“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 timeline 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 7 wastes are and how one can remove them from the order to cash (OTC) cycle.
In manufacturing, waiting is time lost due to a shortage of parts, bottlenecks, breakdowns, etc. 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 in backup retrieval. 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 AR teams address the waste associated with waiting time.
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, for example, 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.
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 disparate systems to make a decision is one such waste. Deductions analysts spend around 60% of their time in classifying deductions and conducting backup retrieval when compared to around 20-30% time for researching and validating the deduction.
Using robotics and Artificial Intelligence (AI) to aggregate data from multiple places including websites, email and customer portals allows the analysts to focus on making the actual decision rather than collating data.
Producing more items than a customer has ordered is considered over-production in manufacturing.
Drawing parallels, overproduction 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% 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.
Over-processing 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 order to cash cycle, customer correspondence is a classic case of over-processing. To clarify, 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, etc.
Deductions analysts spend 20%-30% of their time sending standardized correspondence to collect relevant information from clients. Over-production 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.
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. 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.
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:
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.
Those were the 7 wastes in the accounts receivable processes. To learn more, attend the webinar by Colleen Zdrojewski, VP Financial Services, Dr. Pepper Snapple Group on how her team was able to eliminate the 7 wastes in A/R and saved $2M by moving A/R operations in-house.
Alleviating Small Business Credit Risk with Data…
1000s of trade and B2B credit managers leave money on the table by…
How to Improve Cash Flow by reducing…
In the current volatile economy, Cash is the undisputed king! And maintaining a…
SAP Order to Cash: 30 Must-Have Capabilities
This e-book is an actionable summary that drills down on identifying gaps left…
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.