Today, as opposed to 50 years ago, the cost drivers are to a high degree rooted in support and indirect costs which has discredited the validity of the traditional cost system. The traditional cost system allocates only direct costs to the product, customer or service and the overhead costs are simply allocated based on guesstimates of correlation. The correlations guesstimates could be volume-based, where overhead costs are distributed by percentage determined by the volume. The approach obviously leaves the management with uncertainty and flawed information regarding the factual costs incurred by producing their goods. The focus on optimisation leaves decision-makers clueless of where to take action, which is why ABC, as one of the most precise cost allocation techniques nowadays, would intuitively seem more relevant nowadays. However, several studies show that ABC is on the retreat in terms usage. The thesis has found barriers that might cause the low usage percentage of ABC and cause dissatisfaction among practitioners. The barriers were related to implementation and maintenance of an ABC system and identified to be related to cost, time and complexity issues. These problems were cause for rejection among practitioners and therefore the thesis sat out to suggest a method that could break down these barriers while limiting the wicked problems connected to the solution. The aim was to create a theoretical solution that allowed precision as opposed to the traditional cost system, while breaking down barriers of cost, time and complexity. The Process Mining techniques were found to potentially facilitate the desired solution through its automation capabilities supported by Machine Learning techniques. Process Mining correlates company data in the form of event logs and creates petri nets illustrating the actual processes of an organisation as opposed to idealised processes. The Process Mining approach where analysed and both advantages and disadvantages of using Process Mining to facilitate ABC implementation and maintenance was detected, along with prerequisites. The Process Mining techniques are methods within the field computer science and through its algorithms it can be used as a tool for process mapping, conformance checking and continuous improvement. Process Mining as a facilitator was then discussed in terms of whether using Process Mining could be justified given Process Mining’s prerequisites, disadvantages and ability of overcoming ABC barriers. It was found that Process Mining compliments ABC in terms of implementation and transition by breaking barriers, easing set-up and measuring, and additionally it was found that Process Mining could facilitate the maintenance as well - all of these relating to the same barriers of cost, time and complexity. However, it was also found to be troublesome to use Process Mining given the need for data of a certain quality and extensive logging, which emphasises the wicked problem issue which is Side 5 af 133 almost inherent in any solution-oriented proposition. The discussion was based on the degree of which Process Mining broke down the barriers or simply shifted problems from being ABC related to Process Mining related e.i. Process Mining being costly, time consuming and complex to implement. The essence of the thesis conclusion was the data quality as the next research area in terms of fully (almost) overcoming the barriers related to implementing and maintaining a well-functioning ABC system.
|Educations||MSc in Supply Chain Management , (Graduate Programme) Final Thesis|
|Number of pages||134|