How to Optimize SAP APO Capable to Match for Process Industries

  • by Arun Negi, Manager, Deloitte Consulting LLP
  • Chris Pingel, Specialist Master, Deloitte Consulting LLP
  • July 28, 2016
Learn how to meet key design challenges in the process industry for supply planning and how to overcome these challenges when the preferred engine for implementation is SAP Advanced Planning and Optimization (SAP APO) Capable to Match (CTM).
Learning Objectives

After reading this article, you’ll know how to:

  • Better understand process industry requirements for supply planning, with specific emphasis on the master scheduling sub-process
  • Consider seasonality requirements, lot size settings, and replenishment lead times when implementing a supply planning solution
  • Use standard settings as well as custom development to optimize the use of the SAP Advanced Planning and Optimization (APO) Capable to Match (SAP APO CTM) planning engine
Key Concept
Generating a supply plan considering material and production capacity constraints is crucial to effective supply chain management in the process industries (including consumer packaged goods, food and beverage, and industrial products). An inaccurate production, capacity, and distribution requirements plan can have adverse impacts on the overall inventory situation and the customer service level. In a multi-tier supply network it becomes important to properly account for constraints in the supply chain when generating a supply plan using the Capable to Match (CTM) planning engine.

The supply planning process creates a feasible forward-looking production plan on a weekly basis by considering the capacity constraints, material constraints, manufacturing run rules, and supply chain network. A typical supply planning process is executed every week in weekly buckets for a six- to 12-month horizon, but may differ from company to company.

Having an accurate supply plan can help businesses with increased visibility to the entire supply chain, shorten order-fulfillment times, reduce inventory levels, and improve customer service. Business requirements in the process industry need to be adequately addressed by the planning engines. Some of the critical challenges when using Supply Network Planning (SNP) enabled by SAP Advanced Planning and Optimization (SAP APO) Capable to Match (CTM) as the engine to generate a weekly supply plan are:

  • Accounting for lot size and available capacity in a weekly bucket to maximize capacity use
  • Prioritizing demand based on cumulative lead time (time required to move the product from the manufacturing location to the distribution center)
  • Minimizing manual intervention when generating a supply plan and performing an inventory build for seasonal products

We first discuss the critical business considerations and questions that need to be answered with respect to supply planning in the process industry. We also describe how with the help of innovative changes to the existing functionalities combined with enhancements to planning engines using various user exits and Business Add-Ins (BAdIs) available in APO CTM, we were able to address these business challenges. We cover how to better solve business challenges using CTM as the preferred engine.

Introduction to SNP and CTM

CTM is a planning engine that enables multi-level supply and demand matching as part of SAP APO. CTM offers two modes containing functionality for use in Production Planning and Detailed Scheduling (PP/DS) and SNP.

 

For the purposes of this analysis, our focus is on the SNP CTM solution. CTM is one of the three major algorithm options companies have when they are implementing SNP for mid- and long-term planning. The basic characteristics of SNP CTM include the following:

  • Order-based planning: Every demand that is stored in the system as a liveCache order is planned individually by the CTM algorithm. The algorithm provides the capability to generate logs that assess each forecast, sales order, or other demand source individually as being met on time, met late, or not met at all. This differs from the time-based, bucket-oriented planning that is used by SNP heuristics and the SNP Optimizer.
  • Demand prioritization: Offers the capability to establish location-based or customer-based demand priorities that drive the solver to consider certain demands in priority sequence during the CTM planning run
  • Finite capacity planning: CTM generates a constrained supply plan that can consider transit times, goods receipt/issue processing times, transportation capacity, and production capacity for in-house manufacturing
  • Supply prioritization and multi-sourcing: When multiple sources of supply are available (e.g., multiple lines at the same plant, multiple in-house manufacturing plants, or a make-versus-buy scenario) CTM can consider a prioritization sequence before scheduling the supply to meet the demand. In scenarios in which capacity is constrained, CTM defaults to planning a receipt in advance before it seeks out a lower priority supply source. The impacts of this phenomenon are discussed in the “Seasonal Pre-Builds” section.

Overall, CTM is becoming a more popular choice for companies in the process industries that seek a middle ground between the simplicity and limited capabilities of SNP heuristics and the additional master data requirements and solution complexity associated with successfully implementing the SNP Optimizer.

 

Arun Negi

Arun Negi is a manager at Deloitte Consulting LLP.

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Chris Pingel

Chris Pingel is a specialist master at Deloitte Consulting LLP with more than eight years of supply chain consulting experience.

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