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Prior work has focused on the impact of using alternative bases for allocating costs to products but there has been little work that evaluates the use of alternative allocation bases for allocating costs to departments. In particular, if different departments of a multi‐national firm are located in settings with different reporting requirements, exchange rate risks, and costs of capital, then the choice of cost allocation base can be important. This paper examines the economic impact of alternative service department allocation bases in a decentralised setting. A non‐linear programming (NLP) approach is used to model the problem. A review of prior literature identifies a method, based on the NLP approach, for determining the economic impact of alternative allocation bases in a multi‐product setting. The method is adapted in this paper for the multi‐divisional context. The study finds that centralised production volume decision‐making is superior to decentralised decision‐making using either revenue or volume‐based cost allocation bases. Under certain conditions, revenue‐based allocation bases are also found to be superior to volume bases. Under the assumptions of the model no distinction can be made between the centralised solution and a profit‐based allocation regime. A practical implication of this study is that designers of cost allocation systems need to consider not only the direct income‐shifting effect of different cost allocation bases but also the indirect economic effect of consequential changes in the operating decisions of the firm.

The purpose of this paper is to develop a new systematic method for a multi-unit organization to cope with the cost allocation problem, which is an extension of the reciprocal method. As uncertainty is the inherent characteristic of business environments, assuming changes in engaged parameters is almost necessary. The outputs of the model determine the total value of each unit/business lines or product.

In the proposed method, contrary to existing models, business units are able to transfer their costs to other units, and also, not necessarily transfer the total costs of support units completely. The DEMATEL approach, which finds all relationships between different parts of a system, is also applied for computing effects of the units’ expense paid to each other. Moreover, a fuzzification approach is used to capture linguistic experts’ judgments about related data.

Being closer to the real-world problem in comparison to the previous approach, the proposed systematic approach encompasses the other cost allocation models.

Applying the proposed model for a system like a multi-unit organization, the total price of each unit/business line can be obtained. Moreover, this cost allocation process guides the related decision-makers to better manage the expenses that each unit pays the others.

In the existing studies, business units cannot pay expense support units. However, in the proposed method, the business units are able to pay expenses for other units, and also, not necessarily pay total expenses for support unit completely. Moreover, considering engaged parameters as fuzzy numbers makes the proposed model closer to real-world problems.

The literature on activity‐based costing (ABC) that deals with the allocation of indirect purchasing costs primarily draws on a transactional approach to purchasing. This presents a problem, since a large share of purchasing takes place within relationships. The purpose of this paper is to point out complexities in applying ABC to indirect purchasing costs, when purchasing takes place within long‐term relationships. The interaction model is used as a framework.

A case study is conducted on a first‐tier supplier in the Swedish automotive industry to illustrate real‐life purchasing practices and to point out subsequent difficulties in applying ABC principles. This firm is selected because efforts have simultaneously been undertaken to employ supplier relationship management (SRM) and to implement costing techniques. About 23 interviews were conducted and approximately 31 hours of interview data were collected.

The case shows that many functions tend to be involved in exchange within long‐term relationships. This generates additional purchasing‐related costs not previously recognized in the costing literature. It also leads to difficulties in allocating these costs.

Complexities when implementing ABC are presented, concerning: allocation of costs relating to SRM; allocation of costs pertaining both to transactions and to relationships; less apparent cost drivers due to involvement of many functions in exchanges; and cost allocation over time.

The purpose of this paper is to develop data envelopment analysis (DEA) models and algorithms for efficiency improvement when the inputs and output weights are restricted and there is fixed availability of inputs in the system.

Limitation on availability of inputs is represented in the form of constant sum of inputs (CSOI) constraint. The amount of excess input of an inefficient decision-making unit (DMU) is redistributed among other DMUs in such a way so that there is no reduction in their efficiency. DEA models have been developed to design the optimum strategy to reallocate the excess input.

The authors have developed the method for reallocating the excess input among DMUs while under CSOI constraint and parameter weight restrictions. It has been shown that in this work to improve the efficiency of an inefficient DMU one needs the cooperation of selected few DMUs. The working of the models and results have been shown through a case study on carbon dioxide emissions of 32 countries.

The limitation of the study is that only one DMU can expect to benefit from the application of these methods at any given time.

Results of the paper are useful in situations when decision maker is exploring the possibility of transferring the excess resources from underperforming DMUs to the other DMUs to improve the performance.

This strategy of reallocation of excess input will be very useful in situations when decision maker is exploring the possibility of transferring the excess resources from underperforming DMUs to the other DMUs to improve the performance. Unlike the existing works on efficiency improvement under CSOI, this work seeks to address the issue of efficiency improvement when the input/output parameter weights are also restricted.

Prior research has used computer-generated data to illustrate the benefits of the recently developed duration-based costing (DBC) and its affiliate modified duration-based costing (MDBC). The purpose of this paper is to use data from a Fortune 500 corporation to compare its traditional, or functional-based, cost allocation method with that of the recently developed DBC and MDBC models.

A Fortune 500 company provided one month of production data for a particular, key machine within its manufacturing process. The data were used to apply DBC and MDBC.

Variations arising from differences in the models’ cost allocation reveal the advantages of using time-based cost allocation over the traditional, mostly non-time-based allocation to estimate profit.

By using actual data, this case study enhances prior theoretical research concerning the benefits of utilizing DBC and MDBC over the traditional costing method.

This case study is of benefit to practitioners who use traditional costing since it will encourage them to explore DBC and/or MDBC that tend to be more accurate in situations where the old adage of “time is money” applies. Implementing DBC and MDBC was not difficult to do for the Fortune 500 company as all of the components to run the models were readily available.

This is the first study to utilize actual company data to illustrate DBC and MDBC, and thus, adding to the literature concerning DBC and MDBC.

The purpose of this study is to examine the design, development and implementation of responsibility centre management at a mid-sized Canadian university, within the context of decentralized decision-making. More specifically our study focused on the design, development and implementation of a revenue and cost allocation process known as transparent activity–based budgeting system (TABBS).

The authors conducted this study using a qualitative case study methodology, rooted in grounded theory, as the primary approach to collect and analyse data, and report the findings. Primary data were collected from ten participants using semi-structured interviews.

The main takeaways from our research are that (1) such systems take time to design, develop and implement, (2) consultation, communication and information sharing and model adjustment and refinement are important enabling mechanisms, (3) internal and external events posed significant challenges, (4) although such systems are often designed keeping in mind several intended outcomes, there exists the possibility of experiencing some unintended consequences and (5) the juxtaposition of the above has the potential to negatively or positively impact organizational performance.

The research demonstrates that the design, development and implementation of a complex resource allocation model is an important element of a responsibility-centred approach to planning and decision-making. It highlights the importance and contribution of enabling mechanisms as well as the challenges that large, complex organizations may confront when introducing change.

Many banks and financial institutions have resisted the opportunity to model customer profiles because of the cost of developing and maintaining customer profitability information based on the capture and storage of individual transactions. This paper describes the development of such a model to calculate customer profitability, by putting into place systems which allow the capture and analysis of accurate, up‐to‐date information regarding the transactions of individual customers. The implementation of the model within one regional Australian bank is detailed, together with an indication of its potential for operational and strategic decision making. We speculate that such a model might be more widely adopted to allow banks to focus effectively on the most profitable segments of their business.

The purpose of this paper is to create a cost model at the general equipment level for industrial maintenance services.

The study is divided into two main sections. In the first phase the idea is to create a framework for a cost model with a literature review. The second, empirical part of the study is based on costing information from interviews and information given by network companies: a pulp mill, a maintenance company and an equipment provider. The maintenance of three different equipment processes is examined in the network through a case study, to get more specific information from real world situations to develop the model.

The findings concern the cost items that should be considered in the model, the structure of the model, and how the general cost model is constructed. During the research the model has been extended, and new cost categories included.

The cost model can be used in various performance measurement and decision‐making situations, such as maintenance service pricing, contract negotiations, outsourcing decisions, and life cycle cost management.

The cost model differs from traditional cost models. Earlier models have focused on the perspective of either the service provider or the customer, but not on both perspectives at the same time. However, in order to achieve a win‐win situation in a business network, open books practice is expected from each member of the network.

The purpose of this paper is to extend the results of Meca et al. (2004) depending on the grey information revealed by the individual firms.

The authors introduce cooperative grey games and focus on sharing ordering cost rule (SOC-rule) to distribute the joint cost.

In this study, the authors introduce a model, where inventory costs are assumed as grey numbers instead of crisp or stochastic ones studied in literature. At first, grey numbers and classical cooperative inventory games are recalled. Then, cooperative grey games are introduced and related results are given. Finally, an application is performed for three shotgun companies in Turkey.

It is an effective approach for theoretical analysis of systems with imprecise information and incomplete samples. Therefore, grey system theory, rather than the traditional probability theory and fuzzy set theory, is better suited to model the inventory problems by using cooperative game theory. To the best of the knowledge no study exists modeling inventory situations by using cooperative grey games. From this point of view this study is a pioneering work on a promising topic.

Maximum-flow of an uncertain multi-owner network has become very important recently. This study aims to evaluate the maximum flow on a cooperated logistic system in the presence of uncertainties, raised by travel time, capacity, cost and failures.

To consider different uncertainties and to promote network efficiency, the proposed model is enriched with a cooperative game methodology and a reliability method. A scenario-based method covers optimistic, pessimistic and most likely estimates time, cost and capacity of each route as well as applies a prior failure pattern for breakdown of any resource.

A linear optimization model, which is enriched with target reliability estimation, is presented. Results on a water distribution network indicate more revenue performance for players. Carrying out sensitivity analysis shows the importance of the model parameters.

Modeling maximum-flow problem in the presence of many sources of uncertainty with the aim of a cooperative game is the main contribution of the present study. Also, a novel method based on the reliability theory is applied to close the chasm on evaluating the real maximum flow in a shared decentralized network which suffers from risky conditions on arcs and nodes.