APS is also an effective tool when making short-term shop scheduling plans. The main reason here is that the ability to react quickly allows workers to flexibly deal with various exceptions on the production site. Workshop production planning and scheduling managers are usually very experienced, and their experience is not easy to learn from computers. However, computers are faster and more accurate than humans. Some people pursue APS scheduling accuracy, which is actually unnecessary. In discrete manufacturing enterprises, especially in production sites where people are the main resource, the main purpose of APS scheduling is scheduling automation, rather than optimization. Scheduling automation can quickly respond, insert a work order, remove a work order minutes can get a new order, far simpler than manual. Don't schedule too many resources, just one or two resources. In the production site with great dispersion, the management level control ability is usually not refined, and even if it is well arranged, it may not be able to be executed. The goal of scheduling is mainly two, either to maximize production capacity or to minimize production cycle. In fact, this is the forward and inverted.

Static data and dynamic data

Static data and dynamic data are relative. In mass production mode, the dynamic characteristics of data are very weak, and all data can be regarded as static. With the increase of small-batch, multi-variety and mixed-line production, some data gradually show obvious dynamic characteristics. We made some generalizations. Department information, equipment tool information, product-related data (process, BOM, etc.), work calendar, organizational structure, team personnel, these data belong to static data; Material inventory, procurement, outsourcing, orders, and scheduling goals, scheduling constraints, scheduling rules are all dynamic data.

In custom production, the process data of the order task may enter the production stage for the first time, and these data may be modified several times during the actual production process. In particular, the working time data given by the design department must have errors, and even the standard "working time" of the process does not exist. If we follow the traditional management mode, it is impossible to control these dynamic data, and it also causes great trouble to the formulation of production planning and scheduling. In the past, in mass production, many enterprises used "inventory" every month or every quarter to solve the accurate problem of inventory data. But in the current small-batch, multi-variety production environment, they will find that the inventory data is never clear. Learning how to manage dynamic data with dynamic data management method is a topic we must face at present. In the small-batch, multi-variety production mode, the goals of enterprise decision-making are also multi-goal, dynamic, and sometimes the goals contradict each other. Complete urgent tasks even if you waste productive capacity; Some orders have to be done at a loss, etc. The goal is to maximize production capacity at a certain time, and to minimize production cycles at a certain time. The capture of dynamic data is also a new topic. For example: inventory, order changes, outsourced capacity, as well as different versions of materials, processes, and various changes in the production site data, etc.

The quality of production planning is related to the competitiveness of enterprises

Production planning and scheduling is the decision-making problem of enterprises. Some management software is an auxiliary tool for production planning and scheduling. However, not all enterprises are concerned about this issue, and find ways to improve the quality of production planning and scheduling. In the current state of overcapacity in many industries, at least half of the enterprises do not need to care about this problem. As long as the production capacity is abundant, it is enough to rely on manual tabulation for production planning and scheduling. For discrete manufacturing enterprises (such as parts production enterprises), when the capacity utilization rate reaches a certain degree (about 60%-70%), if the production plan scheduling problem is not studied, it will produce inefficiency and waste, which will affect the order delivery time, but it can be dealt with by relying on manual experience. When the capacity utilization rate reaches more than 75%, the manual experience is already slightly insufficient, and computers and software are needed to help. Enterprises that intend to implement production planning automation/optimization technology should be enterprises with certain market competitiveness. Their ability to pay attention to detail shows that their management level has improved. On the other hand, it is very difficult for enterprises with poor management level to implement APS.