ENHANCING PERFORMANCE MANAGEMENT AND SUSTAINABLE ORGANIZATIONAL GROWTH THROUGH SYSTEM DYNAMICS MODELING

Online Appendix

Mapping Products, Processes and Performance Measures in a Retail Bank: Case Study

SD modeling has been used to map the product system, processes and perfor-mance indicators in a bank delivering a deposit service to so-called “Personal” clients (i.e. whose savings are higher than € 100,000). Such service is character-ized by a system of inter-related sub-services, among which financial consulting is an important component. “Financial consulting” is a final product, delivered to the external client. In relation to such product, in a budget session, the Retail Division explores alternative strategies to attain a set of end-results, in terms of: 1) change in the customer base; 2) change in customer satisfaction, and 3) gross operating in-come from services to personal clients.

In respect to these end-results, the underlying processes through which finan-cial consulting is delivered have been mapped. This allowed us to find out the drivers, strategic resources and policy levers to manage such resources, to affect financial and competitive performance,

Each branch of the retail division delivers such services. Within each branch, a Personal Banker/Consultant is entrusted of the results, for the achievement of the targets included in the operating budget at divisional level. To this end, the bank consultants outline a plan of meetings with the branch’s Personal Clients, with the goal to analyze with each of them his/her current financial position and investment needs, based on four main kinds of needs, i.e.:

Liquidity the client will have to hold in his deposits, to face short term financial expenditures;

Speculative investments on potentially profitable financial tools, which do not provide any guarantees;

Possible specific objectives (e.g..: property purchase) to plan; The increase of invested capital, based on the expectation of prof-

itability and risk.In relation to such categories, after an analysis of financial decisions made by

the client in the past has been done, the consultant estimates the customer’s risk

profile. Through an interview, the level of risk the client is willing to tolerate is as-sessed. For each client, the consultant tries to pursue an equilibrium point between the objective to maximize yield and the need to minimize investment risks. In or-der to optimize the “Risk/Yield” ratio on a medium-long time horizon, the consul-tant uses an “efficient frontier” financial tool. Such tool supports the consultant in setting the pool of financial portfolios maximizing yield or minimizing risk, given a pre-defined yield. This is called the efficient frontier, i.e. the financial portfolio that the consultant proposes to the client, based on his risk profile and investment time horizon.

This implies that the consultant is always in contact with clients, and periodi-cally visits them, to understand their needs and to examine the consistency of the allocation of their financial portfolio with market opportunities.

The macro-processes underlying the financial consulting service delivery to the Personal Client are: 1) interview; 2) analysis of the client’s portfolio; 3) sale of fi-nancial products; 4) post-sale assistance. Each macro-process underlies the deliv-ery of instrumental products, which are the results of the activities carried out by back-office units, in order to attain the final product (“consulting”) to the benefit of the client. The different processes are outlined in Figure 1.

Figure 1: Process detail in financial consulting delivery

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Based on this analysis, the “products” generated by the listed macro-processes can be identified; planned/achieved results can be tracked, and responsibility areas can be made accountable on them. Figure 2 shows how the goal of the macro-process-1 can be referred to “customer profiling”. This is an intermediate product, in respect to the final goal of the described processes, i.e., financial consulting contracts subscription.

Figure 2: Sequential product conceptual levels in final product delivery

The number of profiled clients is a performance measure that can be associated to such intermediate product. In order to affect this measure, a 2nd level intermedi-ate product is identified, i.e., “contacts with customers to profile”. A performance measure related to this product is the “number of interviews to profile clients”.

Therefore, the identification of such two levels of intermediate products sup-ports the setting of corresponding performance measures. Regarding such mea-sures, if we consider the Retail Division, the ratio between “Profiled clients” (ac-tual performance) and “Clients to interview and profile” (planned performance), defines a “Profiling effort ratio”. Since client profiling is a competitive success factor, this ratio is a driver of the Division’s end-results. It affects the “Clients to contact loss rate”, which in turn affects the “Change in the customer base”. This “last layer” of divisional end-results provides a synthetic measure of competitive performance and determines the new level of the “Customer base” stock 1 (Figure 3).

1 This stock is a strategic resource affected by management routines; since it cannot be purchased on the market, its accumulation process only depends on the organization’s ability to generate results that can allow it to retain and, possibly, acquire new customers.

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Figure 3: Strategic resources, performance drivers and end-results on a divi-sional level

Also, if we consider a single branch in the Retail Division, the described prod-uct and process analysis allows us to understand how to affect the profiling effort ratio. If we analyze process-1, we can observe how the number of profiled cus-tomers is an end-result for such process, and its driver is the “number of inter-views to customers to profile”. Such end-result is a flow in the “Profiled clients” strategic resource, which is used in process-2 (Figure 4).

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Figure 4: Strategic resources, performance drivers and end-results on a process/branch level

The same reasoning applies for the other macro-processes. So, regarding process-2, the sale proposal and customer positioning on the “efficient frontier” are first and second level intermediate products, respectively. Their corresponding performance measures are, respectively: the “sale proposal ratio” (i.e. between ac-tual and planned sales proposals) and the ratio between the actual and planned number of clients positioned on the “efficient frontier”. If observed on a divisional perspective, the “sales proposal ratio” is a driver of the “clients to contact loss rate”.

Since process-3 and process-4 are closer to the final “product” delivery, only one level of intermediate products has been outlined. Such products are, respec-tively related to the “meetings setting up for sale proposal” and to “post sale assis-tance contacts”.

More specifically, concerning process-3, corresponding performance measures are, respectively: the “contract subscription ratio and the ratio between the actual and planned number of meetings with clients to sell financial products (volume measure). If observed on a divisional perspective, the “contract subscription ratio” is again a driver of the “clients to contact loss rate”. It also affects the gross oper-ating margin per client, which in turn affects the total gross operating margin flow from “personal” clients.2

Concerning process-4, a performance measure corresponding to the detected intermediate product is the “assistance effort ratio” (i.e. between actual and planned meetings with clients). If observed on a divisional perspective, the “assis-

2 For reasons of synthesis, figure 8 does not show how such last layer end-result affects the gross oper-ating margin and cash flows. Both of them, in turn, accumulate into the equity and liquidity strategic resource stocks, respectively.

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tance effort ratio” is a driver of the “clients in assistance loss rate”, which affects the change in the customer base. The “assistance effort ratio” also affects the change in customer satisfaction (end-result), which affects the company image stock.

To show the usefulness of SD in mapping the described products, processes, and performance measures, a simple stock-and-flow model will be commented now. The model structure will not be described with the purpose to identify alter-native policies from problem behavior. The purpose is, rather, to show how SD can add rigor to performance analysis by implementing the conceptual framework here discussed.3 The value SD adds to the design of performance management sys-tems refers to a proper identification of: performance measures, strategic re-sources, policy levers and performance standards, and budget objectives.

The model portrayed in figure E describes the four macro-processes previously commented. Through a stock-and-flow chain it identifies the factors affecting fi-nancial consulting performance management. The unit of measure of stocks is “clients”; the unit of measure of flows is “clients/time”.

Performance standards are a percentage of maximum potential workload. For instance, in process-1”, to get the budget objective “Clients to interview and pro-file”, the “% STD Clients to profile” is multiplied by “Total Clients Profiling” (i.e. the sum of the three stocks in the process).4

Performance drivers are ratios between a stock representing a delayed informa-tion value of the “product” volume resulting from each process 5 and the corre-sponding objective. So, drivers (e.g. the “Profiling effort ratio”) are expressed in relative terms. The effect that each driver generates on corresponding end-results is gauged through normalized graph functions, which take the driver as an input. Such functions are expressed as multipliers. For instance, the driver named “Effect of profiling on customer loss” is a value comprised between 0 and 0.2. It repre-sents a percentage of the “Customers to contact” stock which is drained as a client loss rate in a given time.

The “Clients to contact loss rate” is also affected by two other multipliers, i.e.: “Effect of subscribed contracts on Customer loss” and “Effect of sale proposal on Customer loss”. The performance drivers affecting the two multipliers are respec-tively originated in process-2 (“Sales proposals ratio”) and process-3 (“Contract subscription ratio).

3 For reasons of synthesis, the stock-and-flow model does not include variables related to financial per-formance (i.e., gross operating margin) and customer satisfaction. Also, details on strategic resources have been intentionally omitted.4 It is worth remarking that the use of an SD model allows one to convert budget objectives from fixed targets to auxiliary variables, which vary over time as a function of their basis. Therefore, the model supports managers in identifying possible reaction of the relevant system to an over or under-absorp-tion of the equivalent available working time, or to a different allocation of such time among the differ-ent processes, as a function of workload. 5 Such “delayed information” variable corresponds to the perceived value by the client of the product generated by each process (e.g., profiling effort, sales proposals, sales contracts subscription, meetings with assisted clients).

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The other performance driver, originated in process-4 (“Assistance effort ra-tio”), affects the “Clients in assistance loss rate”. It is a ratio between the number of agreed meetings with clients in assistance, and the corresponding number of clients to assist in a given time.

SD modeling also contributes to dynamic performance management since it helps managers to avoid common errors in BSC practice in the identification of causalities between measures. For instance, in static BSCs, performance indexes are often confused with performance drivers. An example is here provided by the customer retention rate.6 This variable is a synthetic expression of performance, which cannot be confused as a driver in this context. In fact, it does not affect the customer loss rate. It is rather an effect of it.

6 This variable is calculated as the complement to 1 of the ratio between the customer loss rate and the corresponding stock.

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Figure 5: A stock-and-flow depiction of products, processes, and performance measures