Planning and managing capacities at credit cooperatives

Research carried out by Steinbeis University Berlin

The success and long-term profitability of client business at any regional savings bank or credit cooperative depends on its ability to create customer loyalty, penetrate the market, and attract new customers. Key drivers in this respect include the quality of service and advice, the continuity of ongoing business relationships, and availability. To ensure they make the grade, savings banks and credit cooperatives recruit overqualified employees for front and back office tasks and sometimes knowingly overstaff. One of the key tasks of management is therefore to carefully distribute cost-intensive staffing capacity according to the level of customer demand, not only to ensure customers are not kept waiting, but also to avoid idle capacity in personnel. As part of his doctoral project at Steinbeis University Berlin, Michael Steinmüller scrutinized operational data to examine the planning and control of capacity at a credit cooperative.

In his field work, PhD student Michael Steinmüller identified factors dictating external and internal customer demand. He did this by working out the frequency of customer processes. From this, he derived insights into repeated cycles of staff availability and inevitable influences on customer demand.

The profits generated by regional savings banks and credit cooperatives depend to a large extent on net income from interest payments. This is a function of contributions from “maturity transformation” and the level of interest rates. When yield curves are much flatter than in previous years, there are an increasingly limited number of ways to exploit gain from maturity transformation (short-term borrowing versus long-term lending at normal interest rates). Income from interest, which reflects the success of client business compared to interbank lending, is suffering more and more from the increasingly transparent price of banking services and an increasingly disloyal customer base. Bank services such as conventional mortgages are turning into commodities and are usually only worth the effort if an investment is made to cross-sell products.

To safeguard financial success, the focus at savings banks and credit cooperatives has shifted in recent years to reducing material resources not immediately obvious to the customers. The banks are now increasingly striving to optimize business processes and related staffing levels. The recent tightening of supervisory guidelines, and an expectation among customers that banks operating at a regional level should offer individual services and solutions, have resulted in a soberingly small number of opportunities to optimize business processes. Additionally, optimizing consulting and processing times rarely has the desired effect in terms of lightening the burden on personnel. This is because many business processes are seasonal and staff capacity cannot be simply turned on or off like a faucet. Instead, banks have to optimize the distribution of staff capacity at different times. By identifying the influences on the frequency of certain processes – in terms of timing and cause – it becomes possible to derive key steps to be taken.

Steinmüller’s empirical field work revolved around three research questions. 1) Which processes are relevant when it comes to periodic fluctuations in staff capacity and are thus pertinent to strategic capacity planning and control? 2) Which key influencers – in terms of timing and cause – account for regular patterns in the frequency of certain processes, on a daily or monthly basis? 3) What possibilities are there to plan operations, according to the need to cope with peaks in demand and avoid gaps in capacity at the banks? To answer these questions, a preliminary study was conducted as part of Steinmüller’s PhD to translate 288 business processes at a regional credit cooperative into the processes used by a turnkey software solution called agree®. Over a two- year period, a process database was filled with data on process frequency and duration.

Even during this preliminary “business process modeling” project and a resulting period in which the times to carry out certain processes began to settle down, a number of important insights were gained. These are best exemplified by what happens when a customer submits a new application for a mortgage: Staff capacities relating to a certain process are determined by the designated approval hierarchies. In recent years, credit advice activities have been separated from administrative tasks, meaning that many decision-making responsibilities have been delegated to administrative areas. This has frequently led to customer advisors no longer developing a personal sensitivity to risk and simply delegating responsibility to administrators. As a result, through-put and processing times have lengthened. Reversing the delegation of responsibility and returning decisions to sales departments in non-risk related areas could therefore produce major advantages in terms of the time taken to process customers, as well as benefits to staffing requirements. Staff capacity requirements are closely linked to the quality of requests submitted by the people in sales. By stringently defining and monitoring internal service levels, many repeated processes could be avoided.

After completion of his preliminary project, Steinmüller used a cluster analysis to identify processes pertinent to the analysis of short and medium-term capacity planning and controls. One important realization was that not all processes fluctuate periodically, or they only tie up low levels of resources. As a result, available personnel should be distributed so that staffing levels can be used flexibly, especially for processes that do fluctuate periodically. If processes only tie up small amounts of staff capacity, but they occur very frequently, employees need the right qualifications to be shifted around as required. A subsequent univariate time series analysis of daily and monthly patterns allowed Steinmüller to identify significant correlations between the frequencies of past processes. Based on this, it was possible to map the historical distribution of process frequencies using conventional time series models and to provide good predictive values for future scenarios. For monthly predictions, the most favored approach was an exponentially smoothed curve. For daily predictions, attention was given to an autoregressive integrated moving average for the best fit. Overall, the more data available, the better the reliability of the time series models. As a result it has recommended that banks store process data over extended periods. A subsequent causal analysis provided some important pointers on controlling customer demand. For example, the frequency of processes related to mortgage requests follows a significant quarterly pattern and is dictated to a large extent by trends in the interest charged on lending and the gross domestic product.

The theoretical preparation undertaken as part of the preliminary PhD work and the modeling of business processes can be immediately adapted for use at other credit cooperatives, since in technical terms many processes are substitutable. The process databases can be accessed by all banks. The capacity planning and control model developed as part of the PhD project offers significant potential to be applied at other companies providing customer services. A simple comparison between mortgage request processes and leasing applications at a car dealership shows that both involve sub-processes in the front and back office. By storing the process data from a leasing application, a carmaker can thus plan staffing capacities according to uniform procedures. The bank looked at in the study had a strong thirst for more empirical process data. If there is a similar thirst for knowledge at other banks, in the future there should be more interesting research findings for banks, as well as special models on production planning and control mechanism.

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