A Teaching And Learning Case Study On Data Mining Using Association Rules For SMES

Big Data Analytics is widely adopted by large companies but to a lesser extent by small to medium-sized enterprises (SMEs). SMEs comprise 99% of all businesses in the UK (6 million), employ 61% of the country’s workforce and generate over half of the turnover of the UK’s private sector (£2.2 trillion). SMEs represent 99% of all businesses in Europe and 90% of all businesses worldwide. Therefore, assisting them to gain competitive advantage by the adoption of technology is important. One of the key barriers to adoption is the shortage of case studies. This paper documents the process in which a positioning tool has been developed to help SMEs analyse their readiness to adopt Big Data Analytics. The positioning tool has been applied to a medium-sized logistics company who are currently analysing Big Data captured through the telematic sensors on their fleet of trucks. The case study proposes how the company can enhance their analytics capability further by undertaking data mining through the form of applying association rule mining to gain competitive advantage. This paper outlines how the positioning scoring tool was used in the case study, and how association rule mining was undertaken and the type of rules which may be identified. The development of this case study provides an approach which could be replicated by educators to develop case studies in other sectors such as manufacturing, retail and the service industry.