Research on a MES-Based Unified Code Generation, Management, and Traceability System for Discrete Manufacturing

Background: Against the backdrop of the transformation and upgrading of the manufacturing industry, lean manufacturing has emerged as a systematic and advanced production paradigm that has deeply permeated the entire value chain of enterprises. Objective: However, there is a lack of systematic and effective lean technology paradigms in aspects such as lean practice processes and improving manufacturing process efficiency. Moreover, a comprehensive analysis of the current status and development strategies of lean technologies in discrete manufacturing enterprises has yet to be conducted to address issues such as the fragmentation of lean technology applications and the ambiguity of implementation strategies in discrete manufacturing enterprises. Methods: This paper conducts an extensive review of the literature on lean technologies and transformation methods in discrete manufacturing enterprises. A multi-stage data analysis approach (including data identification, screening, eligibility assessment, classification, and comprehensive analysis) is utilized to analyze 369 highly relevant documents. Results: The main contributions of this study are as follows: (1) A comprehensive review of existing lean manufacturing technologies and methods is provided, classifying, comparing, and summarizing the current status of lean technology and strategy applications, and delineating nine categories of lean technology application directions. (2) A “5P” theoretical framework (Philosophy, Process, People, Problem-solving, and Product) is proposed, redefining a lean technology framework that covers the value streams of discrete manufacturing. (3) Future application trends of lean technologies in discrete manufacturing are summarized and predicted, and an implementation strategy for lean technologies tailored to small and medium-sized discrete manufacturing enterprises, along with six lean technology development strategies, are proposed. The results indicate that many enterprises have derived diversified methods based on their own circumstances, which compensate for the deficiencies of the original lean models. Discussion and Conclusions: This paper organizes these methods to serve as a reference for future research on the lean technology system. The proposed strategies include formulating lean transformation strategies for discrete manufacturing enterprises, enhancing proactive lean capabilities, adapting to passive lean factors, and creating value for the enterprises’ reasonable lean needs from three levels: strategic philosophy, objective principles, and tool technologies. This research will play a guiding role in promoting the coordinated development of lean implementation and achieving high-quality development in discrete manufacturing enterprises.

This paper uses NVIVO as a software tool to study the realization of digital transformation in discrete manufacturing enterprises in Grounded theory. To explore the key influencing factors of digital transformation, in-depth interviews were conducted with 43 employees of typical discrete manufacturing enterprises in China, and qualitative analysis of the interview raw data was conducted. Firstly, the original data were open coded to identify the initial categories of digital transformation behaviors. Secondly, based on the open coding, the connections that exist at the conceptual level between different initial categories are summarized and inducted. Finally, the relationships between the core categories and other categories are analyzed, and the typical relationships between each category are summarized. Based on the analysis of the raw data statements, the dimensions of the direct and indirect roles of discrete manufacturing enterprises in realizing digital transformation are summarized, and the role model of the influencing elements of digital transformation in discrete manufacturing enterprises is constructed to analyze the behavior of digital transformation in discrete manufacturing enterprises. The research results show that there are five main influencing factors for digital transformation. The digital transformation measures of discrete manufacturing enterprises are proposed accordingly.

Managing Functional Conflicts and Response Time Delays Of Customer Orders in Discrete Manufacturing Workflows

Modern manufacturing shopfloors suffer from a bottleneck of capturing and collection of real-time field information. According to the analysis of the new requirements of the shopfloor management faced by current discrete manufacturing enterprises, a real-time visibility and traceability framework is proposed. Under this framework, the main modules including the configuration of the IoT-enabled sensing environment, manufacturing information process, cloud based real-time manufacturing services, are integrated. The presented framework and modules will provide an important foundation for enhancing the capabilities of real-time sensing, dynamical tracing, real-time predication and intelligent management of the discrete manufacturing system.

Order to discrete manufacturing enterprises typical business process as background, analyzes its in the order lifecycle process. Extract the key nodes information in the business process, business process model, data chain model is established. Finally, the use of middleware technology for the actual situation of discrete manufacturing enterprises, established the order lifecycle execution process architecture model. Has important significance for discrete manufacturing enterprise order lifecycle information management system.

<p indent="0mm">In the realm of intelligent manufacturing, discrete manufacturing enterprises are in urgent need of enhancing control levels in their production processes. Therefore, addressing the critical issue of leveraging next-generation information technology for precise and rapid control over disruptive events has become imperative to accelerate progress in intelligent manufacturing. This paper commences by elucidating the temporal logic behind production anomaly control, thereby clarifying the core research problem. Subsequently, it delves into characterizing and modeling production anomalies, encompassing representation of production elements, process modeling, and an overview of current anomaly modeling approaches. Furthermore, a detailed exposition is provided on the optimization of production anomaly scheduling while analyzing existing methods based on “perceive-evaluate-schedule-optimize”. Additionally, this paper explores closed-loop control logic within manufacturing processes that include data collection, anomaly prediction, and real-time control. Moreover, significant emphasis is placed on discussing the potential advantages and challenges associated with applying next-generation artificial intelligence information technology in anomaly control. Finally, considering the current research status and industry requirements for intelligent manufacturing systems, prospective future trends in anomaly control technology are presented.

This paper first expounds the concepts related to digital transformation and the concept of profit model, and then takes the digital transformation of Midea Group as an example to analyze the positive role played by its digital transformation in the profit model and some problems, and then puts forward some suggestions.

The laboratory organization is essential in the university to maximize system convenience, accuracy, appropriateness, and sustainability. The laboratory data system is an information supplier that aids in arranging data sources and decision-making processes. In this work, unified laboratories management system (ULMS) was presented in the educational field for managing and scheduling lab equipment information. There are two steps in the research process: the first is to examine the old approach for organizing and scheduling laboratory equipment and suggest a better one. The approach adopted in this system is the first come, first served rule governs task priority (FCFS). The second step is creating a computerized system for scheduling and managing laboratory equipment. For programming, C# and structured query language (SQL) are used. This system's design included user and administrative integration (manager, technicians, and researchers). An information application system plan was created because of this research to manage the scheduling information for lab equipment. As a result, it can help the management of the laboratory describe all operational tasks in a way that is considerably simpler and produces outcomes more quickly and accurately.

Large language models (LLMs) have transformed software development by enabling automated code generation, yet they frequently suffer from systematic errors that limit practical deployment. We identify two critical failure modes: \textit{logical hallucination} (incorrect control/data-flow reasoning) and \textit{schematic hallucination} (type mismatches, signature violations, and architectural inconsistencies). These errors stem from the absence of explicit, queryable representations of repository-wide semantics. This paper presents \textbf{\framework}, a novel framework for code generation that addresses these limitations through knowledge graph-guided constraint satisfaction. Our approach proceeds in four integrated stages: (1) constructing heterogeneous repository knowledge graphs that capture both static analysis and dynamic execution traces; (2) learning neural query planners that extract task-relevant context from these graphs; (3) employing satisfiability modulo theories (SMT)-guided beam search to ensure generated code satisfies semantic constraints; and (4) maintaining graph fidelity through continual incremental updates. Our comprehensive evaluation on \dataset, a curated benchmark of 4,250 repository-level tasks across 50 Python projects, demonstrates significant improvements over state-of-the-art baselines: 49.8\% Pass@1 (18.1\% absolute improvement), 52\% reduction in schematic hallucination, and 31\% reduction in logical hallucination. Cross-repository generalization analysis shows strong transfer capabilities with only 4.3\% average performance degradation across architectural patterns. These results establish new benchmarks for repository-level code generation while providing theoretical foundations and practical tools for semantically-aware automated software development. The explicit semantic representation and constraint satisfaction framework introduced in \framework\ enables more reliable automated development tools and provides a foundation for future advances in AI-assisted software engineering.

Characteristic of Auto electronic parts production industry are introduced at first. And with the background of the practical demands of Auto electronic parts production industry, with model of Browser/Server (B/S) and program as Microsoft.NET, Visual C++, SQLSever2000 was introduced. Meanwhile, this architecture of Manufacturing Execution System (MES) design theory and database structural charts of the whole system are presented. Information integration and production scheduling optimization function of MES implement information fusion and combination between PCS and ERP, realization the optimization control and management of the Auto electronic parts enterprise production process, which improves enterprise's synthesis competition ability. The two layers integrated automation system production indexes and scheduling as the design core realization optimization of production scheduling indexes. The MES that adopting this method is achieved the integration of management and control. This system has been applied to the Auto electronic parts plant and the production efficiency is increased. The successful implementation of this system has explored a new method for the overall optimization of the large systems in discrete manufacturing enterprise.

Research on flexible job shop scheduling under finite transportation conditions for digital twin workshop

The objective of this study is to monitor manufacturing energy consumption (MEC) in a discrete manufacturing enterprise, where energy is consumed everywhere and anytime with creating ubiquitous MEC data. The ubiquitous manufacturing (UbiM) technology, including radio frequency identification (RFID) technique, is employed to automate real-time data acquisition and processing for an order fulfilment. An MEC model for the order fulfilment is constructed according to the bill of materials (BOM). In this model, the computation is triggered by an RFID read event (RRE) enabling a digital energy metre to acquire energy consumption value of a workstation for processing a certain material, and then the acquired value is assigned to an energy consumption event (ECE). To reflect the effect of an ECE on energy consumption of a production task, a station-material energy consumption matrix (smECM) is constructed to store the relevant event data, which plays a key role in alleviating MEC monitoring restrictions caused by big energy data. By operating the matrix, the MEC monitoring information can be effectively extracted. To assist manufacturing enterprises to better employ it, the proposed method was demonstrated by monitoring MEC of an order in an auto-part manufacturer.

To optimize the production logistics in discrete manufacturing enterprises, we put forward a new thought of workstation-oriented production logistics for the solution of low picking efficiency and high delivery cost of JIT (Just in Time) mode. A distribution model is constructed to minimize the total distribution time in workshop. An improved genetic algorithm is designed to solve the proposed optimization model. Finally, a case study is presented to demonstrate the feasibility and validity of the proposed model.

Hierarchical dynamics-aware deep learning for electricity consumption prediction in discrete manufacturing enterprises

The variety of product types and spesifications make new product development (NPD) tasks tough work in discrete manufacturing enterprises, which makes it a common strategy to refer to similar outcomes (e.g. the product drawings, work instructions, etc.) of former tasks during NPD processes. To improve the efficiency of discovering similar historical outcomes, this paper presents a novel approach to measure the similarity between task execution contexts in process-aware information systems, and exploit it for runtime task knowledge recommendation. In our framework, the measurement of similarity is preceded by 1) modeling the task context with ontology theory, 2) using the ontology matching algorithms to evaluate the similarities between the corresponding context ontology entities of different tasks instances. The TD-IDF is then utilized to compute the context cohesion between the user’s current task and historical tasks, and the tasks with the highest similarity will be recommended to the task executors, along with their outcomes. Comparative evaluation is performed using TF-IDF, Levenshtein and Affine Gaps, and results demonstrate that the proposed approach achieves good precision and recall, and is efficient in task knowledge recommendation.