Order-to-Cash teams are long due for an upgrade!
To avoid irrelevance and to meet the expectations in the new economy, teams must embrace a ‘new playbook’ to bolster
their day-to-day operations.
The Order-to-Cash department has long played a crucial role in helping organizations maintain seamless business ﬂow, avoid unnecessary risks, recover cash tied up in receivables quickly, and much more.
However, in the current economy, as corporations face increasing pressure to reduce costs, improve working capital, and critical metrics like DSO and bad debt, the Order-to-Cash department, like every aspect of the business, is under pressure to demonstrate increased value to the company’s bottom line. Order-to-Cash teams can no longer afford to only focus on preventing bad credit decisions or increasing collection efforts; ﬁnance executives also expect the Order-to-Cash department to strategize and contribute towards their company’s proﬁtable growth.
Teams that realize the shift in the executive perception towards the Order-to-Cash function have the opportunity to command tremendous leadership respect by enabling the cautious and proﬁtable growth of their organizations, even in a volatile economy.
To approach the Order-to-Cash process holistically in the new economy, businesses are now looking to implement various technology solutions in the market that can help overcome the process gaps, increase their teams” productivity, and improve key metrics like working capital DSO, and bad debt.
One emerging pattern in leading enterprise companies is connecting and bringing all their different Order-to-Cash processes – credit, collection, cash application, billing, and invoicing, on a single platform driven by robotic process automation and artiﬁcial intelligence.
In this whitepaper, we highlight the popular trends reshaping the world of Order-to-Cash in the new economy. We also discuss the renewed perception towards the Order-to-Cash department in the eyes of Finance executives and a new playbook that can help one beat market trends and establish a best-in-class order-to-cash management process.
The unexpected economic downturn triggered due to the COVID-19 pandemic in 2020 exposed several weaknesses that have always existed in Order-to-Cash processes. Despite these weaknesses, Order-to-Cash teams worldwide put their best foot forward to continue their operations and maintain their KPIs and goals for the year.
We studied 200+ Order-to-Cash teams to understand how they responded in this situation and handled the crisis. Here were some key observations from our research on the biggest trends that influenced Order-to-Cash in 2021, strategies and solutions used by teams to combat these trends, and how they will continue affecting the Order-to-Cash departments in the present decade.
In 2020, credit teams became more vigilant as there was a need to analyze more information and tighten controls on extending credit limits. This is how teams responded:
Six-fold Increase in Frequency of Credit Limit and Risk Class Changes:Most credit departments responded by tightening control on extending credit limits and increasing the number of credit reviews they conduct each month. The number of credit limit changes made by a credit department in a month, on average, increased six-fold. These risk class changes were mostly in the same direction: the number of customers in the high-risk category increased by almost 4x in 2020.
Fivefold Increase in Number of Order Lockouts/Blocked Orders
Twofold Increase in the Frequency of Credit Reviews
Twofold Increase in the Need for Latest Credit Data
Twofold Increase in Due Diligence in Conducting Credit Reviews
With a lot of executive focus coming in on “cash,” collection efforts became top priority across the ﬁnance department. Collectors and AR specialists tasked with collecting invoices saw the double pressure of balancing the customer relationships with the need to collect receivables as fast as they could. Here are some of the most interesting trends that we noticed:
7.1% Rise in the Average Days to Pay
15% decline in Customers Paying Monthly
21.50% Decline in Payment Commitment Honoring
Twofold Increase in Due Diligence in Conducting Credit Reviews
During our interviews with several Order-to-Cash teams, the most popular pain point shared was that their current processes had many gaps and their existing technology and systems were supportive in handling the challenges triggered by the crisis in 2020.
Siloed Structure of Working Teams
Inflexible Legacy Systems
The fragility of RPA systems and rule-based automation
A recent Gartner report forecasts a 7% YoY growth in companies investing in enterprise technology and automation. However, for O2C the technology options need to deliver on creating a more integrated receivables process, ability to manage global A/R complexity as well as more robust automation through AI and ML and looking beyond RPA.
A ﬂuctuating economy demands more credit data in real-time. The most common and immediate response in 2020 by credit teams has been the reassessment of customers’ creditworthiness.
The months of June-July 2020 saw a whopping 6x increase in the number of credit limit changes done on average by any company.
Credit Management was one of the biggest challenges for the A/R department. With a heavy reliance on outdated paper credit applications that were faxed to the credit department, onboarding new customers or releasing blocked orders was a lengthy and tedious process. Credit reviews were performed with no scoring system in place.
To solve these problems, they used an automated cloud solution. It delivered the following:
World-class organizations have proactive collections teams that are embracing the advantages provided by Big Data, Machine Learning, and Artiﬁcial Intelligence. Utilizing these resources allows them to receive real-time risk signals from public ﬁnancials, credit agencies, and customer payment patterns and to take preventative action to stave off a delinquency event.
Switching to a modern proactive dunning model means taking full advantage of the power of digital technology. In the game of getting paid quickly and on time, using such technology is like receiving valuable silent hand signals from the head coach before your opponent makes a move. Instead of focusing on customers who are already behind on payments, proactive collections is all about having your collections team focus on customers who have a high probability of becoming delinquent and thus can work on changing the outcome.
As companies struggle to ﬁnd more cash, Accounts Receivables executives are under more pressure than ever before to ﬁnd opportunities to reduce operating costs.
The Hackett Group Credit and Collections Performance Study (2019) clearly highlighted the advantages enjoyed by companies that had invested in automation. The study found that the top-performing teams held a strong lead over their peer group across most process metrics. For example, top-performing credit teams employed only one-third as many staff as the peer group and spent only half as much on the credit process as a percentage of credit sales. This was attributed in large part to the fact that top performers had automated 40% of their credit reviews, compared to only a 15% automation rate in the peer group, resulting in less need for manual intervention while increasing the completion rate for new credit reviews. Top performers’ billing process was also more automated, resulting in 75% fewer billing mistakes, so they had much lower dispute-resolution expenses and higher customer satisfaction.
It is clear that automation not only results in freeing up cash by providing managers the ability to reallocate resources across the organization but also in improving process performance and saving time spent in remedial work such as reviewing billing disputes and cash application exceptions.
FIG.2 Accounts receivable KPIs
These differences in automation levels also reﬂect in the overall quality of the receivables portfolio. The same Hackett Group study further concluded that organizations that had invested in automation enjoyed dramatically lower average days delinquent (ADD), at 0.6 versus 8.0 days for the peer group. They also carried ﬁve times less bad debt as a percentage of credit sales. There’s even more bad news for the peer group: Top performers were able to reduce ADD and bad debt levels since 2017, while peers’ scores had worsened.
Accounts Receivable as a function has lagged behind automation and transformation when compared to other enterprise ﬁnance functions including accounts payable, FP&A and treasury. As an example, many organizations still have their AR specialists managing their collection portfolios with spreadsheet-based aging reports and decide whom to contact based on the largest aging amounts. Only one in ﬁve AR teams have some form of automation for the creation of a dispute case from an incoming short payment, and fewer than one-third have automated credit-risk scoring and modeling.
FIG.3 Extent of current automation, by activity
It is no surprise that Order-to-Cash and Accounts Receivable teams have not received a large focus during most digital transformation projects. The typical tendency has been to outsource O2C processes at the earliest possible instance and then enter a cycle of constantly renegotiating performance-linked contracts with BPM (Business Process Management) providers towards lower-cost targets.
However, the fallout from the COVID-19 situation revealed that while offshoring and outsourcing were useful ways to reduce costs, they didn’t necessarily translate into business results such as lower DSO, highly-regulated credit risk and overall customer experience. Pressure to quicken the cash conversion cycle has increased interest in cloud-based digital solutions that provide real-time monitoring/alerts of the total customer portfolio, accurate capturing of details from bad-check images and seamless collection of data from multiple sources. As conclusion, investing in function-speciﬁc technology solutions can deliver both efﬁciency (lower costs) and effectiveness (better business results) gains much faster than other approaches, such as building bolt-on solutions for the ERP system, or simply outsourcing the AR problem and hoping it will solve itself.
A Covid-19 Response Poll (April 2020) found that, despite the recession, almost all ﬁnance organizations are powering ahead with digital transformation initiatives already in ﬂight, and some are even accelerating them. Even more encouraging, 64% are launching select new digital projects. It helps that 77% of CIOs responding to the poll reported they plan a moderate or signiﬁcant increase in technology investment.
High-performance culture drives better business outcomes — this could be in the form of hitting annual sales targets for a business team, or keeping DSO under control for the A/R team.
Business is like a sport – and for a consistently high-performing team, the most essential components are the right players, the right playbooks and the right player analytics. Most companies spend a lot of time in their recruitment process to ensure that they are picking the right players. Then they spend a lot of time creating detailed SOPs and deﬁning the guardrails for people to do the work. But what they miss out on is the importance of player analytics. Think about a football match with the coach giving real-time feedback to the Quarterback on the next play. Companies need to invest in systems that can provide real-time, actionable feedback to front-line staff and ensure that their daily work and decisions result in the right long-term business results.
This will require incorporating two levels of KPIs — leading indicators and lagging indicators.
Companies that can institute a system to deliver real-time info to managers and individual contributors on their leading indicators will be able to create more impact on the performance. Further, if a company can make these numbers available in a transparent manner across the board, then it drives the accountability, ownership and long-term high-performance culture.
Our conversations with A/R leaders from 200+ Fortune 1000 companies over the past six months reveal two contrasting truths about the state of Order-to-Cash in this new economy.
The new economy, triggered by the COVID-19 pandemic, was assailed by a series of employee safety concerns and economic shocks. During such turbulent times, most organizations realized that Order-to-Cash teams hold the potential to do a lot more than number-crunching and updating spreadsheets. They can be important drivers in improving major ﬁnance metrics and KPIs like bad debt, DSO, working capital, recovering cash quickly and much more.
However, even though the Order-to-Cash department has a lot of potential just waiting to be unlocked, it is almost a goose chase to try to achieve anything in a complex IT landscape with multiple ERP systems handling your data, heavy paper and excel based operations and outdated credit policies and collection strategies. Order-to-Cash teams are long due for an upgrade!
Today your Credit teams really need a commercially viable solution to enable automatic updates to the credit health of their entire customer portfolio in real-time. Your collection teams need more time in strategizing how to decrease their bad debt and write-offs, so they can’t afford to keep losing days in just identifying which customers to call and write dunning emails, especially as the number of delinquent customers keeps piling up. Your customers need a better experience with fewer touchpoints across credit, cash application, collection and deduction teams. And let’s face it – basic ERP advancements or process improvement tactics are not enough to achieve all this.
To avoid irrelevance and to meet the expectations from Order-to-Cash in the new economy, teams must embrace a ‘new playbook’ to bolster their day-to-day operations.
The months of June and July 2020 saw a massive 6x increase in the average number of credit limit changes undertaken by companies.
During the early months of 2020, credit teams witnessed a higher demand from executives for up-to-date information about which customers are likely to default in making payments.
The short term solution applied by most credit departments worldwide was to pull more ﬁnancial and credit reports, analyze them, and conduct frequent credit reviews. This helped them survive in the short term, however it was very difﬁcult for credit professionals to maintain this strategy in the later months of 2020.
This is because the immediate need for frequent credit reviews means an increase in cost as more credit reports need to be pulled from credit agencies like D&B, Experian, and Equifax. Looking at innumerable credit reports might help you be on top of risks to some extent, but it will also burn a hole in your pocket.
To avoid all this grunt work which is also prone to oversight and human error, credit departments need real-time risk monitoring, done by an AI-powered automated credit management solution, which is also a commercially viable solution that gives you:
In collections management systems, AI is a prerequisite for preemptive dunning. It can assist teams in multiple capacities such as: identifying at-risk customers before they default, generating prioritized worklists, and predicting the date on which a customer is most likely to pay.
It can also identify the appropriate actions on customers at any given point in time and then trigger them; actions such as automated dunning of customers by sending out automated emails, scheduling notices of default by mail, and by making automated phone calls for “touchless” collections.
*Collections payment date prediction: Collection rules and prioritization are based on static customer segments that do not change with time. Artiﬁcial Intelligence and Machine Learning algorithms can help Collections Management become proactive by predicting payment dates at a customer or account level based on past payment behavior and current open invoices.
Having this type of data enables collectors to act before invoices and customers go delinquent or past due. This in turn reduces the cost of dunning activities, and allows a 10-15% improvement on DSO, while also increasing available working capital. It additionally improves collector efﬁciency by letting them focus on difﬁcult customers rather than low-risk ones.
In reactive dunning models, collectors can expand up to 30% of their work time deciding which delinquent buyers to contact and how to contact them. In reactive AR departments, collectors rely on their intuition, skill, and experience to build worklists (prioritized lists of accounts to contact, how to contact them, and when.
Instead of basing the analysis on the best available real-time data, collectors rely on backward-looking static indicators such as Average Days Delinquent (ADD) to prioritize customers to develop their collections strategies.
Collections teams traditionally look at static indicators, such as Average Days Delinquent (ADD) to estimate payment date for a customer and, consequently, to implement dynamic strategies for dunning, however, reliance on this metric has failed to produce optimal results.
ADD is the average number of days that invoices are past due – the amount of time between the invoice due date and the date it is paid. The calculation helps a company evaluate the overall performance of the collections department and its ability to convert accounts receivable to cash, but because this metric is dependent on averages, it’s affected
by extreme values.
Predict Expected Invoice Payment Date
Static data and human intuition are grossly inefﬁcient when matched against the tactics employed by digitally transformed AR departments. Considering that a single collector can be assigned hundreds of thousands of accounts (depending on the size of a company), it’s practically impossible to expect a high level of efﬁciency from the process without the assistance of digital technology.
Automatically Generate a Prioritized List of Customers Every Day Based on AI-Predicted Payment Dates and Improvise Your Dunning Strategies With AI-Recommended Next Steps
Change is the Only Constant in Today’s Digital Age
The dynamic shift from a reactive to a proactive collections process is the biggest advantage of the AI-powered collections management process. Leveraging ML, the collections team could use accurate predictions to enhance collections output and key KPIs such as DSO and the Collections Effectiveness Index. Predicting payment dates and delays boosts efﬁciency as the entire approach is a proactive one where collectors no longer have to wait for defaults and then request payments from them. Instead, historical data is fed into the system to proactively derive the payment date and contact only those customers who have a higher risk of default.
Organizations managing Order-to-Cash across a global setup run into some key challenges as they continue to reﬁne and build their technology stack around their main ERP systems.
The solution is to choose an “integrated approach” to O2C technology. The key attributes are:
Every AR leader is concerned about the ﬁnal impact that the technology has on the business metrics such as DSO or percentage of receivables that is past-due. However, given that close to 70% digital transformation projects fail it is painful for an AR leader to wait for anywhere between 12-16 months before they even start seeing any of these numbers tick in the right direction.
There are several indicators used by AR leaders to estimate the eventual ROI that their digital transformation projects are delivering
While these are good measures, it takes time for the changes being made at a user and analyst level (through automation and best practices) to start translating into cycle-time reduction or DSO reduction. As such, these are what we refer to as lagging indicators.
AR leaders today need to start looking at ‘leading indicators’ that can give them a sense of whether their digital transformation project is trending in the right direction and give them the required early warning signals to take corrective action and keep transformation projects on course to deliver real ROI.
Here are the key leading indicators AR leaders should be looking at:
Adoption Metrics: Your technology will fail to deliver the results if your users are not using the software. For example, if you just deployed technology to automate collections then you should be tracking whether your collectors are using the automated dunning functionality or recording payment commitments in the software. When your teams fully leverage the technology, you are more likely to get real business results. As an AR leader who is responsible for delivering positive ROI from a transformation project – it is your responsibility to ensure that you are closely tracking technology adoption before proceeding to look at other metrics of success.
Analyst Performance Metrics: Once your team has started using the software to its fullest potential, you should start tracking metrics which will help you assess whether the software is resulting in improved performance at the analyst level. Continuing on the example of the collections software, you should be looking at signs of improvement in number of customers being reached out to per day, number of payment commitments recorded, number of dunning notices sent out. Once you see this data trending positively, you can be more conﬁdent of seeing these trends translate into improved lagging indicators such as lower DSO and past-dues.
At the end of the day, as an AR leader, you are interested in technology to enable your teams to get to best-in-class performance. The ﬁrst step towards getting to best-in-class performance starts with peer benchmarking. Now, peer-benchmarking can be a very complicated process – from building parameters and measures for your study, to identifying and surveying a peer group of companies to analyzing that data.
However, technology combined with the availability of public databases of performance data are making it easier for you as an AR leader to not only benchmark against best-in-class but also translate that data into goals for your teams at a process and sub-process level.
AR leaders need to look for performance tracking systems that can incorporate industry benchmarks into user activity as well as management dashboards. For example, what is the best-in-class rate of capturing payments from payment commitments and how do your collectors perform against that benchmark? Or, what is the best-in-class benchmark for outstanding payment days as a proportion of standard payment days and how does your overall AR process stack up against that?
Having an eye on the best-in-class numbers is essential to drive the right set of process changes.
Benchmarking data that is integrated into the workﬂow software used by teams and dashboards used by AR leaders should be high on the priority list for any AR leader looking to drive long-term best-in-class performance from their AR function.
As per BCG (analysis in 2020), there are 7 key processes and 27 sub-processes in an O2C process. These processes run in the background of several departments such as marketing, sales, pricing, contracting, collections, ﬁnance, and customer service. Businesses expect O2C processes to deliver customer satisfaction and bring about cost-efﬁciency. However, the current state of the O2C process entails several manual steps on account of information stored across siloed systems. This increases the processing time of the information. Traditionally, businesses solved their process issues by adopting standalone applications. This creates an environment of siloed operations at the department, data, and technology level, with each department relying on an individual legacy system, having individual core responsibilities and functions, and do not focus on collaboration with other systems. Each department ends up having their own datasets, which might differ from others on how they are collected and stored which hinders automation of processes and adoption of technology. Overall siloed operations lead to operational inefﬁciencies, affecting the customer experience thereby hindering the business growth.
Separate ERPs for different lines of businesses becomes challenging for GPOs to consolidate their reporting. They end up having dedicated teams that extract data separately from each line of business. Furthermore, it becomes difﬁcult to have control over the system. It ends up having a tailor-made control for every system.
Tammy from Martin Marietta highlights that earlier they had to involve the IT team for generating reports from a siloed reporting tool. With the implementation of a reporting tool that sits over all of their databases, it has become easier to get data and mine data faster.
Traditional ERP systems are doing an excellent job at digitizing and storing information. According to Linda from Uber, accessing and analyzing information from ERPs require manual effort. To provide monthly or biweekly reports of business performance on parameters such as expected payments or weekly processed payments, information has to be collected from individual systems and then analyzed; several processes need customization, which is a limitation for the ERP systems. Further, customizing existing ERP systems to manage the workﬂow can lead to further investments. To tackle these issues, current ERP systems should be integrated with cloud solutions that require less funding, seamless connectivity, and accessibility across siloed processes.
Over the years, working dynamics have evolved. From physical proximity considered to be essential for running effective processes to achieving the same efﬁciency while working from home. From running critical processes from highly secure physical brick-and-mortar sites to running them from any geographical location by adopting technology providing the same level of security.
Organizations should focus on building a virtual environment for the future, wherein business can be run from any location or system with no issues of data availability or accessibility and security. Automation should be a part of the core strategy to make processes agile, transparent, and cost-effective. To enable this, running disparate ERP systems will not be an ideal approach. Organizations should integrate ERP systems with the cloud to facilitate global connectivity. Cloud adoption offers the following beneﬁts:
Seamless data availability provides real-time information to customers such as real-time updates on shipments and payments. Furthermore, the ability to transfer data automatically in any format to any device or system, to share details on an order, shipping notice, tracking details, payment information, or remittance advice, improves customers’ experience and empowers them with data and insights to optimize order and payment processes. As highlighted above, the cloud allows smooth data integration across a distributed network, where data is veriﬁed, validated, transformed, and transmitted according to the receiver’s requirements. Overall, the cloud provides a connected ecosystem where relevant users can share data as and when needed.
A cloud-based integrated platform is needed to create a layer of the system of engagement for managing end-to-end O2C processes. A uniﬁed platform is placed above all the processes which ensure a seamless exchange of information, irrespective of geography, process, or business function. It also eliminates the hurdle of integrating ERP systems with cloud solutions. Furthermore, business leaders are also keen to accept an integrated platform to improve workforce productivity, cost-effectiveness, and efﬁciency.
To adopt a platform approach, process integration, customer centricity, and shared responsibility across the process should be at the core of the platform. These must-have imperatives are explained below:
A uniﬁed platform-based O2C process helps SSCs with efﬁcient resource utilization, healthier cash ﬂows and process transparency. Key beneﬁts of the uniﬁed O2C platform are:
Cargill, a global food corporation, with operations in 70 countries, wanted to streamline its O2C process due to existing inefﬁciencies, including limited DSO opportunities, the variability of bad-debt reserves, and high-cost execution. Cargill lacked a standardized O2C process across its business verticals due to which it was facing the following issues:
Cargill faced multiple challenges because of this disparate IT landscape including, but not limited to, the lack of visibility across processes, absence of reliable data for reporting, less control over A/R outcomes, and limited data insight on customer payment behavior. Cargill wanted to centralize and integrate its entire O2C system to ensure end-to-end visibility in credit risk. With multiple business units and ERP instances, Cargill struggled to reduce cost as it worked to improve process efﬁciency and customer satisfaction. The core challenges for the company were limited visibility across the AR process and moving to a single, global integrated platform for managing the receivables across processes. Challenges faced by Cargill in the credit-to-cash process were:
Cargill deployed a solution that integrated receivables from multiple ERPs, maintained a consistent ﬂow of data in the form of a uniﬁed dataset, enabled process standardization and data communication across different BUs, and tracked performance on deﬁned parameters. To overcome business challenges, Cargill implemented a series of third-party ERP accelerators, which enhanced the functionality of the existing ERPs through full integration. Some solutions and their advantages are:
Post the implementation of ERP accelerators, Cargill modiﬁed and reviewed all its business operations through a dedicated master database. At present, it has a one-stop solution that seamlessly converts all its credit into cash.
Whirlpool’s digital transformation showcases a utopian view of how adoption of digital technologies such as cloud and AI, can help in generating business value through O2C activities. However, the adoption of technology alone does not guarantee a successful digital transformation. Businesses often overlook certain critical aspects of the transformation process, which could lead to considerable losses in terms of effort and investments. In fact, the ratio of failure of digital transformations is as high as ~70%.
Tony Saldana highlights that “there are very speciﬁc reliability drivers (and therefore checklists) to improve the success rate of each of the ﬁve stages of digital transformation.” He deﬁned two disciplines or risk factors that are necessary for the successful delivery of each maturity stage.
They are as follows:
The checklist enables organizations to take care of the risk factors, based on the stage they want to leapfrog to. After assessing the stage of maturity and deﬁning the risk factors, organizations need to work on becoming fully prepared to follow a phased framework for their digital transformation.
Most businesses are looking to adopt digital tools and technologies that ensure timely and accurate ﬂow of comprehensive customer data from all departments, a 360-degree view of existing information for all stakeholders, and automation of routine and repetitive tasks such as invoice entry, payment reminders, and inventory database, with an additional layer of AI / ML to generate actionable insights or assist in intelligent decisions. Technology has the potential to offer customized solutions for speciﬁc processes or sub-processes, e.g., it can be used to run a real-time cash application process and bring the process at par with any other process in an organization. It creates an interconnected environment, optimizing all Order-to-Cash operations and allowing these processes to feed into core business decisions. With a single intelligent platform, businesses can overcome all of the concerns associated with traditional systems. This single platform is capable of providing seamless integration across processes while enabling cross-functional collaboration; serving as the top layer of all the processes and systems, overcoming the hurdle of data integration and improving process efﬁciency and customer satisfaction. For long-term success, businesses need to continuously keep benchmarking their processes and technologies and keep upgrading their systems to be future-ready.
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HighRadius Integrated Receivables Software Platform is the world’s only end-to-end accounts receivable software platform to lower DSO and bad-debt, automate cash posting, speed-up collections, and dispute resolution, and improve team productivity. It leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively by using machine learning for accurate decision making across both credit and receivable processes and also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier accounts receivable process to the buyer accounts payable process. Integrated Receivables have been divided into 6 distinct applications: Credit Software, EIPP Software, Cash Application Software, Deductions Software, Collections Software, and ERP Payment Gateway – covering the entire gamut of credit-to-cash.