Precise cost forecasts: Diebold Nixdorf improves its service management thanks to data analysis
The accurate estimation of service costs poses considerable challenges for many companies. An inadequate overview of failure rates and the associated service costs can lead to considerable misjudgements. This often results in inflated cost estimates or contracts with disappointing service margins. This is where the it’s OWL project ‘Data-based product management (product.intelligence)’ comes in. Diebold Nixdorf, a provider of IT solutions and services for financial institutions and retailers, is developing a transparent cost analysis in the project by means of a fact-based determination of failure rates on the basis of historical service deployment and usage data. This analysis is intended to ensure a more accurate forecast of expected service costs and more precise pricing, as well as to identify improvements in product design.
“A lack of overview of failure rates and therefore service costs at module and component level can lead to incorrect assumptions when estimating service costs,” says Dr. Andre Kolle, Senior Manager Process Automation and Data Analytics at Diebold Nixdorf. This in turn can lead to costs being set too high and the order being awarded to a competitor or the future service margin being below expectations when the contract is signed. The it’s OWL project aims to solve this problem by determining failure rates on the basis of historical service deployment data and usage data. “Among other things, this involves breaking down the deployment data from our ticket system to module and component level and determining the resulting spare parts and repair costs,” says Kolle. “By implementing the data analysis, the initial aim is to create transparency of service costs at component level.”
Accurate forecast of service costs
On this basis, Diebold Nixdorf can make a more precise forecast of the expected service costs and therefore more accurate pricing based on the device configuration, which should increase the proportion of product sales with an accompanying service contract and minimize contracts with low service margins. “On the other hand, these findings can also be used to derive starting points for improvement measures, including in product design,” adds Kolle.
These are the challenges facing Diebold Nixdorf
The global consolidation of data poses various challenges, such as the linking and harmonization of data from different ticket systems, various master data problems such as inconsistent product descriptions and the identification of the affected component of a fault based on the technician debriefing. “In addition, technician and spare part costs from different sources must be combined and the service costs must be correlated with the utilization of a device,” says Kolle.
