The manufacturing industry is increasingly accountable for the environmental impact resulting from its activities. Research indicates that specific production processes within manufacturing plants generate significant environmental impact through energy consumption. To understand the consumption of energy in a production environment, it is necessary to outline the energy flow within the facility, along with the classification of energy usage and its relationship to processes and production outputs. It is also important to identify auxiliary (non-value added) energy within production as the area with the greatest potential for savings through changes in operational behaviour. This article introduces a practical process mapping methodology that combines energy management with value stream mapping. The methodology is based on 'Lean' manufacturing principles and on application to a couple of industry use cases has been shown to successfully illustrate the relationship between the energy usage and production activities for a particular value stream. Furthermore, the significant energy users in relation to the actual production process steps have been identified, and energy reduction opportunities of 42% and 50% have been quantified.
A methodology was developed that accurately and flexibly determines the auxiliary (AU) and value-added electricity in manufacturing operations. A tool was developed for production engineers which allows for the verification of machine efficiency in relation to their energy consumption. Historical production and electricity consumption data were collected for a period of three months from four different machines in a value stream at a manufacturing facility. The data were examined using a methodology based on statistical analysis of the historical data collected and were verified using heuristic machines profiles. Results showed AU electricity consumption varied between 10 and 26% per machine. When weekend data (non-productive periods) were excluded from calculations, AU electricity consumption reduced. Past work focuses on optimising single machine, and the quantification of wasted electricity is not always clear. This research work can be applied to one or more machines, and to single or multiple products passing through the same machine. It places particular attention to AU electricity since potential energy and cost reduction of up to 20% could be achieved. Hence, this work can aid in developing key performance indicators to measure energy usage in manufacturing operations, particularly focused towards reducing AU electricity consumption.
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