Business Process Services. White Paper. Improving Agility in Accounts Receivables with Statistical Prediction and Modeling

Transcription

1 Business Process Services White Paper Improving Agility in Accounts Receivables with Statistical Prediction and Modeling

2 About the Authors R Rengesh Siva Rengesh Siva has over 14 years of experience in process excellence and quality assurance across the manufacturing and services industry. He has worked on several transformation projects in procure to pay, hire to retire, and invoice to cash processes. Siva is a Six Sigma Black Belt, ISO 9001 lead auditor (TUV), and Lean coach. Amitabha Mandal Mandal has been playing the role of a business finance analyst for more than four years. He has worked in the areas of delivery, content editing, and quality analysis in a career spanning 15 years.

3 Abstract Days Sales Outstanding (DSO) is an important metric for companies as it indicates the liquidity and cash flow situation of the firm, Lowering DSO or keeping it within an acceptable range is critical since its inefficient management can significantly impact the company's financial health. This can, however, prove challenging, especially for large companies with huge cash flows and continuous billing. Often, collections teams are expected to reduce DSO by significant margins without considering the least possible DSO. This results in undue pressure on the team. Besides, a significant reduction in the outstanding value may not be sufficient to achieve the desired quarter end DSO. This paper talks about the key factors contributing to DSO, and how applying a statistical prediction model to arrive at the base level DSO for operations can help in taking focused actions for reducing the overall DSO. We also discuss process efficiency improvement measures to help optimize DSO.

4 Contents Improving Efficiency of Accounts Receivables 5 The DSO Challenge 5 Calculating the Base Level DSO 6 The Benefits of Calculating Base Level DSO 7 Causal Analysis for Higher DSO 8 Making the DSO Count with Analytics 10

5 Improving Efficiency of Accounts Receivables The core collections activities in the invoice-to-cash process include prioritizing accounts by outstanding value, regularly communicating with customers, making collection calls, and resolving disputes. Though there are several software and tools available to organize these activities, Accounts Receivables (AR) teams often ignore some of the key customer-specific aspects in the collections processes. For instance, follow ups for payments are done on regular intervals, without considering the customer's historic payment pattern or a delivery issue in current bills. Inefficiencies in this area can lead to significant delays or failure to collect before deadlines in AR collections. Sometimes, customers are also unhappy with impersonalized and incoherent routines. The efficacy of AR collections can be improved through predictive analysis and modeling. Identifying and taking proactive action on potentially delinquent invoices can reduce the collection time. Furthermore, prioritizing delinquent invoices for action based on the expected time of payment can optimize the utilization of collections resources. The DSO Challenge Traditionally, organizations have worked on collections based on overall outstanding value. Managing outstanding payments is of critical importance to companies. However, a targeted intervention in reducing outstanding for a particular set of invoices or clients, for example, long pending invoices, often does not help lower the DSO. By the time previous invoices are cleared, a new set of invoices and corresponding defaults is added to the outstanding balance as per the business cycle. This issue is not so evident in smaller organizations with batch billing or month end billing systems. However, large companies with huge transactional cash flows and a continuous billing mode find it challenging to achieve the desirable DSO. They struggle with the process even though they are supported by enterprise software applications that help produce large amounts of data and reports. As the organization grows, the operational targets for the collections team are continuously amended. These collections targets are typically calculated from outstanding values. This can often be confusing for the team working on collections since the outstanding values constantly vary. This method of calculation can be flawed and reflects DSO that is higher than ideal in that industry or for a given set of operating conditions. Shareholders and top management in large organizations often see this gap in numbers and expect it to be bridged quickly. Operational inefficiencies further impact the DSO. Table 1 highlights a list of typical operational factors impacting the DSO and the measures identifying these factors. 5

6 Operational factors impacting DSO Typical metrics underlying the factors Timely cash application Percentage of transactions unaccounted for a particular time Delay due to remittance Dispute management Write off value Resources deployed Efficiency in pursuing collections Real time prioritization by value Lag in follow up Inconsistent payment terms Adherence to credit policy Billing issues Accuracy in invoicing Application of rebates and discounts Order processing issues Credit assessment of customers Expiry of forthcoming contracts Most of these operational factors are now measured and reported with the help of advanced applications. However, organizations need to make huge investments in these applications, business intelligence, and regular reviews. Due to macro-economic factors such as rise in interest rates, inflation of raw material prices, recession or liquidity issues such as repayment of debts and unplanned expenses, fluctuations in these measures can cause several complications. Adequate administrative discipline must be exercised to ensure that the cash inflow is not disrupted. Calculating the Base Level DSO Table 1: DSO: Impacting factors and measures Base level DSO is the best possible DSO at which an organization can operate. Expressed in number of days of sales outstanding, it is the DSO value arrived at, when all due collections are received exactly within the credit period. This is the ideal value that an organization must strive for while launching an improvement initiative. Furthermore, the inherent variation of DSO between various organizations or industries is best explained with base level DSO. In a static business environment (constant revenues, same customers quarter-on-quarter), it is possible to calculate this value by simply working out the given formula. However, in a dynamic business situation, an analytical model that uses the data points of the influencing parameters can be used to arrive at the precise value of base level DSO. When calculating DSO, it is important to understand the pattern of fresh invoices and newly defaulted invoices. One of the key business measures that clearly indicate the flow of fresh invoices is the quarteron-quarter growth rate, which, as shown above, is a statistically significant business metric for DSO. 6

7 Globally, DSO is calculated with the following formula, DSO = Total outstanding Sales per day Sales per day is calculated by the trailing twelve month revenue over 365 days. Total outstanding corresponds to the actuals at the end of the year. If this equation is applied to an organization with a flat growth rate, there is no impact on DSO. However, when the organization is growing or even ramping down, the sales per day at the beginning of the year, and the sales per day at the end of the year vary hugely. Hence, for a growing organization, the calculated DSO tends to be higher, and vice versa. The various payment terms agreed upon by different customers, whether 30, 45 or 60 days can also significantly impact an organization's DSO. Though customer payment terms usually follow industry norms, it is important to compare a company's agreed payment terms with the industry average. This is an important step towards computing DSO accurately. If growth rates and patterns in payment terms are not considered for calculating DSO, the published DSO will not reduce at a rate proportional to the mitigation efforts. Clear insights into the DSO configuration can thus reduce the pressure on the collections and operations teams. The base level DSO can be calculated by adopting the customer surface response design model (discussed in the next section). This statistical model is based on the organization's growth rate and payment terms agreed with the customer, assuming that all the payments are made on time according to these terms. This model gives us the benchmark DSO for the organization to work towards. Adopting the Customer Surface Response Design Model for Calculating Base Level DSO The configuration elements in the analytical model for base level DSO prediction include growth rate, revenue credit period mix by value, and the operating range for the organization. The historical quarter on quarter growth rates for at least eight quarters are required to create a predictive model for the base level DSO. The base level DSO is generally correlated to a quadratic relationship with growth rate and the composition of credit periods. There could be a correlation between revenue and actual DSO, at very low coefficients, albeit rarely at practical values of DSO. This is because there is a general assumption that higher the value of collections, the more difficult it is to collect for a particular set of resources deployed. Table 2 shows the calculation of the base level DSO using this predictive statistical model. 7

8 Input data Calculated values Monthly revenue Quarterly growth rate Credit period composition P (30 day) P(60 day) P (60 day) Base level outstanding Trailing 12 month revenue Base level DSO Table 2: Calculation of base level DSO The base level outstanding, trailing twelve month revenues, and base level DSO are mathematically calculated based on monthly revenue, quarterly growth rate, and credit period composition for a select data set. This is done by selecting a few sets of input levels in the entire operating range of values for the organization. If the input factors are identified at different levels covering the entire operating range, the typical regression procedure is not required. This, in turn, would involve multiple iterations and incremental change in accuracy (adjusted R2) of the model. The custom surface response design method helps us to identify the various operating levels and corresponding outcomes quickly, and arrive at an accurate prediction of DSO. Causal Analysis to Determine Factors Resulting in Higher DSO Levels Having arrived at the base level DSO, the relationship between the causal factors contributing to the gap (higher DSO levels) is determined. Figure 1 shows the gap between the actual DSO and base level DSO for an organization. 8

9 90 Days Q1 FY14 Q2 FY14 Q3 FY14 Q4 FY14 Q1 FY15 Q2 FY15 Q4 FY15 Actual DSO Base level DSO Figure 1: Gap between actual DSO and base level DSO The factors contributing to this gap can be categorized as process inefficiencies, delivery issues, and customer default. Figure 2 indicates the areas of process inefficiencies attributed to higher DSO. Live dashboards with visual representation of the Red, Amber, and Green (RAG) status on collections are published for these areas. This helps in prioritization of collection actions. Bill to party verification Credit terms rationalization Prioritization of collections based on RAG status On time application of cash Invoice standardization RCA for disputes On time billing Figure 2 : Process inefficiencies contributing to higher DSO In large organizations, even capturing and publishing performance measures with accountability information can motivate and influence stakeholders to move towards the target. Log analyzer tools in conjunction with ERP can provide real-time dashboards. Analytical insights on overdue outstanding can be represented through a heat map indicating customer behavior, transaction issues, and disputes. Figure 3 depicts an example wherein a few payments were made by customers beyond the credit period. Customers are prioritized for long term corrective actions depending on the value of the delayed payments. 9

10 Lead time vs Credit period Credit period Days Customer A Customer B Customer C Figure 3: Determining collections prioritization based on value of delayed payments Process inefficiencies are managed through improvements in line with lean principles, elimination of non-value added activities, and automation. In the example given in Figure 3, the lead time for collection for high value customers is plotted in a box plot. While customer C makes most payments within the credit period, a good proportion of payments from Customers A and B is beyond the credit period. In this instance, statistical analysis revealed that inefficiency in on-time application of cash and dispute lags contributed significantly to the higher lead time. Making DSO Count with Statistical Modeling The base level DSO that an organization can achieve is derived from their credit term mix ratio and growth rate. With improved processes, a company's performance can be measured as days beyond base level DSO, and compared with peers. Most organizations make the mistake of comparing their DSOs with that of other verticals and domains, thus putting inordinate pressure on their collection resources. DSO is an important indicator of an organization's efficiency and profitability. The model discussed in this paper gives a custom surface response design for predicting base level DSO. This helps the operations team manage the DSO better, set realistic targets for the collections teams, and set practical expectations among stakeholders. 10

11 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. TCS TM creates value through its FORE simplification and transformation methodology, backed by its deep TM domain expertise, extensive technology experience, and TRAPEZE suite of solution accelerators and governance enablers. 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 Business Process Services Unit, visit: ( 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 2015 Tata Consultancy Services Limited TCS BPS Design Services I 04 I 15