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CLIENT CASE STUDY


Ducati is Analytics Driven

Analytics takes Ducati around the world at speed and precision.

DUCATI

About Ducati- Motor Holding S.p.A. - A Sole Shareholder Company - A Company subject to the Management and Coordination activities of AUDI AG

Since 1946 Ducati has been producing sport-inspired motorcycles characterised by high-performance Desmodromic engines, innovative design and cutting-edge technology. Situated in Bologna, the factory is located in the Borgo Panigale district. The model range covers several market segments with the following families: Diavel, Hypermotard, Monster, Multistrada, and Superbike. In 2015 Ducati presented the Ducati Scrambler: a new brand made of bikes, accessories and apparel that provide the last word in creativity and self-expression. These authentic icons of “made in Italy”, together with an extensive range of associated accessories and technical and lifestyle apparel, are distributed in 90 countries around the world. Ducati competes in both the World Superbike and MotoGP World Championships. In Superbike Ducati has won 17 Manufacturers' titles and 14 Riders' titles and in 2011 passed the historic milestone of 300 race victories. Ducati have participated in MotoGP since 2003, winning both the Manufacturers' and Riders' titles in 2007.

The Story

Opportunity

Ducati Motor Holding S.p.A. seeks to continually improve every aspect of its business to enhance business performance in today’s highly competitive motorcycle industry. Ducati wanted to improve its spare parts demand planning capabilities in order to increase the quality and efficiency of service levels while optimizing stock levels of spare parts and improving cash flow.

Solution

Ducati chose to leverage the Spare Parts Inventory Optimization application on the Accenture Insight Platform to improve forecast accuracy. The project was developed between 2012 and 2013 and Ducati’s “demand planning” department is still using the same application, continuously improving performance and results with Accenture analytics services and forecast modeling provided on a monthly basis.

An important activity during the initial phase of the project, was to analyze the historical orders, data and configure an automatic algorithm that classifies each SKU according to historical data characteristics (length, variance, trend, number of no observations, etc.) and additional business information (discount class SKU, SKU with specific quality campaigns, etc.). SKUs with the same classification were then grouped by SKU aggregates that were subsequently associated with a statistical model. In so doing, every SKU in the same aggregate shares the same model specification, but the parameter estimate and the forecast run separately for each SKU. Critical SKUs for which there could be some performance improvement using a different model were isolated in a “single association” aggregate, where each SKU has its own model specification.

The Spare Parts Inventory Optimization application was quickly installed in just a few weeks and customized in a few months. Fine-tuning activities were related to specific configurations of Ducati business processes, incorporating the logic and complexity of IT, including SKU attributes and substitution logic, key performance indicators, actual and reconstructed orders, time series and other aspects. The advanced analytics forecasting solution is now completely integrated within Ducati’s existing enterprise systems.

Results

With Accenture’s Spare Parts Inventory Optimization application, Ducati is experiencing increased accuracy of spare parts forecasts due in large part to the classification and custom modeling of time series. It has also seen elapsed time for updating spare parts orders forecasts decline from hours to minutes, allowing for the evaluation of many mid-month scenarios. The application encompasses all of the company’s spare parts stock items and serves all stories and suppliers with a single distribution point. Additionally, Ducati is now able to optimize the inventory for dealers, suppliers and stores across 80 countries, attaining positive best-service-level efficiency records.