全球运营的商业基准:啤酒行业的水和能源消耗案例

J. Kirstein, A. Brent
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引用次数: 0

摘要

随着对资源的需求和成本的增加,从可持续发展的角度来看,酿造行业面临的风险越来越大。作为回应,全球的酿酒商都开始了提高效率的计划,通常伴随着雄心勃勃的、公开承诺的目标。基准通常用于帮助酿酒商确定改进机会,但在没有标准化工厂之间差异的情况下,对啤酒厂的水和能源表现进行比较是缺乏的。随后,以南非米勒啤酒厂为例,对水和能源使用的正常化进行了调查;提出变量(工厂之间的差异)和标准化模型,使啤酒厂(不同的)能够进行比较,以便进行基准测试,并确定改进机会。从文献中获得的水、电和热能使用的驱动因素被选择、合理化并在行业专家的德尔菲调查中排名,并与64个SABMiller站点的数据相关联。研究人员利用来自58个SABMiller站点的主要驱动因素和数据,建立了水、电和热能使用的多变量线性回归(MVLR)模型。通过对南非米勒公司位于不同枢纽的6个站点的数据进行测试,该模型能够预测水、电和热能的使用情况,误差在7%以内,将已识别的变量作为输入。通过消除在啤酒厂工作人员控制范围内的驾驶员的可变性,MVLR模型用于标准化性能指标,并使工厂之间的直接比较成为可能。结果表明,使用范围主要由工厂体积驱动,而水和能源使用性能的大部分变化是由于工厂和工程人员控制范围内的驱动因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Business benchmarking of global operations: The case of water and energy consumption in the brewery industry
As the demand for, and cost of, resources increase, the brewing sector is increasingly at risk from a sustainability perspective. In response, brewers globally have embarked on efficiency improvement programs, often accompanied by ambitious, publicly committed targets. Benchmarks are then often used to assist brewers in identifying improvement opportunities, but a comparison of water and energy performances in breweries is deficient without normalizing for differences between plants. The normalization of water and energy use was subsequently investigated, by using SABMiller breweries as a case study; to propose variables (differences between plants), and a normalization model to enable breweries (that are different) to be compared for the purpose of benchmarking, and to identify improvement opportunities. Drivers of water, electricity, and thermal energy usage obtained from literature were selected, rationalized and ranked in a Delphi survey of industry experts, and correlated with data from 64 SABMiller sites. The main drivers identified and data from 58 SABMiller sites were utilized to develop multi-variable linear regression (MVLR) models of water, electricity, and thermal energy usage. The models, tested with data from 6 SABMiller sites of different hubs, were able to predict water, electrical, and thermal energy usage to within a 7% error, given the identified variables as inputs. By eliminating the variability in drivers within the control of brewery staff, the MVLR models were used to normalize the performance indices and enabled the direct comparisons between plants. It is shown that the range in usages is driven mostly by plant volume, and that much of the variability in water and energy usage performances are due to drivers within the control of plant and engineering personnel.
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