the risk of lending, i.e. it leads to an increase in their profits. The identification of the individual firms’ sales optimums enables agents to determine the terms of the contracts of these interactions, which are compromises from the positions of each transaction participants. In addition, the firms’ optimums indicate the predictions of the equilibrium market prices.

In comparison with the studies in the contract theory framework, first, the mathematical description of the complicated (three-agent) system of interactions is proposed; second, the optimal choice non-linear models are developed, which take into account the non-linear demand functions in the monopolistic competition markets; third, the equilibrium of the agents with contradictory interests is investigated. In the later item, the authors establish that the revenue sharing contracts in the complimentary demands functions systems do not require the payments between the participants. Fourth, the authors prove that, in the equilibrium of these markets, the housing prices, the mortgage interest rates and the insurance rates are mutually independent and equal to the prices in the isolated markets.

This chapter reviews the specification and application of the Deaton and Muellbauer's (1980) almost ideal demand system (AIDS) and the Christensen et al. (1975) translog (TL) demand system. In so doing we examine various refinements to these models, including ways of incorporating demographic effects, methods by which curvature conditions can be imposed, and issues associated with incorporating structural change and seasonal effects. We also review methods for adjusting for autocorrelation in the models' residuals. A set of empirical examples for the AIDS and the log TL version of the translog based on historical meat price and consumption data for the United States are also presented.

In order to ensure that an inventory system is performing correctly, management must continually monitor the system and take corrective action as necessary. Only a few previous articles have suggested techniques for monitoring and diagnosing the performance of a reorder point system. Uses statistical process control charts to monitor the systems performance on an ongoing basis. The proposed system is triggered by unplanned stockouts or by a periodic review of each item′s inventory turnover rate. The system then systematically analyses the stockout and the control charts and identifies the causes, which can be fitness‐related and/or operations related. Problems can be quickly diagnosed and corrective action taken so the reorder point system will perform as intended.

Motivated by recent research indicating that the operational performance of an enterprise can be enhanced by building a supporting data-driven environment in which to operate, this paper presents a simulation framework that enables an examination of the effects of applying smart manufacturing principles to conventional production systems, intending to transition to digital platforms.

To investigate the extent to which conventional production systems can be transformed into novel data-driven environments, the well-known constant work-in-process (CONWIP) production systems and considered production sequencing assignments in flowshops were studied. As a result, a novel data-driven priority heuristic, Net-CONWIP was designed and studied, based on the ability to collect real-time information about customer demand and work-in-process inventory, which was applied as part of a distributed and decentralised production sequencing analysis. Application of heuristics like the Net-CONWIP is only possible through the ability to collect and use real-time data offered by a data-driven system. A four-stage application framework to assist practitioners in applying the proposed model was created.

To assess the robustness of the Net-CONWIP heuristic under the simultaneous effects of different levels of demand, its different levels of variability and the presence of bottlenecks, the performance of Net-CONWIP with conventional CONWIP systems that use first come, first served priority rule was compared. The results show that the Net-CONWIP priority rule significantly reduced customer wait time in all cases relative to FCFS.

Previous research suggests there is considerable value in creating data-driven environments. This study provides a simulation framework that guides the construction of a digital transformation environment. The suggested framework facilitates the inclusion and analysis of relevant smart manufacturing principles in production systems and enables the design and testing of new heuristics that employ real-time data to improve operational performance. An approach that can guide the structuring of data-driven environments in production systems is currently lacking. This paper bridges this gap by proposing a framework to facilitate the design of digital transformation activities, explore their impact on production systems and improve their operational performance.

Considers a class of control systems known as generalized kanban control systems (GKCS) which can be used to implement a pull control mechanism in a manufacturing system. In a GKCS, the production system is decomposed into stages, where each stage consists of a production sub‐system. There are two design parameters per stage: one controls the work‐in‐process in the stage and the other determines the maximum number of finished products of this stage. Investigates the influence of these design parameters on the efficiency of generalized kanban control policies by deriving qualitative properties as well as using experimental results on the behaviour of GKCS.