About Mallow International
Mallow International is a leading Exporter, Manufacturer, Supplier of Bed Linen , Cushion Covers, Table Linen from Karur, Tamil Nadu, India. They export custom made unique home textile products to most of the EU countries along with USA, Australia, Brazil and few Latin American countries.
Key Results:
An Intuitive and Proactive Demand and Inventory Planning Model is developed and implemented for Mallow International, Karur, India. The model is built using RPA in Google sheets for specifically run the Amazon FBA Operations. The model has the capability of demand planning, replenishment triggers, stock out flags and much more!
1. Business Case
Business case explains the Key Business Issues the Replenishment Model targets to solve & the Game Plan is the Methodology and Steps to solve those Issues.
Milestone-1: Map the Current State
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- Stocking Points
- Cost Components
- Lead Times at each Node
- Demand Variability
- Lead Time Variability
- Other Parameters as required
Output: A Schematic Visualization (inspired by VSM) to show the supply chain in a single page focusing on Lead Times, Cost and Inventory. Replenishment Conceptual Document (Solutioning) explaining the Key Functionalities to solve the Business case.
Milestone-2: Build an Intuitive Integrated Demand and Inventory Planning Model (refine/build) the following functionalities
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- Ability to predict stock-out (leading indicator)
- Estimate Recommended Stocking Quantities (RSQ)
Output: Automated ‘Integrated Planning Model’ using RPA.
Milestone-3: Integrated Planning Model Implementation
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- Update Live data & Model Go-Live
- Model Usage SOP (Video Work Instruction)
Output: Model Go-Live & Standard Operating Procedure
2. Structure of the Replenishment Model
Model Platform & Highlights:
• Built in Google Sheets with RPA!
• No Complex Macros! Only Inline formulae and easy to use.
• Model Ownership & Access Controls set in Google Sheets.
• No Complex workflow. The final key insights at product level will be brought into a single page i.e., one row for each products with all the key data points, all calculated data and output.
• Variables set as an User Input. Ex: Additional stock % for Gold Products
3. Key Databases / Data
| DATA SET | DESCRIPTION |
| Demand History | Sales data at Product level – 1-3 years data |
| Lead Time Data | Lead time data from the order for replenishment till the product is available at the Stocking point available at the Stocking point |
| Storage Cost Data | Slab-wise storage cost information at each stocking point |
| Open Orders Data | Transfer Orders (PO) which are open at stages of Manufacturing, FG (Ready to Ship) and In-Transit |
| Stock On-Hand Data | Inventory available for sale at each stocking point for each product at the defined Unit of Measurement |
| Variability Data | Focus is on the Demand Variability. Based on the sales pattern, Month-wise variability will be estimated by product |
| Gold Products | It is an estimated data sets once in a Quarter based on the sales and the contribution to the overall Top Line |
| Seasonality Products | It would be a Sub-set of Variability data or a hard coded Seasonality based on Client Input |
| Demand Data (Seasonality) | We need to arrive at a logic to estimate the demand and starting month of Spike for Products with Seasonality |
| Run-Out Date Projection | Run-out date is the date in which an out of stock is estimated based on the forecasted consumption and current stock on hand |
| Run-Out Date Projection Post Shipments | Run-out date is the date in which an out of stock is estimated based on the forecasted consumption and current stock on hand + Estimated shipment ETA & Receipt quantity |
| Run-Out Date Projection In-Between Shipments | Run-out date is the date in which an out of stock is estimated based on the forecasted consumption, current stock on hand + Estimated shipment ETA & Receipt quantity + Estimated shipment ETA & Receipt quantity for 2nd Shipment |
| Forecasted Quantities | Forecast is on monthly basis for each product and it will be updated field-wise by the forecaster (User). |
| Interim Datasets | There could be other data sets which may be required while we get onto building the Replenishment model. |
| Space Forecast | Estimated space data in-terms of space storage units which would show the capacity utilization based on forecasted stocking pattern. |
| Cash Flow | An estimated cash flow situation based on the order flow, frequency and timing converted to $ based on the procurement parameters. |
4. Key Functionalities
- Data Insights for the Forecaster for Forecast Updates each Week
- Differentiated Stocking for Gold Products
- Differentiated Planning for Seasonal Products
- When to Order and How Much to Order?
- Excess Stock and How Much?
- Open Orders Visibility and Tracking
- Run out Dates at each Level (On Hand, On-Hand + 1st Shipment, + 2nd Shipment and so on)
- Potential Stock Out in Days (On Hand, On-Hand + 1st Shipment, + 2nd Shipment and so on)
- Dead Stock – Products in stock with no sale for past one Year
- Potential Stock Transfer suggestion from 3PL Warehouse in-case of a projected stock out
- Potential Stock Move suggestion from Amazon FBA to 3PL
- Space Forecasting based on the Supply Chain performance
- Capacity Utilization % and proactive actions