From mass-production to personalization through mass-customization, the culture of modern companies has had to adapt in recent years to meet the ever more demanding needs of consumers. This change towards customer-centricity has quickly proven to be effective in differentiating itself from the competition, notably thanks to product configurators. But it has also brought new managerial issues that challenge the achievement of business objectives. In this Master's thesis, we try to understand how data produced by a configurator can be transformed to extract and present relevant information for decision-making. In a first step, a complete review of the literature has allowed us to clearly identify the business issues that a customer-centric company may face. Then, we have applied the V-Shaped Business Intelligence approach to a case study to produce a reporting platform allowing to monitor the human-machine interface of an industrial shelving configurator. This platform relates the abandonments to a series of measures identified in the literature. The results show that users seem to be satisfied and that navigation does not seem to be complex while the abortion rate is very high. We suggest that trial-and-error process (i.e learning by doing) is behind this finding although a lack of explicit/qualitative data may also be a determining factor.
|Date of Award||21 Jun 2022|
|Supervisor||Patrick Heymans (Supervisor)|
- business intelligence