University–industry collaboration: using meta-rules to overcome barriers to knowledge transfer

This is the final version of the article. Available from Springer Verlag via the DOI in this record.University–industry knowledge transfer is an important source wealth of creation for all partners; however, the practical management of this activity within universities is often hampered by procedural rigidity either through the absence of decision-making protocols to reconcile conflicting priorities or through the inconsistent implementation of existing policies. This is problematic, since it can impede operational effectiveness, prevent inter-organisational knowledge-creation and hamper organisational learning. This paper addresses this issue by adopting a cross-discipline approach and presenting meta-rules as a solution to aid organisational decision making. It is proposed that meta-rules can help resolve tensions arising from conflicting priorities between academics, knowledge transfer offices and industry and help facilitate strategic alignment of processes and policies within and between organisations. This research contributes to the growing debate on the strategic challenges of managing knowledge transfer and presents meta-rules as a practical solution to facilitate strategic alignment of internal and external stakeholder tensions. Meta-rules has previously only been applied in a computer intelligence context however, this research proves the efficacy of meta rules in a university–industry knowledge transfer context. This research also has practical implications for knowledge transfer office managers who can use meta-rules to help overcome resource limitations, conflicting priorities and goals of diverse internal and external stakeholders

A novel data-driven algorithm for the automated detection of unexpectedly high traffic flow in uncongested traffic states

We present an algorithm to identify days that exhibit the seemingly paradoxical behaviour of high traffic flow and, simultaneously, a striking absence of traffic jams. We introduce the notion of high-performance days to refer to these days. The developed algorithm consists of three steps: step 1, based on the fundamental diagram (i.e. an empirical relation between the traffic flow and traffic density), we estimate the critical speed by using robust regression as a tool for labelling congested and uncongested data points; step 2, based on this labelling of the data, the breakdown probability can be estimated (i.e. the probability that the average speed drops below the critical speed); step 3, we identify unperturbed moments (i.e. moments when a breakdown is expected, but does not occur) and subsequently identify the high-performance days based on the number of unperturbed moments. Identifying high-performance days could be a building block in the quest for traffic jam reduction; using more detailed data one might be able to identify specific characteristics of high-performance days. The algorithm is applied to a case study featuring the highly congested A15 motorway in the Netherlands

Disaggregate and aggregate inventory to sales ratios over time: the case of German corporations 1993–2005

Although inventory reduction has been a major topic in production and operations management research for many years, there is a lack of empirically confirmed answers for questions such as: Have inventories in fully industrialized economies such as Germany decreased, overall, during the past decades? To the extent, inventory reductions were successfully realized, in which industries did they occur? Are there differences in inventory reduction achievements between raw materials, work-in-process, or finished goods? Are there measurable effects of inventory reductions upon the financial performance? To the best of our knowledge, this empirical study is the first one to investigate long-term inventory development on a firm as well as on industry level in a major European economy. It is based on data from German corporations and provides answers to the research questions stated above. The study’s findings indicate that total inventory to sales ratio decreased in a statistically significant extent in four out of six industry sectors during the time frame investigated. Further results suggest that the overall impact of inventory reductions to the financial performance of companies is only of a small degree