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This simple case study tells the story of three young men who started an online business-to-business trading portal for fun, and to help fund them through university. They seized the opportunity of a major assignment to ask a new lecturer, the narrator, to guide them into profitability. Reluctantly, the young men were coerced into a literature survey, which proved surprisingly helpful to them. A simple research project followed, using mixed methods (survey, expert opinion, key account interviews). Based on the survey results and some simple frameworks from the literature, the young men not only completed their exercise, but also went on to turn their hobby into a sustainable business. The business still exists today, based on the simple study conducted some 12 years ago.

This study examines challenges to the retrospective financial evaluation of continuous improvement (CI) activities. Through a review of the literature and active engagement with CI implementations, we identify several issues that may lead to divergence between operational and financial assessments. Out of this conflict emerges a set of concepts that we find important − the delineation of soft versus hard capacity benefits, the distinction between capacity used and capacity paid for, and the data gaps that relate to these benefits – and recognize operational improvement and financial improvement as distinct, yet interrelated, theoretical constructs. This study helps explain a series of persistent gaps in the management accounting literature: Conflict between operations and accounting managers, the divergent perspectives of Johnson and Kaplan after their publication of Relevance Lost (Johnson & Kaplan, 1987), and the need for both operational control (including detailed capacity control) and accounting control in CI firms. Instead of one control system being at odds with the other, or co-existing despite each other, each of these systems support a different component of the financial improvement process. Operational control systems in CI firms emphasize non-financial information and social and behavioral controls that empower decision-making by employees, while accounting control systems seek to motivate and translate operational gains into financial gains. Soft and hard benefits linked to capacity play an integral role in understanding the difference in focus of each control system, while data limitations help to explain why these systems remain loosely coupled in practice (or absent, as seems to be the case with detailed Capacity Management Systems).

Supplier selection is a complex and strategic activity needed in every organization, involving many stakeholders and different attributes as price, delivery performance, and product quality. Globalization, in the last decades, increased the competitiveness between vendors, enhancing the use of decision models to support the best choice based on optimizations and bidding variations due to specific needs. This chapter presents three models of multi-dimensional auctions to improve an international humanitarian NGO process procurement efficiency by reducing procurement costs and the decision-making process time. These models have the advantage to be easily implementable in typically complex environments where there is a large number of categories, suppliers, and other features.

The first proposed model uses combinatorial auctions and is suited for procurement, where suppliers can benefit from cost complementarity. The second one uses volume discount auctions and is suited for volumetric purchases, where discounts for large quantities are common. The third one is a multi-attribute model, which computes the best possible solution considering several criteria and can be used in case of complex purchases that involve various categories and trade-offs and are subject to spot prices.

Several design considerations for this type of auctions are reviewed, as well as the mathematical formulation to determine the best alternative (i.e., winner) that can be solved using simple tools like Microsoft Excel. The models are optimized by a mixed-integer programming, and the multi-attribute one is developed using multi-criteria decision analysis (MCDA). All three models developed in this research showed superior results compared to the baseline, being between 9% and 20% more efficient than a regular supplier selection (singly choosing the lowest price) and improving the bidding compliance.