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Revolutionizing Demand Planning with IoT: A Case Study
Technology Category
- Application Infrastructure & Middleware - Data Visualization
- Platform as a Service (PaaS) - Application Development Platforms
Use Cases
- Demand Planning & Forecasting
- Inventory Management
The Challenge
The customer, a pioneer in water and housing products, was facing significant challenges in demand planning due to low forecast accuracy and heavy reliance on Excel spreadsheets. The company's demand planners were spending a lot of time manually copying data from sheets and manipulating it to generate demand scenarios. This manual process was not only time-consuming but also limited the company's ability to react quickly to changes in demand. Furthermore, the company's forecasting process was flawed as it predominantly used only lagging indicators, resulting in low forecast accuracy. The planning processes also varied widely across countries, with no single process or overview, leading to inconsistencies and inefficiencies.
About The Customer
The customer is a leading manufacturer of water and housing products that are designed to solve everyday, real-life challenges and improve home quality. They are pioneers in their field, constantly innovating to meet the changing needs of their customers. However, they were facing significant challenges in demand planning due to low forecast accuracy and heavy reliance on manual processes. Their planning processes were inconsistent and inefficient, varying widely across different countries. They needed a solution that could streamline their demand planning process, improve forecast accuracy, and enable them to react quickly to changes in demand.
The Solution
The company adopted o9's unique integrated platform to overcome their demand planning challenges. With o9, the company could still use Excel files but no longer needed to manually move data, saving valuable time. o9's platform also improved the company's forecasting process by incorporating internal and external drivers of demand, such as GDP, into its machine learning forecasting capabilities. This helped improve forecast accuracy. Additionally, o9's platform provided the capability to connect all planning processes across time horizons on a single integrated, cloud-native platform, eliminating inconsistencies and inefficiencies. The o9 Enterprise Knowledge Graph was used to build demand knowledge models that incorporated leading indicators of sell-out, such as trade promotions and marketing initiatives. The company also leveraged o9's open architecture to use best-in-class algorithms from R and Python to optimize their forecast.
Operational Impact
Quantitative Benefit
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