Volume 9, Number 1, 2009

Improving Procurement through Regression Analysis: A Case Study of Predicting Argentine Jet Fuel Prices .......... 1
J. A. Salaverry and E.. D. White III

Public Procurement and Innovation: Towards a Taxonomy ........................................................................................ 17
L. Hommen and M. Rolfstam

SYMPOSIUM

Symposium on International Public Procurement, Part II ............................................................................................. 57
J. Telgen and K. V. Thai

Does e-Procurement Save The State Money? ............................................................................................................. 58
M. Singer, G. Konstantinidis, E. Roubik and E. Beffermann

Bridging the Divide – Commercial Procurement and Supply Chain Management: Are There Lessons for Health Care Commissioning in England? ................................................................................................................................ 79
B. A. Allen, E. Wade and H. Dickinson

The Purchase of Technology in Health Organisations: An Analysis of its Impact on Performance ............................. 109
A. Ancarani, C. D. Mauro and M. D. Giammanco

ABSTRACT. The paper presents an investigation carried out in an Italian health organisation, aimed at studying the purchasing process of medical equipment at the hospital ward level, and at assessing its impact on hospital ward performance. A model of the decision process that leads to purchase is developed. The results show that the acquisition of technology has a positive impact on the ward’s relative efficiency, and that efficiency is further linked to the specific goals pursued by the head of ward and by the constraints faced.

ABSTRACT. Scientific literature reports scarce evidence of whether Internetbased procurement systems improve the efficiency of State purchases. We propose a methodology to estimate savings in: (i) the centralization of administrative tasks, and (ii) price differentials due to a larger number of contractors and suppliers bidding on contracts. We test our methods with ChileCompra, the Chilean e-procurement agency. During 2007, 885 Chilean State agencies used this system to purchase US$4.5 billion in products and services. Our preliminary results show price reductions of 2.65% and administrative cost savings of 0.28%-0.38% between 2006 and 2007.

ABSTRACT. Current English health policy is focused on strengthening the ‘demand-side’ of the health care system. Recent reforms are designed to significantly enhance the capability and status of the organisations responsible for commissioning health care services and, in so doing, to address some of the perceived problems of a historically provider/supplierled health system. In this context, commissioning organisations are being encouraged to draw on concepts and processes derived from commercial procurement and supply chain management (SCM) as they develop their expertise. While the application of such principles in the health sector is not new, existing work in the UK has not often considered the role of health care purchasers in the management of health service supply-chains. This paper describes the status of commissioning in the NHS, briefly reviews the procurement and SCM literature and begins to explore the links between them. It lays the foundations for further work which will test the extent to which lessons can be extracted in principle from the procurement literature and applied in practice by health care commissioners.

ABSTRACT. Of all oil products consumed by the Argentine Air Force (AAF), jet fuel is the resource with highest demand and at the end of the day the most expensive support item procured by the AAF. Accurate predictions of Argentine jet fuel prices are necessary to improve AAF financial and logistics planning. Multiple regression analysis is one such tool that can aid in accurately forecasting the amount required when procuring this valuable commodity. Using this methodology, we develop and illustrate a highly predictive model that has an adjusted R2 of 0.98 and an average percentage absolute error of 4%.

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