o9 Solutions, Inc. Case Studies Transforming Retail Operations with IoT: A Case Study of an American Clothing and Home Decor Retailer

Edit This Case Study Record

	Transforming Retail Operations with IoT: A Case Study of an American Clothing and Home Decor Retailer o9 Solutions, Inc.

Transforming Retail Operations with IoT: A Case Study of an American Clothing and Home Decor Retailer

o9 Solutions, Inc.

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	The implementation of o9 led to a transformation in the company's operations. The platform's ability to connect all planning processes on a single integrated cloud-native platform eliminated the silos that previously existed. The company now has access to sophisticated, collaborative, and ML-driven forecasts, which has significantly reduced the time spent on manual tasks. The improved replenishment planning has led to a more balanced inventory in the stores. The company also benefited from the implementation of the Enterprise Knowledge Graph, which replaced Oracle and Excel systems. The functionalities implemented have increased efficiency and accuracy in demand and replenishment planning. The company also appreciated the dedication of the o9 team to deliver value, rather than just executing the project, and their deep industry knowledge in the retail industry, which allowed for a faster time to value.

Impact #1

The implementation of o9 led to a transformation in the company's operations. The platform's ability to connect all planning processes on a single integrated cloud-native platform eliminated the silos that previously existed. The company now has access to sophisticated, collaborative, and ML-driven forecasts, which has significantly reduced the time spent on manual tasks. The improved replenishment planning has led to a more balanced inventory in the stores. The company also benefited from the implementation of the Enterprise Knowledge Graph, which replaced Oracle and Excel systems. The functionalities implemented have increased efficiency and accuracy in demand and replenishment planning. The company also appreciated the dedication of the o9 team to deliver value, rather than just executing the project, and their deep industry knowledge in the retail industry, which allowed for a faster time to value.

Benefit #1	Significant hours saved on manual planning and inventory management.
Benefit #2	Substantial reduction in inventory and expedite costs.
Benefit #3	Notable improvement of instock/fill rate.

Benefit #1

Significant hours saved on manual planning and inventory management.

Benefit #2

Substantial reduction in inventory and expedite costs.

Benefit #3

Notable improvement of instock/fill rate.

Download PDF Version

Overview

Transforming Retail Operations with IoT: A Case Study of an American Clothing and Home Decor Retailer

Operational Impact

Quantitative Benefit