
Abstract:
Forecasting & Replenishment (F&R) is the process of demand as well as replenishment planning generating order proposals and exceptions for the retail industry. SAP F&R is a solution meant for efficient replenishment of stores and distribution centers in retail industry. Its main goal is to simultaneously reduce inventory while optimizing actual service levels.
F&R is a multi-step process, focusing on forecasting for individual products and locations and replenishment planning based on forecast / reorder point and/or target stock. F&R focuses on products which are replenished regularly from external and/or internal vendors. Solution is capable to leverage quantitative techniques to forecast true market demand based on historical performance, past promotions, future events and demand generation activities. Solution requires the products to have a measurable and repeated sales history as well as up-to-date stock information to generate appropriate order proposals.
1.0 Business Process Definition
Forecasting & Replenishment (F&R) is the process of forecasting and replenishment planning generating order proposals and exceptions for the retail industry. F&R is a multi-step process and , focusing on forecasting for individual products and locations followed by replenishment planning based on forecast / reorder point and/or target stock.
F&R typically leverages quantitative techniques to forecast true market demand based on historical Performance, past promotions, future events and planned demand generation activities. Forecasting is followed by Replenishment Planning to generate order proposals and exceptions. Inventory Analysts review the order proposals and exceptions before releasing the order proposals for execution.
SAP F&R optimizes the internal logistics of retail companies by improving the replenishment processes and aims to achieve the following:
-Cut surplus stock in distribution centers and stores
-Reduce stock-outs in distribution centers and stores
-Lessen the large amount of manual work required by implementing highly automated replenishment planning in stores and distribution centers
-Increase transparency in the supply chain through effective analyses
-SAP F&R helps to minimize the total cost of ownership.
2.0 Forecasting & Replenishment Challenges Faced by Business
When clients focus on Forecasting & Replenishment for their DCs and Stores, they will face issues that could span supply, demand and even the nature of the products being forecasted.
Product Challenges:
Demand Challenges:
Replenishment Challenges:
3.0 F&R Process Flow Overview
Following diagram depicts an overview of the business processes involved in the execution cycle of F&R.
To ensure a smooth execution of the above processes, business needs to adopt certain best practices with regard to the information management related to master data management, sale history and forecasting processes, replenishment processes as well as analysis of KPIs.
4.0 F&R Best Practices
4.1 Best Practices - Master Data Management
4.2 Best Practices - Forecasting Process
4.3 Best Practices - Historical Data Management
4.4 Best Practices - Statistical Forecasting
4.5 Best Practices - Replenishment Process
4.6 Best Practices - Performance Measurement
5.0 Conclusion
Adherence to the best practices facilitates the smooth functioning of the planning process in F&R while optimizing the system performance and output of the planning algorithm. Data inconsistencies play a crucial role in F&R landscape. Unlike CIF in APO, where the transaction data are transferred from ECC to APO in real-time, buffer interface in F&R is asynchronous. Any synchronization between ECC and F&R is an on-going maintenance activity and needs to be ensured before every FRP run. While planning the data management strategy, planners must consider the performance issues due to data volume and schedule of the jobs to generate inconsistencies and their manual cleanup.
Best practices need to be established for respective organizations suiting to the specific needs of the business with regard to the master data management, forecasting process and application of demand influencing factors, replenishment process and optimization of order proposals followed by performance assessment of KPIs. Most importantly, bets practices should be treated as a set of guidelines and needs to be fine-tuned and optimized as the solution landscape as well as experience of the planners mature over a period of time.
6.0 Conclusion
Pravat Dash, CPIM is a Managing Consultant in the Business Consulting Services Group of IBM Global Services. He has over 16 years of SAP SCM/ERP implementation and industry experience in the area of Supply Chain Management and Logistics. He has worked as Lead Consultant and APO Team Lead for implementing SAP SCM solutions for clients in various industry verticals for the past 12 years. He has authored multiple papers in Supply Chain Planning space. You may reach him via email prabhatdash2003@yahoo.co.in.
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