基于用电量的制造企业剩余生产能力预测
发布时间：2018-10-30 18:59
【摘要】：在供应链中，采购商往往有着众多的制造供应商可供选择，在采购决策之前，，需要对供应商进行评估，掌握供应商订单期内剩余生产能力信息是关键评估内容之一。由于采购商与制造供应商之间不存在所属关系，评估涉及到的订单、生产计划等信息属于企业隐私，同时供应商为了追逐利益最大化，极有可能提供虚假信息，因此如何获取准确客观的生产信息成为掌握供应商剩余生产能力信息的瓶颈。针对这一问题，根据用电量信息难以造假、容易考证、获取方便、能够反映现实生产情况等特征，提出了基于用电量预测制造供应商剩余生产能力的方法，同时也为制造企业掌握自身未来生产情况提供新的思路。 论文从制造系统角度分析了基于用电量进行制造企业剩余生产能力预测的可行性，然后进行建模。建模分三个过程进行：制造企业月用电量与该月剩余生产能力关系分析建模、基于企业月用电量时间序列对未来月份剩余生产能力的预测建模、模型验证。 首先，根据企业用电分布，分析了用电量除了受产品生产耗电影响之外，时节气候变化是主要的非生产影响因素，其具有增长性和季节波动特点，引入用电量时节影响变量概念描述时节气候影响因素，利用部分样本求出用电量与产量、剩余生产能力的线性回归关系，再对各月用电量时节影响变量进行量化计算，从而得到了整体样本用电量与产量、剩余生产能力的非线性回归关系。 其次，根据灰色系统理论“少数据、贫信息、增长性”的特点，结合生产能力度量、预测期内用电量时节影响变量计算、剩余生产能力利用率计算，进行了基于用电量的制造企业剩余生产能力灰色预测，分析误差产生原因，利用突变数据去除法、趋势移动平均法改进灰色预测技术，建立了最终预测模型，并建立了基于用电量进行同类型企业的剩余生产能力预测模型。 最后，文章对所建预测模型进行实例分析，验证了预测模型的有效性。
[Abstract]:In the supply chain, buyers often have a large number of manufacturing suppliers to choose from. Before purchasing decision, the supplier needs to be evaluated. It is one of the key evaluation contents to grasp the information of the supplier's surplus production capacity during the order period. Since there is no ownership relationship between the purchaser and the manufacturing supplier, the order, production plan and other information involved in the evaluation is private to the enterprise. In order to maximize the benefits, the supplier is likely to provide false information. Therefore, how to obtain accurate and objective production information becomes the bottleneck to grasp the information of supplier's surplus production capacity. In order to solve this problem, according to the characteristics of electricity consumption information is difficult to fake, easy to verify, easy to obtain, and can reflect the actual production situation, the method of forecasting the surplus production capacity of manufacturing suppliers based on electricity consumption is put forward. At the same time also for manufacturing enterprises to grasp their own future production situation to provide new ideas. This paper analyzes the feasibility of forecasting the surplus production capacity of manufacturing enterprises based on electricity consumption from the point of view of manufacturing system, and then models the model. The modeling is divided into three processes: the analysis and modeling of the relationship between the monthly electricity consumption of manufacturing enterprises and the surplus production capacity in that month, and the prediction and modeling of the remaining production capacity in the future month based on the monthly electricity consumption time series of the manufacturing enterprises, and the model verification. First of all, according to the distribution of electricity consumption in enterprises, it is analyzed that the seasonal climate change is the main non-production factor, which has the characteristics of growth and seasonal fluctuation. The concept of the influence variable of electricity consumption season is introduced to describe the seasonal climate influence factors, and the linear regression relationship between electricity consumption and output, surplus production capacity is obtained by using some samples, and then the quantitative calculation is carried out on the influence variables of each month's electricity consumption season. The nonlinear regression relationship between electricity consumption, output and surplus production capacity of the whole sample is obtained. Secondly, according to the characteristics of grey system theory, such as "less data, poor information, growth", combined with the measurement of production capacity, the calculation of the influence variables and the utilization rate of surplus production capacity in the forecasting period are carried out. In this paper, grey prediction of surplus production capacity of manufacturing enterprises based on electricity consumption is carried out. The causes of errors are analyzed. The grey prediction technology is improved by using abrupt data removal method and trend moving average method, and the final prediction model is established. A model for forecasting the surplus production capacity of the same type of enterprises based on electricity consumption is established. Finally, an example is given to verify the validity of the prediction model.
【学位授予单位】：宁波大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH186
[Abstract]:In the supply chain, buyers often have a large number of manufacturing suppliers to choose from. Before purchasing decision, the supplier needs to be evaluated. It is one of the key evaluation contents to grasp the information of the supplier's surplus production capacity during the order period. Since there is no ownership relationship between the purchaser and the manufacturing supplier, the order, production plan and other information involved in the evaluation is private to the enterprise. In order to maximize the benefits, the supplier is likely to provide false information. Therefore, how to obtain accurate and objective production information becomes the bottleneck to grasp the information of supplier's surplus production capacity. In order to solve this problem, according to the characteristics of electricity consumption information is difficult to fake, easy to verify, easy to obtain, and can reflect the actual production situation, the method of forecasting the surplus production capacity of manufacturing suppliers based on electricity consumption is put forward. At the same time also for manufacturing enterprises to grasp their own future production situation to provide new ideas. This paper analyzes the feasibility of forecasting the surplus production capacity of manufacturing enterprises based on electricity consumption from the point of view of manufacturing system, and then models the model. The modeling is divided into three processes: the analysis and modeling of the relationship between the monthly electricity consumption of manufacturing enterprises and the surplus production capacity in that month, and the prediction and modeling of the remaining production capacity in the future month based on the monthly electricity consumption time series of the manufacturing enterprises, and the model verification. First of all, according to the distribution of electricity consumption in enterprises, it is analyzed that the seasonal climate change is the main non-production factor, which has the characteristics of growth and seasonal fluctuation. The concept of the influence variable of electricity consumption season is introduced to describe the seasonal climate influence factors, and the linear regression relationship between electricity consumption and output, surplus production capacity is obtained by using some samples, and then the quantitative calculation is carried out on the influence variables of each month's electricity consumption season. The nonlinear regression relationship between electricity consumption, output and surplus production capacity of the whole sample is obtained. Secondly, according to the characteristics of grey system theory, such as "less data, poor information, growth", combined with the measurement of production capacity, the calculation of the influence variables and the utilization rate of surplus production capacity in the forecasting period are carried out. In this paper, grey prediction of surplus production capacity of manufacturing enterprises based on electricity consumption is carried out. The causes of errors are analyzed. The grey prediction technology is improved by using abrupt data removal method and trend moving average method, and the final prediction model is established. A model for forecasting the surplus production capacity of the same type of enterprises based on electricity consumption is established. Finally, an example is given to verify the validity of the prediction model.
【学位授予单位】：宁波大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH186
【参考文献】
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