Get Lean By Removing 7 Wastes Of Accounts Receivable

Jay Tchakarov

“All we are doing is looking at the timeline, from the moment the customer gives us an order to the point when we collect the cash. And we are reducing the time line by reducing the non-value adding wastes.” – Taiichi Ohno, the father of Toyota Production System

While Mr. Ohno was talking about the production line in Toyota, his quote is applicable to any process in a business. Operating with lean and mean teams is a business priority for the accounts receivable departments, as they too deal with challenges to convert orders to cash as quickly as possible. One of the ways to achieve lean operations is by removing wastes associated with business processes. If you look at the lean methodology, these wastes are placed in seven buckets. In this blog, I will draw parallels between a production line and the A/R processes to explain what those seven wastes are and how one would remove them from the order-to-cash (OTC) cycle.

Waiting

In manufacturing, waiting is time lost due to shortage of parts, bottlenecks, breakdowns, and so on. In an accounts receivable process, waiting relates to various bottlenecks, including waiting on analysts to collect backup and obtain approvals. Our customers have reported that credit and collections analysts spend at least 30% of their time retrieving backup documentation. Eliminating wait time becomes critical in freeing up the analysts’ time to clear worklist items that they wouldn’t have been able to address because of time constraints. Automating the backup retrieval helps A/R teams address the waste associated with waiting time.

Inventory

Having excess inventory is detrimental to manufacturers as it ties up capital in a form that is difficult to convert to cash. In A/R terms, inventory is the amount of work that needs to be completed by a deduction analyst and the worklist is the warehouse. Given that the analyst has to clear the worklists as fast as possible, it makes more sense to concentrate on actual invalid deductions than trade promotions, for example. The systems have to be robust enough to categorize deductions into various areas so that the time spent by deductions analysts is better utilized to clear the inventory of “invalid” deductions.

Transportation

In manufacturing, transportation is usually about moving the material, while in A/R, it is about moving information and data from diverse systems and spreadsheets. In a dispute case, spending excessive time retrieving proof of delivery (POD), claims documents, and other backup from disparate systems is one such waste. Deductions analysts spend around 60% of their time in classifying deductions and conducting backup retrieval and only around 20%-30% of their time researching and validating the deduction. Using robotic process automation (RPA) and artificial intelligence (AI) to aggregate data from multiple places, including websites, emails, and customer portals, allows the analysts to focus on making the actual decision rather than collating data.

Overproduction

Producing more items than a customer has ordered is considered over-production in manufacturing. Drawing parallels, over-production for A/R means excessive dunning by collections agents. According to a McKinsey study, 70% of collections calls are made to customers who would have paid without being reminded. Collections analysts spend around 20% of their time sending standardized customer correspondence, which becomes a very costly process to initiate a wasted correspondence. Correspondence programmed for automation would be configured by rules and would circumvent this problem.

Overprocessing

Overprocessing is about doing work that adds no value; in manufacturing, it would be equivalent to adding features that customers don’t want or won’t use. In the order-to-cash cycle, customer correspondence is a classic case of overprocessing. Sometimes collections and deductions analysts provide too much backup that does not impact the case for collections or dispute denial. This leads to wasted time, confused customers, potential delays in payments, and the like. Deductions analysts spend 20%-30% of their time sending standardized correspondence to collect relevant information from clients. Overproduction leads to repeat work and impacts the deductions analysts’ time, which is already limited even as their worklists are long. Correspondence templates help by implementing best practices and providing the right information every time.

Errors/defects

Manufacturing defective parts is very costly for any manufacturer, so eliminating errors/defects becomes extremely critical. Similarly, errors by analysts in a credit review process are very expensive for the company. The credit teams have to make sure that there are no errors in credit reviews and approvals to prevent write-offs and avert risk. Another area that is impacted by errors is the matching of trade deductions to promotions. Automating data entry and standardized workflows helps prevent these errors.

Excess motion

In an assembly line, shop floor managers make efforts to remove every unnecessary motion by people and machines in order to optimize the time to produce an output. Drawing parallels, a collections analyst repeatedly calls customers where:

  • Promise to pay exists
  • Dispute cases exist
  • Dunning notices exist
  • Payment has been initiated

All of this is unnecessary motion, and is both time-consuming and costly for a collections team. Using robotics and AI to have smart worklists that give collections analysts “clean receivables” will prevent excess motion and remove the associated wastage of time.

This article is republished by permission. For more information, visit HighRadius. 


Jay Tchakarov

About Jay Tchakarov

Jay Tchakarov is vice president of Product Management and Marketing at HighRadius Corporation. As part of HighRadius’ executive team, he is responsible for defining HighRadius’ Credit and A/R products and for educating the market about the value of automation and advanced technologies. He and his team work closely with sales, consultants, and customers to make sure the products address critical pain points and provide quantifiable, high-value solutions. Jay has more than 15 years of experience in software development, product management, and marketing, and numerous successful product launches. Jay graduated summa cum laude and received a Bachelor of Science in Computer Science from the University of Louisiana at Lafayette, a Master of Science in Computer Science from the University of Illinois at Urbana-Champaign, and an MBA from Rice University.