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Transforming Demand Planning in Beauty Retail with IoT
Technology Category
- Platform as a Service (PaaS) - Application Development Platforms
- Sensors - Level Sensors
Applicable Industries
- E-Commerce
- Retail
Applicable Functions
- Logistics & Transportation
- Sales & Marketing
Use Cases
- Demand Planning & Forecasting
- Last Mile Delivery
The Challenge
The beauty retailer, marketing 15 brands and 2000 SKUs across various distribution channels, faced a significant shift from physical retail channels to online due to the COVID-19 pandemic. This shift necessitated a future-proof planning tool to support their ambitious growth plans and to gain a deeper understanding of demand drivers. The company was heavily reliant on Excel, which led to latency and siloed processes. They lacked a comprehensive understanding of the main drivers of demand for their five channels. The company was also unable to plan at the desired level of granularity, leading to shortages and delivery delays. Demand planners were spending most of their time crunching Excel spreadsheets, unable to focus on higher-level strategic tasks. Manual interventions were frequently needed, especially for estimating the effect of New Product Introductions.
About The Customer
The customer is a leading beauty retailer that markets a total of 15 brands, covering 2000 SKUs, and uses a wide variety of distribution channels: mass market, hair and beauty salons, and selective distribution for various brands. The company experienced a significant shift in delivery from physical retail channels to online, mainly due to the COVID-19 pandemic. This shift required a future-proof planning tool to support their ambitious growth plans and help obtain a deeper understanding of the drivers of demand. The company was heavily reliant on Excel, which created latency and siloed processes.
The Solution
The company deployed o9's demand sensing features to understand the drivers behind the growing channel shift across e-commerce, modern trade, and general trade channels. This allowed them to determine the right level to forecast at and perform demand planning with high granularity for all 2000 SKUs. The company also used the o9 Enterprise Knowledge Graph to build demand models, enabling it to run key demand planning processes for all product lines in a single integrated platform. o9's open architecture was leveraged to ensure data integration with Matelabs.ai. This solution removed low-value manual work by enabling data-driven exception workflows and ML-based New Product Introduction forecasting. The company replaced Excel with o9, gaining flexibility of having a platform, rather than a product, which permits modeling flexibility.
Operational Impact
Quantitative Benefit
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