Transforming Supply Chain Management with IoT: A Case Study of a Large Cigar and Tobacco Manufacturer
Technology Category
- Analytics & Modeling - Digital Twin / Simulation
- Functional Applications - Inventory Management Systems
Applicable Industries
- E-Commerce
- Retail
Applicable Functions
- Logistics & Transportation
- Sales & Marketing
Use Cases
- Demand Planning & Forecasting
- Digital Twin
Services
- System Integration
About The Customer
The customer is a large manufacturer of cigars and traditional pipe tobacco, owning about 200 leading brands. Over the years, the company has grown extensively, leading to a complex IT landscape with fifteen different ERPs and a diverse business structure involving various channels such as retail, wholesale, and e-commerce. The company's operations are characterized by omnichannel complexity, with each go-to-market channel having its own supply chain configuration and complexities. Despite these challenges, the company is committed to improving its operations and enhancing its supply chain management.
The Challenge
The customer, a large manufacturer of cigars and traditional pipe tobacco, had grown extensively over the years. This growth led to a scattered IT landscape with fifteen different ERPs and a complex business structure involving different channels such as retail, wholesale, and e-commerce. The company faced challenges with its omnichannel complexity, where each go-to-market channel had its own supply chain configuration and complexities. This led to forecast accuracy issues as the company applied a one-size-fits-all stat forecasting model. Additionally, the company lacked end-to-end visibility across all nodes on their supply chain, leading to an unconstrained supply chain operation. The company also struggled with scenario planning, running its operations based on an inaccurate forecast and an unconstrained supply plan without the ability to run business scenarios and translate that into financial consequences.
The Solution
The company partnered with o9 to address these challenges. o9 provided end-to-end planning capabilities, enabling the company to segment go-to-market channels and demand. This allowed the company to incorporate internal and external drivers of demand and key market trends into o9’s highly differentiated ML forecasting models, thereby limiting excess inventory and stock-out occurrences. o9 also provided a unique capability of a digital twin for all 30,000 SKUs, allowing for constraint supply planning and modeling of all constraints and costs. Furthermore, o9 offered advanced scenario planning capabilities, enabling the company to understand the implications of different demand and supply scenarios. The o9 Enterprise Knowledge Graph was used to move towards driver-based forecasting, supply planning across time horizons, inventory optimization, control tower, and IBP. These capabilities allowed the company to respond faster to changes in the market and reduce the total cost to serve.
Operational Impact
Quantitative Benefit
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