Introduction to an Overarching SAP Integrated Business Planning Optimizer Methodology

Objective

After completing this lesson, you will be able to understand the SAP holistic approach to inventory optimization.

SAP's Holistic Approach to Inventory Optimization

What are the main challenges that can appear when a company plans its inventory?

  • Accurate inventory targets: Inventory targets are planned but they do not match the reality of the current business.
  • Sustainable and integrated process: There is no continued feedback with proven process integration.
  • Performance management: Stock is planned for non-priority SKUs, causing delays in other critical parts of the supply chain. At the end, important customers are not prioritized.

Why do other approaches fall short?

  • Targets are being calculated one stage at a time. 
  • Service levels are being managed only using broad ABCD item classifications. 
  • Dynamics across future time periods are not considered. 
  • Manual rules-of-thumb, based on experience. 
  • Simple safety stock calculators or spreadsheets. 
  • Key inputs not tracked in systems, for example, LT Variability. 
  • Same targets for all periods. 
  • Infrequent review – quarterly or longer.
  • Manual entry into existing systems. 
  • Reactive, ad hoc meetings. 
  • No visibility into what drives need for inventory. 

SAP's Holistic Approach addresses these challenges through:

  • A total supply chain view. 
  • A stochastic process engine. 
  • Multi-stage inventory optimization. 
  • Proven integration. 
  • Continuous insight. 
  • Operational synchronization. 
  • What-if capabilities. 
The figure describes the SAP's Holistic Approach to Inventory Optimization.

Multistage Inventory Optimization

Multistage Inventory Optimization

SAP IBP for inventory simultaneously optimizes inventory across the end-to-end supply chain, constrained by customer service requirements with the lowest inventory holding cost. That means:

  • Coordinated planning eliminates over-buffering of inventory, and ensures services objectives are met.
  • Intra-site fill rate optimization, also known as internal service level optimization, provides significant inventory reduction.
  • Demand variability propagation to upstream stages is essential in multistage optimization to avoid bullwhip effect.
The figure describes the Multistage Inventory Optimization.

Other approaches imply:

  • Isolated planning results in over-buffering of inventory across the supply chain (leaves money on the table).
  • Determining postponement strategy is challenging.
  • Use heuristics for demand variability propagation or forecast at each stage (significant over-buffering).
  • Cannot handle bill of material (BOM), lot sizes, or other supply chain complexities.

Multistage Inventory Optimization Approach

Multistage Inventory optimization (MIO) Approach

This approach presents a self-developed (confidential intellectual property), stochastic, non-linear, and optimization-solved approach. This is subjected to a hard constraint of meeting desired customer service levels, while calculating internal service levels as decision variables.

What is the objective of MIO's approach?

  • Find an inventory plan that minimizes the sum of all holding cost rates, and multiply it by the safety stock in units for each product location within the entire planning horizon for all products in all locations.
  • Where holding cost rate can include working capital opportunity, storage costs, obsolescence costs, and taxes.

What are the constraints within the objective function?

  • Subjected to the hard constraint of meeting desired customer service levels.
  • The following planning parameters and replenishment constraints are factored into the calculation of inventory requirements:
    • Uncertain and time-varying demands.
    • Lead time and lead time variability.
    • Periods between reviews.
    • Minimum and incremental lot size.
    • Stocking/non-stocking policy.

What are the decision variables?

Internal service levels at each internal product location.

The following shows a summary of the objective, contraints, and decision variables.

The figure describes the Multi-stage Inventory Optimization Approach.

Stochastic Process Engine Steps