The Industrial and Commercial Bank of China (ICBC) has more than 16,000 branches and serves over 230 million individual customers and 3.6 million corporate clients. Its daily financial transactions total about $180 million. It is also the largest publicly traded bank in the world in terms of market capitalization, deposit volume, and profitability. To stay competitive and increase profitability, ICBC was faced with the challenge to quickly adapt to the fastpaced economic growth, urbanization, and increase in personal wealth of the Chinese. Changes had to be implemented in over 300 cities with high variability in customer behavior and financial status. Obviously, the nature of the challenges in such a huge economy meant that a large-scale optimization solution had to be developed to locate branches in the right places, with right services, to serve the right customers. With their existing method, ICBC used to decide where to open new branches through a scoring model in which different variables with varying weight were used as inputs. Some of the variables were customer flow, number of residential households, and number of competitors in the intended geographic region. This method was deficient in determining the customer distribution of a geographic area. The existing method was also unable to optimize the distribution of bank branches in the branch network. With support from IBM, a branch reconfiguration (BR) tool was developed. Inputs for the BR system are in three parts:
a. Geographic data with 83 different categories
b. Demographic and economic data with 22 different categories
c. Branch transactions and performance data that consisted of more than 60 million transaction records each day These three inputs helped generate accurate customer distribution for each area and, hence, helped the bank optimize its branch network. The BR system consisted of a market potential calculation model, a branch network optimization model, and a branch site evaluation model. In the market potential model, the customer volume and value is measured based on input data and expert knowledge. For instance, expert knowledge would help determine if personal income should be weighted more than gross domestic product (GDP). The geographic areas are also demarcated into cells, and the preference of one cell over the other is determined. In the branch network optimization model, mixed integer programming is used to locate branches in candidate cells so that they cover the largest market potential areas. In the branch site evaluation model, the value for establishing bank branches at specific locations is determined.

Questions for Discussion
1. How can analytical techniques help organizations to retain competitive advantage?
2. How can descriptive and predictive analytics help in pursuing prescriptive analytics?
3. What kinds of prescriptive analytic techniques are employed in the case study?
4. Are the prescriptive models once built good forever?