MES (Manufacturing Execution System) serves as the core execution system for manufacturing enterprises’ production processes and an information-based production management system tailored to the workshop execution layer of manufacturing enterprises. With the in-depth integration of new-generation information technologies such as artificial intelligence and the Internet of Things (IoT) with traditional manufacturing technologies, traditional domestic enterprises are gradually transforming toward intelligent manufacturing. In this transformation process, MES is also confronted with a host of challenges.

The construction of MES has long been a heated topic, yet it still involves multiple difficulties that test enterprises’ thinking modes and problem-solving capabilities. Among various problem-solving methodologies, the dimensional upgrading solution provides a viable reference approach. It helps break inherent thinking limitations and achieve a state of thorough breakthroughs and efficient problem-solving similar to the "dimensionality reduction strike".

The dimensional upgrading discussed in this paper does not focus on business model elaboration. Instead, it targets practical technical and business pain points, aiming to address problems fundamentally by focusing on their essence and core rather than adopting compromised and trade-off solutions, so as to realize one-off and permanent problem resolution.

Based on personal practical experience and insights, this paper puts forward targeted thoughts and case analyses, hoping to offer valuable references for peer researchers and practitioners. It also serves as a preliminary summary of the author’s research in this field.

1. Problems of Organization and Traceability in Complex Batch Splitting and Circulation

In workshop order production, order splitting is extremely frequent due to practical production demands and traditional manual operation habits. It includes overall batch splitting before production execution, in-process batch splitting during production, and complex batch merging in subsequent processes.

This process involves not only order splitting and the reorganization of subsequent information flow, but also the coordination of material logistics.

In addition, batch splitting often occurs in processing and inspection circulation links, and the splitting rules of one process may differ completely from those of the next, forming a complex cross-process batch circulation scenario.

Solving only specific individual scenarios can achieve temporary effects but fails to adapt to subsequent changes and support diversified and variable production demands.

From the perspective of dimensional upgrading, orders should no longer be regarded as the basic management unit. Instead, each workpiece or part should be taken as the finest granularity management object, while orders are defined as a logical combination of workpieces and parts. The same logic applies to production batch splitting and circulation batch splitting. Such refined management can adapt to all production organization requirements, endowing the system with maximum compatibility and fundamentally solving the persistent problems of production organization and full-process traceability in one go.

From a long-term perspective, even mass production scenarios require such refined management capabilities, which pose extreme challenges to system architecture, big data organization and complex computing performance.

2. Capability Evaluation Defects in Production Planning Formulation

The current hierarchical planning system, including comprehensive production planning, master production planning, material requirement planning and workshop operation planning, has prominent inherent defects. In actual enterprise operation, most production tasks are managed in a project-based mode, with mandatory control of decomposed delivery nodes based on delivery deadlines. Although rough and even refined capacity evaluation is implemented, poor executability of planning remains the core cause of chaotic workshop production.

This traditional hierarchical planning model has three major drawbacks. First, it is extremely difficult to adjust plans across layers, and plan propagation and coordination between different levels face huge obstacles. Second, practical enterprise experience shows that workshop planning chaos is rarely caused by internal workshop problems, but mostly stems from unreasonable and uncertain upper-level planning or resource constraints from other workshops. Third, upper-level planning adopts static capacity evaluation rather than dynamic assessment based on the organic coupling of operating procedures, resulting in poor executability of formulated plans.

Following the principle of "long-divided situations tend to unite", dimensional upgrading proposes an integrated and unified planning solution. Supported by modern advanced computing technology, enterprises can integrate all hierarchical plans and adopt the refined operation-level process resource scheduling mode. Traditional hierarchical and distributed planning is merely the extraction and combination of refined scheduling results. The corresponding capacity evaluation will be transformed into refined, feasible and dynamic finite capacity evaluation, which can fundamentally eliminate the drawbacks of traditional hierarchical planning systems.

Nevertheless, the implementation of cross-level integrated refined operation scheduling needs to solve problems such as incomplete or missing basic input data. The entry point and scope of unified operation planning should be determined according to enterprise actual conditions. For example, there are significant differences between R&D-oriented enterprises and mass-production-oriented enterprises, and the transformation will also involve adjustments to enterprises’ basic management modes.

3. Ordered Management and Control of Flexible Production Lines

Against the backdrop of intelligent manufacturing, enterprise production organization falls into two extreme modes: multi-variety and variable-batch flexible production, and dedicated automated line production. In fact, most dedicated automated lines are nominal applications of intelligent manufacturing, which only support single-product production and are far from the automated flexible production advocated by Industry 4.0. Nevertheless, automated replacement of manual operation is an inevitable trend for improving production efficiency and promoting technological progress. The core problem to be solved currently is how to balance and integrate automation and flexibility, with the key lying in realizing true production flexibility.

Flexibility refers to the capability to dynamically allocate production factors and resources and implement coordinated control in response to changing production tasks. Single-product automated lines lack flexibility and can be basically regarded as black-box systems in production management.

Many academic studies attempt to establish analytical or probabilistic models to describe and solve manufacturing system operation problems, yet such methods are inefficient. They rely on constant simplification of actual scenarios, resulting in distorted models that cannot solve practical production pain points.

From the perspective of dimensional upgrading, this paper proposes a calculus-based problem-solving idea: hierarchize and discretize continuously coupled production systems. Specifically, production factors, resources and control links are discretized, and resource and control chains are reconstructed through establishing logical correlations. The strategy of "deconstruction first, reconstruction second" enables the system to cope with changing scenarios with fixed logical frameworks, and solve all specific operational combination problems through diversified portfolio changes. This approach maximizes the utilization of manufacturing resources and releases the full potential of flexible control.

The dimensional upgrading problem-solving idea is still being refined. The above cases focus on bottom-level flattening and refinement to provide an innovative perspective for industrial problem analysis. With the rapid development of information and computing technology, and the continuous penetration of IoT, big data and cloud computing in the industrial field, traditional inherent thinking should not restrict the application of new technologies. The potential of emerging technologies will drive the innovation of industrial operation modes. Gradual accumulation of technological and methodological improvements will trigger qualitative changes, forming a new manufacturing system operation and management mode with dimensionality reduction advantages over traditional models.

Source: Information and Software Service Network