Order to cash (O2C), the process by which customers make orders and companies receive payment, is the lifeblood of any business. The concept is simple in theory, but in practice is frequently corrupted by process deviations. Some are unavoidable, but many of the chief offenders are actually quite simple to address.
In the first of this three-part series we looked at on-time delivery as a pillar of the O2C process, and how failure to effect deliveries on time can hinder the successful completion of O2C.
In this installment we’re going to look at another common stumbling block, order rejection. Here, delivery isn’t a factor at all—because orders don’t even get to the acceptance stage.
There are occasions where rejection is a legitimate response to an order, but they are generally few and far between. In most instances, a rejected order results from something which could have been avoided.
It goes without saying that having an order rejected is also extremely dissatisfying for a customer. If one rejection doesn’t make that customer into an ex-customer, repeat instances surely will, so it’s very important that businesses have a clear picture of their order rejections.
But the key question is not “How many orders have been rejected?” but rather “Why are these order rejections occurring?”
Simple interrogations can produce some of the desired information, but nowhere near the level of detail required to begin addressing the issues. Likewise, reference to KPIs will only tell you part of the story, providing little more than the rates of order rejection.
It is in this area that intelligent solutions built on process mining technology excel. A real-time MRI of the operational data pinpoints exactly what the issues are and where in the process they are arising, and automatically recommends corrective action to operationalize process improvement.
Some of the most common culprits in order rejections are simple errors in the orders themselves. Whether the mistake relates to variables such as quantity and delivery dates, or mismatched order codes, the end result is anything from outright rejection to long periods of delay because manual intervention is required to amend mistakes.
Where errors are the principle cause of order rejection, it may be that a further degree of automation is a possible solution. With intelligent process mining-based solutions, areas which would benefit from being automated can clearly be identified.
Order rejection may also be initiated by the customer; they might refuse to accept delivery of an incomplete order, or one that is attached to an incorrect invoice. Something as basic as an unexpected delay in supply time might be enough to cause cancellation by customers who place a high value on prompt order fulfilment.
Another frequent cause of order rejection stems from companies refusing to accept some orders because they have a perceived level of risk attached. This is a complex area. On the one hand, a rejected customer is likely to feel aggrieved and thereby become an ex-customer. On the other, no business wants to extend credit to clients who are probably or possibly not going to be able to pay their bill.
Businesses will commonly create automated limits on order values, preventing the placing of orders over a certain value. They may also have automated credit limits in place which prevent existing customers from adding to their order above a certain predetermined limit.
The idea is sound in principle, but in practice can lead to absurdities in which an existing customer with $99,000-worth of their orders fulfilled encounters an order limit of $100,000. They then find themselves unable to order anything over $1,000 without first clearing their bill in full.
Imagine a restaurant serving your group with starters and mains but then declining to furnish your wine selection until you pay for the food orders. You are unlikely to enjoy the experience or be in a hurry to revisit.
These types of scenarios play out more often than most people realize and can go totally undetected in perpetuity. But creating barriers over such relatively low-value items is an impediment to the practical conduct of business and a deterrent to clients.
Here again, process mining technology comes into its own.
By conducting a thorough examination of the relevant data, process mining finds ways in which a company can improve its order limit policy or, if that limit is operating entirely to the company’s detriment, provide reassurance that it can be scrapped completely.
Rejecting orders on the grounds of credit checks is trickier. It goes without saying that a certain amount of caution is necessary, but excessive caution works against the company by closing the door on revenue which could have been realized.
In an analog world, a high degree of insight was not possible, and so businesses would set a credit bar as best they could. But this meant that many legitimate potential customers were prevented from doing business.
Fast-forward to today’s digital era and the advent of process mining solutions means that companies can get a much clearer picture of their credit check policy and how it affects their potential customer base. Policies can be more flexible and customized instead of one-size-fits-all, unlocking further sources of revenue.
Widespread computerization among businesses and consumers means that the businesses of today hold more data about their operation and their clients than at any time in history. Yet if that data is not properly put to use, its benefit is being squandered.
Process mining technology is the key to realizing that data’s potential, transforming it from raw material into a valuable asset which streamlines the business operation, helps satisfy customers and ultimately creates conditions for greater profits.
In short, process mining offers a new depth of insight into issues commonly faced by businesses, including some that they never even realized existed. Which makes intelligent business solutions—like the one Celonis has built on top of its powerful process mining technology—something no modern company can afford to ignore.