Competitive-intelligence from public data: The service facility location problem
Facility location is one of the most important strategic decisions for a firm. In many contexts it is an irrevocable decision, affecting profits and operations for many years in the future, and usually involves a large fixed cost for setting up the facility. Firms therefore evaluate many sites and consider the decision to locate very carefully. In this paper we consider the location problem for retail service facilities, that is consumer-facing storefronts that provide a service and compete with existing facilities to some degree or the other. The problem is challenging for the following reasons: (i) The models require estimates of how demand will expand and shift when we locate a new facility, but the firm, since it has not yet started operations, has no historical demand of its own, and demand, costs and profits for the existing, competing, retail facilities is almost never available, simply because they are owned by competitors. (ii) Future competitive entry (and also exits of incumbents) may affect the firm’s revenues profoundly, and hence the decision on whether and where to locate. For instance the most profitable location may also be the most vulnerable to new entry and not the best decision. In this paper, we model competitive entry and exit and tackle the estimation challenge based only on public data (thus avoiding the data access problem). We apply the model to a service industry as an example, specifically the restaurant industry. Our estimation is based only on public data, addressing the data-availability issue.