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The purpose of this paper is to propose data mining techniques to model the return on investment from various types of promotional spending to market a drug and then use the model to draw conclusions on how the pharmaceutical industry might go about allocating promotion expenditures in a more efficient manner, potentially reducing costs to the consumer. The main contributions of the paper are two-fold. First, it demonstrates how to undertake a promotion mix optimization process in the pharmaceutical context and carry it through from the beginning to the end. Second, the paper proposes using directed acyclic graphs (DAGs) to help unravel the direct and indirect effects of various promotional media on sales volume.

A synthetic data set was constructed to prototype proposed data mining techniques and two analyses approaches were investigated.

The two methods were found to yield insights into the problem of the promotion mix in the context of the healthcare industry. First, a factor analysis followed by a regression analysis and an optimization algorithm applied to the resulting equation were used. Second, DAG was used to unravel direct and indirect effects of promotional expenditures on new prescriptions.

The data are synthetic and do not incorporate any time autocorrelations.

The promotion mix optimization process is demonstrated from the beginning to the end, and the issue of negative coefficient in promotion mix models are addressed. In addition, a method is proposed to identify direct and indirect effects on new prescriptions.

A better allocation of promotional expenditures has the potential for reducing the cost of healthcare to consumers.

The contributions of the paper are two-fold: for the first time in the literature (to the best of the authors’ knowledge), the authors have undertaken a promotion mix optimization process and have carried it through from the beginning to the end Second, the authors propose the use of DAGs to help unravel the effects of various promotion media on sales volume, notably direct and indirect effects.

The paper presents one of the first attempts to identify and categorise the fundamental barriers currently preventing the multibillion semiconductor equipment manufacturing industry from implementing existing B2B e-trading models for its secondary market. It furthermore proposes a global e-business strategy supporting aftermarket integration with the industry's supply chain.

Because of the global nature of the industry, the research employs a multiple case-study design to explore the state-of-the-art in semiconductor excess management. The data for this analysis are obtained through a number of in-depth interviews with experts from a cross-section of the industry, and further supplemented and validated with a systematic literature review and public corporate data.

The results indicate that significant market imperfections still exist in the industry due to information and knowledge deficits, organisational inefficiency and IP-related concerns. The considerable levels of third-party competition to the original equipment manufacturers raise questions about the existence and efficacy of reverse logistics processes and Closed-Loop Supply Chain (CLSC) management strategies in this industry. It has been shown that a leaner semiconductor supply chain is achievable through the implementation of the proposed B2B e-marketplace, maintaining the information exchange on the surplus/obsolete equipment and parts.

These outcomes are unique for supporting the design of the first global e-marketplace for the secondary semiconductor equipment and spares. The results can, furthermore, inform the standardisation of the semiconductor aftermarket transactions, streamline knowledge exchange mechanisms amongst different industry players and improve pricing strategies. These contribute to knowledge of principles allowing the aftermarket e-trading to become a key part of the value network in high-tech manufacturing industries.