From BI to insights to predictive and prescriptive analysis – data is the holy grail at TNT

On a quest for business-driven costing insights

TNT’s objective was to improve business insights, predictive and prescriptive models and ensure alignment with business strategies and drivers. This would bring many benefits, such as improved customer insights, pricing capability, product profitability, transfer pricing, cost management and traffic lane profitability. With both TNT and delaware already mature thought leaders in these fields, driving more innovation and additional improvements wasn’t going to be easy. As a high-volume business delivering millions of consignments every month, the tiniest change to any business driver could have significant impacts on TNT’s top and bottom lines.

Rethinking the journey from costing to pricing

“We wanted to provide our management team with an end-to-end overview of how our revenues and profit were being affected by both external market forces and internal efficiency,” explained Anwar Mirza, currently global head of data governance at TNT. “With the help of delaware, we realized that we needed to rethink the journey from activity-based costing (ABC) to advanced analytics, cost-to-serve, pricing and transfer pricing. Since the early 90s, TNT has been an advanced practitioner in the use of ABC, calculating the unit cost of every aspect of its entire business and translating this to pricing as part of a sales process.”

Making the best of people, processes, technology and data

TNT’s cost modeling system relied on a clear understanding of the business drivers affecting employee roles and business processes. As a result, ensuring that software provided information and data of the highest quality was crucial. “For this project, it was essential for delaware to seamlessly deliver solutions where cloud processing and other technology considerations would not become the focus,” says Jos Gilissen, finance functional head at delaware.

“We needed to apply a structured and well-governed methodology to the preparation of our datasets for the business analytics environment,” explains Anwar. “Data quality is always an unacceptable reason to question our analytics and business models. By assuring the data quality from the outset, we guaranteed an on-time and on-budget project.”

Using business analytics to calculate the tangible value of data and its quality

Smart data preparation is a major factor in any business process in the IoT and digital era. Anwar’s data governance approach took into account the direct links between data quality, and key performance indicators and business actions. “We performed a profitability analysis with a high-quality dataset and another using a poor-quality dataset. The differences in our resulting unit costs were huge. We clearly demonstrated a movement in the unit cost through poor data quality. In this way, we could demonstrate the tangible value of data quality.”