Transitory alliances: an instrument for surviving turbulent industries?

Over the past decades, firms have constantly struggled to deal effectively with their rapidly changing environment. Especially in high tech industries, costs of R&D have rocketed, whereas steep learning curves and ever shortening product and technology life cycles have reduced the time to recoup these costs. Under such conditions of turbulence, a new form of alliances becomes an important part of the innovator's toolkit: transitory alliances. Transitory alliances can be defined as short–lived alliances that focus on completing narrowly defined tasks in a very short time frame. Companies can no longer rely exclusively on their traditional alliance and M&A practices to survive industry turbulence. Furthermore, transitory alliances can be a wellspring of innovation and provide companies with access to a portfolio of new ideas. They can help companies to acquire knowledge in a swift manner, thereby strengthening their ability to survive the high speed of change. The specific characteristics of this alliance type are discussed in this paper. Typical examples are taken from Internet related sectors.     

Redesigning the supply of gasses in a hospital

Developing buyer–supplier relationships has been relatively ignored in the literature. This paper proposes a practical approach to evaluate and plan improvements in a supply relation, consisting of two parts. Firstly, a supply chain concept is proposed that consists of four areas: physical supply, planning and control, organisation and relation, and flow of information. This concept is useful in describing and analysing a supply relation. Secondly, a method based on scenario planning is used to generate alternative designs of a supply relation and to stimulate discussion. The approach is applied for a hospital and its supplier of gasses. The application helped considerably in gaining knowledge about the actual working of the supply relation of both organisations. The alternative scenarios stimulate discussion and development of their relation. The paper also contributes to the knowledge of applying supply chain management concepts in a hospital.     

Operation flood: impacts and issues

This article investigates whether Operation Flood - the Indian dairy programme supported by the WFP, the EEC and other international donor agencies - has achieved its objectives of enhancing dairy production in India while also offering poor and landless farmers a chance to participate in dairying. Detailed empirical work on the project did not confirm the grand claims made for Operation Flood as a success story of global significance. In particular, conclusions are highlighted with regard to Operation Flood's impact on India's dependency on foreign (EEC) deliveries of skim milk powder; India's domestic dairy production; the incomes of participating farmers; existing informal dairy production and marketing; the nutritional levels in rural areas; and the position of rural women. Various options for the future are outlined.     

Robust bootstrap procedures for the chain-ladder method

Insurers are faced with the challenge of estimating the future reserves needed to handle historic and outstanding claims that are not fully settled. A well-known and widely used technique is the chain-ladder method, which is a deterministic algorithm. To include a stochastic component one may apply generalized linear models to the run-off triangles based on past claims data. Analytical expressions for the standard deviation of the resulting reserve estimates are typically difficult to derive. A popular alternative approach to obtain inference is to use the bootstrap technique. However, the standard procedures are very sensitive to the possible presence of outliers. These atypical observations, deviating from the pattern of the majority of the data, may both inflate or deflate traditional reserve estimates and corresponding inference such as their standard errors. Even when paired with a robust chain-ladder method, classical bootstrap inference may break down. Therefore, we discuss and implement several robust bootstrap procedures in the claims reserving framework and we investigate and compare their performance on both simulated and real data. We also illustrate their use for obtaining the distribution of one year risk measures.