This is a blog on the optimizers available within the Response & Supply application of SAP IBP, including the Time-Series (TS) and Order Based Planning (OBP) optimizer.
Please note that for the TS optimizer both R&S and S&O license are required. For the OBP optimizer the R&S license is sufficient. See also SAP Help for Applications and Features.
Expert help for Optimization in IBP (SAP Consulting):
- Note 2427153 - IBP Supply Optimization Consulting
Blogs on TS Optimizer
- IBP for Supply Optimizer: the mathematics behind
- Expert settings for IBP supply optimization
- Time-Series-Based Supply Optimizer: notes on performance
- Overview of SAP IBP Supply Planning Algorithms (Webinar in Sept 2020) PDF | Recording
- IBP Product Webinar Series, R&S - Tactical & Operational Supply Planning: Recording & Presentation
- SAP IBP Operational Supply Planning - Overview and Capabilities (May (2020) PDF | Recording
- SAP IBP Operational Supply Planning - Deployment Planning (Oct 2020): PDF | Recording
- IBP OBP optimizer webinar (Nov 2020): PDF | Recording
Useful links to SAP IBP Help, specific for R&S (TS = Time-Series, OBP = Order Based Planning):
- Getting Started with Time-Series-Based Supply Planning
- Recommendations for Making Your Start with TS Planning Easier
- How Order Based Planning Works
- Table with Supported Functions of TS Algorithms
- Table with Supported Functions of OBP Algorithms
SAP Notes with useful information for R&S:
- SAP Note 2907554: Optimization in S&OP
- SAP Note 2238074: Additional information on supply planning > look for the attachments
- SAP Note 2922352 - Numerical issues in Supply or Deployment optimization
- IBP Learning journey
- IBP 700: TS Optimizer
- IBP 800: Order Based Supply Planning
- IBP 820: Deployment Optimizer
- Webinar on IBP Training: PDF | Recording | FAQ
IBP optimizer scenario guide, including runtime benchmark data
The Scenario Guide for the IBP Response& Supply Optimizer is a document that helps you to classify scenarios for Supply Optimization in SAP IBP.
Very often there are questions like “can supply optimization handle such a volume?”, or “what will be the runtime to solve a specific scenario?”
An answer to such questions is very difficult. The final behaviour of the optimizer depends on a lot of details. For example, a very large scenario
can be solved very fast, if the constraints are easy to satisfy. But if we have very challenging constraints, a smaller scenario could need much
As these details often are not known at the beginning of a project, no one can answer questions like above upfront in an exact
manner. Estimations which are too conservative (leaving a large buffer for example for the runtime) are also not very helpful. And setting up
prototypes is either inaccurate or causes too much effort.
The best and most convincing alternative is to compare the current project with already realized projects in IBP. On this level you don’t need to
investigate all details, but you get very soon an impression about the planning of your scenarios, if you look into some similar scenarios. This is
the approach we want to establish with this document.
The scenarios and numbers in this document are based on the experiences we made with different customers, partners, and projects.
Fundamentals of classifying optimizer scenarios:
- Details vs. Runtime: Details meaning granularity of time buckets and length of time horizon, number of products, lot sizes, etc.
- Runtime vs. Quality: If runtime is critical define maximal runtime with a solution gap that is acceptable for the business
- Global planning vs. decomposed planning: Can you split the model into sub-problems, or do you need to solve it in one global planning run?
Drivers of complexity:
- Scenario Size: Horizons & Planning Periods; number of products, customers, locations; faire share feature
- Lot-Sizes: keep the horizon to consider lot sizes as short as possible
- Type of algorithm: For example Dual Simplex or Barrier method
- Numeric: In most cases it is sufficient to follow the warnings and instructions in the log of the optimization run, that is, to switch on the recommended additional
features to transform your supply chain in numerically more robust optimization model
You can get a sense of the complexity of your optimizer model by looking at the number of the variables on the "Optimizer Run Details" screen in your IBP system and compare it to the numbers in the scenario guide:
IBP optimizer safeguarding service
The service is meant to be as support for partner led optimizer implementations to give optimizer modeling recommendations in an early stage.
The service will be performed at two distinct points of time:
- Middle to end of Business Blueprint Phase: Design Evaluation
- Before volume test: Performance Optimization
The length of the service is 3-5 days for each of the two checkpoints and depends on the complexity of the optimizer model.
The service is performed by SAP optimizer experts and is a paid service. The SAP service account team can find out what the rate is in a specific country.
For SAP MaxAttention customers the service days are usually covered by the MaxAttention contract.
Contact: Please contact your assigned SAP Digital Supply Chain Customer Engagement Executive (CEE) if you are interested in the service.
Do´s and Don´ts Document
The document answers questions on the following topics:
- How to address complexity in optimization
- Optimizer Expert Parameters
- Explanation of optimizer runs
- Runtime and complexity
- How to reduce complexity
- How to deal with Numerical Problemas
How to address complexity in Supply Optimization:
- General advisory: Keep unnecessary complexity out of the model!
- Remember, it’s mid-term planning in most of the cases.
- Careful use of discretization (lot sizes, fixed cost, setup):
- Use discretization only in short-term horizon.
- Use minimum lot sizes instead of integral lot sizes (rounding values) where possible.
- Avoid using integral lot sizes on several levels (e.g. production and transportation).
- If multi-level lot sizing cannot be avoided, make sure lot sizes are aligned.
- Reduce model scope:
- Use shorter and/or time aggregation.
- Only plan relevant location levels in network. Consider propagating demand using heuristic for certain network levels.
- Only plan relevant SKUs. Components/Raw materials may not be required.
- Split Optimization scope into separate runs:
- Plan by product group.
- Identify bottleneck resources – non-bottleneck resources can be part of multiple runs.
- This also improves planner experience / usability / fail safeness!
- Take care modeling alternative sourcing:
- Avoid using ‘exception only’ alternatives in automated planning. Handle exceptions via alerts and manual planning.
- Avoid modeling ‘don’t care’ options (identical cost for alternative sources). Use ‘virtual’ priorities if necessary, to avoid strong fluctuations in optimizer results.
- Consider restricting multi-sourcing to selected products (e.g. fast movers).
- Shift part of complexity to short-term distribution and/or production planning
- Distribution planning – use of Deployment (optimization)
- Plan part of the distribution network in deployment only
- Consider transport lot sizing only in deployment
- Planning in daily periods only in deployment
- Allows consideration of additional constraints (e.g. storage, handling)
- Fair-share considerations (location/region level) may only be considered in deployment
- Production planning
- Use PP/DS for detailed production planning
- Alternative: Use ‚production-only‘Supply Optimizer model in short term
- Consider production lot sizes etc. only in short-term planning
- Consider raw materials/component only in short-term planning
- Distribution planning – use of Deployment (optimization)
- Do not consider Optimizer as a black box! Build understanding of working principles of optimizer:
- It’s not rocket science…
- Build a ‘formal’ tool translating business rules into an optimizer cost model
- Especially useful if there are complex business requirements
- Involve (power) users in design of optimizer cost model
- Set up ‘How-to’ guides for analyzing / addressing frequent issues
- In most scenarios, the same issues come up again and again
- Document approach for new issues as they occur
- Document resolutions provided by the experts in a way that non-experts can understand
IBP Best Practices
IBP Best Practices Explorer, processes specific for R&S in screenshot below