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This chapter presents the indirect preferences for all full rank Gorman and Lewbel demand systems. Each member in this class of demand models is a generalized quadratic expenditure system (GQES). This representation allows applied researchers to choose a small number of price indices and a function of income to specify any exactly aggregable demand system, without the need to revisit the questions of integrability of the demand equations or the implied form and structure of indirect preferences. This characterization also allows for the calculation of exact welfare measures for consumers, either in the aggregate or for specific classes of individuals, and other valuations of interest to applied researchers.

This study investigates the impacts of changing the length of review period on the performance of a periodic review system with lost sales under varying demand patterns by using simulation method.

The simulation model has been built using a spreadsheet program and depicts a special case of several retailers supplied by a central warehouse. Simulation scenarios are created based on two factors: length of the review period, and product type in terms of the average and standard deviation of the corresponding product's demand distribution.

Results obtained from the simulation model show that inventory performance is quite sensitive to the duration of review periods, and selecting the appropriate period length is largely dependent on the variability of demand. Specifically, relatively shorter review periods are required for products with high variable demand, while the inventory system can tolerate longer review periods if the product demand is less fluctuating.

The study provides some practical guidelines for the inventory managers regarding the selection of an appropriate length for a review period in a periodic review system with lost sales.

The purpose of this paper is to assess failure demand as a lean concept that assists in waste analysis during quality improvement activity. The authors assess whether the concept’s limited use is a missed opportunity to help us understand improvement priorities, given that a UK Government requirement for public service managers to report failure demand has been removed.

The authors look at the literature across the public sector and then apply the failure demand concept to the UK’s primary healthcare system. The UK National Health Service (NHS) demand data are analysed and the impact on patient care is elicited from patient interviews.

The study highlighted the concept’s value, showing how primary care systems often generate failure demand partly owing to existing demand and capacity management practices. This demand is deflected to other systems, such as the accident and emergency department, with a considerable detrimental impact on patient experience.

More research is needed to fully understand how best to exploit the failure demand concept within wider healthcare as there are many potential barriers to its appropriate and successful application.

The authors highlight three practical barriers to using failure demand: first, demand within the healthcare system is poorly understood; second, systems improvement understanding is limited; and third, need to apply the concept for improvement and not just for reporting purposes.

The authors provide an objective and independent insight into failure demand that has not previously been seen in the academic literature, specifically in relation to primary healthcare.

This chapter presents the differential approach to applied demand analysis. The demand systems of this approach are general, having coefficients that are not necessarily constant. We consider the Rotterdam parameterization of differential demand systems and derive the absolute and relative price versions of the Rotterdam model, due to Theil (1965) and Barten (1966). We address estimation issues and point out that, unlike most parametric and semi-nonparametric demand systems, the Rotterdam model is econometrically regular.

This research aims to consider the impact of common inventory system inaccuracies that occur in retail outlets on the inventory levels, fill rate, and service level of those outlets by simulating daily customer demand and random error in the inventory system.

The simulation experiments vary the amount of inventory system error, the frequency of inventory record error correction, the size of the daily demand, the number of days in the replenishment system's review interval, and the replenishment system's customer service level.

Inventory system error and the frequency with which the error is corrected are statistically significant for fill rate and service level. Thus, inaccuracies in inventory levels affect a retail outlet's ability to service its customers, though at the single SKU level, the results do not indicate a practical impact due to countervailing effects.

Retail outlets must be aware that error exists and can influence the behavior of their replenishment systems, but the overall impact may not be as significant as it might appear.

This research extends prior work on the effects of inventory inaccuracies and clarifies the debate pertaining to their ultimate effects on retail performance outcomes.

A mixed two‐level system is considered for parts distribution. Three approaches are studied for the division of the total item set S into subsets S⊂1 and S⊂2. The subset S⊂1 includes items directly served by the company to the end‐users and subset S⊂2 includes items served through dealers. These approaches are dealer item‐based, company item‐based and system‐based. The systems approach is compared with the two item‐based approaches from the dealer′s and company′s point of view. A market sensitivity model is developed in the systems approach, which considers the effect of customer service (primarily average delivery time and number of items in set S⊂2) on the captured market share. Optimisation of total expected profit, based on a system′s approach, leads to higher profit levels and to a larger subset S⊂2 than obtained from the two item‐based approaches.

Line balancing has been an important technique for manufacturing system design, because a completely balanced system can provide maximum resource utilization at the designed capacity. However, even if a system is completely balanced, it still has capacity waste when the entire product life cycle is considered, because real production is often significantly less than capacity. Avoiding this mismatch requires scalable systems such as reconfigurable manufacturing systems (RMSs) to meet changing product demand. Stage paralleling is suggested as an approach to scalability for RMSs. By comparing the economic feasibility of such manufacturing systems with completely balanced transfer line systems with respect to station cost, it is shown that line balancing is not necessarily desirable with this approach. The effect of station cost differences for unbalanced systems is also considered.

Most demand studies have concentrated on the estimation of expenditure elasticities for single commodity at a time thereby not being able to reveal the details of the relationships among various food items demanded by households. The purpose of this paper is to simultaneously estimate the demand equations for a number of food items and to estimate cross-price elasticities which are necessary for studying consumer behaviours, marketing, production planning and policy making.

Relevant data were collected from 320 randomly selected households in a multistage sampling procedure. The normalized data were analysed in a system of equation with symmetry, adding-up and homogeneity restrictions imposed on the model.

Expenditure elasticities show that gaari and palm oil were inferior food items while others could be classified as normal. Own-price elasticities showed that beans, plantain, yam flour and rice were luxuries while others were necessities. Cross-price elasticities revealed that some were substitutes of one another while others were compliments and some were not related.

The data were collected using a month recall approach and generalizing its findings beyond such months of a year may be misleading. Therefore, other researchers should repeat the study across months and locations.

The study recommended that food policies should be broad based to encompass majority of the food items consumed in the study area given the intrinsic relationship inherent among them as their demands were interrelated and consumer behaviours as revealed by various elasticities be considered in formulating food-related policies.

The paper emphasized the need to model food demand in a system of equations as against single equation modelling.

Briefly reviews previous literature by the author before presenting an original 12 step system integration protocol designed to ensure the success of companies or countries in their efforts to develop and market new products. Looks at the issues from different strategic levels such as corporate, international, military and economic. Presents 31 case studies, including the success of Japan in microchips to the failure of Xerox to sell its invention of the Alto personal computer 3 years before Apple: from the success in DNA and Superconductor research to the success of Sunbeam in inventing and marketing food processors: and from the daring invention and production of atomic energy for survival to the successes of sewing machine inventor Howe in co‐operating on patents to compete in markets. Includes 306 questions and answers in order to qualify concepts introduced.

The purpose of this paper is to study joint inventory and pricing strategy for a continuous inventory review system. While dynamic pricing decisions are often studied in the literature along with inventory management, the authors' aim in this study is to obtain a single long‐run optimal price; also to gain insight about how to obtain the optimal price and inventory control variables simultaneously and then the benefits of joint optimization of the inventory and pricing decisions over the sequential optimization policy often followed in practice.

A general (R;Q) policy system with fixed cost of ordering is modelled and then the case where unsatisfied demand is lost is studied. General forms of both the additive and multiplicative demand models are used to obtain structural results.

By showing optimality conditions on the price and inventory decision variables, two algorithms on how to obtain optimal decision variables, one for additive and another for multiplicative demand‐price model are provided. Through extensive numerical analyses, the potential profit increases are reported if the price and inventory problem are solved simultaneously instead of sequentially. In addition, the sensitivities of optimal decision variables to system parameters are revealed.

Although there are several studies in the literature investigating emergency price change models, they use arbitrary exogenous prices menus. However, the value of a price change can be better appreciated if the long‐run price is optimal for the system.

Very few researchers have investigated constant price and inventory optimization, and while there are several past studies demonstrating the benefits of dynamic pricing over a static one, there still are not many findings on the benefit of joint price and inventory optimization.