Delayed deliveries, employee absenteeism (COVID), changing production priorities, urgent orders, breakdowns, co-op trouble, repair orders, poor quality, you know it?
HAL APS has been designed to effectively solve production problems in an environment of high volatility of everything that occurs on the side of the supply chain and internal factory processes. The APS must reflect what is really happening on the production hall, so only the goal is important, even if it changes several times a day.
The most convenient form of presenting the production schedule is the Gantt’s chart. It shows the relationships between technological operations carried out on resources on the timeline. HAL can do something amazing, it can change these dependencies as part of What-if scenarios. Thanks to a very efficient HAL mechanism, it can contain tens of thousands of objects on one screen. A multitude of single-click operations, zooms, sliders, filtering, sorting creates a powerful real-time simulation tool.
Analyze your plan from general to specific. The Gantt’s chart performs calculations on the fly, allowing for any schedule modifications. A moving red timeline separates the past from the future. The color of the block on the chart may depend on many factors, and the content displayed on it is fully customizable. When you hover over the block, a balloon will appear, which will display detailed information about the technological operation, e.g. about the time parameters, demand sources or the current progress of the operation.
Imagine that without investing in a machine park, you will get 10-15% more from what you have. So you will produce more at the same time. It sounds interesting? What if we release much more? Hmm ... or maybe we will try to shorten the total Lead Time, reduce TKW or optimize changeovers. HAL uses very effective and complex scheduling algorithms. Always solves the planning problem. HAL is a journey to the digital world where problems are solved in real time.
Industry differences are important. The problems of the machinery industry are not the problems of the pharmaceutical industry. The assembly line in the automotive industry is scheduled differently than the assembly line in the electrotechnical industry. The cutting sheet of metal is subject to slightly different rules, and the cutting of glass or melamine boards is different. The quality control rigor in the gas industry is different from that in the ceramics industry. The conversion of the injection molding machine is planned in a slightly different way than the CNC machine tool. HAL offers you Industry Know How as standard.
The HAL must be supplied with input data having its Master Data outside of the HAL, and it must supply information to other process participants. HAL makes decisions and suggests the best possible action scenarios. It is the most important system in a production organization, but for its effectiveness it has to query other systems involved. Data sources may have different locations, depending on the specific architecture. For example, the source of data about the product structure, i.e. BOM / BOM 150%, can be PLM, ERP, MES, CPQ, APS. Any variant is possible.
Late delivery of materials
Delays of cooperators
Breakdowns and faults
Uneven work performance
Problems with quality
There are many reasons why the scheduling algorithms embedded in the most expensive ERP systems have not proved successful.
First, MRP does not solve the planning problem. MRP generates unrealistic production plan suggestions. Thousands of exception messages specify what must be met to complete the plan.
n practice, it is easier to enter production orders manually than to handle messages. The planning system must reflect the reality of production, it must represent physical objects and their scheduling characteristics. Scheduling digital twin.
The effectiveness of HAL is not only the simplicity of the interface or the gigantic ergonomics of the single-click actions used, but also the strength of the algorithms. Intelligent algorithms looking for an increase in labor productivity through a combination of rolling improvements as in Amdahl's law. Algorithms that release production capacity, algorithms that load parallel resources, aimed at the maximum use of all resources. Unique algorithms that take into account the variable performance of human resources competences.
The world of science has not found any effective algorithms for many optimization tasks, and the prospect of their discovery in the future seems very doubtful. Therefore, appropriately selected heuristics are used so that a solution can be found quickly, guided by the objective function.
Heuristics are used in the machine learning process.