BOOST 4.0 IX
PILOT : AFTER-SALE CONSUMER SERVICES POWERED BY PREDICTIVE ANALYTICS
SPARE PARTS SUPPLY CHAIN OPTIMIZATION
CONTACT NAME MISS BARBARA VILLA
EMAIL barbara_villa @whirlpool.com
PILOT PRODUCT DESCRIPTION
Spare part production and management is one of the most complex and important challenges for whirlpool consumer service department.
The analysis of the historical data is a key element to predict in an accurate way the future needs of spares in order to anticipate the consumer demand. The pilot will put together different types of information from different source systems inside the organization in order to automatize the planning of spare parts along the entire supply chain.
OBJETIVE OF THE PILOT
The scope of the use case is mainly the implementation of a forecast process that considers both historical data and other company information related to customer feedback and production feedback. The objective is to gather as much information as possible from the company and the customer perspective in order to improve the effectiveness of the forecasting process, in order to make more efficient the Demand Planning Process and to facilitate the Supply Planning Process.
To reach this goal, an Event Stream Process will be created in order to analyze data of connected appliances and use them as additional input in the forecast of the spare parts. Moreover, additional analysis will be possible with the aim of intercepting fails and damages, enabling new customer-care actions and services.
The new forecast models will use big data technology and analytical techniques like machine learning in order to improve the accuracy of the forecast estimations, while the data of connected appliances will be acquired by the SAS ESP Engine.
The IT landscape that support the business process should change, incorporating since the beginning of the process all the relevant information needed to produce a better forecast directly into the planning system.
In this scenario BOOST will provide a new contribution to the forecast and supply generation process by collecting and elaborating data from different data sources inside the organisation:
- Finish Goods Sales (sell in / sell out)
- Finish Goods Product Registrations and Extended Warranty Registrations
- Service Network Product Interventions
- Customers recorded calls and claims
- Internal Factory Production Feedback and Quality Tests
Smart Appliances data coming from connectivity
KPIS AND METRICS
The main KPIs of the pilot are:
Spare Part Stock Reduction (-30%)
Increase Inventory Turnover (+35%)
Lead Time to Consumer (-25%)
Plant Service Level (+1%)