Business Process Services. White Paper. A Framework to Analyze Errors for Effective Business Operations
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1 Business Process Services White Paper A Framework to Analyze Errors for Effective Business Operations
2 About the Author Mahesh Kshirsagar Mahesh has a vast experience across various technologies spread over 24 years. He has played a key role in addressing the ever changing demands from the business by architecting state-of-the-art IT solutions. As a key technology leader, he has successfully incubated several high impact solutions, high revenue potential IT Service Lines and Technology CoE(s). His expertise spans across various technology and architecture domains, and while he has been associated with various industry verticals, he has largely been associated with Banking and Financial Services.
3 Errors in business operations often occur as a result of unintentional failures of transactions, resulting in problems for the business and end-customers as well. While most errors may only cause minor delays and inconveniences, a few may cause critical issues. Errors are usually symptoms of issues or failures at different levels the task or the process, the resources, the time of the transaction, etc. But corrective actions usually focus on only one aspect such as the individual tasks, or the associates, or a single transaction. What is required is a comprehensive examination of all such transactions and all errors that are similar. In this paper, we address the need for a systematic approach to reduce errors in business operations and suggest a framework for effectively analyzing the errors. This framework enumerates the various factors that influence the success of business operations and suggests ways to use the results of such analysis for redesigning processes to prevent errors and enhance the quality of operations.
4 Contents Introduction 5 Building a Framework for Analyzing Errors in Business Operations 5 Defining Dimensions of Errors in BPS Operations 6 Reporting the Results of Error Analysis Canned Reports 8 Error Analysis Using Advanced Analytics 8 Descriptive Techniques 9 Predictive Techniques 10 Redesigning Processes to Reduce Errors 10 Conclusion 11
5 Introduction Third party sourcing and global in-house centers for business operations or shared services are an integral part of running business operations today. In fact, a significant amount of operations are being run outside the enterprise today. And this is where there has been significant growth in business process services as well. This growth has been accompanied by increasing expectations of enhanced quality of BPS operations. This fundamentally requires managing service level agreements (SLAs) linked to Right First Time (RFT) and On Time Delivery (OTD). For achieving the desired quality, BPS organizations require a continuous and dedicated focus on eliminating or minimizing errors in business operations. Recurring errors in business operations typically indicate a strong need for enhancing the existing checks and controls deployed for monitoring processes. This also calls for better training and support for BPS associates in order to help them become more efficient and effective. In addition, we recommend building a framework to analyze the errors in their relevant dimensions. Such a framework can provide insights for defining and continuously refining the actions required for error-free management of BPS operations. This paper provides the fundamental inputs and thought processes required to build an effective framework for analyzing errors, and identifies the dimensions of errors that need to be considered. It also suggests how the results from such a framework can be used to make business operations more effective. Building a Framework for Analyzing Errors in Business Operations In order to build a framework that is comprehensive and effective, it is important to gainfully capture the relevant dimensions of the errors that occur in business operations. It is equally important to keep in mind the uses of those dimensions, and then gather the right data points associated with each of them. Such a framework can also be used to design canned reports to improve understanding of the nature of errors and the location of their occurrence within the process. In addition, descriptive analytics can be employed for mining error data to look for patterns and causative factors. Predictive analytics can be used to determine the likelihood of errors, and thereby, further strengthen the framework. Finally, the outputs gained from this analysis can be used for process design initiatives targeted at either eliminating or significantly reducing the errors in operations. Figure 1 below schematically depicts the framework for analyzing errors. For transaction data processed incorrectly in business operations (that is, errors), this framework helps to: Identify attributes of errors Link attributes of errors to transactions through transaction ID 5
6 Present a unified view in error analysis data store Generate a set of canned reports Use analytics (descriptive and predictive) to gather insights into causes and error forecasting Get recommendations for process redesign Error Attributes Canned Reports (Health of Operations) Criteria Robustness Error Analysis Data Store Stability Linkage through Transaction ID Transaction ID Transaction Date & Time Operational Error Error Description Type of Error Nature of Error Source of Error Injection Mode of Error Impact of Error Customer Name & Profile Customer s Customer Profile Process Name, Sub-process Name, Task ID Error Correction Time Maturity Risk Sensitivity Profitability Descriptive Analytics (Patterns & Correlation) Focus-Impact of Errors Type of Errors Nature of Errors Approach Factor Analysis Time Series Analysis Predictive Analytics (Error Forecasting) Process Re-design Focus - Impact of Errors Injection Mode of Errors Approach Process Analysis Process Change Simulation Pilot Implementation Solutions Process Transformation Targeted Training Knowledge Codification IT Systems Enhancement Transactions with Error Error Correction Point Focus-Impact of Errors Sources Errors Approach Classification Time Series Figure 1: Suggested Framework for Identifying and Analyzing Errors, and Implementing Process Re-design for Improved Efficacy Defining Dimensions of Errors in BPS Operations The suggested framework is based on the definition of the dimensions of errors in business operations. These dimensions take into account the key influencing factors in a business operations ecosystem. The key dimensions observed across operations can be categorized as follows. 1. Transaction based errors: This refers to the nature of errors that occur in a typical transaction. These include incorrect data entry, incorrect data checking, incorrect decision making and incorrect data transfer. 2. Injection point of errors: This refers to the nature of errors that occur during execution. These errors include incorrect allocation of work to the agent, incomplete creation of work content carried out by the agents under normal and high pressure situations, and errors that occur while sharing data during business transactions. 6
7 3. Nature of errors: This could be due to data error or oversight that occur as a result of agents overlooking data contents, or due to data misinterpretation that occurs due to insufficient understanding, typically in knowledge intensive business operations. There may also be procedural oversight errors on account of not adhering to the standard operating procedure (SOP). 4. Mode of error injection: This refers to the inherent inefficiencies and shortcomings of the underlying systems or processes that lead to errors, and can be classified as: a. Process definition: These are errors that occur due to ineffective definition of a process. b. Training issues: These depend on how well the BPS agent has mastered a process and may arise due to insufficient or in-effective training. c. Knowledge management: These are repeated errors that are not effectively managed. They arise due to a lack of proper knowledge management that can be leveraged to learn from earlier mistakes. d. IT systems deficiency: Inherent shortcomings of the underlying IT system can also introduce errors in a process. 5. Impact of errors: Depending on the area or function that is impacted, these errors can be classified as: a. Financial: Operational loss or penalty levied on errors committed b. Regulatory: Non-adherence to legal rules and regulations that must be followed c. Compliance: Overall non-compliance with operating procedures d. Customer not contactable: Inability to contact the customer within the required time e. Customer dissatisfaction: Inability to meet customer expectations adequately 6. Correction time: This is the turnaround time (TAT) required to correct an error after its detection. Error correction TAT can be rated as high, medium, or low. 7. Error correction point: This refers to the point within the process where the correction has to be made to prevent the error from occurring again. It could either be at the origin of the transaction, i.e. when the transaction is booked or downstream in the transaction cycle. In case the correction point cannot be pinpointed, the error can be classified as an undiscovered error. Undiscovered errors have a higher chance of recurring and going undetected. 8. Process name: This indicates the business process, sub-processes or tasks within processes where the operational error has occurred. 9. Customer profile: This includes the identity of the client for whom the business operations are being executed and the revenues provided by the customer, i.e. high, moderate or low. 10. End-customer profile: This refers to the customer's end-customer and the degree of importance attributed to them by the customer, i.e., extremely important, very important or important. 7
8 Reporting the Results of Error Analysis Canned Reports Once the schema for errors is available, the canned reports can be checked against it to understand the types of errors occurring, where they are occurring, the cause of these errors, and their impact. Typical canned reports are used to assess the health of the business operations as listed in Table 1. Reports Robustness of business operations Description Overall error rates categorized by type and nature of errors Categorized by process, sub process, tasks within processes Categorized by end customer profile Categorized by error correction time Stability of business processes Categorized by sources of errors, by customer, by customer profile, by end-customer profile Maturity of business operations Categorized by mode of injection of errors, by customer, by customer profile Categorized by correction point of errors, by customer, by customer profile, by end-customer profile Risk sensitivity of business operations Categorized by impact of errors, by customer, by customer profile, by end-customer profile Profitable vs. non-profitable business operations Categorized by error correction time, by customer, by customer profile, by end-customer profile Figure 1: Suggested canned reports to understand the results of error analysis Error Analysis Using Advanced Analytics The framework for analyzing business operations errors is best leveraged by performing 'advanced analytics' on the available data. Advanced analytics use the attributes of core business transactions for which the aforesaid dimensions of errors have been specified. Illustrative case: Consider the following illustration for payment processing. In this case, payments are being made from one entity to another, but the payments either reach the receiving entity in the incorrect amount, in the wrong currency, in the incorrect account number, or at the wrong time. Representative fields associated with a typical payment-processing transaction are as in Table 2 below. 8
9 Transaction Fields Transaction ID Transaction date time Originating entity Receiving entity Payment currency Payment amount Bank of originating entity A/c number originating entity Bank of receiving entity A/c number of receiving entity Payment type Payment mode Payment channel Success-failure code Payment failure code Description Reference number of the transaction Date and time of processing the transaction Entity that is making the payment Entity that is receiving the payment Currency of the payment being made Amount of payment being made Identity of the bank of the originating entity Account number of originating entity at its bank Identity of the bank of receiving entity Account number of receiving entity at its bank Reason for payment like cost of goods, fees, commission, utility bills Mode of payment like cash, cheque, account-to-account transfer Channel used for making the payment Indicates success or failure of the payment transaction Failure codes which uniquely represent the reason for failure of payment Table 2: Representative fields for payment processing transactions The transaction ID acts as the unique key here that relates information regarding the various dimensions of BPS errors with the transaction specific data. The rest of the fields given in Table 2 are attributes associated with payment-processing transaction, and analysis of these attributes plays a significant role in performing advanced analytics. Descriptive Techniques The focus here is on leveraging descriptive analytics techniques for mining error data to look for patterns in it. An insight into 'impact of errors caused by customer process' requires an analysis of data patterns and correlation in 'type' and 'nature' of errors to understand possible causes for the errors. Considering the transaction attributes can expand the scope of this analysis to understanding how these attributes are influencing errors. In this case, they are 'payment type', 'payment mode', 'payment channel' and 'success-failure code'. First, patterns in the errors should be identified, e.g. are there more errors appearing when the transaction is in a particular currency or when the transaction is between two specific banks, etc. Simulation techniques can then be used to study the error potential of those processes or sub-processes. Analyzing patterns of occurrence as well as variations across the error characteristics and across the various error attributes captured can help identify error patterns. Techniques like factor analysis resolve these measurements and observations into distinct patterns of occurrence. This can help analyze the interrelationships among the error attributes for providing inputs for causal analysis. 9
10 To gain insights into 'error correction time' the data patterns and correlations between 'type of errors' and 'nature of errors' must be analyzed first, and then they must be linked to transaction attributes. For example, it is likely that some errors are seasonal in nature. These can be studied using time series analysis. Time series analysis highlights an internal structure (such as autocorrelation, trend or seasonal variations) for the data points over a period of time. It studies the error trends, i.e. how errors and error distribution change over a period, or how errors repeat themselves at intervals over time, or highlight cyclical variations in the errors. The pattern of errors related to team competencies is also an important consideration. Agent based simulation can be utilized for the purpose. The processes can be simulated to see whether the error patterns are correlated to the composition and competencies of the team. Predictive Techniques The focus here is on leveraging predictive analytics to measure the likelihood of errors. This requires a deep dive into 'sources' of errors to predict the 'type' of errors that can occur. This can be linked with a 'time' factor so that predictions can also be made on when certain errors might occur. An analysis of data patterns and correlation in 'sources of errors' can predict the probability of errors and the 'sources' linked to those errors. It can also highlight the possible time frame in which these errors are likely to occur. This analysis needs to consider the associated transaction attributes, which in the illustration happen to be 'payment type', 'payment mode', 'payment channel' and 'payment failure code'. For greater effectiveness, this analysis would need to be supported by efficient classification. Classification divides the records into distinct segments or classes. Since classification necessitates that these classes are defined upfront, it can be used for prediction as well. In our illustrative case, payment characteristics such as type, mode, channel, and success failure code can be used to classify errors by 'source'. A time series analysis can be used to extrapolate past behavior into the future and predict when errors are likely to occur. Redesigning Processes to Reduce Errors A study of the relationships between the number and type of errors to other features of the process (such as transaction type, originating entity, etc.) in combination with regression techniques can help predict the number of errors of a particular type. If the features of the process are strongly correlated to the errors, this technique can be used to determine the efficiency of existing processes, and find means of improving and redesigning them. 10
11 Redesigning processes requires a thorough analysis of the 'injection mode of errors' for a particular process so that appropriate changes can be made to that process. Simulations can be run to validate the impact, and subsequently the process can be piloted to collect required data points. This can help a manager of BPS operations make a vetted decision to implement a redesigned process. Process redesign that is linked to the 'injection mode of errors' would typically lead to the following: Identifying process definition level inefficiencies and addressing them Measuring adequacy of training, from the perspective of both content and coverage Codifying knowledge and de-skilling through systemic capture of tacit knowledge Overcoming inherent shortcomings in underlying IT systems as well as design shortcomings Conclusion The ultimate aim of any approach for eliminating errors is to help managers improve the overall quality of BPS operations. The quality of operations can be improved only if the potential for error is identified and acknowledged, and error reduction strategies are built into all aspects of business operations. The framework suggested here requires analysts to examine the various factors that influence transactions in business operations. These are then used to identify points for intervention and formulate appropriate error reducing actions. This implies that the resulting actions will be aimed at multiple levels of intervention, thereby focusing on all factors that influence transactions in business operations. We suggest that business managers use this framework both for assessing the potential for occurrence of errors and for identifying ways to prevent errors in order to ensure improved operations. 11
12 About TCS' Business Process Services Unit Enterprises seek to drive business growth and agility through innovation in an increasingly regulated, competitive, and global market. TCS helps clients achieve these goals by managing and executing their business operations effectively and efficiently. TCS' Business Process Services (BPS) include core industry-specific processes, analytics and insights, and enterprise services such as finance and accounting, HR, and supply chain management. TM TCS creates value through its FORE simplification and transformation methodology, backed by its TM deep domain expertise, extensive technology experience, and TRAPEZE governance enablers and solutions. TCS complements its experience and expertise with innovative delivery models such as using robotic automation and providing Business Processes as a Service (BPaaS). TCS' BPS unit has been positioned in the leaders' quadrant for various service lines by many leading analyst firms. With over four decades of global experience and a delivery footprint spanning six continents, TCS is one of the largest BPS providers today. Contact For more information about TCS consulting services, contact bps.connect@tcs.com Subscribe to TCS White Papers TCS.com RSS: Feedburner: About Tata Consultancy Services (TCS) Tata Consultancy Services is an IT services, consulting and business solutions organization that delivers real results to global business, ensuring a level of certainty no other firm can match. TCS offers a consulting-led, integrated portfolio of IT and IT-enabled infrastructure, engineering and TM assurance services. This is delivered through its unique Global Network Delivery Model, recognized as the benchmark of excellence in software development. A part of the Tata Group, India s largest industrial conglomerate, TCS has a global footprint and is listed on the National Stock Exchange and Bombay Stock Exchange in India. For more information, visit us at IT Services Business Solutions Consulting All content / information present here is the exclusive property of Tata Consultancy Services Limited (TCS). The content / information contained here is correct at the time of publishing. No material from here may be copied, modified, reproduced, republished, uploaded, transmitted, posted or distributed in any form without prior written permission from TCS. Unauthorized use of the content / information appearing here may violate copyright, trademark and other applicable laws, and could result in criminal or civil penalties. Copyright 2014 Tata Consultancy Services Limited TCS Design Services I M I 07 I14
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