The evolution and future of Advanced Planning and Scheduling (APS) systems
Prototypes are easy, production is hard.
In this blog post, we will look into a type of industrial software systems known as Advanced Planning and Scheduling softwares, how they evolved, what does SAP offer in this software category and what does the future look like.
Photo by ThisisEngineering RAEng on Unsplash
First let’s look at the definition of Advanced planning and Scheduling (APS).
Advanced planning and scheduling (APS, also known as advanced manufacturing) refers to a manufacturing management process by which raw materials and production capacity are optimally allocated to meet demand. APS is especially well-suited to environments where simpler planning methods cannot adequately address complex trade-offs between competing priorities.
The evolution of APS systems started with the era before MRP (Material requirements Planning) with simpler inventory management methodologies like reorder point planning and economic order quantity governed the production process. MRP aimed to co-ordinate the various departments in the company to organize manufacturing resources, ensure raw material is available for production and finished material is available to serve customer orders. A good MRP system aimed to solve material shortages, keep optimum work in capital and generate a production schedule. The MRP approach often assumed infinite resource capacity and was not equipped to handle constraints in manufacturing. Later on, capacity was considered and the new paradigm came to be known as MRP II (Manufacturing Resources Planning).
The next step in this evolution was the birth of the first ERP systems where Software companies like SAP led the charge and aimed to create software that integrated all departments under a single source of truth to create the ultimate system of record. ERP systems by definition increased the scope beyond manufacturing to include other lines of business such as marketing, finance and HR.
The immense popularity of a single system of truth for the whole organization resulted in exponential adoption of ERP globally and ultimately led to the emergence of specialized applications for different lines of businesses to extend the ERP system.
When MRP II was no longer enough to cater to the business requirements because it required a stepwise procedure to plan material and capacity separately, was difficult to create adaptive production plans that considered volatility (in demand, resource capacity and material availability) – Advanced Planning and scheduling systems filled the gap with simultaneous planning and scheduling of production based on available resources (material, labor and machine). Most APS systems also allow visualization of the planning results and simulation capabilities.
Advanced Planning systems use complex mathematical algorithms to forecast demand, to plan and schedule production within specified constraints, and to derive optimal source and product-mix solutions.
Vieira, J., Deschamps, F., & Valle, P. D. (2021). Advanced Planning and Scheduling (APS) Systems: A Systematic Literature Review.
How to APS systems solve planning problems?
The are a few common approaches that are usually taken by APS systems to solve the problem of production scheduling they are:
Heuristic Approach: A heuristic is any approach to problem solving or self-discovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediate, short-term goal or approximation. For APS solutions, a heuristic is a planning function that executes planning for selected objects (e.g. materials, resources, production operations, or line networks, depending on the planning focus). As an example SAP Manufacturing for Planning and Manufacturing – an APS solution from the SAP Digital Supply Chain Planning Suite offers multiple standard heuristics for:
Production Planning – These heuristics are used for procurement planning of products and other planning tasks.
Detailed Scheduling – These heuristics are used for sequencing of operations on resources.Your planning focus is on resources and operations. Examples of these heuristics include Minimize Runtime and Remove Backlog.
Repetitive Manufacturing – These heuristics generate planned orders for requirements and take the resource capacity for all periods into account. The planning focus is on resources, line networks and materials.
Model Mix Planning – This heuristic optimizes the sequence of configurable products while taking any restrictions into account. The planning focus is on resource and line networks. (uses the optimizer function)*
Note: In addition to using standard heuristics, you also have the possibility of defining your own based on unique algorithms with SAP Manufacturing for Planning and Scheduling (aka ePP/DS).
2. Mixed Integer Linear Programming: A mixed integer linear programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers. The algorithms used to plan the entire supply chain network or multiple locations to generate production and procurement proposals would use these algorithms. SAP IBP Response & Supply and SAP Manufacturing for Planning and Scheduling PPO use this algorithm.
- Check out this great post by Carsten Schumm on the Mathematics behind the IBP Optimizer
- Also check out this wonderful blog about SAP Manufacturing for Planning and Scheduling PPO
Note: The IBP Optimizer and SAP S/4HANA Manufacturing for Planning and Scheduling (ePP/DS) PPO complement each other. On the one hand, IBP Optimizer plans the entire supply chain network whereas ePP/DS PPO focuses on Manufacturing plants.
3. The Genetic Algorithm: The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population “evolves” toward an optimal solution. You can apply the genetic algorithm to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, nondifferentiable, stochastic, or highly nonlinear. The genetic algorithm can address problems of mixed integer programming, where some components are restricted to be integer-valued. The SAP Manufacturing for Planning and Scheduling detailed scheduling (DS) optimizer uses the genetic algorithm to create optimized production schedules.
Here’s a great Youtube video that takes you through the genetic algorithm.
What does the future look like for SAP S/4HANA Manufacturing for Planning and Scheduling (ePP/DS)?
The SAP S/4HANA Manufacturing for Planning and Scheduling solution with its roots in SAP APO is a mature application and now after being embedded to S/4HANA (additional add-on subscription) has a well defined roadmap with more features and functionalities across various dimensions being delivered to customers with each release. These areas of improvements include user experience, industry specific planning algorithms, cross app integration as well as architectural updates. To view the roadmap, click the link.
Supply chains today need planning solutions and APS systems form a critical piece of any end to end planning solution. With the increased adoption of Artificial and Machine learning , we will see new algorithms infused into APS systems to solve even harder planning problems. At the same time, user experience for these traditionally difficult to use applications will also see great improvements.
In conclusion, you now know more about the evolution of advanced planning systems like SAP S/4HANA Manufacturing for Planning and Scheduling (ePP/DS) and Integrated Business Planning. We have also delved into different types of planning approaches and algorithms these systems use. Lastly, we learnt how to view and know more future updates in the SAP Road Map Explorer.
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