EIS Educational Materials
This page is an index of EIS topics to help customers, consultants and supply chain experts learn more about SAP Enterprise Inventory and Service Level Optimization. These presentation files are each on a unique topic. These are topics covered in our customer education sessions and may need additional explanation to apply to your situation.
There are also formal education classes about EIS offered by SAP at this link: https://learningportal.wdf.sap.corp/ - enter SCM EIS into the search box for a list of EIS classes offered by SAP.
We also have a page specifically for EIS Support materials to help our customers over the last few years. If you are a customer with a need for support, click here for help at sap.com or click here for the SAP Service Marketplace link to create an incident (note you have to register and search for SCM-EIS). But browse through this material first to see if you can find the answer to your questions. There are also many support documents, solutions and ideas on the EIS WIKI Support page if you click here.
This topicis an introduction to SAP Enterprise Inventory and Service Level Optimization (EIS). It includes a definition of inventory optimization, lists demand factors and supply factors that impact safety stock, and shows how oversimplified planning wastes resources and puts your business at risk.
This topic covers inventory replenishment definitions, the impact of key drivers on inventory and the impact of time-varying demand.
Safety Stock calculations are based on forecast error and service level targets. Forecast error is modeled using Gamma distribution. Gamma can be asymmetric or symmetric. If it is symmetric, then normal distribution can be used in the calculation. Select Gamma distribution to improve the accuracy of inventory target for items with lumpy demand, items with intermittent demand, or slow-moving items.
This topic has the definitions of Non-Stockout Probability (NSP) and Fill Rate (FR) as the two different methods of determining service level targets in EIS. There is also an example of how these are calculated.
Different supply chain stages should optimally share the inventory and uncertainty risk. Propagate demand from downstream to upstream to model the impact of internal service level (ISL) decision of the upstream stage at the downstream stage. In multi-stage supply chains, stages are all linked together. Orders placed by the customer-facing node create demand for product upstream while upstream service levels have an impact on ability to meet service level downstream. Multi-stage models allow the internal service level of the upstream stage to be modeled at the downstream stage and ensure that service level targets are met.
This topic focuses on characterizing demand uncertainty. It describes best practices for calculating demand uncertainty, the impact of demand uncertainty on safety stock requirements. The following topics are covered:
- Incorporating Forecast Lag
- Intermittent Demand
- Missing Data
- Forecast Bias
- Recency Factor
This topic covers the impact of supply variability factors on the calculation of safety stock. These factors include:
- Lead Time Uncertainty
- Schedule Attainment Problems
Service Time is the duration between when the order is placed and when it is expected to be fulfilled. If your customers are placing orders in
advance of their delivery date requirements, this can be reflected as Service Time. Service Time does have an impact on the safety stock calculations.
Most users of EIS follow periodic planning models which are fixed period, variable order quantity. The alternative described here is Reorder Point which is fixed order quantity, variable period.
Reorder point (ROP) planning is a consumption-based MRP procedure. An order or production is triggered when the total amount of on-hand and on-order stock falls below the reorder point.
EIS calculates both quantities; you choose which one to use in your supply chain.
Carrying Cost in EIS
Inventory Holding Cost has a significant impact on inventory optimization. Carrying cost, or inventory holding cost, is the effective “interest rate” at which inventory costs are carried. Inventory holding cost has both financial and operational components. Tax effects should also be considered in calculating inventory holding costs.
Any stocking point may service multiple demand streams. For example, a customer facing node can ship to multiple customer classes. A hybrid node has both internal and customer demand. These demand streams can be differentiated based on service level requirements, service time, forecast bias and the life cycle of the product. Execution options are also described here.
Supply chains often have constraints on how much product can be supplied or shipped in a given period. These capacity constraints are modeled as inputs on the supply chain’s supply paths in EIS. These inputs can be static or time-varying. When supply capacity constraints exist, “pre-build” inventory is necessary to satisfy future demand. This inventory is shown as “pre-build” in prior periods when supply capacity exceeds demand.
The Storage Capacity Constraint feature of EIS allows the modeling of limited storage for one item at one location like a liquid in a storage tank. The model redistributes safety stock to respect storage availability to other locations in the supply chain, upstream or downstream where storage capacity exists but at a higher cost. Accounting for storage constraints requires properly modeling cycle stock, especially the impact of shipment frequency.
The EIS application includes a New Item Processor to help estimate the uncertainty and calculate safety stock.
If a New item is introduced that is actually an existing item with a new stock number, the existing item’s forecast error history can apply to the new item. A cut over date should be used. Ideally, sales of both new and existing items should be tracked. If a new package size is introduced, a ratio should be applied to arrive at the right number “units” of demand if forecasts are transferred. Ratios should be recalculated. New Item introduction does not change the “old” item’s forecast, but applies the “old” item characteristics to the “new” item forecast accordingly. New items introduced with no similarities to existing items must use either estimates of required safety stock (hard input), or estimates of uncertainty.
This topic describes how EIS handles time varying bills of materials. The components in a bill of material can be planned to change over time therefore the safety stock requirements for those components has to change.
This topic lists all the data that can be input into the EIS model. For example:
- Inventory unit cost
- holding cost percentage
- period between review
- forecast mean
- forecast standard deviation
- target service level
- total lead time mean plus many more
With SLO, service levels are set based on the unique characteristics of each demand stream. There are two methods for optimizing service levels. Global Target Optimization: Differentiated service levels are set to meet corporate service objectives with the minimum investment in inventory. Profit Optimization: The tradeoff between revenue generation and inventory investment is evaluated to determine optimal service level for each demand stream.
This topic goes through the eight steps to validate your results after successfully running the EIS model. As a user, you need to become comfortable with your safety stock targets, develop an understanding of key drivers of inventory and identify and rectify any modeling or master data errors.
This topic summarizes the EIS intuition takeaways and benefits:
- Improve Customer Service Levels
- Reduced Inventory & Working Capital
- Improve Planner Productivity
- Reduced Production & Distribution Costs