Renewable Energy Supply Chain Companies are Using Data Analytics to Increase Sales
Wind turbine supply chain companies can take advantage of data analytics to predict the probability of a specific make and model winning a competitive tender for an equipment supply contract. Proactive companies are already using this methodology to enhance their bid probabilities and their win percentage by identifying which products in their portfolio would be the best fit, as well as which competitor products are the most likely to show relative strength on power output and CapEx performance.
This process involves first gaining visibility to the current market landscape. You need to be able to answer the question about whether you have the product range which would be capable of competing in the market. This is facilitated by looking at the evolution of rotor size, power ratings, hub heights and other turbine factors in the market.
Additionally, metrics like specific power versus the power rating will inform as to where the concentration of product development has occurred. Gaps in the market represent potential opportunities for product introduction to the market, but the analysis must go even further.
Next the market potential must be understood, which can be broken down by the specific power range. Taken against the capacity already installed, it is easy to visualize how the gaps in the market have manifested, as many markets have evolved installing wind turbines that only correspond to narrow ranges.
The real opportunity lies in being able to overlay the product portfolio of any OEM to see how the breadth of their products lines up with the existing installed base and the overall market capacity potential. This indicates the extent to which they will face competition in a market segment, or whether their product(s) could fill an unmet market gap that has yet gone under-exploited.
Getting even more granular with this analysis we can see not only the total capacity installed with a breakdown by specific power range over time, but it is also possible to drill through to the next layer down to see how product sales in the market segment have grown over time.
The evolving trend in capacity additions broken down by specific power range will tell a compelling story about the nature of the market and the wind regime. Compared against the market potential chart, it will be clear how quickly certain segments of the market have grown.
It will also serve as a relative indication for how saturated a market segment is and the pace at which the market will shift from exploiting the high wind speed sites with better payback to the developer and owner, versus the lower wind speed sites. Low average wind speed sites may have close proximity to existing transmission capacity to evacuate the power, but it requires more cost effective technology with the ability to achieve a lower LCOE profile than would be acceptable at a high wind speed site.
This view also has the added benefit of offering companies an opportunity to identify their specific competitors in a given market segment, and seeing trends in capacity additions which have occurred in the past and how that pattern has shifted over time.
Companies will need to ensure that they can compete effectively in a competitive tender for turbine supply by benchmarking their bill of materials (BOM) cost against other companies. This BOM cost benchmark will help codify the relative cost differential for different makes and models to ensure you price the products in a manner that will allow you to win tenders for turbine supply contracts.
Lastly, the companies will need to know the historical trends in buying patterns of developers as well as the selling patterns of their competing OEMs, particularly within the market segments in which they intend to compete. This is visualized through a matrix heatmap showing the amount of capacity for a developer or asset owner which was installed for a given OEM.
This will indicate the extent to which a developer or owner will be likely to source from a particular OEM, and also provides companies the opportunity to target developers with the knowledge in hand that your product beats your competitors on BOM cost and LCOE.
The net result is a product benchmarking analysis which shows the probability of a product winning a competitive tender for a project site with an average specific power range between 210 – 219 W/m2 in this case. The annual energy production is calculated from the power curve, and compared to the CapEx cost analysis from the Bill of Materials tables. These percentiles can then be aggregated to provide a bid prediction capability for all products within a specific power range.
In this example, the GE 2.3-116, Goldwind 3S model with 3.3MW power rating and the Vestas V110 2.0 will have the highest probability of winning bids in turbine tenders in global markets where the project site has an average specific power range of 210 - 219 W/m2.
To achieve a solid return on capital, which exceeds parity to what was invested and results in the product development leading to a profitable product, companies must proactively pursue a strategy for product development and global sales which ensures net positive ROC is achievable.
For years companies lacked the tools and sophistication of analysis to understand these market dynamics. Now, with this new set of data analytics available to them, they can intelligently position themselves to achieve profitable returns on their capital investments.