The most effective industrial energy productivity projects integrate end-use efficiency, electrical and thermal distribution efficiency, and increasingly, on-site cogeneration and renewable supply. They are also rarely implemented due to unrealistic hurdle rates or overly optimistic view of future energy price risk. This rarity introduces another reason why so few see the light of day — the learning curve.
Suppliers that can create and assess a fully integrated energy model and make detailed recommendations for a complex manufacturing site are hard to find, even on a global basis. Large, highly talented engineering companies stumble when asked to develop a complete energy strategy rather than a specific subproject. At the same time, it is the natural instinct of a customer to select from a group of known and trusted suppliers.
In effect, two learning curves are at play at this early stage. The customer is learning to ask the appropriate questions and judge the capability of the vendor. The vendor is being asked unfamiliar questions, often causing a push back implying they are, in fact, the wrong questions. This sets up a potential game-ending dynamic. The customer begins to lose confidence in the validity of the game plan, and the vendor reverts to the trusted menu of single energy efficiency actions. This often results in cherry-picking a few subprojects and not in a transformative energy plan.
Even if this initial barrier is passed and inexperienced vendors respond, the tendency is to price the learning curve and add substantially higher contingencies than are really necessary. This alone may be sufficient cause to stop the energy strategy going forward. The customer may now react by asking for less costly alternatives, with the cherry-picking-projects approach being the typical response, again reinforcing the status quo ante.
If this second barrier is jumped, another learning-curve effect appears with potential to derail a successful energy strategy. Possible approaches will probably include elements that are relatively unusual in the local markets. Examples could be on-site cogeneration or renewable energy supplies, interactive metering, and control systems covering multiple energy types and dynamic pricing. Unfamiliarity will cause investment and cost estimates to include a substantial part of the learning curve to assess the local market.
|Peter Garforth heads a specialist consultancy based in Toledo, Ohio and Brussels, Belgium. He advises major companies, cities, communities, property developers and policy makers on developing competitive approaches that reduce the economic and environmental impact of energy use. Peter has long been interested in energy productivity as a profitable business opportunity and has a considerable track record establishing successful businesses and programs in the US, Canada, Western and Eastern Europe, Indonesia, India, Brazil and China. Peter is a published author, has been a traveling professor at the University of Indiana at Purdue, and is well connected in the energy productivity business sector and regulatory community around the world. He can be reached at email@example.com.
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So now the energy strategy has learning curves priced and timed into the initial planning, feasibility and engineering assessment efforts, and into the estimated investment for the recommendations. This may kill the strategy even before it goes for senior management review, which leaves the energy team to explain what they got for the expensive consulting and engineering services spent to date.
However, the strategy may still be attractive to warrant review. But now all the other barriers we have covered many times in this column kick in. Unrealistic hurdle rates are demanded. Future upside energy price and other risks are minimized. Even worse, the fact that the plan includes unfamiliar elements may cause even more cost contingencies to be added. In effect, this is a response to a senior management learning curve. Inevitably the energy plan fails at this point. It is seen as overcomplicated and having taken too long to produce. The combination of unrealistic expectations, undervaluation of risk, and excessive high costs due to the learning-curve effect kill it.
Countering this is not rocket science. The energy team and the senior management that will ultimately approve the final plan should establish clear and realistic return expectations up front. Before doing any detailed assessments, they should agree on the profile of critical risks around energy pricing, production volumes, and regulatory changes.
The energy team should then open up the procurement philosophy. This must include companies that can prove they have developed transformative energy strategies for complex sites. This is likely to be a small pool and may not even be from the same country. At the same time, the team must include sensible levels of local knowledge. This may mean creating a hybrid team where the local players might be in uncomfortable territory. The selection of the project management both from the customer and the vendor sides will be different. It will require different diplomatic and team-building skills and active dispute resolution. It will also require all the team members to be open to new ideas and even new benchmarks when it comes to cost estimation and overall performance benefits.
To give this a real-world perspective, on a recent project, my team was confronted with North American estimates for locally unfamiliar energy solutions that were two to three times higher than proven costs in Scandinavia, which is hardly a cheap market. The North American local estimates clearly included learning-curve costs and excessive contingencies. The experienced Scandinavia estimates were based on two to three decades of implementation, which, if achieved, made the North American projects highly attractive.
This should change the customer objective from: “How do I live with lower efficiencies?” to: “How do I make sure I can capture global best practices and costs to beat my competition?” This is a very different perspective for the energy manager.