Pharmaceutical Formulation Company
Module: Distribution Replenishment PlanningIndustry: PharmaceuticalsSchedule a FREE Demo
Client Overview: Mid Sized Pharmaceuticals company with manufacturing and distribution of both APIs and Formulations. Client is present in more than 150 countries with an annual turnover of 1.3 Billion Euros. They have 37 subsidiaries and presence in 6 continents employing more than 8,000 employees worldwide.
Key Features
- Multi Echelon Inventory Planning
- Inventory Planning both on Procurement and Dispatch side
- Fixed Supply Network
- Periodic and Continuous Replenishment cycles
Problem Method and Result
The Puzzle
- The Client has an extensive network of distributors (and CFAs) distributing the drugs across the country
- They have three plants that manufactures the formulation in different parts of the country, apart from plants for API and a dedicated plant for Exports.
- The company wanted to streamline its distribution. So that, it could reduce the overall inventory in the system and improve order fulfilment.
- The client also wanted to automate the overall Sales and Operations Planning Process
The Solution
- Using historical data of Sales, Lead time for clients fixed Multi echelon the Supply Chain Network Map.
- For the Service Level requirements, reorder points were dynamically created at SKU-Depot level.
- Based on the Inventory levels at depot level and the Reorder Points, an automatic order is generated to replenish the stock based on economic order quantity.
- For dispatches, logic for Full Truck Load were written to ensure optimal usage.
- The solution used Demand Planning as well as Production and Procurement Planning to create the inventory at the Factory / Mother Warehouse.
The Result
- Greater visibility on the transport plan.
- Greater visibility on the probable date when a product could reach reorder point and thereby can plan production and trucking.
- In addition, the reorder points are dynamic and hence the inventory levels are controlled on a continuous basis.
- Periodic and Continuous Replenishment Strategies in 10 day buckets were implemented thereby allowing for course correction
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SOLUTIONS DESIGNED TO SYSTEMATICALLY ESTIMATE MARKET DEMAND
Be it New Product Development, Planning, or Promotions, implementing collaborative Demand Forecasting provides you with statistical / ML / AI data to support the course of action in the medium term.
the puzzle
Demand Planning comprises key step of arriving at a baseline number. This is done by using Statistical and ML/AI methods. The Module applies various methods of forecasting on the historical Sales data to arrive at the forecast in the coming months. The forecasting can be done at various levels such as SKU-Depot / Brand-Region / SKU Region
the solution
the result
Multiple Heirarchy
Forecasting at multiple Hierarchies e.g. SKU-Location, SKU-Sales Office, Brand-Channel etc.
Supersession
Supersession that allows linking of old to new items to provide meaningful continuity in history for effective forecasting new product
Statistical Methods
Variety of Forecasting Techniques using efficient Python to take care of different distributions such as seasonality, promotions etc. Also allows for Outlier Detection
Classification of Products
Dynamic Classification of Products across Runner / Repeater / Stranger using different parameters of frequency, variability etc.
ML / AI Methods
Selection of different AI / ML Methods for cases where ML can be implemented
Choice of Error Methods
Selection of Techniques based on different Error Measures with ability to add custom Statistical Techniques