Much has been written about the 4th Industrial Revolution (4IR or Industry 4.0), but really it is about the pace and velocity of change that we are all seeing in modern manufacturing and even supply chain operations. It is about the introduction of the so-called intelligent technologies and the intelligent applications that these technologies are giving birth to and how these technologies are transforming (and disrupting) the value chains of traditional business operations. Whether the technologies are being used to automate processes (through machine learning or robotic process information), to streamline decision making (through natural language processing and smart workflow or Situation Handling) or to accelerate innovations through predictive analytics, machine learning or the internet of things (IOT), manufacturing processes are being forever transformed. In addition, managers no longer look for the secret key to improve a single function or task using a singular, focused technology, but rather they see that real, measurable value is locked in an end to end process. With these exciting 4IR technologies, we can now afford to take a broader process perspective to see how multiple activities, multiple applications, and multiple technologies are brought to bear through linked business activities that rely on specific business capabilities to deliver a situation. The modern manufacturing manager must therefore be a generalist in a sense in order to be able to understand not only a single business process, but to understand how business processes link together to form business solutions. In addition, the production planner must be able to bring a variety of technologies together to unlock the value that is locked in the end-to-end process – from product Manufacturing Engineering through Planning, Operations, Replenishment, Quality and analytics. The purpose of this blog is to present a model for addressing this challenge.
First, let’s look at the “Big Picture”:
That’s a lot of moving parts, isn’t it? But wait, there’s more. The real “value levers” available to unlock the hidden value in this Production Planning and Execution value chain may be hidden at a level below this picture. For example, Product Definition issues can be decomposed into Lot Sizing Procedures and a variety of product material type definitions (consumption, plan, order and forecast driven). The product structure (BOM) can be managed at a category, usage, item or reporting level). Production processes can be described in terms of product routings and infrastructure in terms of work centers. It’s easy to see how manufacturing managers and production planners have opted to fix what’s broken and sometimes lose sight of the broader process. As they say, the squeaky wheel gets the grease.
But is a 4IR world, that is not enough. Luckily, modern production planners increasingly have a broad range of tools at their disposal to widen the lens a little bit and take a process perspective than a capability or worse, a tools perspective. The tools are there in SAP S/4HANA to do the work for the planner if only the planner can see the relevance of the end to end process.
In this diagram, we highlight where Machine Learning Tools exist to help the planner in the Production Engineering and Master Data activities, Robotic Processing bots exist to help in Forecasting, Situation Handling scenarios and Machine Learning have been configured to assist with the Capacity planning and managing / forecasting buffer levels to assist with Demand Driven MRP and finally how BOTS and ML Algorithms can be configured to assist with Quality Management.
“Come on” ….you might say…”There’s got to be an easier way.” Well, you would be right. There is a recorded demo of this process available on YouTube that shows how Lucy Williams, Production Planner for ABC corporation deploys a smart demand driven replenishment control system with SAP S/4HANA. First, Lucy can easily improve her accuracy in forecasting and planning a well-balanced buffer to ensure that raw materials and product components are available when they are needed. Since she is using an ML based tool, the buffer can be trained to “learn” how to accurately forecast buffer levels based on prior experience. Lucy also utilizes BOTs with pre-defined content using SAP Intelligent RPA to automate the uploading of Planned Independent Requirements (PIR) into the system. After the MRP run decomposes requirements according to the BOM structure, Lucy needs to identify and solve capacity issues early in the requirement planning process. With SAP S/4HANA Predictive Material and Resource Planning, also known as predictive MRP, she can easily simulate capacity impacts and receive resolution proposals for potential conflicts. Not only does it simulate the impact of capacity on the planned requirements for selected materials, but it also simulates in real-time how potential requirements, production and capacity adjustments could impact the overall delivery performance. Capacity Management is also addressed using intelligent tools. Using simulation, Lucy can test multiple scenarios based on her past experience to ensure that the right materials are available where – and when – she needs them. With Predictive MRP, Lucy can do prework, get system proposals on the best option, and can easily simulate different options to adapt the capacity, demand, and source of supply, in order to solve material and capacity bottlenecks. There is no need to program the configuration. It can also be nicely integrated with other SAP planning tools like Integrated Business Planning or Demand Driven MRP.
The recorded demo goes on to show how SAP Situation Handling can be incorporated into Lucy’s workflow to help her to quickly notice when MRP run exceptions occur and she can react in a timely fashion. Then, while dispatching orders, she can see which other orders are blocking timeslots in the work center she is currently planning. While dispatching the last of her orders, she realizes that this critical order should probably start at the earliest time possible, and because the system supports drag and drop, Lucy is able to quickly rearrange the orders to suit to her needs.
Lucy is clearly a 4IL production Planner and SAP S/4HANA Manufacturing and SAP Intelligent Technologies help her to run a more efficient production control operation. Let us know how you are using intelligent technologies or contact your SAP representative for more information.
First, let’s look at the “Big Picture”:
That’s a lot of moving parts, isn’t it? But wait, there’s more. The real “value levers” available to unlock the hidden value in this Production Planning and Execution value chain may be hidden at a level below this picture. For example, Product Definition issues can be decomposed into Lot Sizing Procedures and a variety of product material type definitions (consumption, plan, order and forecast driven). The product structure (BOM) can be managed at a category, usage, item or reporting level). Production processes can be described in terms of product routings and infrastructure in terms of work centers. It’s easy to see how manufacturing managers and production planners have opted to fix what’s broken and sometimes lose sight of the broader process. As they say, the squeaky wheel gets the grease.
But is a 4IR world, that is not enough. Luckily, modern production planners increasingly have a broad range of tools at their disposal to widen the lens a little bit and take a process perspective than a capability or worse, a tools perspective. The tools are there in SAP S/4HANA to do the work for the planner if only the planner can see the relevance of the end to end process.
In this diagram, we highlight where Machine Learning Tools exist to help the planner in the Production Engineering and Master Data activities, Robotic Processing bots exist to help in Forecasting, Situation Handling scenarios and Machine Learning have been configured to assist with the Capacity planning and managing / forecasting buffer levels to assist with Demand Driven MRP and finally how BOTS and ML Algorithms can be configured to assist with Quality Management.
“Come on” ….you might say…”There’s got to be an easier way.” Well, you would be right. There is a recorded demo of this process available on YouTube that shows how Lucy Williams, Production Planner for ABC corporation deploys a smart demand driven replenishment control system with SAP S/4HANA. First, Lucy can easily improve her accuracy in forecasting and planning a well-balanced buffer to ensure that raw materials and product components are available when they are needed. Since she is using an ML based tool, the buffer can be trained to “learn” how to accurately forecast buffer levels based on prior experience. Lucy also utilizes BOTs with pre-defined content using SAP Intelligent RPA to automate the uploading of Planned Independent Requirements (PIR) into the system. After the MRP run decomposes requirements according to the BOM structure, Lucy needs to identify and solve capacity issues early in the requirement planning process. With SAP S/4HANA Predictive Material and Resource Planning, also known as predictive MRP, she can easily simulate capacity impacts and receive resolution proposals for potential conflicts. Not only does it simulate the impact of capacity on the planned requirements for selected materials, but it also simulates in real-time how potential requirements, production and capacity adjustments could impact the overall delivery performance. Capacity Management is also addressed using intelligent tools. Using simulation, Lucy can test multiple scenarios based on her past experience to ensure that the right materials are available where – and when – she needs them. With Predictive MRP, Lucy can do prework, get system proposals on the best option, and can easily simulate different options to adapt the capacity, demand, and source of supply, in order to solve material and capacity bottlenecks. There is no need to program the configuration. It can also be nicely integrated with other SAP planning tools like Integrated Business Planning or Demand Driven MRP.
The recorded demo goes on to show how SAP Situation Handling can be incorporated into Lucy’s workflow to help her to quickly notice when MRP run exceptions occur and she can react in a timely fashion. Then, while dispatching orders, she can see which other orders are blocking timeslots in the work center she is currently planning. While dispatching the last of her orders, she realizes that this critical order should probably start at the earliest time possible, and because the system supports drag and drop, Lucy is able to quickly rearrange the orders to suit to her needs.
Lucy is clearly a 4IL production Planner and SAP S/4HANA Manufacturing and SAP Intelligent Technologies help her to run a more efficient production control operation. Let us know how you are using intelligent technologies or contact your SAP representative for more information.
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