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RETAIL BANKINGACADEMYCourse Code 108 - Operations I

108.Operations I

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179© Retail Banking Academy, 2014

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RETAIL BANKINGACADEMY

Course Code 108Operations I

Introduction

As shown in Retail Banking I, Banking Overview, a retail bank delivers core services such as financial intermediation (e.g., channelling savings from depositors into loans from consumers and companies); payment services (e.g., transactions accounts, debit cards and credit cards); money creation (e.g., loan process and fractional reserve banking) as well as other auxiliary services such as cash management and advisory services. In delivering these core services, retail banking creates operations. We now present a discussion on the definition of ‘banking operations’.

Definition of Banking Operations

Enabled by information technology (IT) and information databases, a banking operation is a process by which the retail bank delivers a financial service or product to a consumer or company, resulting in an ongoing contractual relationship (explicit or implicit).

This contract may have a definitive end (e.g., loan contracts) or may have a contingent end (e.g., the depositor may end the relationship at will or a mortgage holder may prepay in case of attractive low mortgage rates).

In other words, a banking operation facilitates the contractual relationship between a retail bank and its counterparty (e.g., depositor, borrower). A banking operation comprises two dimensions. First, a banking operation is a process* enabled by people, technology and information databases. Second, the process is created to deliver a financial service or product.

The key point is that customers demand banking products to meet their respective needs. Banking operations are the medium (i.e., process) by which banking products are delivered to customers.

What Is a Process?

A process describes how some activity is done; that is, how the bank expects to deliver the

* T. Ilin, The Functional Integration of Operations Management in Banking, Cranfield University, (2008).

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financial service/product. Specifically, Ilin (2008)* describes a process as follows:

“A process is a coherent set of activities to be carried out in some order, that can be executed repeatedly, involving one or more entities, with a definable beginning and ending point, having identifiable inputs and outputs, achieving one or more goals of a function, identified by a verb or verb phrase that describes what must be accomplished.”

Note that in this definition, a function is part of a process. A function is what is delivered and by whom. In addition, a process has certain attributes:

a) A repeatable set of activities that flows from one node (i.e., person) to another in some order.

b) There is a presumed beginning of the process and an end.

c) There are identifiable inputs into a node, and identifiable outputs as well.

We make a point of departure from traditional discussions on banking operations. In many textbooks and research papers, banking operations are viewed from the perspective of back-office, middle-office and front-office operations. We believe that this is a myopic view. Indeed, as we shall see later, all banking operations typically begin with the customer interfacing with the front office through its various channels. They are then evaluated by functional professionals in the middle office, and recorded and stored by the back office.

The final result from this chain of events is reported back to the front office and the customer. So the front office, middle office and back office are part of the decision-making process. By emphasising an approach based on the back office, middle office and front office, as is commonly done, we can miss the full view of a banking operation and create an unnecessary and inefficient silo approach.

At this point, we state our main point that we believe will help to explain the concept of ‘banking operations’.

A retail-banking operation may be viewed as a set of explicit or implicit contracts between the bank and its customers. The terms and conditions of the contracts are delivered through processes that are enabled by information technology and information databases. This places into focus the crucial role of people, channels and IT infrastructure in retail banking. By implication, the main sources of operational risk in a retail bank are derived from people risk, process risk and systems risk.

The remainder of this module is organised as follows:

Chapter 1: Banking Operations

Chapter 2: Process Efficiency

Chapter 3: Lean Six Sigma Processes

Chapter 4: Theory of Constraints and Associated Metrics

This module concludes with a summary and review questions.

We now analyse banking operations from the liability side as well as the asset side of the balance sheet. We do not consider banking operations that deal simultaneously with both sides of the balance sheet. This is covered in Asset Liability Management (Retail Banking II).

* Ibid, Footnote on previous page.


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Chapter 1: Banking Operations

As we have shown in Retail Banking Overview, a retail bank is a financial intermediary. It borrows from depositors (banks’ liabilities) and then lends the funds (banks’ assets) to consumers and companies. We examine an important banking operation from the liability side of the balance sheet: deposit taking. In explaining this operation, we first present an explanation of the front office, middle office and back office functions. Please note that we do not subscribe to the view that these operations are functionally separated. Our objective is to describe the main functions in each office.

The Front Office

The front office is a direct interface between the customer and the bank through channels such as branches, call centres and mobile channels. The front office interprets the requirements (derived from needs) of the potential customer. It performs a marketing function to acquire new customers, and tries to create a longer-term relationship with existing customers.

Among the specific functions that professionals in the front office perform is gathering information with the objective of ‘knowing the customer and the customer needs’, so that interpretation of the customer’s expectations/needs are not misplaced. This may be done in conjunction with the marketing research group and the back office, which stores information on customer profiles.

The Middle Office

Professionals in the middle office are not directly customer-facing but rather enable the process by assessing, for example, the credit risk to the bank in lending to a customer, or the money-laundering risk for the bank in accepting a deposit. Apart from risk management, the middle office (e.g., legal department) also ensures that all banking operations comply with regulatory and statutory requirements such as the Sarbanes-Oxley Act, Dodd-Frank Act and Basel III, as well as the ethical principles and guidelines that the bank has adopted. The middle office also sets the terms and conditions (e.g., price, repayment terms) of the financial service. It also includes the human resources department as well as finance and accounting.


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The Back Office

Professionals in the back office, similar to the middle office, are not directly customer-facing but enable the process by recording and storing the information arising from the operation. For example, the back office processes loan applications, facilitates payment services and settles trades. The back office manages various information databases, keeping track of future payments to depositors, receipt of due payments from loans etc.

We now describe a typical operation arising from the liability side of the balance sheet. The process begins with a customer. Assume that the customer has decided to make a deposit based on, for example, promotion materials and information on the bank’s website or internet advertisement. The potential customer fills out an application to open an account. This part of the process is normally conducted by the front office. The application is then assessed by the compliance department in the middle office for money-laundering risk or other compliance requirements (e.g., USA Patriot Act).

From a bank’s perspective, the cost of borrowing can be more than the interest rate it pays on the deposit and the operational cost of the processes involved in delivering the financial service. It can be disastrous for the bank if it inadvertently accepts deposits from a client that is deemed unacceptable because of compliance obligations.* Upon passing these hurdles, the application is then sent to the back office for recording and storing. The accepted application is sent to the client and to the front office for information purposes.

We illustrate this banking operation of deposit gathering below.

....

..

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Arguably, it is less risky for a retail bank when it borrows from depositors. We recognise that there could be liquidity risk problems. When a bank lends to consumers and companies, the process is similar in principle but relatively riskier. Let us look at how this works.

When a customer seeks to borrow from a bank, it seeks the advice of a front-office professional. This professional could be, for example, a dedicated relationship manager or a contact person at a bank branch. The application is sent to the middle office mainly for credit risk assessment

* It was reported by BBC News (12 December, 2012) that the “UK-based bank, Standard Chartered, has now paid a total of $674m (£419m) to US regulators and authorities for illegally hiding transactions with Iran and other countries under US sanctions. The New York State Department of Financial Services (DFS), one of the regulators that issued the fines, said the bank laundered as much as $250bn (£161bn) for nearly a decade.”.

108.1: The bank operation of deposit gathering


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that is based on the customer’s financial health (e.g., wealth and income levels), financial history, existence of possible collateral etc. The objective is to obtain estimates of the customer’s probability of default, the bank’s exposure at default and loss given default.

The risk manager also considers the bank’s current portfolio of loans for concentration risk. The middle office decides on the price of the loan and terms of repayment. Experts in the middle office consider Basel III implications if the loan is approved. This includes provisioning for expected loan loss and capital allocation for unexpected loan loss. Subject to legal approval, if the loan is approved, the documentation is sent to the back office for recording and storing. The final approved documentation is sent to the customer and the front office for information purposes.

Clearly, all banking operations are based on processes that originate with the customer and end with the customer. In the interim, the front office, middle office and back office are actively involved to varying degrees. Banking operations on the asset side are relatively riskier; hence the middle office is highly involved in risk management, capital and provisioning implications, compliance and ethical issues.

Here is the key point:

It is not very useful to study front office, middle office and back office activities in isolation. Rather, front office, middle office and back office activities are part of every banking operation. The importance of the middle office is predominant, especially for banking operations that arise on the asset side of the balance sheet. The main reason is that risks on the asset side can have a very major impact on bank profits and thereby affect the risks on the liability side of the bank’s balance sheet. This cross-correlation of risks – emanating from the asset side of the balance sheet – places a great onus on the front office, middle office and back office professionals, but mostly on those in the middle office.

At this point, it is clear that people and processes stand out as two additional P’s in the traditional 4P’s model of traditional marketing.* In terms of banking operations, the people factor is paramount.

Retail banking is clearly a ‘people business’ and so it goes without saying that people matter most. In terms of operations, people manage transactions, evaluate and monitor risk, monitor compliance, create customer databases, track regulator constraints etc. But, as we have shown above, the second P (i.e., process) is intertwined with people. People and process are joined together in banking operations. This has an important implication for human resource management (HRM).

Ensuring that the right people with the right behaviour, skills and experience are matched with the right function in the right office (front office, middle office and back office) is, arguably, the most important function of human resource management in retail banking. The effectiveness of the process is enhanced by this HRM function.† Of course, the efficiency of the process is important. But efficiency is not necessarily achieved without competent people placed at the right node in the process. The ways of improving the efficiency of a banking process are considered in Chapter 2 and partially in Chapter 3 of this module.

Finally, we would like to highlight the complexity of banking operations and do not want to leave the impression that banking operations are based on processes which are uni-dimensional, a simple process without cross-correlation with other processes. We explain this key point.

We stated in Retail Banking Overview that modern retail banking is characterised by multiple products, multiple channels and multiple customers. This means that processes in retail banks may be initiated by a client in one channel (e.g., branches, call centres, bank’s website, mobile handset), but completed in another. For example, a client may apply for a loan on the internet

* The 4P model is introduced in the Marketing module of Retail Banking I.

† A comprehensive study of human resource issues is conducted in the module titled People Management in Retail Banking II.


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and have the funds deposited in a branch account. This is an example of depth complexity, where a client can deal with several points of contact that are provided by the bank for the convenience of customers.

In a similar way, the customer may access a portfolio of products within each distribution system. For example, a customer can access information and application documentation within each channel on deposits (in all variations), loans, mortgages, debit cards, credit cards etc. This is called breadth complexity.

Accordingly, it is clear that retail banking operations typically reflect both depth and breadth complexity. This will likely lead to substantial IT complexity and process bottlenecks. Dealing with this major issue affords much scope for process efficiency. But when management of a retail bank endeavours to achieve optimal operational efficiency, it faces the following fundamental trade-off.

Management desires process productivity (lean process, i.e., high speed) with consistency of service delivery at each customer contact point (i.e., low process variation). But the other side of the coin is customer service quality (i.e., zero error rate). Therein lies the conflict. A fast process will likely increase the probability of human error and mistakes. This gives rise to operational risk. Managing this trade-off between speed and uniformity on one hand and customer service quality on the other hand is crucial.


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Chapter 2: Process Efficiency

Optimising banking processes is a key objective of management. The main reason for this is related to maximising profitability by reducing the cost-income ratio (CIR). Let us recall the importance of CIR that was summarised in Retail Banking Overview. In a retail bank, a measure of operational efficiency is cost-income ratio (CIR). The relationship between gross profit and cost-income ratio is observed in the following algebraic calculation:

Gross Profit = Underlying Income (UI) minus Operating Expenses (OPEX)=UI * (1 – CIR) =

Clearly, there is a negative relationship between cost income ratio (CIR) and gross profit (GR). A lower value of cost income ratio leads to a higher gross profit. Academic research has provided evidence to support this relationship. For example, Mathuva (2009)* (as well as several references therein) found a negative relationship between CIR and a bank’s profitability.

It is interesting to note that a key determinant of CIR is interest result. This is not surprising, since interest result is a predominant proportion of the retail bank’s total underlying income.

We note that CIR should be viewed with caution. This is because CIR may rise even if actions taken to reduce operational costs are successful. This is because income may fall faster than operational costs.

But we note that retail banking is conducted in a very competitive industry. This means margins are very narrow and expected to remain that way for the foreseeable future, at least for mature economies. Hence, management typically focuses on efficiency gains from banking processes by focusing on operational costs.

We now deal with another fundamental issue that affects CIR – the role of process efficiency in improving CIR with income unchanged. This might be the differentiator among retail banks.

We consider some findings in operations management that seem to possess some empirical regularity. They are useful for management to consider in its efforts to reduce operational costs (OPEX) and improve CIR.

* D. M. Mathuva, “Capital Adequacy, Cost to Income Ratio and Performance of Commercial Banks: The Kenyan Scenario”, The International Journal of Applied Economics and Finance, Vol 3 Issue 2: 35-47, (2009).


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First Law: The Law of Velocity or Little’s Law

This law states that the velocity* of a process is proportional to its degree of flexibility. The more flexible a banking process is, the more conducive it is for quick turnaround from origination to completion of a process.

This law has an important consequence for retail banking. First of all, professional literature has shown that flexibility is inversely related to the degree of complexity. Hence, the greater the degree of complexity of a process, the lower the velocity, and the process to final completion becomes slower.

Why is this important for retail banking? In the previous chapter, we noted that retail banking processes have two main sources of complexity – depth and breadth. These will reduce the speed with which the customer’s requirements are delivered from the time of origination. This is because the IT requirements for cross-functional channels (depth complexity) may be complex in design and hence subject to systems failure. Indeed, failure of systems is a source of operational risk according to Basel III.

The law of velocity is also called Little’s Law† and was developed in queuing theory. Little’s Law is the basis for lean strategies, which are considered within the context of call centre management in Operations II.

A version of Little’s Law states that:

CT = WIP/TH where CT = cycle time (i.e., the average time in the system or completion time); WIP = work in process at various stages in the system; TH = throughput (arrival rate). Note that it is a steady state relationship and so the arrival rate is equal to the departure rate.

Here is an example in banking that illustrates Little’s Law:

ExampleWe assume the following:

Mortgage applications arrive at the front office at 25 applications per day steadily. This means that arrival rate, also called throughput (TH), is TH = 25; we also assume that there are 100 mortgage applications at various stages in the approval process, i.e., WIP = 100. Then, by Little’s Law, the average completion time is CT = 100/25 = 4. This means that the average time it takes to complete one mortgage application is four days.

From a retail-banking perspective, managers take action with the aim of reducing the number of incomplete tasks (e.g., applications), which is the extent of work in process (WIP). Then for a given arrival rate, the average time to final decision will be reduced. This is quite clear from Little’s law. If WIP is reduced, then for a given level of throughput rate, the average completion time is reduced.

We summarise this lesson as follows:

Lesson: (Corollary to Little’s Law)

Little’s Law has an important lesson for bank managers. Concentrate first on the numerator. Take actions to reduce the WIP. This does not require an improvement in the throughput rate. This latter case would require investment in training and HR management. This is a longer-term

* The transfer of information requires a medium of transfer. If two people talk to each other, the medium of transfer is by sound waves. If there is interference in the environment, then the receiver of the information may receive warped signals or even ambiguous messages that create misinformation. Electrical current flowing through a wire is even more efficient as a means of communication between two people. Light is used to communicate information over fibre-optic communication networks. In the same way, a process that is interlinked by many nodes where communication between nodes is delayed leads to an overall poor process in terms of information flow.

† J. D. C. Little, “A Proof of the Queuing Formula: L =A”, Operations Research, Vol 9, issue 3, 1961.


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solution that is discussed later.

We now consider another law that is important for achieving process efficiency.

Second Law: Law of Non-Valued Activity

This law is related to Little’s Law. The second law gives an explanation for slower process speed. It states that when WIP increases, the amount of non-value-added work increases in non-linear fashion – that is, at a faster rate. Non-value-added activities have a zero or negative rate of return and may be eliminated from the process without impairing the process of value-added activities. In other words, the price of a non-valued activity is zero. From the first law, an increase in WIP arises from an increase in complexity. The second law states that complexity also increases non-value-added activities even faster than it creates an increase in WIP. *

ComplexityIncrease in Non-Value Activities

Increase in WIP

108.2: How complexity increases work-in-process

A third principle is quite important to locate the greatest source of non-value activities.

The Pareto Principle

The Pareto Principle is named after an Italian economist, Vilfredo Pareto (1848-1923).† It is known as the 80/20 rule. For instance, 80 percent of repeat purchases at a retail store will come from 20 percent of customers. In the current context, this rule states that 80 percent of non-value added activities may be attributed to 20 percent of people in the process. Furthermore, 80 percent of the sources of complexity in the process are due to 20 percent of the nodes in the network.

In summary, the source of non-value activities is not symmetrical. Most non-value activities arise from a relatively small number of people in the bank.

If we consider the Pareto Principle within the context of process efficiency, we see that the sources of complexity and consequent non-value-added activities, are localised – i.e., not widespread. The implication is that it may not be necessary to make large-scale changes to the bank’s processes – local optimisation is the preferred route for managers to adopt. The idea is to identify the 20 percent and make local optimisation changes.

This is a very big-bang idea – the greatest improvement is obtained by considering the few sources of greatest delay. This is what Juran‡ called “vital few, trivial many”.

* M. George, Lean Six Sigma for Service. (McGraw Hill, 2003).

† The Pareto Principle is based on an empirical finding first discovered by Vilfredo Pareto in 1897 when he observed that 80 percent of the land in England (and every country he subsequently studied) was owned by 20 percent of the population.

‡ Joseph M. Juran, Architect of Quality. (New York, NY: McGraw-Hill, 2004).


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Chapter 3: Lean Six Sigma (LSS) Processes

The aspirational objective of operational excellence is customer satisfaction, which is determined by competitive product or service attributes such as price, matching customer needs, as well as positive customer experience. This objective is enabled by efficient bank processes and systems support. Efficient operations are defined by three essential properties: predictability; transparency; and measurability. We examine these properties in detail.

PredictabilityPredictable operations are distinguished by consistent service quality. Customers expect that banking services will be reliable in terms of quality and delivery time. The strategic objective of Six Sigma is consistency. Experience has shown that a key factor in a successful Six Sigma programme is people. This will be discussed later.

TransparencyTransparent operations refer to clarity of sequential steps from the customer’s perspective. Customers have a clear idea what information is required and why it is required.

MeasurabilityThis property is quite important for assessing the effectiveness of the bank’s operations in delivering customer service quality. Common measures of bank operations are ‘time to complete’ (TTC) and ‘cost to complete’ (CTC).

We now consider the concept of lean six sigma processes in relation to these three main properties. First, we deal with lean processes.

LeanIn Chapter 1 it is demonstrated through the application of Little’s Law that there are mainly two ways to improve process efficiency: 1) reduce work in process (WIP) and 2) improve throughput yield.


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108.3: Time and Complexity

The objective of a lean strategy is to reduce complexity (i.e., reduce non value-added activities) and this will lead to lower average completion time.

As shown in Chapter 2,WIPTH

CT= where CT = cycle time (i.e., the average time in the system); WIP = work in process at various stages in the system; and TH = throughput or departing rate. But complexity of process is highly correlated with work in process. Indeed, George* states that “Complexity increases WIP. Some WIP spends 90 percent of its time in in-boxes waiting to be worked”. Using Little’s Law, we get the following relationship where average time to complete an activity (CT) and the degree of complexity of the process (WIP):

CT is proportional to Complexity

This relationship provides the basis for an important lesson to obtain lean operations:

Lesson 1

By reducing complexity (and hence WIP), the bank manager achieves a lower cycle time. This improves service levels, reduces cost and increases customer satisfaction.

This lesson assumes that throughput remains constant. But if, by reducing complexity, staff productivity is increased and hence throughput or departure rate is increased, then the reduction in the completion time will be even higher.

But this relationship may also work in the opposite direction and create an inordinately high completion time. For example, if complexity rises, then WIP is expected to increase as well. However, this may also cause a decline in the throughput rate (TH). The result is a disproportionate increase in completion time (CT).

One reason for this result is that an increase in WIP creates a higher utilisation rate, which is what causes the longer completion rate. What is the culprit? Complexity.

Action point:

Consider each process from beginning to end and view each step from the customer’s perspective. Eliminate steps that are neither necessary nor required. This will reduce WIP.

* Michael L. George, Lean Six Sigma for Service, (New York: McGraw-Hill, 2003).


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Additional benefit of process simplification

By simplifying processes, bank staff are likely to be more able to substitute for each other. This enables better resource planning.

We now state the second lesson derived from Little’s Law.

Lesson 2

All else being equal, an increase in capacity (i.e., staff) will increase departure rate (TH) and hence reduce completion time (CT).

To see this, consider Little’s Law again: WIPTH

CT=

Since throughput (TH) is correlated with capacity, we may state that completion time (CT) is proportional to WIP

Capacity. Hence, by adding staff directly, capacity is increased and CT is

decreased.

There is a caveat here. While adding staff increases capacity and hence reduces completion time, there is a need to ensure that current staff is productive.

Action point:Before adding capacity, ensure that the right person with the right behaviour is in the right job.

We conclude this section with the consideration of process bottlenecks. The reason for this is that the entire system, even if correctly configured, may be slowed down at a bottleneck point. A bottleneck must be identified and mitigated.

Bottlenecks

A bottleneck is a stage in a process that slows down the entire process. It is identified as the node or stage with the smallest throughput yield – i.e., the least productive part of the process. By identifying a bottleneck and eliminating it, the process speeds up (i.e., is leaner).

However, the manager must exercise caution when trying to locate potential bottlenecks. The reason for this is that it is not uncommon for bottlenecks to reside at nodes that require a higher level of human interaction (e.g., home inspection and value appraisal in the mortgage application process in some markets). This can be a relatively long process and just applying a measure of throughput to identify a possible bottleneck can be misleading since the bank staff may be attempting to reduce the risk of adverse selection and exercise due diligence.

In addition, experience shows that apparent bottlenecks can arise later in a process because of errors that have been created in the prior stages, only to be corrected at a later time. This extra correction time may falsely deem this node or person to be a bottleneck.

Lesson 3

In order to reduce the risk of falsely identifying a bottleneck, ensure that the bottleneck is not correcting errors from previous stages in the process.


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Identifying a Bottleneck

To recognise a bottleneck, a value stream methodology is recommended.

Value Stream Mapping

A value stream map is a visual representation of the banking process from beginning to end, showing what work is being performed at each node or step. At each step, data on the resources used, the throughput yield, turnaround time, and frequency and severity of complaints are collected.

The objective is to visualise the entire process and cross-linkages, as well as local problems at each node. Most importantly, bottlenecks may be discovered, non-value-added work may be identified and general sources of inefficiencies determined. This is a great first step in the LSSprocess.

The process may be viewed as a collection of nodes and hence there is effort to identify the time between the arrival time at the node and the departure time. This is sometimes called ‘time to complete’ (TTC) a task at a specific node.

In another approach, the expert can calculate the full cost of completing a task at a selected node. This is sometimes called ‘cost to complete’ (CTC) at each node so that high cost nodes are identified.

TTC at each server in the process has the potential for greater improvement than CTC. The justification for this conclusion is the observation that most costs at each node are largely fixed ‘people’ costs. The impact on CTC can be bigger if the node is eliminated in a reconfiguration of the process.

The implementation plan for Value Stream mapping is as follows:

1. Select a process for analysis.2. Map the current state of the process and calculate the throughput rate for each stage. The

map shows detailed information on performance (e.g., throughput rate) at each node of the process.

3. Propose a reconfiguration of the process with all identified waste removed. Map the future state of the revised process.

4. Plan and implement future changes and recalculate throughput rate periodically.

Remember that value stream maps identify the source of waste and not why it has occurred. The LSS expert will likely face opposition if the alleged waste is a sacred cow of a power-holder.

Lesson 4

Value Stream Mapping identifies waste and Little’s Law shows the effect on average completion time.

We now consider the effect of Six Sigma strategies in affecting CT.

Six Sigma is a strategy that aims for consistency in the delivery of (banking) services. Variationin service delivery may create uncertainty or anxiety for the customer. While a successful leanstrategy eliminates or minimises non-value activities or waste, variation creates risk*. The goal of Six Sigma is the reduction in variability that also implies an adherence to the ‘one and done’ resolution principle. Rework not only causes unreliable service, but also affects profitability.

* In investment theory, a measure of total risk is volatility (i.e., standard deviation represented by term, sigma).


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George (2003) states that in service organisations 30-50 percent of costs are caused by slow speed and rework.

108.4: Reliability and WIP

This graph shows that as the reliability of service time increases (equivalently, as consistency increases), the work in process (WIP) decreases. By Little’s Law, this means that the average completion time decreases.

The Ford Motor Company is probably a shining example of the application of an extensive Six Sigma programme, which is reflected in their tag line: “Quality is Job 1”. The following is an excerpt from the company’s 2000 annual report, showing the benefits of Six Sigma in terms of consumer satisfaction and cost reduction:

“In the past year we launched Consumer Driven 6-Sigma, a scientific, data-driven process to uncover the root cause of customer concerns and drive out defects. We now have more than 1,800 full-time, trained problem solvers called Black Belts leading customer satisfaction team projects. The 215 projects they completed last year have already saved us $52 million, and will save us more than $200 million in the next two years. More importantly, these projects made a real difference to our customers by eliminating high priority concerns and increasing satisfaction.In addition, Consumer Driven 6-Sigma is changing the culture within the company.”*

It may be argued that Six Sigma and its objective of minimum defects per million is more appropriate for manufacturing processes than banking processes with people intervention. It is well recognised that banking services are naturally delivered by people and hence exhibit some degree of variability.

Accordingly, issues like rework and the ‘one and done’ principle are more related to people than mechanical processes. Hence, the important objective of placing the right people in the right jobs may have a bigger impact on addressing the costs of rework and variability in service times. Therefore, we view the ‘people-function’ fit as key for banking in achieving similar results to Six Sigma for manufacturing processes.

* Source: Annual Report (2000).


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Chapter 4: Theory of Constraints and Associated Metrics

The last chapter brought out a key imperative for banking operations. To optimise operational costs and so to improve profitability by lowering the overall cost-income ratio, management should direct its efforts to the few sources of the greatest delays in the system. This is the basis of the Pareto Principle.

But, while the manager must consider making localised changes, the process must be viewed as a whole. By looking at the entire bank process end-to-end, the manager will be able to see the greatest source of non-value activity. This is not a myopic point of view. A system’s productivity is not increased by improving the already well-performing parts or nodes. The overall efficiency of a process is limited or constrained by the weakest link.

Only by improving the weakest link will the overall process efficiency be enhanced. This is the basis of the ‘Theory of Constraints’, developed by Eliyahu Goldratt.* The philosophy is that management should focus on the most constraining part of the process and restructure the process by improving this node or eliminate it. Goldratt proposes a five-step process, which we reference below. But it is worth noting the similarity between the Pareto Principle (the 80/20 rule) and the Theory of Constraints. Both approaches seek local optimisation for the greatest improvement. The theory of constraints is more concentrated and is sometimes called the 99/1 rule. It is not just the 20 percent that matter, as in the Pareto Principle, but the single most important constraining factor. Goal improvement requires that managers deal with this constraint – first and foremost.

The Five-Step Model is described as follows:

1. Identify the constraint (the source of greatest delay or lowest throughput yield).

Throughput yield (for a given time period) at a given node in the process = number of exits/number of entries.

For example, suppose that 10 loan applications are placed on a person’s desk at the beginning of the day. These applications require approval. Suppose at the end of

* Jeff Cox and Eliyahu M. Goldratt, The Goal: a Process of Ongoing Improvement. (Croton-on-Hudson, NY, North River Press: 1986).


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the day, eight applications have been processed. Then the throughput yield for the day is eight processed applications / 10 applications seeking approval = 80 percent.

In the process of loan-application approval that begins with the customer via the front office and then the middle office and finally the back office, we seek to identify the position (i.e., the node) in the process with the lowest throughput yield.

According to the Theory of Constraints, this is the most constraining node in the process and hence the only one that requires the greatest improvement.

2. Decide how to exploit the constraint. This could involve a HR solution where the function is currently not optimally matched with the job function at this node of the process. It may just require a short training session on time management or similar process training.

3. Subordinate all other processes to the above decision. Ensure that the solution imposed on the most constraining node does not negatively affect other parts of the system. It could very well be that an improvement in the throughput yield at the weakest link could reduce the throughput yield at other links.

4. Elevate the constraint (make other major changes needed to break the constraint).

5. If, as a result of these steps, the constraint has moved, return to Step 1. Don’t let inertia become the constraint. That is, be vigilant and watch out for new constraints.

The logic of the theory of constraints, as outlined above, is best understood if we consider the rolled throughput yield (RTY) for a process. Here is how the calculation is done.

Position (i.e., node) in the Process Throughput YieldFirst node 0.99

Second node 0.98

Third node 0.90

Fourth node 0.99

108.5: Illustration of the Theory of Constraints

Note that the third node is the most constraining in terms of process efficiency. To see the effect of the fourth node on the overall process efficiency, we calculate the rolled throughput yield (RTY). This value is calculated as the product of the individual throughput yields. So RTY = 0.99 * 0.98 * 0.90 * 0.99 = 0.86. Hence, the overall productivity of the system is 86 percent. If this example refers to a loan application approval process, then the RTY of 86 percent means that 86 percent of the loan applications exit the approval process without delay. Hence, RTY is a measure of the productivity of the process.

Notice that the RTY (86 percent) of the entire system is lower than the lowest throughput yield of 90 percent. This is always the case unless each node has a productivity yield of 100 percent.

The logic of the theory of constraints is now clear.

Emphasise the worst case and solve it. But note that the worst case is not necessarily the one with the lowest throughput yield – 90 percent in this case. It could be that the person located at this node is correcting errors made in the prior stages in the process. The manager must ensure that this is not the case.

Even if all previous stages in the process are error-free, it is advisable to calculate other process metrics for each node in the process to identify bottlenecks. We recommend that the manager calculates Cost to Complete (CTC) and Time to Complete (TTC) for each position in the process. Generally, a longer time to complete the activity at a position is correlated with a higher cost to


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complete. The manager may choose these process metrics to identify bottlenecks or constraints. But be focused on the worst case. Even if the productive desks in the loan-approval process are increased to 100 percent but the productivity of the third desk remains at 90 percent, then the overall productivity as measured by RTY is 90 percent. It is not worth the effort. But if no action is taken with respect to the productive desks and the third desk is improved to 98 percent, the RTY is now 0.99 * 0.98 * 0.98 * 0.99 = 94 percent. This result is higher than the previous case, where the already productive desks were improved to 100 percent with no action on the third constraining desk.

Lesson (Theory of Constraints operationalises the Pareto Principle)

Identify and enhance the most constraining bottleneck. Identify bottlenecks by rolled throughput yield (RTY) in combination with cost to complete (CTC) or time to complete (TTC).

The last question we consider in this chapter is the effect of improvement in people productivity on overall process productivity. The improvement is obtained by removing costs of complexity and its resulting non-linear increase in non-value-added activities. Hence, local optimisation, according to the theory of constraints, leads to cost reduction, which is a natural result of improvement in productivity. Of course, there are knock-on effects on customer satisfaction arising from fast turnaround of customer requirements, which may lead to increased customer satisfaction/loyalty.*

† So we see that there is an immediate measureable benefit on the cost income ratio (CIR). But recall that for a retail bank, gross profit (GP), which is underlying income (UI) less operational costs (OPEX), is given as follows:

GP= UI * (1 - CIR)

This means that improved process efficiency reduces operational cost without any loss in revenue.‡ But management must be careful about seeking to lower the CIR by aggressively increasing process efficiency. There could be a consequent high level of operational risk.

† This topic is covered in the module called Service Quality I.

‡ The knock-on effects on increased customer loyalty may increase income as well leading to a lower CIR and to a more optimistic effect on gross income.


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Summary

In this module, we emphasised that a retail banking operation can be viewed as a set of processes that are enabled by an IT infrastructure and information databases to deliver financial services to the client. Each process originates with the customer and ends with the customer. In between, each process involves the front office, middle office and back office. Hence, the key to optimal banking operations is efficient processes.

We introduced some concepts from queuing theory – Little’s Law – which is also known as the Law of Velocity, the basis for lean processing, the subject of Operations II (Retail Banking II). This law helps the bank manager to understand that improved processing time may be accomplished by reducing the work in process (WIP). As a corollary, it shows that a dramatic rise in the arrival rate into the process increases WIP, and velocity is reduced. Finally, a higher WIP is typically accompanied by a non-linear increase in non-valued-added work, a source of cost with no reward.

We also introduced the Pareto Principle. This principle is also called the 80/20 rule where, in the case of banking processes, 20 percent of the desks (a figurative analogy for nodes in the process or chain) are responsible for 80 percent of the inefficiencies or delays. This principle directs management to concentrate on the 20 percent of nodes. The Pareto Principle is consistent with Goldratt’s Theory of Constraints, which directs management to optimise locally by zeroing in on the single most constraining node. This may be called the 99/1 rule, based on the thesis that RTY is a measure of process productivity and the RTY for an entire process is lower than the weakest link.

We recognise that trying to reduce cost-income ratio (CIR) will lead to improved financial performance, since a lower CIR increases gross result. But there comes a point where the higher operational risk may enter the equation.

To achieve operational efficiency we recommend:

a) Simplify banking operations by simplifying processes (remove complexity and hence non-value-added activities) for greater efficiency and increased profitability. Audit the process regularly so as to identify bottlenecks (i.e., the most inefficient desk). Remember Goldratt’s fifth step in his five-step model: do not let inertia into the process.

b) Seek the cooperation of human resource management (HRM) professionals in the bank to ensure that the talent of each employee is matched with his/her respective function in the banking operation. This is sometimes called ‘talent management’ and can give rise to increased employee engagement and hence increased productivity. That is, there is an expectation of a higher throughput yield. Some of the causes of employee error and mistakes are high employee turnover, poor management practices and over-reliance on key employees. Talent management is likely to mitigate these problems and hence reduce people errors. Finally, audit the process regularly to identify potential fraudulent activities.

c) Employ IT that is scalable and flexible. Third-party software can be a reliable proven solution but must be a good fit with stated requirements. If it is too complex this is not consistent with optimal banking operations. Finally, develop and regularly test a business-resumption plan.

We also showed that an indiscriminate shortening of a banking process can lead to high levels of people risk and hence operational risk. There is no free lunch in actions to increase the efficiency of banking operations.

Lesson (Do not focus on cost reduction only)

Process efficiency is not an end but just a means to optimise customer-service quality. Cost-income ratio is a consequence and not a strategic goal. Retail banks that aggressively seek to reduce cost-income ratio at all costs drive customer satisfaction downwards.


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Multiple Choice Questions

1. Mortgage applications arrive at a rate of 25 per day and there are 100 mortgage applications at various stages in the approval process. By Little’s Law, the average time to complete one mortgage application is:

a) 0.25 dayb) 4 daysc) 5 daysd) 0.20 day

2. Which of the following statements is incorrect?

a) The Pareto Principle implies that 20 percent of customers create 80 percent of non-value-added activities.b) When work in process (WIP) increases, the amount of non-value activities increases in the same proportion.c) Non-value-added activities add cost but produce no reward.d) Reputational risk is highlighted in terms of other risk types.

3. Risk management, capital and provisioning activities, compliance and ethical issues in a retail bank are typically associated with:

a) Front office b) Middle office c) Back office d) None of the above

4. A retail banking process for opening an account is structured sequentially as follows:

A: Account opened (forms sent by customer)B: Forms received by bank and allocatedC: Customer-review processD: Processing of formsE: Account openedF: Customer informed

The arrival rate from A to B is 95%; B to C is 91%; C to D is 96%; D to E is 96% and E to F is 97%. The rolled throughput yield is:

a) 96%b) 86.8%c) 77.2%d) None of the above

5. Which statement is incorrect?

a) A lean process is closely associated with the implications of Little’s Law.b) Six Sigma actions are intended to deliver uniform customer service.c) The Pareto Principle states that the occurrence of non-value added activities is symmetrical.d) The theory of constraints implies that managers must deal with local bottlenecks.


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6. Which of the following is a more direct measure of operational efficiency?

a) Net promoter scoreb) Cost-income ratioc) Net interest margin d) Net interest spread

7. Which of the following is correct? Administration and management of loan applications is typically associated with the

a) Front officeb) Middle officec) Back officed) Mortgage sales team

8. Consider the process efficiency metrics:

a) Cost to completeb) Net promoter scorec) Time to completed) Rolled throughput yield

Which option is most appropriate to measure process efficiency?

I: a) only II: b) only III: a) and c) onlyIV: a), c) and d) only V: a), b) ,c) and d)

9. According to Little’s Law, a short-term positive effect on average completion time (CT) is achieved by actions to:

a) Reduce throughput rateb) Increase throughput ratec) Increase WIPd) Decrease WIP

10. According to Little’s Law, a long-term positive effect on average completion time (CT) is achieved by actions to:

a) Increase throughput rateb) Decrease throughput ratec) Increase WIPd) Decrease WIP

Answers:

1 2 3 4 5 6 7 8 9 10

b b b c c b c IV d a


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