What Is Advanced Planning and Scheduling?

APS Advanced Planning and Scheduling (Advanced Planning and Scheduling) is a solution to production scheduling and production scheduling problems, often referred to as sequencing problems or resource allocation problems.

Since the 1940s, it has been a traditional research topic to use mathematical methods to make accurate calculations to arrange production plans. Some of the main ideas of high-level scheduling have been around long before computers existed. The two major contributions to APS are: one is the Gantt Chart that appeared early in the early 20th century; the other is the use of mathematical planning models to solve planning problems. Both the United States and the former Soviet Union applied new optimization linear programming techniques to solve war-related logistics management problems. These ideas and methods have played a fundamental role in the germination of APS.
With the development of multinational companies around the world, manufacturing issues have become more and more complex, and the scale of variables has reached tens of thousands. Although technologies such as linear programming have also been expanded to handle more complex problems, they still cannot meet the needs of enterprises. As a result, many companies develop their own APS in-house, while others develop based on purchasing programs that solve linear programming problems.
In the mid-1960s, IBM developed an MRP system based on product structure decomposition and developed into a closed-loop MRP system in the 1970s. In addition to material requirements planning, production capacity requirements planning, workshop operation planning, and purchasing operation planning were also all incorporated into MRP. Form a closed system. This laid the foundation for the emergence of MRPII in the 1980s, but in fact this closed loop of MRPII is due to preset lead times, unlimited production capacity scheduling and unconstrained material planning, so it can only be closed manually, which is difficult to match the actual Complex dynamic manufacturing environment. During this time, simulation technology began to enter the planning field, and simulation-based planning tools began to appear; in the early 1980s, tire manufacturer Kelly Springfield and tobacco company Philip Morris began to apply planning and scheduling systems. Subsequently, the rapid MRP simulation technology can simulate complex production operations on independent computers. Some of them adopt batch processing in resident memory mode, leaving the host computer that dominated the business calculation at that time, enabling manufacturing companies to complete production planning and scheduling. It only took a few hours instead of the more than 20 hours recognized at the time, greatly reducing planned run times. [1]
From the beginning of the last century to the present, it is the concept promotion phase of APS in China. Some university researchers have moved the theory of APS from university labs to corporate application sites, and overseas APS practitioners have returned to China for APS applications At the same time, there are some domestic enterprise pioneers who practice APS. These three groups constitute the mainstream forces in the current domestic APS boom. When it comes to optimizing algorithms and scheduling software, I have to mention the famous ILOG (now part of IBM) and the famous Cplex. Cplex is a high-performance mathematical programming problem solver from IBM. Cplex is used by many companies as the core engine of APS, such as ILOG's PPO, SAP's APO, and i2's optimization software. In addition, foreign APS brands include Asprova, FlexSche, Preactor (Siemens), AspenTeech, Quintiq and so on. And domestic products include Shidayou, Andafa, Languang Innovation, Yongkai, Yuangong International and other products.
However, it is also embarrassing to mention that APS does not have a large company, or when it was about to grow up, it was eaten by ERP vendors. Professional software that wants to develop independently will face such growth troubles.
The core of APS is the optimization algorithm, which has gone through four generations. The latest fourth generation is an intelligent algorithm that integrates artificial intelligence with dynamic adjustment algorithms. The intelligent algorithm is used for static scheduling, and multi-Agent negotiation is used to perform distributed calculation dynamic adjustment. So far, planning management based on MPS and MRP calculations has become the standard and core function of modern enterprise ERP. However, from the perspective of decades of application results, it is still difficult to meet the planning management needs of enterprises. In fact, the system and method of MPS main production planning and MRP material demand planning have been difficult to adapt to the on-demand production environment, especially the large-scale personality.
Information exchange between ERP, APS, MES
The era of customized industry 4.0. APS can comprehensively consider constraints such as production capacity, tooling, and processing batches, and can implement rolling scheduling in conjunction with MES. This also explains why in the era of Industry 4.0, MES and APS systems have become far more dazzling stars than ERP. APS is divided into supply chain level APS and factory level APS. The manufacturing execution system MES was the earliest used in the workshop control and management work center, and issued work orders, mainly by manually reporting progress and man-hours; along with the lean manufacturing and manufacturing digitalization, the MES manufacturing execution system was formed. Therefore, the two functions overlap in intelligent scheduling. At present, as an advanced planning and scheduling system that represents today's advanced management thinking, the core of APS is a proven mathematical algorithm or solution. In the digital economy, the development of APS shows a trend of diversification. One is to integrate more closely with ERP and MES. [3]
It is worth noting that the data source of APS is changing. APS data such as i2 and Oracle's APS are derived from ERP; and with the popularity of MES, the production system has also become a source of huge data pools. This is a new development opportunity for APS. One is the combination with multi-variety small-volume order manufacturing and project manufacturing: in fact, only APS can achieve multi-variety small-volume personalized custom planning models. It is also worth noting that cloud platformization, due to the intermittent nature of APS services, has caused the purchase of high core algorithms and idle server runtimes. For this reason, APS cloud platformization can greatly reduce the investment of planned production of enterprises. Enterprises at home and abroad have deployed APS for SaaS.
Although APS has powerful functions, it is very suitable for the solution of the overall planning of the supply chain. However, there are still many problems that need to be solved in the application of APS in China's enterprise management. For example, the function of APS is mainly used in supply chain management, while the supply chain management of Chinese enterprises is still at a very early stage, and the company's awareness of supply chain competition is still very vague. Therefore, it is difficult for APS to find a real application. However, the basic information, processes and planning systems of internal enterprise information are weak. The operation of APS requires data support provided by ERP, MES, PLM and other systems. Previously, the reasons for the poor implementation of many APS projects were mostly because of the lack of MES to help APS achieve closed-loop and rolling production schedules, which resulted in poor planned redemption rates. Nevertheless, with the increasing demand for large-scale personalized customization in the market, APS has gradually become an important hub for smart factories in the discrete manufacturing industry. In some industries, such as the tobacco industry that is processed in groups, the large-scale personalized production of the automobile and the home appliance industry, without APS, it is difficult for people to plan based on experience. Along with this, personalized demand has gradually become the mainstream market demand. It is expected that in the next three to five years, the application of APS in China will begin to enter a large number of implementation stages and become the command center of smart factories [3] .

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