Putting O.R. in the start-up business plan
The other day I had a serendipitous meeting with the owner of a small business. It was my birthday, and we had gone out to lunch in an unusual restaurant about 20 miles west of here. As I parked the car, we noticed that there was a waste collection vehicle in action. While Tina took our guests into the restaurant, I checked that the car was not going to be an obstacle to the collection truck. The driver was the owner of the company, standing in for the usual driver, and we stopped to discuss the business. The vehicle collected all the food waste from the restaurant, both that from food preparation and uneaten food. What happens to that waste?
The vehicle was going to take all the food waste to an anaerobic digester where it is turned into an energy source and organic fertiliser. Food waste is macerated into tiny pieces to allow for better digestion from the bacteria in the digester. As the organisms eat their way through the waste, methane gas is created as a by-product. This gas is siphoned off and used to power an engine and, in turn, an electrical generator. Whilst some of the heat generated is used as an eco-power source for a local dairy, the surplus is directed to the National Grid for electric power. The only waste product from this process is organic digested material which, when dry, is an effective fertiliser.
We talked about the process, which seems to have widespread application. It is a new business, hence the title of this article referring to "start-up". And we spoke about several aspects of the process and business, including the radius that could be served by the plant, which is in Plymouth (about 20 miles further west).
Afterwards, I reflected on the scope for O.R. in such a business. Even in a short conversation, I could see that there are now - now the business is up and running - several opportunities for an O.R. professional to help with decision-making (such as inventory control, vehicle routing, scheduling, forecasting). But, I realised that the scope for O.R. in the first stages of a start-up is limited. We tend to assume that there are datasets which we can analyse from the past history of the enterprise - datasets which the entrepreneur will not have. What help could I have offered this man if he had come to ask for O.R. help before he launched the business? The answer is surprisingly little. In O.R. we are biased towards the enterprise that is up and running.
Postscript: From the Western Morning News of 26-Nov-2012, reporting on the South West Green Energy Awards:
Best renewable energy scheme went to Langage Farm in Devon, which has integrated an anaerobic digester into its operations. The plant uses waste material to provide enough heat and energy to run both the farm and the dairy produce factory.
The bio-fertiliser output is applied to the farmland which grows the grass, which cows eat to produce milk, which is then used to make dairy products, creating a carbon- neutral, closed loop.
The vehicle was going to take all the food waste to an anaerobic digester where it is turned into an energy source and organic fertiliser. Food waste is macerated into tiny pieces to allow for better digestion from the bacteria in the digester. As the organisms eat their way through the waste, methane gas is created as a by-product. This gas is siphoned off and used to power an engine and, in turn, an electrical generator. Whilst some of the heat generated is used as an eco-power source for a local dairy, the surplus is directed to the National Grid for electric power. The only waste product from this process is organic digested material which, when dry, is an effective fertiliser.
We talked about the process, which seems to have widespread application. It is a new business, hence the title of this article referring to "start-up". And we spoke about several aspects of the process and business, including the radius that could be served by the plant, which is in Plymouth (about 20 miles further west).
Afterwards, I reflected on the scope for O.R. in such a business. Even in a short conversation, I could see that there are now - now the business is up and running - several opportunities for an O.R. professional to help with decision-making (such as inventory control, vehicle routing, scheduling, forecasting). But, I realised that the scope for O.R. in the first stages of a start-up is limited. We tend to assume that there are datasets which we can analyse from the past history of the enterprise - datasets which the entrepreneur will not have. What help could I have offered this man if he had come to ask for O.R. help before he launched the business? The answer is surprisingly little. In O.R. we are biased towards the enterprise that is up and running.
Postscript: From the Western Morning News of 26-Nov-2012, reporting on the South West Green Energy Awards:
Best renewable energy scheme went to Langage Farm in Devon, which has integrated an anaerobic digester into its operations. The plant uses waste material to provide enough heat and energy to run both the farm and the dairy produce factory.
The bio-fertiliser output is applied to the farmland which grows the grass, which cows eat to produce milk, which is then used to make dairy products, creating a carbon- neutral, closed loop.
I agree with your final statement, but to some extent I think the bias results in short-sightedness on our part rather than a systemic inability. First, given access to data from other startups (both successful and failed), we might be able to extract useful information sufficient to model some decisions. Second, while the startup lacks its own data, it deals with other entities (the public, government, other firms) for which data has been amassed. If the data is publicly available (the major stumbling block, I suspect), useful information can again be extracted.
ReplyDeleteAs an example, a colleague teach procurement management. One task he routinely assigns to students requires them to build a regression model to predict a supplier's actual manufacturing cost for a procured component. This allows the buyer to negotiate a price from a stronger position. No buyer data is needed, just commodity prices and industry-standard labor rates and costs.
What about simulation? Besides, vehicle routing could be already done.
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