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While cost allocation decisions attract considerable attention in the management accounting literature, many studies are contradicting and inconclusive. They often seek to develop product or service weights in order to make operating decisions with the sole objective of maximizing the firm’s profitability. But before developing these weights, the studies must first rank these products – which is a complex endeavor that is often driven by many hierarchical financial and nonfinancial goals and objectives. Ranking is also difficult due to using such complex concepts as time, uncertainty, cost, and interdependencies between accounting systems and manufacturing systems and among the products of the product mix. These concepts are inherently fuzzy and coextensively applied often with a confluence of variables operating simultaneously.

This paper applies an advanced mathematical model to account for a hospital cost allocation decisions in treating spinal cord injuries (SCI). The model combines the powers of fuzzy set theory (Zadeh, 1965) and the analytic hierarchy process (Saaty, 1978). The precise ratings required in the conventional analytic hierarchy process but practically hard to obtain are replaced by naturally semantic variables by using the fuzzy set concept. de Korvin and Kleyle’s (1999) fuzzy-analytic-hierarchical process (FAHP) then develop these ambiguous variables. FAHP can help to optimize decisions involving ambiguous variables and the web of prioritized strategies and goals of cost leadership, product differentiation, financial objectives of earnings, cash flows, and market share and nonfinancial goals such as tradition and owners’ convictions and philosophies.

We use data from seven Michigan SCI facilities in applying the FAHP model to rank and otherwise develop more optimal strategies and goals and compare our results to the decisions of hospital management.

Economic security is one of the crucial dimensions of the welfare state. High-income individuals are able to purchase private insurance, but a large portion of the individuals remains uninsured. The authors have tried to rationalize the problem of the study over the reason why people remain uninsured. Hence, the purpose of the study is to identify an insurance model that can cover the risk of the heterogeneous segments. The study is qualitative in nature and applies a fuzzy analytic hierarchy process (FAHP). Based on seven criteria, process is applied to arrive at an alternative model among basic models of insurance, namely, conventional private insurance, mutual, and social insurance. Since social insurance has emerged with the highest score of 41% in the study, it is implied that social insurance works best in a situation where the market is full of private information and moral hazard. The findings reaffirm that government intervention is required in an insurance market to provide coverage to both covariate and idiosyncratic risks. The findings are especially relevant in the context of emerging markets where a sizeable poor population goes uninsured. The study contributes to the literature by proposing alternative insurance to address the problem of insuring the voluntarily uninsured.

This article contributes to the fuzzy logic application literature in accounting by examining a key issue in the use of fuzzy logic: how to find an optimum number of classes to minimize the decision maker's cost. Two costs are assumed: (1) we assume fuzziness is costly and thus should be minimized and (2) we assume that adding categories is costly. In order to address the issue of finding the optimal number of classes, we define the objective function as being cost minimization. We seek to determine the costs and benefits of increasing the number of classifications and ask whether an internal optimum is identifiable and achievable. We assume, ceteris paribus, less fuzziness is preferable to more fuzziness, but fuzziness can only be reduced through the use of more categories whose creation is costly. More fuzziness is costly, but so is the creation of additional categories to alleviate the fuzziness. When we arrive at the optimal number of clusters that corresponds to a minimal total cost, that number may not be the same as the “natural” number of categories. It is, nonetheless, a useful and practical way of deciding on the number of classifications. The approach we employ in this study is not confined to a management accounting information environment. It can be applied to any information environment where measurable classifications exist.

Since quality cannot be manufactured or tested into a product but must be designed in, effective product design is a prerequisite for effective manufacturing. However, the concept of effective product design involves a number of complexities. First, product design often overlaps with such design types as engineering design, industrial design and assembly design. Second, while costs are key variables in product design, costing issues often arise that add more complexities to this concept.

The management accounting literature provides activity-based costing (ABC) and target costing techniques to assist product design teams. However, when applied to product design these techniques are often flawed. First, the product “user” and “consumer” are not identical as often assumed in target costing projects, and instead of activities driving up the costs, managers may use budgeted costs to create activities to augment their managerial power by bigger budgets and to protect their subordinates from being laid off. Second, each of the two techniques has a limited costing focus, activity-based costing (ABC) focusing on indirect costs and target costing on unit-level costs. Third, neither technique accounts for resource interactions and cost associations.

This paper applies the new method of associative costing (Bayou & Reinstein, 2000) that does not contain these limitations. To simplify the intricate procedures of this method, the paper outlines and illustrates nine steps and applies them to a hypothetical scenario, a design of a laptop computer intended for the college-student market. This method uses the well-known statistical techniques of clustering, Full Factorial design and analysis-of-variance. It concludes that in product design programs, the design team may need to make tradeoff decisions on a continuum beginning with the design-to-cost point and ending at the cost-to-design extreme, as when the best perceived design and the acceptable cost level of this design are incongruent.

The United Nations Industrial Development Organisation (UNIDO) proposed the development of eco-industrial parks (EIP) related to the 9th, 12th, and 13th of the sustainable development goals (SDGs) for ensuring green industrial practice to deal with climate change. The first approach to EIP is choosing a suitable site, however, it is not a simple task, because it involves spatial factors and is always impaired by uncertainties that require more than one decision module. There is a fewer study in objectively assessing the criteria for the selection of suitable sites for EIP development to contribute to the SDG initiative. This study provides an integrated process for assessing a consistent weight of criteria for EIP site selection. Nine steps were used in the fuzzy-analytical hierarchy process namely criterion identification, hierarchical structure construction, triangular fuzzy number matrix, geometric ratio, fuzzy relative weight, defuzzification, normalisation, sensitivity analysis (SA) and weight ranking. When tested using spatial and non-spatial criteria for EIP site selection, results show transportation infrastructure (13%), raw materials (12%), water bodies (12%), climate (10%), labour (9%), land use (9%), markets (9%), governmental policies (8%), existing industries (7%), urban settlement (6%), and restricted areas (5%). The SA verified that any evaluation error of 2% or 5% on the criteria weight is insignificant, but for 10% error, results can be distorted. The study has developed a consistent, simple approach integrating hierarchical and uncertainty modules for choosing EIP locations, and it is proposed as a guide for selecting suitable greenfield or brownfield EIP sites for sustainable industrial practices.

An issue that is becoming yet more relevant to modern manufacturers is that of flexibility. As life cycles become shorter a manufacturing firm can easily be left with redundant stock and dated processes. In Taiwan this issue has been addressed at several levels, this case study describes one such project. A Taiwanese academic conducted a study, gaining business acceptance of a hierarchical set of theoretical flexibility factors, then rearranging these via pictorial representations of fuzzy logic-derived plane surfaces, and finally re-presenting them to business as a set of ordered propositions designed to identify the key factors contributing to flexibility. The learning points relate, first, to the empirical facts uncovered about the specific factors that have a major bearing on manufacturing flexibility. These factors are, of course, specific to Taiwan and the current environment there. Second, though, is the more enduring illustration of a mixed-method case approach; where interviews, fuzzy logic analytical methods, and pictorial representation of the fuzzy logic output all combine to give clear guidance to managers of an industrial sector under stress, and to the policy makers who exert significant control over their environment.

Managing complex construction projects is a challenging task because it involves multiple factors and decision-making processes. A systematic evaluation of these complex factors is imperative for achieving project success. As most of these factors are qualitative or intangible in nature, decision makers often rely on subjective judgements when comparing and evaluating them. The hybrid techniques that integrate fuzzy set theory and the analytic hierarchy process (AHP) are able to deal with such problems. This chapter discusses various hybrid techniques of the fuzzy AHP and presents an application of these techniques to the evaluation of transportation project complexity, which is essential for prioritising resource allocation and assessing project performance. Project complexity can be quantified and visualised effectively with the application of the fuzzy AHP. This chapter enhances the understanding of construction project complexity and fuzzy hybrid computing in construction engineering and management. Future research should address the calibration of fuzzy membership functions in pairwise comparisons for each individual decision maker and develop computational tools for solving optimisation problems in the constrained fuzzy AHP. In the area of construction project complexity, future research should investigate how scarce resources are allocated to better manage complex projects and how appropriate resource allocation improves their performance.