According to the analysis, the company’s service performance was on 79% level on an average. Although the performance was not on the desired level (85% at the time), the biggest performance gaps were found in high prices parts that had sporadic demand. As the service performance level was below 80% in majority of the groups, it suggested that variances and their causers were also distributed evenly in the distribution chain. One reason for this was that there were several processes changes being made in the company recently and the new processes had not been fully implemented. While the service performance KPI was below the target relatively evenly, the stock-out costs analysis revealed that there are five groups where the costs are considerably higher than in the other groups. From Fig. 8 it is seen that in these groups (marked with gray), 30% of total parts were causing 80% of stock-out costs.