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Revolutionizing Retail Operations with IoT: A Case Study of a Major Canadian Retailer
Technology Category
- Sensors - Flow Meters
- Sensors - Liquid Detection Sensors
Applicable Industries
- Electronics
- Transportation
Applicable Functions
- Logistics & Transportation
Use Cases
- Demand Planning & Forecasting
- Transportation Simulation
Services
- System Integration
The Challenge
One of Canada's largest retailers, with a network of over 400 stores, was facing significant challenges in its supply chain operations. The company was struggling with network flow volatility, which was causing bottlenecks and creating issues with labor and transportation planning. The goal was to improve on-shelf availability by smoothing demand and aligning labor and transportation capacity. The company was also challenged with moving goods efficiently while reacting to merchant requests. They needed to evaluate options such as adjusting demands, adding another shift at the distribution center (DC), accessing the temporary labor pool, or accessing flexible transportation capacity. Furthermore, the company needed to plan daily for the next day, taking into account near-term capacity problems. There were challenges in aligning capacity with demand and blocking flows of excess demand based on revised capacities and merchant priorities.
About The Customer
The customer is one of Canada's largest retailers, offering a wide range of merchandise including apparel, housewares, small appliances, electronics, hardware, and grocery items. The company also provides specialty services such as pharmacies, garden centers, and vision centers. With a network of over 400 stores, the retailer has a significant presence across the country. The company was facing challenges in its supply chain operations, particularly in terms of network flow volatility, labor and transportation planning, and daily planning for near-term capacity problems. The goal was to improve on-shelf availability and efficiency in moving goods while reacting to merchant requests.
The Solution
The company partnered with o9, a leading provider of AI-powered supply chain solutions, to address these challenges. o9 helped the company create a logistics forecast by taking the previous year's data and applying machine learning algorithms that were enriched based on future events planned by the merchants. This approach allowed for scenario planning and helped the company anticipate and manage demand more effectively. With o9, a weekly plan was created based on current store demands, operating at an item/store/day granularity. This workflow drove tactical capacity planning changes before smoothing flow based on capacity limitations and flow prioritization defined by the merchant teams. The day plan, executed once a day, was driven off the confirmed store order drop for execution and was at an item/store/day granularity. This workflow also drove next day capacity planning changes before blocking flow based on capacity limitations and flow prioritization defined by the merchant teams. The o9 knowledge graph was used to build fully integrated end-to-end flow planning models and machine learning-based logistics forecasting.
Operational Impact
Quantitative Benefit
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