Operations II Y - Retail Banking Academy · Y 159 Retail Banking Academy, 214 RET RE TA IL BANIN...

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207.Operations II

Course Code 207 - Operations II

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157© Retail Banking Academy, 2014

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Course Code 207Operations II

IntroductionThe focus of the Operations I Module in Retail Banking I was on process efficiency through the application of Little’s Law for lean processes and talent management for consistent delivery of customer products and services. It was also shown that complexity hides sources of non value-added activities, places undue stress on IT systems and exponentially increases people errors and mistakes. Actions to simplify processes result in a reduction of operating costs for the bank and higher customer service quality.

This module continues in the same vein.

Modern consumer banking has becomes a highly technology-dependent business as senior management seeks to gain competitive advantage through process efficiency and cost reduction, multichannel integration for better customer experience and robust risk management. While the core value of IT projects is reflected in their enabling of new business initiatives, their failure to deliver promised business benefits on time and within budget can lead to financial loss and non-financial loss in the form of reputational, regulatory and personal impact*. These consequences are the result of operational risk, which, as defined by Basel III, is the ‘risk of loss resulting from inadequate or failed internal processes, people and systems or from external events’.

It is therefore imperative that IT professionals conduct optimal project risk management so as to mitigate the risk of poor project planning (i.e., project execution risk) and simultaneously reduce the risk of undesirable outcomes (i.e., project result risk).

Chapter 1 deals with efficient and effective IT project management. IT innovation also enables the optimal management of the bank branch and call centre through the application of effective queue management. For example, bank branches are becoming advisory and self-service centres aided by innovative IT infrastructure. In recent times, at Toronto Dominion Bank in Canada, call centre employees have been supported by state-of-the-art technology so that they become problem solvers for customers. With the emphasis on customer satisfaction rather than

* Module 107 of Retail Banking I provides examples where systems failure has contributed to a loss of customer trust with consequent damage to the bank’s reputation. Evidence reported by Professors Bent Flyvbjerg and Alexander Budzier from the Said Business School (Oxford University) showed that for mega IT projects, the failure rate was inordinately high, which they attributed to significant cost overruns and over-estimation of business benefits.


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the number of telephone calls completed per unit of time, the bank introduced KPIs based on first-call resolution and favourable customer feedback. We emphasise that the main objective of Chapter 1 is to link IT project management to the business objectives of the bank and not to provide technical details related to software, hardware and network development.

Chapter 2 considers managerial actions for the effective management of branch and call centres through an understanding of behavioural issues involved in queue management as well as an advanced approach to Little’s Law.

The remainder of this module provides a summary and multiple choice questions including a mini-case.


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RETAIL BANKINGACADEMYCourse Code 207 - Operations II

Chapter 1: IT Project Management

In this chapter, we introduce a three-stage model of project management that we label ‘The 3D Model’. The first stage involves the definition of the project. The second phase is the development stage and the third is the deployment stage. This sequence is illustrated below.

207.1: The 3D Model

We now discuss each stage of this model and identify the project management risks that the IT project manager must mitigate to reduce unintended outcomes.

Definition Stage

The definition stage is where project planning occurs and business objectives are determined. The success of an IT project is not only about technical success but, more importantly, about delivering on the agreed-upon goals set by business stakeholders. For example, if the IT project is intended to support the introduction of a new product on existing channels in the bank’s existing market, then the stakeholders may include business managers from credit risk, operational risk, marketing and sales, capital management and finance.

It is important that the IT project leader communicate and coordinate the needs of all stakeholders so that the project scope does not become overwhelming. Two sources of risk arise at this point. The first is the risk of miscommunication by the project leader when the number of stakeholders increases. This point is raised by Anbari*, who shows using a simple calculation†

* Frank T. Anbari, Quantitative Methods for Project Management (2nd Edition), International Institute for Learning, New York.† It is shown that for n stakeholders, the number of two-way communications (i.e., communications channels) is 2

)1( �nn . For example, for five project stakeholders (including the project leader), the number of communication

channels is 10. But if we add one more project stakeholder (so that n=6), the number of communication channels is 15. The likelihood of miscommunication has increased significantly.

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from communications management that there is an “increased number of possible ‘side bar’ communications and resulting miscommunication as the number of project stakeholders increases”.

Comment

IT projects span many functional units in a retail bank. If the bank is organised along functional units, then communication between the IT project manager and business managers will be difficult and the risk of miscommunication is high.

The second source of project risk arises when each stakeholder has several, seemingly independent priorities that the project must deliver. Each stakeholder may have his/her own ‘wish list’. With a large number of project stakeholders, the aggregate list of stakeholder priorities can be overwhelming. This creates a lack of focus for the project leader and complicates the definition stage.

The analysis of this point provides some advice for the project leader.

Advice for the IT Project Leader

Communicate with all project stakeholders as a group and not individually. This will reduce the risk of miscommunication.

Coordinate all stakeholders’ priorities by removing duplication and provide a ranking of priorities according to alignment with business objectives.

Seek final approval for a selected number of the ranked priorities from all stakeholders. There must be a limited number of clear business objectives.

The Iron Triangle

The project leader must determine what constraints must be placed on the Project Budget (Cost to Complete), Project Schedule (Time to Complete) and Scope (Number of Business Priorities or Deliverables). A graphical representation of the inherent trade-off between these three project characteristics is called the Iron Triangle.

207.2: The Iron Triangle

As a general rule, if the project manager changes any one of these constraints, then he/she must adjust the other two constraints in order to maintain control over the project in the later stages. For example, if all project stakeholders agree that schedule is a constraint (i.e., not flexible) then unexpected delays later in the development stage will require that the project manager add staff (increase the budget) or reduce the number of business priorities (decrease scope). Similarly, an increase in project scope (e.g., additional deliverables) would likely lead to cost overruns and time delay.


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Advice for the Project Manager

Ensure that there is unambiguous agreement with project stakeholders in regard to which one of the triple constraints is flexible.

Ensure that project priorities or deliverables are not open-ended and subject to discretionary change later in the process. An increase in scope will lead to cost overruns and time delay.

Consider the Iron Triangle throughout the IT project management process from definition to development and deployment. (Collyer & Warren, 2009).*

*In addition, the project manager will establish project milestones with appropriate KPIs so that the project performance can be evaluated if the decision is made to proceed to the development stage. We conclude this section with conditions that affect the decision to exit the project or proceed with the development stage.

If project stakeholders decide that the list of business objectives (scope) cannot be compromised and the project manager concludes that there is a risk of cost overrun, then a decision to exit must be considered. Since the cost incurred in the definition stage is approximately 10 to 15 percent of the total project cost, then the negative consequences of the sunk-cost bias will be avoided. For IT project management, the sunk-cost bias implies that if high unrecoverable costs are incurred in the development stage the project manager will likely proceed to the development stage, even if there is no objective reason to do so. This is because high sunk costs are a barrier to exit.

Advice for the Project Manager

A decision to exit the project should be considered in the definition stage. If the decision is made to proceed to the next (development) stage, then it is more difficult to exit even if there is unambiguous evidence to do so. The reason is that when sunk costs are high, there is a subjective bias for the project manager to continue with development and deployment.

The decision to exit is easier earlier than later in the process.

Development Stage

The next phase in the IT project management model is the development stage. This stage involves project design that incorporates stakeholder (user) specifications. Complexity of in-house development is usually measured by the number of designers required to achieve technical success. In many cases, retail banks do not have adequate in-house expertise. However, whether there is adequate in-house expertise or not, many experts recommend that IT project managers purchase off-the-shelf software packages that are parameter-driven and hence flexible. There is some evidence† that using off-the-shelf software leads to less cost overruns and less time to complete IT projects when compared to in-house customised software.

Comment

The development phase typically incurs about 50 percent of the total project cost. Both the definition and development stages would have incurred about 65 percent of the total cost and so it becomes more difficult for the project manager to exit at this stage.

Typically the development stage comprises some milestones that keep track of the project performance. At a specified milestone, the project manager will typically conduct an earned value analysis (EVA) that integrates the three constraints of the Iron Triangle to evaluate the

* S. Collyer and C. Warren, “Project Management Appraisal for Dynamic Environments”, International Journal of Project Management, 27 (4), 355-364, (2009). † Henry Mance, “Why big IT projects crash”, FT.com (18 September 2013).


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project performance to date. The earned value method (EVM) is a classical evaluation procedure in project management and considers the interaction of three metrics calculated at the milestone date.

• Planned Value (PV) is the evolution of the scheduled budget line over time and is typically assumed to follow an S-Curve. This shape reflects the lowest budget in the definition stage followed by a rapid increase in the development stage and a slower rate of growth in the deployment stage. Note that, in this analysis, we consider the total schedule budget to be cost allocated for the development and deployment stages only. This is because we are assessing the project performance in terms of cost, time and scope, which are relevant when development starts.

• Actual Cost (AC) is the cumulative actual expenditure from the start of the project to the milestone date.

• Earned Value (EV) is the cumulative earned value of the project at the milestone date and is calculated as the PV multiplied by the completion rate of deliverables.

Three key performance metrics are as follows:

Cost Performance Index (CPI) = EV/AC.

A value of CPI that is greater than 1 means that the project is under budget while a value that is lower than 1 indicates cost over-run. The project manager should pay attention to the trend in this value and take appropriate actions to mitigate the risk of cost overruns. A value of unity for CPI is unusual since, as indicated above, there is a potential for unexpected changes to the project scope, change in the timing of budget approval and design errors.

Schedule Performance Index (SPI) = EV/PV.

A value of SPI that is greater than 1 indicates that the project is ahead of schedule; if SPI is lower than 1, then the project is behind schedule and if SPI =1 the project is exactly on schedule. For the same reason discussed above, a value of unity for SPI is unusual.

Finally, we consider a product of these two ratios to obtain an overall performance indicator of the project.

Critical Ratio (CR) = CPI x SPI.

This is called the Cost-scheduled Index. If CR=1, then the overall performance of the project is on track. The reason may be that both efficiency ratios (CPI and SPI) are on target, or if one index is below unity then the other index must compensate with superior performance to bring the overall project closer to target.

We provide an example to show the calculation and interpretation of these project management metrics.

Example of an IT project and Project Performance

• Project Schedule (Development and Deployment) = 24 months;

• Total Budget (including definition stage) at Completion = $10 million with an allocation as follows:

• Definition Stage ($1.5 million); Development Stage ($5 million); Deployment Stage ($3.5 million);

• The first milestone is six months after the start of development and the scheduled budget is $3 million;

• At the milestone date, the actual cost is $2.5 million when the promised deliverables are 90


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percent complete.

Using project management terminology, we see that PV = $3 million, AC = $2.5 million and EV = $3 million * 0.90 = $2.7 million.

The cost performance index (CPI) = EV/AC = $2.7 m / $2.5 m = 1.08. This value indicates that the project evaluated at the milestone date is under budget.

The schedule performance index (SPI) = EV/PV = $2.7 m / $3 m = 0.90. This value indicates that the project is behind schedule.

The value of the critical ratio (CR) = CPI x SPI = 0.97. This value provides a snapshot of the overall project performance at the milestone date and indicates that the project is just below target. The benefit of being under budget does not fully compensate for the project being behind schedule.

Deployment Stage

The final stage in our 3D model of IT project management involves the deployment of the IT product.

This would typically include robust:

• Systems testing to evaluate the end-to-end functionality

• Stress testing of the system to evaluate potential breakpoints

• Security testing to evaluate the effectiveness of appropriate firewalls and potential for breaches

The project manager will also ensure that the budget allocated for this stage includes resources to train dedicated employees and provide maintenance costs. As a guideline, maintenance costs are typically about 20 percent of the total project budget.

Comment

Note that this guideline is a good reminder for the project manager to take actions to reduce project complexity at the definition stage. Higher project complexity not only leads to higher project risks but also higher total costs and so higher maintenance costs.

Project complexity is not only derived from a large number of stakeholder priorities but also from the expected time to completion. For projects with long duration, the likelihood of turnover of key personnel with core technical expertise in the development stage increases. The same chance of high turnover is also true for key stakeholders in business units. New stakeholders may introduce changes to the expected business outcomes resulting in higher levels of complexity in design. Consequently, the project execution and outcome risks increase.

Lesson

Projects with a time to completion of more than two years are likely to be exposed to high project execution and outcome risks.

This completes our analysis of IT project management from a business perspective. IT project failures can have devastating effects on the bank’s profitability. For this reason, IT projects must not be assessed on the basis of technical success only, but on whether business objectives are enabled within the constraints set by the Iron Triangle.

We now consider other key operational issues in retail banking as they related to bank branch and call centre management. These are considered in Chapter 2.


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Chapter 2: Bank Branch and Call Centre Management

The study of bank branches and call centres presented in this chapter involves an application of queuing theory, which is simply defined as the study of waiting in all its forms. If one believes that ‘time is money’, then it may be concluded that customers do not like to wait. Branch and call centre managers in particular are interested in the relationship between customer satisfaction and customer waiting time for a service. While there are analytical approaches to studying waiting time (Little’s Law is a statement about waiting time in queues), there are also customer perceptions about waiting time that are significant.

But how do customers in a queue form perceptions about waiting time?

Fortunately, psychologists can help in this regard and research has provided some interesting conclusions. The following pages outline some of the most notable findings relevant to branch and call centre management in retail banking:

The Peak-End Rule

This rule, attributed to Daniel Kahneman*, states that people evaluate an event by the intensity of the most pleasant or unpleasant experience (i.e., the peak) and the end. Shakespeare was likely right: all’s well that ends well. It applies to experiences that have a beginning and an end. The peak-end rule is explained graphically as follows:

* Daniel Kahneman, Experienced utility and objective happiness: a moment-based approach, (Princeton, 1999).


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207.3: The Peak-End Rule

It is not the totality of experience that is remembered. Two snapshots matter – the peak and the end – the most intense perception and the experience at the end.

Although the bank seeks to create a long-term relationship with the customer, each customer-bank interaction has a defined beginning and an end. Hence the peak-end rule applies. Branch managers can utilise this rule to affect customers’ regret of waiting for a long time before they are served.

Advice for Bank Branch Managers

If customers have to wait a long time before they are served (high intensity negative experience – the peak), then a positive average customer experience is still possible by ensuring a memorable positive customer experience at the end of the customer-bank interaction.

End-of-Wait Experience

A concept that is related to the peak-end rule is called ‘end-of-wait’ satisfaction. A research report* shows that when customer service times are greater than promised, then the end-of-wait satisfaction was more negative for customers who were given a waiting time guarantee by the bank compared to another group of customers who were not offered the same guarantee.

This result has an implication for the management of incoming telephone calls to the bank’s call centre. It is common to offer customers maximum waiting time guarantees that vary by time of day. The research finding implies that meeting the waiting time guarantees will create positive customer experience.

But a word of caution is required at this time.

Advice for Call Centre Management

Banks must ensure that adequate investment is made in efficient and effective IT infrastructure that will enable the provision of waiting time guarantees or else they are likely to create negative customer satisfaction.

Appointment Syndrome and Customer Satisfaction

The modern bank branch is evolving from a centre of transactions to one providing mainly advisory services. Branch staff typically schedule appointments with customers to provide customers with

* P. Kumar, M. Kalwani and M. Dada, “The impact of waiting time guarantees on customers’ waiting experiences”, Purdue University, ISBM Report l-(1998).


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advice on investment and portfolio management (e.g., private banking), retirement planning, trust and estate planning and cash management, and lending to SMEs. Management of customer appointments is important to avoid what is commonly called ‘appointment syndrome’. This is illustrated as follows:

207.4: Appointment Syndrome

The scheduled appointment time creates a definitive expectation for the customer. The customer does not experience dissatisfaction from waiting between the arrival time (which is at the discretion of the customer) and the appointment time. But a delay beyond this time creates dissatisfaction that increases exponentially. The reason is that, beyond the appointment time, the customer experiences anxiety since he/she does not know how long the delay will last.

Lesson

Bank branch personnel should set customer appointment times that must be respected. Customer dissatisfaction can reach very high levels even for a relatively short delay.

We now consider resource planning issues in bank branch and call centre management. However, we present advice for managers when dealing with issues related to waiting time.

• From a bank branch perspective, be mindful of the perception of unfairness when there is more than one customer queue. It may permit jockeying that occurs when there are two or more parallel queues and the customer moves from one queue to the other. This is common in supermarkets and it can create customer dissatisfaction.

• Parallel queues may instead be recommended when different segments of customers are served within the same bank branch (e.g., business customers versus retail customers). Jockeying in this case should be zero and the length of the line should be shorter. This may also prevent ‘baulking’, which occurs when a customer does not enter a queue because he/she thinks it is too long. Of course, a slow-moving long line is probably the worst case for the branch. For example, if the customer enters the queue in a branch and after some time loses patience and leaves, he/she is said to have ‘reneged’. For a call centre dealing in telephone calls, the analogous situation where people feel that they have waited too long is called ‘abandonment’.

This chapter deals with call centre and bank branch management with an emphasis on resource planning. We restrict the mathematics and formulae to a strict minimum, and instead provide graphical illustrations of one of the most important problems facing the retail banking manager: how to staff for congestion periods when line length can grow sharply. This may lead to baulking and reneging; incoming calls to a call centre can create blocking, leading to a high degree of


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abandonment. These all contribute to customer dissatisfaction.

To facilitate the discussion, we begin with presenting simple scenarios of waiting in queues in a branch or call centre. We then add more realistic assumptions to the scenarios that would mimic the reality of branch and call centre operations. The main objective in all scenarios is to recommend lessons for the bank manager.

First, we model a typical queuing system in a bank branch or call centre.

NPS • Net Promoter Score is a measure of the extent of the consumer as an advocate. NPS = percentage of advocates less percentage of detractors.

CEM • NPS measures the success of customer-experience management. Banking operations are optimised when customer experience is optimised. The stimulus for banking-operations optimisation is the customer's requirements – tangible and intangible.

LSS • Lean Six Sigma is a methodology that is intended to make the customer's experience unexpectedly positive. This is accomplished by Lean, which seeks to eliminate non-valued activities and Six Sigma that seeks to deliver expected customer-centric outcomes (i.e. mean) with minimum variation (Sigma).

Queue = WaitingLine

CustomersArrive

CustomersExit

S servers

Maturity Analysis

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207.5: Parallel servers/single queue

To facilitate the following analysis, we present some important definitions and assumptions.

• Customers arrive at a constant average rate per unit of time. For example, recent experience in a bank may show that customers arrive in a queue at an average of 10 customers per hour. This is assumed to be constant. In a later scenario, we will permit uneven arrival rates, which can create congestion periods. This average arrival rate is customarily labelled by a Greek symbol λ (pronounced ‘lambda’).

• When customers arrive at a bank, they enter a queue – the waiting line. It is assumed that there is one waiting line. Similarly, telephone calls enter a ‘waiting line’ until answered.

• There can be one or more servers (e.g., tellers or telephone operators). In the model above, we show the possibility of S servers. It is assumed that all servers are equally efficient. For example, a server may be able to have an average service rate of 20 customers per hour or equivalently, an average of one customer every 3 minutes. The average service for each server is denoted by another Greek letter µ (pronounced ‘mu’). Of course, the average service time =

µ1 . In our example, the average service time is 1/20 of an hour = 3 minutes.

• We define the ‘utilisation rate’. We first illustrate using an example. Assume that 15 customers enter the bank branch every hour or lambda = 15. Also assume that there is only one teller who has an average service rate of 20 customers per hour or mu = 20. Then the teller is utilised for 15/20 = 75 percent of each hour. In other words, the teller is free for an average of 25 percent of each hour.

For one server (i.e., teller or telephone operator), utilisation rate = incoming rate divided by outgoing rate. In our example, this is 15/20 = 75 percent. In symbols, we can write that the utilisation rate =

µλ

• It is customary to represent the utilisation rate by the Greek symbol ρ (pronounced ‘rho’)

Now suppose that there are two tellers in the bank each with an average service rate of 20 customers per hour. Then the two can serve 40 customers per hour. So the utilisation rate is the incoming rate / outgoing rate = 15/40 = 37.5 percent. This means that each teller is idle for an average of 62.5 percent of the time.

So for two servers, utilisation rate = 15/ (2*20) = 37.5 percent. Symbolically, ρ=

µ

λ*2

Hence for S servers, the utilisation rate, ρ =

µ

λ*S

.


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This definition is intuitive. More servers create a bigger capacity. For the same number of customer arrivals or incoming calls, a bigger capacity reduces the utilisation rate of each server and increases their idle time.

• We define the cycle time or completion time (CT) as the sum of the average waiting time in the queue and the average service time. Recall from above that the average service time is

µ1 .

• As we stated at the beginning of this chapter, it is the average waiting time in the queue that matters most for customer satisfaction. Of course, poor service will only add to customers’ regret. Baulking or reneging is related to the average waiting time in the queue. A similar comment relates to the potential for call abandonment in the call centre. There is an approximation formula for the average cycle time (CT) that was proposed by Sakasegawa*.† It can be intimidating for some and so we place it in a footnote and consider its qualitative implications for branch and call centre management.

It is important to note that we presented the formula as the CT = A+B. In this case, A = average waiting time in the queue and B = average service time. The focus of capacity planning is mainly to reduce the value of A, the average waiting time.

First Scenario

Assumptions:

1) The average arrival rate is 10 customers per hour (lambda =10)

2) The average service rate is 12 customers per hour (mu = 12)

(Note that for one server, the utilisation rate is 10/12 = 83.3 percent)

We calculate – from the formula by Sakasegawa – the waiting time in the queue, the average service time, and the average cycle time, by increasing the number of servers (i.e., increasing the resource base). The following table shows the details of the calculation:

S Waiting Time in Queue (hour)

Average Service Time (1/µ) (hour)

Average Cycle Time (hour)

1 0.420 0.08 0.500

2 0.020 0.08 0.100

3 0.007 0.08 0.087

4 0.001 0.08 0.081

5 0.000 0.08 0.080 A graph showing the effect of increasing capacity (number of servers) on average cycle time is shown below.

*

( )( ) µµλµµλ 1

/1/ )1(21

=−

=+≈++−

BandSS

SAwhereBACTS

; This is a special case where particular distributions are assumed. We will modify later on for a more general case. The reference is H. Sakasegawa, ‘An Approximation Formula Lq ~ α.ρβ/ (1-ρ)’, Ann Inst Statist Math, 29 A, 67-75 (1977).


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207.6: Effect of customer arrival rates on average cycle time

Our first scenario shows that the biggest impact on average waiting time in the queue is obtained by just adding one more teller or telephone operator. The optimal number of tellers is three at all times during working hours. This is because the utilisation rate drops sharply even for one more server and the waiting time in the queue is reduced to insignificant levels.

By adding more servers beyond the second, the idle time of all servers increases significantly. In summary, as the number of servers is increased, the average time in the queue approaches zero and the total time in the bank branch is approximately the average service time. This represents a trade-off between expense and customer satisfaction which management, will need to make.

Second Scenario

Assumptions (same as in the first scenario):

1) The average arrival rate is 10 customers per hour (lambda =10)

2) The average service rate is 12 customers per hour (mu = 12)

Based on the first scenario, we assume that the bank manager hires three tellers who are present at all times during working hours.

We now use the same formula to estimate the impact of increasing arrival rates on the average cycle time. We get the following results:

Lambda (λ) = arrival rate per hour

Waiting Time in Queue (hour)



10 0.007 0.08 0.087

15 0.013 0.08 0.093

20 0.025 0.08 0.105

30 0.123 0.08 0.203

35 0.953 0.08 1.033

A graph showing the effect of increasing customer arrival rates on average cycle time is shown below. Three tellers are adequate except when the arrival rate is more than 30 customers per hour.


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Customer Arrival Rates

207.7: Effect of variable arrival rates on customer service

There is a dramatic rise in waiting time in the queue when the arrival rate is 35 customers per hour. This is because for 3 tellers (which was optimal in the first scenario), the utilisation rate is now 35/ (3* 12) = 35/36 = 97 percent. This scenario shows that while capacity planning is less difficult when arrival rates to a bank branch or call centre are relatively uniform and predictable, sudden increases in customer arrival rates can cause significantly long waiting lines and greater likelihood of high levels of customer dissatisfaction.

Here is a case in point. Assume that under normal conditions, as demonstrated in the first scenario, the bank or call centre manager hires 3 tellers or telephone operators and the resulting average cycle time (or completion time) is 0.084 hour.

Suppose that the manager expects that an average of 35 customers will arrive during lunch hour. It is assumed that other times of the day reflect normal arrival rates.

Clearly, this waiting period is likely to lead to baulking and even reneging with significantly high levels of customer dissatisfaction. One solution is to add one more teller for the one-hour period. In this case, the utilisation rate drops to 35 / (4.12) = 73 percent, with the average waiting time in the queue reduced to 0.04 hour. This is a remarkable decrease from its original level (with 3 tellers) of 0.95 hour.

Lesson

When there is an expectation of temporary but higher-than-normal customer arrival rates to a bank branch or call centre, a temporary increase in staff levels will have a dramatic effect on utilisation rates and hence on waiting time in the queue. Customer dissatisfaction during this period is likely to be significantly reduced.

In the two scenarios considered, we assumed that arrival rates and service rates are constant and predictable. We also considered the case when there is a spike in arrival rates; for example, during lunch hour at the bank branch. The lesson above summarises the key point made so far. But a more realistic case is based on customer arrival rates and employee service rates that are variable and unpredictable. It may still be the case that the branch manager expects a spike in arrival rates during the lunch hour, but the level of arrival is not known with certainty. We consider this case in a third scenario.

Third Scenario

The assumptions are the same as in the first scenario where it was optimal to have three servers.


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To restate, we assumed that the average arrival rate is 10 customers per hour (lambda =10) and the average service rate is 12 customers per hour (mu = 12). Now we assume that the arrival rate and service rate are variable. We assume that the standard deviation, a measure of volatility of arrival time and service time, is twice the average. We then calculate* the average cycle time with various levels of servers as we did in the first scenario. We present our results in the table below:




1 1.67 0.08 1.75

2 0.084 0.08 0.164

3 0.018 0.08 0.098

4 0.007 0.08 0.087

5 0.004 0.08 0.084

If we compare the first scenario of (non-variability) with the third scenario, where the variability of both arrival and service times are included, we see that the same level of optimal cycle is reached with one more server. The cycle time of 0.087 is reached for 3 servers in the first scenario, but with 4 servers in the third scenario.

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Capacity (with Variable Arrival Rates)

207.8: Effect of server productivity on average cycle time

The main point is that variability in arrivals and departures in the bank branch or call centre requires a higher staff level to reach the same customer service level.

Our last scenario considers the role of increased staff efficiency that may be obtained through eliminating waste, training or by placing the right person in the right job.

Fourth Scenario

The assumptions for this scenario are the same as in the first scenario. However, we assume that there is a 25 percent increase in server productivity that is achieved either through employee training, ensuring a better talent-function fit or further automating certain process steps. Of course, all three actions may be undertaken by management.

By applying the same general formula as in the third scenario, (but now with an average service rate at 15, which is 25 percent higher than the status quo of 12) we obtain the following:

* The more general Sakasegawa formula applies. It is stated as

( )( ) µµλµµλ 1

2*

/1/ 22)1(21

++

−=

++−STIAT

S CVCVSS

SCT

CV = coefficient of variation = ms

where σ=standard deviation.


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1 0.437 0.067 0.504

2 0.033 0.067 0.100

3 0.006 0.067 0.073

4 0.001 0.067 0.068

5 0.000 0.067 0.0867 When compared with the third scenario, there are fewer servers required for the same level of average cycle time. The graph that follows illustrates this point:

0

0.1

0.2

0.3

0.4

0.5

0.6

0 1 2 3 4 5 6

Average Cycle Time

Capacity (with Server Productivity)

207.9: Increased productivity with fewer servers

We come to our final lesson derived from the four scenarios.

Lesson

If there is an anticipated spike in customer arrival rates in the bank branch or call centre, then a temporary increase in staff to reduce the increased utilisation rate is recommended to reduce average time in the queue and enhance customer satisfaction.

If there is increased variability in customer arrival rates and/or service rates, increased employee productivity through training programmes, talent management and enabling technology will reduce the number of servers required.

The four scenarios considered have highlighted further insights for managing the bank branch or the call centre.

a. As capacity utilisation approaches 100 percent, the average waiting time in the queue rises sharply. This has the potential for significant customer dissatisfaction which may manifest in reneging and baulking or abandonment.

b. The seasonal flow of arrivals in a queue is best dealt with through temporary increases in capacity. The trade-off is always between customer satisfaction and total cost. Increases in capacity have a dramatic positive effect on cycle time and hence customer satisfaction. Addressing seasonality or intraday spikes with only temporary increases therefore contains cost.

c. There is no substitute for putting the right person in the right job. Increases in employee


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productivity lower the number of servers required. Clearly, an increase in employee productivity reduces total cost with a positive effect on customer satisfaction. This is the best of both worlds.


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Summary

This module dealt with operational issues and business implications of IT project management in retail banking. We analysed the 3D model of definition, development and deployment. The definition stage is where project planning occurs and business objectives are determined. The success of an IT project is not only about technical success but more importantly about delivering on the agreed-upon goals set by business stakeholders. We discussed the implications of the Iron Triangle of triple constraints. We offered important advice for the project manager to consider in the definition stage: a decision to exit the project should be considered in the definition stage. If the decision is made to proceed to the next (development) stage, then it is more difficult to exit even if there is unambiguous evidence to do so. The reason is that, when sunk costs are high, there is a subjective bias for the project manager to continue with development and deployment. The decision to exit is easier earlier rather than later in the process.

The development stage involves project design that incorporates stakeholder (user) specifications. We presented classical project management metrics to evaluate project performance at each milestone in the development stage. These included Cost Performance Index, Schedule Performance Index and the Critical Ratio. These metrics assess potential for cost overruns and schedule delay.

Finally, the deployment stage comprises testing of end-to-end functionality, firewalls and potential for security breaches and business objectives.

Chapter 2 considered behaviour issues, as well as resource planning in bank branch and call centre management. Behaviour issues include the implications of the peak-end rule, end-of-wait experience and the appointment syndrome. We concluded this chapter with resource planning by a consideration of a more advanced treatment of Little’s Law (presented in Operations I) with graphical illustrations of key implications based on selected scenarios. The main lessons from these scenarios were:

a. As capacity utilisation approaches 100 percent, the average waiting time in the queue rises sharply. This has the potential for significant customer dissatisfaction that may manifest in reneging and baulking or abandonment.

b. The seasonal flow of arrivals in a queue is best dealt with through temporary increases in capacity. The trade-off is always between customer satisfaction and total cost. Increases in capacity have a dramatic positive effect on cycle time and hence customer satisfaction. Addressing seasonality or intraday spikes with only temporary increases therefore contains cost.

c. There is no substitute for putting the right person in the right job. Increases in employee productivity lower the number of servers required. Clearly, increase in employee productivity reduces total cost with a positive effect on customer satisfaction. This is the best of both worlds.


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Multiple Choice Questions

1. In what stage of the 3D model of definition, development and deployment is the proportion of total project cost the least?

a) Definitionb) Development c) Deployment d) Systems testing

2. At a milestone during the development stage, the project manager calculated typical project management performance metrics. The project manager concluded that there was evidence of a potential for a cost overrun. This conclusion is most likely reflected in which of the following:

a) Cost performance index <1b) Schedule performance index >1 c) Critical ratio = 1d) Critical ratio >1

3. The components of the Iron Triangle (triple constraint) are all of the following except:

a) Budgetb) Schedulec) Scoped) Scale

4. At a project milestone, the project manager calculates that the Cost Performance Index is 0.90 and the Schedule Performance index is 0.80. Which one of the following is incorrect?

a) Actual cost exceeds earned valueb) Earned value is less that planned valuec) The cost-scheduled index indicates that the project is on trackd) The critical ratio is less than unity

5. The ‘appointment syndrome’ states that customer regret is highest:

a) Between the time the customer arrives and the time of appointmentb) Immediately after the appointment time c) When the delay after the appointment time is overly longd) Immediately before the appointment time

6. The peak-end rule implies that:

a) The average customer experience during the customer-bank interaction is a better predictor of customer overall experience. b) The average of the intensity of the peak experience (pleasant or unpleasant) and the ending experience is a better predictor of customer overall experience.c) The intensity of the peak experience (pleasant or unpleasant) is a better predictor of customer overall experience.d) The intensity of the ending experience is a better predictor of customer overall experience.


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7. Which of the following statements is most likely incorrect?

a) Temporary spikes in arrival rates in a queue are best addressed by temporary staff allocation.b) A high level of WIP can affect employee productivity leading to high throughput (TH).c) An overly high employee utilisation rate can lead to low completion time arising from high error rates. d) Even a small increase in capacity (e.g., in staff) from low levels, can have a big impact on cycle (i.e., completion) time.

8. Which statement is incorrect?

a) End-of-Wait experience implies that when a bank meets its customer waiting time guarantees, customer experience is positive.b) Having the right person in the right job can lower the need to have additional staff to meet congestion needs in bank branches.c) Parallel queues may be recommended when different segments of customers are served within the same bank branch.d) Capacity planning is rendered more difficult when arrival rates of telephone calls to the call centre are more uniform.

Answers

1 2 3 4 5 6 7 8

a a d c c b b d


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Mini-case Study

MidWest Bank is a regional bank in the United States that provides residential mortgage loans up to 90 percent LTV. About a year ago senior management become concerned about the credit risk associated with such high LTV ratios and adopted a strict lending policy: loan-to-value ratios must not exceed 75 percent. As a consequence, a much lower percentage of loan applications were approved.

Currently, consumer data show that the bank receives an average of 100 mortgage applications per 30-day month and decisions are made on an accept/reject basis. There is a single department that deals with all mortgage loan applications that come to the bank, irrespective of the channel in which each was initiated. The bank’s internal audit department shows that, on average, 50 mortgage applications per month are in various stages of the review/approval procedure.

Recently, there has been a flood of customer complaints regarding the length of time it takes for the bank to decide on whether the loan application is approved. Many loan applicants indicated in their complaint letters that other regional banks took an average of 10 days to make a final decision on residential mortgage applications.

In response to customer complaints, the bank has decided to revamp its decision-making process. It hired a consulting company that made the following observation: most applications were processed very quickly but some applications took an inordinate amount of time due to a lack of required information or unclear documentation.

The consulting company recommended that all applications be subject to a pre-process sorting (i.e., a pre-screening stage).

Specifically, each application would be deemed as either:

A: Looking acceptable in terms of required information and supporting documentationB: Needing further analysis and documentationC: For rejection with no further consideration

The first two categories – A and B – would be sent to specialist teams that would make the final decision on these applications.

This plan was put into effect over the last six months on a trial basis. After this period, the audit department provided the following data for this six-month period:

– 60 percent of applications were classified as type A– 30 percent of applications were classified as type B– 10 percent of applications were classified as type C

The two specialist teams also provided the following approval rates and average completion times to the audit department:

– 90 percent of type A applications were approved with an average completion time of six days.– 70 percent of type B applications were approved with an average completion time of 12 days.

These average completion times are calculated after the pre-screening stage. The audit department concluded that the average time an application spends in the pre-screening stage affects the overall average completion time but depends on the resources, in terms of people, process and systems, that have been allocated.


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Multiple Choice Questions

1. Which of the following statements about loan to value (LTV) ratios is incorrect?

a) A high LTV ratio creates a higher credit risk exposure for the lender.b) A high LTV indicates that the loan amount is high compared to the appraised value of the residential property.c) The new policy in regard to LTV implies that loan applicants will make at least 25 percent down payment.d) The lower percentage of approved residential mortgage applicants is due to a higher LTV ratio policy by senior management.

2. Currently the average time for MidWest bank to make a final decision on a residential mortgage application is:

a) lower than the average time for competitorsb) 15 daysc) 0.5 dayd) 10 days

3. Which statement is incorrect?

a) Customers complained that the rejection rate of residential mortgage applications was too high at MidWest compared to competitors.b) At MidWest most residential mortgage applications were processed very quickly.c) Some applications were delayed due to a lack of proper documentation. d) Some applicants did not provide all required information and this resulted in an undue delay of the final decision.

4. The consultant’s recommendation that a pre-process sorting (pre-screening) be implemented would most likely lead to which of the following?

a) Higher arrival rate of residential mortgage applications to MidWest bankb) Lower number of residential mortgage applications per month in various stages of the review/approval procedurec) Higher number of residential mortgage applications arriving at the bank with unclear documentation d) Ensure that residential mortgage applications with loan-to-value ratios below 75 percent will be submitted to the bank

5. The results from the six-month trial period show all of the following except:

a) Most applications met information and documentation standardsb) 10 percent of applications were rejected with no further considerationc) Class C applications were mostly rejected and a small percentage approved after a lengthy delayd) Thirty percent of applications were classified as requiring further analysis

6. The overall average mortgage approval rate during the six-month trial period was:

a) 54%b) 90%c) 70%d) 75%


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7. The overall average time to make a decision on all applications after the pre-screening stage is:

a) 10 days b) 12 days c) Lower than the average time of 10 days for MidWest’s competitorsd) Higher than the average time of 10 days for MidWest’s competitors

8. Which of the following statements is incorrect in relation to MidWest’s experience with the six-month pre-screening of applications?

a) Pre-screening of applications can lead to fewer applications in the process awaiting final decision.b) Pre-screening of applications can lead to higher productivity in terms of a faster time to the final decision.c) Pre-screening of applications can lead to higher operational efficiency in that completion time is faster.d) Time for pre-screening does not materially affect the overall time to final decision on mortgage applications.

Answers

1 2 3 4 5 6 7 8

d b a b c d c d


Operations II Y - Retail Banking Academy · Y 159 Retail Banking Academy, 214 RET RE TA IL BANIN...

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