Automate regular planning tasks

AI-assisted optimization of production planning

Manual production planning always carries the risk of losing sight of the overarching context while focusing on individual adjustments. Capacitive leveling, when done manually, is time-consuming and makes it challenging to maintain an overview of the entire situation. For instance, overtime may be scheduled to address capacity issues at a workstation, without realizing that this action is actually counterproductive due to a shortage of parts in subsequent stages of production.

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Automate regular planning tasks: AI in production and production planning

wayRTS takes on tasks that can be done more efficiently with AI

Manual production planning always carries the risk of losing sight of the big picture while making individual adjustments. If done manually, capacitive leveling is time-consuming, and it is difficult to understand the overall situation. For example, overtime can be ordered in the event of capacity problems in the workplace, although this measure does not make sense at all due to a shortage of parts in downstream operations.

Benefits for your processes

Shorter throughput times

Reduced planning costs

Unburdening from routine activities

Inventory optimization

No more troubleshooting

Mockup Product Sheet AI-assisted optimization of production planning with wayRTS

Product flyer: AI-assisted optimization of production planning with wayRTS

In our product sheet, we show you how Artificial Intelligence (AI) can help you optimize production planning and automate regular planning tasks.

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AI-based automation in wayRTS applies to these three areas:

Rough planning

In rough planning, production orders are planned automatically. Over a specified period of time, orders are smoothed out approximately, scheduled, and a check is conducted to ensure that there is enough material and capacities to fill all orders.

Order networks are planned in the rough planning based on the priority of customer orders. In the end, there are enough materials and capacities to fill all orders. This means that the current planning image has been smoothed out. In addition, new customer orders ATP & CTP can be checked, and realistic delivery dates forecast. Other functions of automated rough planning are planning inventory optimization and scheduling evaluation. Scheduled inventory optimization checks whether processes can be moved closer to the demand date in order to reduce inventories without compromising punctual material supply for the order. This shortens the overall lead time and reduces inventory, which can save on inventory costs. The schedule evaluation plans how many shifts there should be in a given time period. The minimum principle applies: As few shifts as possible, as many as necessary.

Detailed planning

Detailed planning is planning on a daily basis. Automated detailed planning includes sequence planning and set-up optimization. An optimized sequence of orders is scheduled for workplaces where there are bottlenecks.

In automatic detailed planning, the AI optimizer performs set-up optimization on workstations where there are bottlenecks. Workplaces with bottlenecks are workplaces where there is high demand and great set-up effort. The goal is to work at these workplaces as efficiently as possible and with minimal set-up operations, so that orders are ready on time. Goals can conflict, for example, set-up optimization and on-time delivery. Intelligent algorithms in wayRTS find the optimal solution for this – according to the trained objectives.

Automation

Various automation systems are also integrated into the application. These handle simple planning activities and can be performed in addition to rough or detailed planning. Different service runs can be combined. Examples:

In contrast to rough planning, backlog clearing is used exclusively to clear backlogs. It does not take into account the capacitive feasibility of orders. Various functions can be used to clear backlogs. These can, among other things, reduce the total throughput time or postpone requirements for replenishing safety stocks into the future.
The plausibility check compares the planning image before and after running automatic optimization functions. If there have been any conspicuous postponements, it warns the user. The user can then specifically check whether or not the postponements make sense.
Pushing up customer orders is an automatic check whether, for example, the breakdown of customer orders and changes in the processing sequence would serve to process orders in time.

Foto der valantic Referenz ottobock, Referenz waySuite, wayRTS, APS Software

Case Study

How Ottobock Increased On-Time Delivery

Because capacity planning was time-consuming, Ottobock implemented sales & operations planning software, which enabled this orthopedic technology company to increase its productivity.

Foto der Grenzebach Gruppe, People, valantic Referenz waySuite

Case Study

Grenzebach plans and controls supply chain processes with valantic’s waySuite

Grenzebach serves the global markets of the glass and construction materials industries as well as interlogistics with custom-tailored automation solutions. Together with valantic, the company implemented a location and function-spanning planning solution for networked control of all value-creation processes.

valantic Reference wayRTS, KNAPP AG, pick it easy

Case Study

KNAPP AG optimizes supply chain with aps system wayRTS

KNAPP is one of the global market leaders among suppliers of complete intralogistics solutions and automated storage systems. KNAPP’s business is project-driven. In order to reduce order throughput times, optimally utilize capacities, and increase on-time delivery, the company continuously improves its planning organization and processes. The APS software wayRTS plays a central role in this.

valantic Reference TGW Logistics - APS Software wayRTS, waySuite

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TGW: Transparent planning with APS software wayRTS waySuite

TGW Logistics relies on the valantic APS software wayRTS / waySuite for top planning reliability, increased efficiency, productivity, transparent planning and performance in the supply chain. Even during its switch to SAP S/4HANA, TGW Logistics Group always had a transparent planning picture with the valantic APS software suite.

MAN Energy Solution Zuerich Production planning APS System waySuite

Case Study

APS waySuite at MAN ES: Production planning AI-optimized

MAN Energy Solutions has been using valantic's APS waySuite production planninng system for more than a decade. Thanks to IT-supported planning processes along the entire value chain - quotation, project management, engineering, production planning, procurement, personnel planning, scheduling, supplier tracking, testing and assembly - MAN ES achieves exceptionally high delivery reliability.

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Case Study

APS Advanced Planning Tool wayRTS improves delivery reliability at motan

motan operations GmbH, an equipment manufacturer for the plastics industry, made improvements at its Isny location with the help of valantic planning, inventory, and supply chain management. Thanks to the introduction of the APS advanced planning & scheduling tool wayRTS (Real Time Simulation), throughput times are much shorter and the company can make delivery promises to customers that are reliably kept.

AI suggests, you decide

The automatic resolution of backlogs and capacitive smoothing by wayRTS makes life easier for you and everyone involved in production planning. Instead of elaborate analyses of manual backlog processing and capacitive harmonization, you simply check what the optimizer suggests – in some cases automatically – and apply it to your normal interactive planning in wayRTS.

AI-supported production planning helps you be as good as you can be. What intelligent algorithms find out about the planning status and possibilities to improve it affects various planning roles, such as purchasing, sales, project management, master rounds, and assembly planners.
Some people with full versions, others with comment or viewing licenses for wayRTS, everyone is working with the same automatically optimized planning image and makes their contribution to the visible overall best. The common view facilitates communication between stakeholders. For example:

Scheduling of new requirements/customer orders – coordination between sales and production planning

If sales sends order confirmations right away when new customer orders are received, they run the risk of not being able to meet the delivery date. It’s better to transfer the customer orders to the ERP system. wayRTS takes over the data and conducts an ATP&CTP check. Sales then sees in wayRTS the date on which the customer order can be processed and can quickly promise the customer a feasible date.
If the customer does not agree to the date, various scenarios can be checked before the customer order is scheduled.
With wayRTS and the automatic feasibility check, sales can understand which delivery dates are actually possible and thus will no longer promise a date for production planning that cannot actually be honored.

Dispatching of new requirements and customer orders AI-in-the-production production planning with APS system-wayrts

Clarification of late orders – coordination between purchasing and production planning

Purchasing can use wayRTS to access their individual dashboard, which displays any delayed orders.
This information allows the purchasing department to check if late orders can arrive on time. If this is not the case, production planning and purchasing must negotiate an agreement. They consider whether increased procurement costs for the timely arrival of the materials or a delay in deadlines will be accepted.

waySuite Dashboard Transparenz über den kompletten S&OP-Prozess

Falling short of customer deadline requests – coordination between sales and production planning

The optimizer AI provides customer dates that are not feasible for planning purposes with reasons for delay. Production planning and sales must consult to decide which customer deadlines must be met and which ones can be postponed. Using the optimizer, it is possible to simulate different scenarios. It illustrates how shifting individual processes affects the entire order situation, and on the basis of this information, it is possible to decide how to deal with customer deadlines that cannot be met.

Mockup vom Dashboard AI in production with APS-system wayRTS analysis of medium term planning

Capacitive increase – coordination between master round and division planning

In the event of increased orders, it may be necessary to increase capacity. Here, the masters have the necessary expertise to be able to assess whether capacity can be increased in the short term. The optimizer can help them with this. In wayRTS, various scenarios can be simulated over a given period of time. Thanks to shift model optimization, it is possible to use AI to determine at which times a shift adjustment would make sense and be necessary in order to complete all orders as soon as possible. This shows how, for example, increasing the capacity of one workstation affects others and the entire order network. Division planning and the master round then confer to decide on the measures for increasing capacity.

Mockup Dashboard AI in production with APS-system wayRTS comparison two scheduling strategies

valantic configures the optimization AI individually for your requirements

Do you want to see how an optimization of production planning might look for your company? Schedule a live demo now. AI-supported optimization of production planning only makes sense if it is precisely tailored to the conditions in your production and supply chain. waySuite comes with a series of mathematical procedures from which we can configure the automation that is right for your requirements. Together, we will teach the system which optimization goals and priorities apply, and in which contexts. This can vary significantly depending on the industry and the company. While one production planning is designed entirely to meet deadlines, cost factors dominate in another.

With our structured implementation methodology for semi-autonomous, optimized production planning, we will help you on your journey to creating a “Smart Factory.”

1

Agile development and customization of the optimizer
2

Step-by-step expansion of complexity
3

Traceability and reproducibility of the optimized production plan
4

Possibility of modular expansion of optimization/automation
5

Adaptation of the requirements and specifications as part of agile development

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AI-assisted optimization of production planning

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