Download PDF
Auto Parts Retailer Optimizes Product Assortment with APT's Test & Learn Software
Technology Category
- Analytics & Modeling - Predictive Analytics
- Functional Applications - Inventory Management Systems
Applicable Industries
- Automotive
- Retail
Applicable Functions
- Sales & Marketing
- Business Operation
Use Cases
- Predictive Replenishment
Services
- Software Design & Engineering Services
- System Integration
The Challenge
The retailer’s merchandising team wanted to optimize its product assortment within the brakes category. As a part of this effort, the retailer planned to introduce an economy brake kit. However, management was unsure how to accurately quantify the total store impact of the new product, net of potential cannibalization effects on the existing brake product line.
About The Customer
The customer is a leading auto parts retailer with over $5 billion in annual sales. The company operates a vast network of stores across the United States, providing a wide range of automotive parts and accessories to both professional mechanics and DIY enthusiasts. With a strong focus on customer satisfaction and product variety, the retailer continuously seeks innovative solutions to enhance its product offerings and improve overall store performance. The company's merchandising team is particularly keen on leveraging data-driven insights to make informed decisions about product assortment and placement.
The Solution
Using APT’s Test & Learn for Sites software, the client designed an in-market experiment to test the new brake kit in a representative subset of stores to measure its incremental impact on profit. The software compared 'test' locations, where the new product was introduced, to customized groups of similar 'control' stores, that did not offer the economy brake kit. This methodology enabled the company to isolate the incremental profit impact of the new product amidst the noise of its sales data. APT software combined these significant drivers of performance into a highly accurate model to generate store-by-store sales performance predictions. Targeting the new product introduction to the subset of stores that were expected to perform well, based on APT’s predictive model, the auto parts retailer increased the value of the program by $8.5MM annually.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
Case Study
Improving Production Line Efficiency with Ethernet Micro RTU Controller
Moxa was asked to provide a connectivity solution for one of the world's leading cosmetics companies. This multinational corporation, with retail presence in 130 countries, 23 global braches, and over 66,000 employees, sought to improve the efficiency of their production process by migrating from manual monitoring to an automatic productivity monitoring system. The production line was being monitored by ABB Real-TPI, a factory information system that offers data collection and analysis to improve plant efficiency. Due to software limitations, the customer needed an OPC server and a corresponding I/O solution to collect data from additional sensor devices for the Real-TPI system. The goal is to enable the factory information system to more thoroughly collect data from every corner of the production line. This will improve its ability to measure Overall Equipment Effectiveness (OEE) and translate into increased production efficiencies. System Requirements • Instant status updates while still consuming minimal bandwidth to relieve strain on limited factory networks • Interoperable with ABB Real-TPI • Small form factor appropriate for deployment where space is scarce • Remote software management and configuration to simplify operations