Dissertation > Economic > Industrial economy > China Industrial Economy > Organization and management of industrial enterprises

Research and Application on Key Performance Indicators of Manufacturing Enterprises

Author ZhuLi
Tutor SuHongYe
School Zhejiang University
Course Control Science and Engineering
Keywords Performance Indicators Evaluation Manufacturing enterprises Object-Process Methodology Coordination strategy ISO22400
CLC F425
Type PhD thesis
Year 2014
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For the sake of increasing the economic benefit, maintaining the ability of sustainable development and enhancing the competitiveness, it’s necessary to keep operation and production process steady and efficient in manufacturing enterprises. In order to deal with the problems and challenges of evaluation for operation and production process, based on the actual situation and characteristics of manufacturing enterprises in China, key performance indicators (KPIs) is introduced and the framework architecture, integration model, implementation method, coordination strategy of KPIs, etc. are analyzed and discussed. According to the analysis of research and application in operation and production process evaluation of manufacturing enterprises, three problems of KPIs evaluation are presented, including lack of the common evaluation framework structure of KPIs, the evaluation for the whole manufacturing enterprises, and the analysis in the relationship of multi-KPIs. For the first problem, the KPIs evaluation framework architecture of manufacturing enterprises is proposed, which consists of two parts, KPIs definition and KPIs application. The KPIs of process control system (PCS), manufacturing execution system (MES) and enterprise resource planning (ERP) are described separately on the basis of KPIs description model and the corresponding KPIs evaluation set is established, which standardizes the evaluation of operation and production process in manufacturing enterprises. For the second problem, KPIs integration model is built based on Object-Process Methodology (OPM) and the hierarchical division of KPIs is formed according to the functional structure of manufacturing enterprises. The KPIs in each level are analyzed by analytic hierarchy process (AHP) to evaluate manufacturing enterprises comprehensively. For the third problem, multi-KPIs coordination strategy is provided. The relationship of multi-KPIs in KPIs evaluation set is coordinated to keep the balance among KPIs. Last but not least, KPIs evaluation is applied in iron and steel company. Based on the detailed analysis of iron and steel manufacturing process, KPIs evaluation is used in economic benefits and process performance of rolling unit respectively and the evaluation results is supported to production planning decision-making in iron and steel company. The main contents and major contributions in this dissertation are described as follows:1) According to the characteristics of operation and production process in manufacturing enterprises, the KPIs evaluation framework architecture of manufacturing enterprises is proposed based on the feedback control theory. The manufacturing enterprises are evaluated reasonably and effectively through KPIs definition and application. Meanwhile, the factors influence KPIs evaluation are deeply discussed, including data factor, time factor and quantity factor. The common KPIs description model is used to normalized description and analysis, and typical KPIs are listed for PCS, MES and ERP level to establish the KPIs evaluation set of manufacturing enterprises. Finally, the effectiveness and validity of KPIs evaluation are verified by applying in overall equipment effectiveness indicator of manufacturing production.2) The KPIs integration model of manufacturing enterprises is provided on the basis of OPM and the influence factors are discussed. The hierarchical division of KPIs is made in line with the functional structure of manufacturing enterprises, forming KPIs evaluation set on enterprise level, site level, area level, work center and work unit level, and control unit level separately. The relevancy among KPIs in each level and the relation between every KPI and the top level KPI are analyzed by AHP. The main factor effects KPIs evaluation target is found to improve the operation and optimize production process. Moreover, the method is applied in evaluating the customer satisfaction of iron and steel company to verify the availability of KPIs integration model and the practicability of AHP.3) Focusing on manufacturing equipments in production process, manufacturing equipment model is established by OPM. The KPIs for evaluating manufacturing equipment are explained and KPIs evaluation set is created. Simultaneously, multi-KPIs coordination strategy is proposed to balance and coordinate the relationship of KPIs in KPIs evaluation set. The trade-off scheme of multi-KPIs evaluation is supported on planning and scheduling of manufacturing enterprises. Finally, methanol-water distillation equipment is introduced to illustrate the applicability of KPIs coordination strategy by coordinating the relationship between production ratio indicator and unit energy consumption indicator, and an efficient solution is obtained.4) Manufacturing process in iron and steel enterprises is analyzed deeply. The order processing and rolling process is modeled by OPM to discuss the influence factors of making production planning and selecting process routes in rolling unit. In the meanwhile, based on the model of rolling unit and considering the constraints of raw materials, products, inventory and production capacity, etc, the evaluation model of rolling unit is established, which subjects to production process cost indicator and inventory cost indicator. The feasible production planning scheme is acquired by multi-KPIs coordination strategy and variable weighted coefficient optimization method. And the production process of the scheme is evaluated by inventory turns indicator and equipment load rate indicator. The KPIs evaluation provides the theoretical basis and application guidance for making and selecting appropriate manufacturing production planning scheme.

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