ford電費(fèi)節(jié)約6sigma項(xiàng)目-文庫吧資料
2025-02-15 08:16本頁面
【正文】 endy. ? Give the impression that everyone participates. ? Efforts of each person is essential for the total success. ? Saving is along with the corporate policy. ? Each person can contribute a lot/ Problems could occur if I do not save. ? Saving effects can be confirmed clearly / We can feel the effects. ? Business achievements are evaluated by the saving effect. ? Not a temporary project. Activities should be continued. The Process of Using (Wasting) Electricity CE Diagram CE Matrix ? Lowering wattage (without influencing on the business environment) is important in this project. Each process input (areas of electricity consumption and appliances, such as lighting of office and PCs) should be evaluated according to this. ? Points mentioned in the VOC can be basically fulfilled equally by all the process inputs. ? A rough estimate of power consumption shows that lighting of office and PCs are two major process inputs in lowering wattage. ? As the most important process inputs are clear, we did not develop a CE matrix for this project. MSA Converting the Monthly Data (1) Monthly power consumption data is reported by the maintenance pany of the building after reading the wattmeters. It has turned out that there are some problems for this data as mentioned below. We must adjust the data to pare the consumption of power of each month correctly. Tenants cannot check the meters by themselves at this building. ? The days of checking meters differ from month to month. The data of the dates of checking can be followed to the past. ? The numbers of business days differ much from month to month. ? The majority of appliances are not used on weekends though some are used just like main frame puters. Therefore, consumption of power significantly differs between business days and holidays. Converting the Monthly Data (2) ? We applied the multiple regression analysis on monthly power consumption (Y), the number of business days (X1) and the number of holidays (X2) in the past data. We estimated the relationship between power consumption and Xs – the number of business days and holidays. ? We converted the consumption data by using this relationship to make correct parison between months with different number of business days and holidays. ? It has turned out that the power consumption was fairly stable between October 2023 and the start of the project by the analysis of the converted past data. Regression Analysis： KWH versus Work days, Holidays The regression equation is KWH = 14486 + 2929 Work days + 1627 Holidays Predictor Coef SE Coef T P Constant 14486 12417 Work day Holidays S = 3649 RSq = % RSq(adj) = % Analysis of Variance Source DF SS MS F P Regression 2 503844693 251922347 Residual Error 17 226339999 13314118 Total 19 730184693 Source DF Seq SS Work day 1 236055862 Holidays 1 267788831 Unusual Observations Obs Work day KWH Fit SE Fit Residual St Resid 5
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