Bohdan W. Oppenheim

Energy Efficiency in Small and Medium-Sized Manufacturing Firms: Order Effects and the Adoption of Process Improvement Recommendations

In many manufacturing operations, profitable energy efficiency opportunities remain unexploited. Although previous studies have tried to explain the underinvestment, we focus on how the way in which a portfolio of opportunities is presented in a list affects adoption decisions. We use information on over 100,000 energy-saving recommendations made to more than 13,000 small and medium-sized manufacturing firms under the Industrial Assessment Centers program of the U.S. Department of Energy. We find that adoption rates are higher for initiatives appearing early in a list of recommendations. This sequence effect is consistent and large: simply moving a recommendation one position lower has the same effect on average as increasing up-front implementation cost by at least 17% from the average value. Given this impact of sequence on adoption of individual recommendations, we utilize variations within our data to examine how various sequencing approaches affect adoption at the portfolio level. Sequences in which recommendations are listed from best to worst payback achieve higher potential energy savings given the investments in energy efficiency made by the firms. We also observe a choice overload effect at the portfolio level, but the magnitude of this effect is small.

Impact of Productivity on Energy Conservation

Productivity has a major impact on energy use and conservation in manufacturing plants. It is often more significant than the optimization of equipment energy efficiency. This article describes a lean manufacturing, which represents the current state of the art in plant productivity. A significant opportunity for energy savings by transforming production into a single-piece lean flow is demonstrated. The impact from major individual productivity elements on energy is discussed. Simple metrics and models are presented as tools for relating productivity to energy. Simple models are preferred because productivity is strongly influenced by intangible human factors, such as work organization and management, learning and training, communications, culture, and motivation, which are difficult to quantify in factories.