It is worth mentioning that Huang Xiang stressed that unlike ChatGPT, which is popular in the circle, the key point for AI large models to play value, whether it is commercial value or social value, is that the output results must be credible. When AI is applied to harsh manufacturing environments, whether it is generative innovation on the R&D side or process optimization on the production line, a small uncontrollable error can have disastrous consequences. For manufacturing enterprises, in the process of applying intelligent decision-making, it is inseparable from the guidance of methodology, scene selection, in-depth understanding of business logic, and the deep integration of machine learning and operations research technology.

For example, for the same underlying business modeling, the digital game platform of Sandata Technology will build a full-dimensional intelligent framework with high fidelity according to the actual business situation of the enterprise, through data description and cleaning, rule analysis, and decision analysis, and finally achieve the “optimal solution” with multiple objectives and constraints.

Deep cultivation scene, designed to meet the actual needs of customers

No matter what kind of coping strategy, the changeable and unstable external situation is an objective existence. At present, although some enterprises have built a variety of large and small platforms and middle platforms, but little effect, digital input-output ratio is not ideal, the fundamental reason is that there is no adaptation to the corresponding scenario, and even many strategic details of supply chain management are only in the minds of enterprise executives, can not be implemented into the daily production and operation of enterprises.

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“This is why, in addition to targeting intelligent decision-making and data-driven, the digital game platform released by Sugi Digital Technology emphasizes the integration of scenes and the division of industries.” Huang Xiang introduced that “engine + decision center + scene” has always been the core strategy of Shanshu Technology development, and “scene” will become the strategic focus of Shanshu Technology this year. “When we focus on the enterprise supply chain, we will find that there are different segments and fragmented needs of customers in different areas. Sugigu Technology will design an organizational structure, business process and operation model that matches Zhongtai from multiple perspectives such as data, operation and innovation.” Huang Xiang said that through deep cultivation of user scenarios, the platform can reduce the instability brought by changes to enterprise development from the whole life cycle of the supply chain, and consolidate the organizational and management foundation for the durability of enterprise digital development:

First of all, the company will build a digital production and marketing master plan for the enterprise based on the algorithm and business, establish a dynamic mid – and long-term multi-level linkage plan, provide decision-making choices for different goals for production and marketing, improve the quality of decision-making and track and monitor the execution data of the plan, in order to achieve the overall goal of optimizing the business and improving operational efficiency. For example, in the face of the “going to sea” boom of manufacturing enterprises in recent years, the platform supports unified data information management for multiple regions, provides a complete plan model covering the demand, supply and financial information of each factory, and can combine the local regional requirements and supplier capabilities to generate a plan group including demand plan, production plan, inventory plan and procurement plan in one click. Achieve cross-organizational collaboration and balance of production and marketing.

When the master plan needs to be decomposed, the platform will generate a production line level production plan accurate to day/shift, and consider the impact of actual production execution on the plan to carry out rolling scheduling to ensure the accuracy and implementability of the planning results. Moreover, the production scheduling module can optimize scheduling according to enterprise objectives and obtain the optimal goal-oriented scheduling results.

At the level of order management, the platform will synthesize all the signed uncompleted order requirements, combine the actual existing capacity and future capacity planning, generate a detailed plan including the order delivery date and delivery quantity, and calculate the order intelligently and quickly based on the existing production capacity of the enterprise, and generate the order delivery response result within a short time range. Able to respond quickly to customer needs and improve customer satisfaction.

For the material plan, the platform will disassemble the demand layer by layer according to the demand plan and BOM data, consider the lead time and proportion relationship between levels, calculate the demand time and demand quantity of materials at all levels, generate the material demand plan, and take the medium and long-term forecast as the demand input, while considering the constraints of long-term production plan and material assembly constraints. Drive and guide the formulation of long-term material purchasing plan.