Transforming Retail Operations with IoT: A Case Study of an American Clothing and Home Decor Retailer
Technology Category
- Functional Applications - Inventory Management Systems
- Sensors - Level Sensors
Applicable Industries
- Apparel
- Retail
Applicable Functions
- Procurement
- Warehouse & Inventory Management
Use Cases
- Inventory Management
- Predictive Replenishment
About The Customer
The customer is an American clothing and home decor retailer that specializes in casual clothing, luggage, and home furnishings. The company was facing challenges with its manual and Excel-driven planning processes across various functions and time horizons. This led to suboptimal decision-making, inventory management, and service level challenges. The company's key planning processes, including demand planning and replenishment planning, were executed in silos, making it difficult to connect the dots and make informed decisions. The company lacked a statistical demand forecast, and the planners were spending a significant amount of time disaggregating the forecast to a size level, leaving little time for actual analysis.
The Challenge
The American clothing and home decor retailer, specializing in casual clothing, luggage, and home furnishings, was grappling with highly manual and Excel-driven planning processes across functions and time horizons. This led to suboptimal decisions, inventory, and service level challenges. The key planning processes, including demand planning and replenishment planning, were executed in silos, without the ability to connect the dots. The company lacked a statistical demand forecast, and planners created forecasts based only on sell-out at an item level. They spent a significant amount of time disaggregating the forecast to a size level, leaving little time for actual analysis. Furthermore, the company faced challenges in accurately performing replenishment planning due to a high level of required manual interventions and processes not supported by analytics.
The Solution
The company adopted o9, a unique platform capable of connecting all planning processes for basic and seasonal product lines across all channels (Retail, Uniform, Catalogue, Online/ Marketplace) and regions on a single integrated cloud-native platform. With o9, the company gained access to sophisticated, collaborative, and ML-driven forecasts, enabling data-driven exception workflows and reducing the time spent on grunt work. The platform also improved the company's ability to manage the replenishment plan for stores and the procurement plan for DCs, at various grains and horizons for all regions. The use of o9’s advanced forecasting and safety stock-driven replenishment plans led to a more balanced inventory in the stores. The Enterprise Knowledge Graph was used to create demand and replenishment knowledge models, replacing Oracle and Excel systems. The functionalities implemented included lost sales correction, outlier cleansing, characteristics-based item mapping for new product introduction, ML forecasting, planner enrichment models to accommodate causal factors, and size scaling functionality.
Operational Impact
Quantitative Benefit
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