The e-cigarette industry involves the production and distribution of electronic nicotine delivery systems (ENDS), commonly known as e-cigarettes or vapes. These devices are designed to simulate the experience of traditional tobacco smoking by vaporizing a liquid solution, often containing nicotine, flavorings, and other chemicals. The industry has witnessed substantial growth, driven by factors such as changing consumer preferences, perceived harm reduction compared to traditional smoking, and marketing strategies.
Use Case – Demand & Operations Planning
The aforementioned company is a manufacturer of electronic cigarettes and nicotine-based items, aiming to offer a tobacco smoking alternative. The company's products include Vapes and a variety of free-base nicotine flavors made up of natural oil and extracts, enabling smokers to enjoy smoking while bypassing the harmful effects of tobacco. They provide various products, including device kits, nicotine pods, and accessories, available in a variety of flavors.
- ~$2B Revenues
- 10+ Countries
- ~2k+ Employees
- Demand Planning done by different teams using different forecasting methods globally resulting in in-efficient decision making.
- Working across different geographies to understand the forecasting methods followed.
- Most of the calculations were done using multiple excel book versions.
- Missed opportunities due to data integrity leading to inaccurate plans.
Identifying expected demand levels for the product or service with multiple subtle changes that add up over time and change the trajectory of demand.
Transforming a strategic plan into a detailed roadmap, delineating precise actions the team will undertake on a weekly, and at times, a daily basis.
The metrics and outcomes that marketing departments look at to determine how well the marketing activities are doing at achieving the goals in marketing plans.
Internal reports used to run the organization, make business decisions, and monitor progress to make more accurate, data-driven decisions.
- Demand Planning done based on SKU level Statistical forecast data, users , Doors planning and penetration.
- Enabled the statistical forecasting model to determine the forecasting based on SKU / Region.
- Statistical forecasting implemented using 31 forecasting models to generate best-fit by either product, product category or SKU.
- Devised an uniform method for demand planning across geographies based on North America demand planning model
- Implementation of scenario analysis on Doors planning
- Streamlined and rationalized demand planning process across geographies.
- Scenario analysis in doors planning helped in effective planning for new doors.
- Better forecast generation with statistical fit forecasts based on Actual or Adjusted history giving planners a baseline demand plan.