Paul Liston

A simulation based supply partner selection decision support tool for service provision in Dell

Partner selection is an important aspect of all outsourcing processes. Traditional partner selection typically involves steps to determine the criteria for outsourcing, followed by a qualification of potential suppliers and concluding with a final selection of partner(s). Reverse auctions (RAs) have widely been used for partner selection in recent times. However, RAs, although proven successful in initial price reduction strategies for product and service provision, can suffer from reduced effectiveness as the number of executions increases. This paper illustrates Dells experience of such diminishing returns for its outsourced after sales product repair service and presents the development of a new partner selection methodology, which incorporates a new process improvement stage to be executed in combination with the final selection phase. This new methodology is underpinned by the development of a computer based simulation supply partner selection decision support tool for service provision. The paper highlights the significant additional cost saving benefits and improvement in service achievable through the use of advanced simulation based decision supports.

Towards automated data-driven model creation for cloud computing simulation

The increasing complexity and scale of cloud computing environments due to widespread data centre heterogeneity makes measurement-based evaluations highly difficult to achieve. Therefore the use of simulation tools to support decision making in cloud computing environments to cope with this problem is an increasing trend. However the data required in order to model cloud computing environments with an appropriate degree of accuracy is typically large, very difficult to collect without some form of automation, often not available in a suitable format and a time consuming process if done manually. In this research, an automated method for cloud computing topology definition, data collection and model creation activities is presented, within the context of a suite of tools that have been developed and integrated to support these activities.

Supply Chain Design: In An Outsourcing World

Supply chain management as a formal technique has been in existence since the mid- to late 1980s. It evolved in the western world from the concept of mass customization in the 1950s and 1960s, through to the use of manufacturing resource planning in the 1970s, to the concept of continuous improvement techniques, such as JIT (just-in-time) and TQM (total quality management) in the 1980s. Since its inception, supply chain management has evolved and adapted to the continually accelerating needs of what is today a truly global economy. With modern supply chains, globalization plays a significant role in their complexity. There is no agreed starting point for globalization, as it can be traced back through the centuries in different guises. Nevertheless, globalization has been rapidly increasing in the last 15 years or so. This has been facilitated greatly by vast improvements in transport, removal of trade barriers (such as the sustained expansion of the European Union and the continued implementation of multilateral trading systems, e.g. GATT/WTO) and the vast advancements of ICT (information and communications technology) (Ethier 2005, Ngowi et al. 2005, Morrissey and Filatotchev 2000).

A View of SME Clusters and Networks in Europe

It is a matter of course that each country in the large European Union presents specific characters and individual features of its own industrial environment. However, a common peculiarity can be recognized, evidenced by two numbers: the percentage of SMEs in any national industrial system, always close to 90% of the total number of enterprises, and the percentage of personnel employed in SMEs, greater than 60% of the active population. What can also be widely recognized in almost all European countries are the recent crises, which have affected SMEs, and the attempt by SMEs to counteract their difficult position by searching for agreements and cooperation. One type of reciprocal support SMEs looked for in a crisis was contracts with larger enterprises: this gave rise to supply chains. But often the desire of SMEs was to have collaborative links with other SMEs, operating in the same industrial sector and mainly located in the same region: this resulted in the rise of networks and districts. In the last decade, the European Commission has started to promote studies devoted solely to supporting these types of clustering. Some countries have also launched programs to finance SME aggregations, defining agencies for pushing the establishment of new SMEs groups. This chapter offers an outline of a number of different national situations, concerning the rise and, sometimes, the fall of SME clusters and networks. Obviously, the scope of this chapter is not to give an exhaustive presentation of the European situation of SME aggregations: it aims to force the reader to recognize similarities, weakness and strength aspects, and to apply these to an analysis of the SME aggregations performance.

Cloud based data capture and representation for simulation in small and medium enterprises

The data collection and representation phase is an important phase within the lifecycle of a DES study. It is recognized that for large companies the data collection and representation phase differs when compared to SMEs. DES is not widely used by small to medium sized enterprises (SMEs) due to complexity and related costs being prohibitively high. DES-related data can be stored in a variety of formats and it is not always evident what data is required (if even available) to support a DES model in relation to specific problem scenarios. Building on previous research output, this paper presents the implementation of a Cloud based SaaS application to process input data from a SME in the medical industry and to output this information to in a usable format towards data-driven automated simulation model building.