What the Firm Does On-Site

Agglomeration, Insurance, and the Organization of the Firm


A firm has a machine that breaks down periodically. In Model 7A, the firm does repairs in-house. The firm minimizes overall cost by balancing an inventory of product (to meet customer demand during downtime) and an inventory of repair staff. In Model 7B, the firm outsources repairs to a contractor as needed. There may now be a delay in starting repairs awaiting the arrival of the contractor’s repair crew. Outsourcing becomes attractive because of an insurance principle. If the client is in an industry using similar machines that break down stochastically over time, a contractor, by redeploying repair staff from client to client, may achieve lower unit costs than a firm doing repairs in-house. A clustering of clients with the same kind of machine enables the repair contractor to be more efficient. In Model 7C, the firm uses cost minimization to choose between in-house repair and outsourcing. Model 7D examines the implications of profit maximization by a repair contractor servicing client firms. This chapter builds on  Chapter 4 and  5. In those chapters, a local supply curve was assumed at each place that could have a different intercept. In  Chapter 6, I explained that difference in part as a result of the variation in the effective prices of non-ubiquitous inputs. Here in  Chapter 7, it is the clustering of clients with the same kind of machine that enables an efficient repair contractor and thereby reduces unit cost for client firms. Model 7D parallels  Chapter 6 in terms of how a firm and its supplier (in this case a repair contractor) co-locate. In this chapter, the repair contractor is a metaphor for any kind of producer service that can be outsourced, and the chapter suggests how we might similarly think about shippers and the endogeneity of the price of shipping and unit shipping cost.


Shipping Cost Inventory Cost Repair Time Agglomeration Economy Economic Order Quantity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department Social SciencesUniversity of Toronto ScarboroughTornotoCanada

Personalised recommendations