It’s a special occasion, so you’ve booked a table at a hot new restaurant downtown and you’re taking a group of your favorite people with you to celebrate.
On arrival things look promising. You’re greeted by the maitre d', shown to a great table and have your drinks orders taken while you peruse the food menus.
But once the food orders have been taken, things soon start going wrong. Some starters arrive, others fail to appear. Some guests receive drinks, others get nothing or get the wrong something they didn’t order. Some even receive their main course instead of their starters.
Insult is added to injury when you attempt to order several bottles of celebratory champagne but are told that you have reached your credit limit and cannot add any more items to the order unless you clear your bill.
That is the final straw. You gather your party, arrange a heavy discount on the bill with the embarrassed maitre d’ and leave in search of a more customer-friendly establishment. On the way, you log onto Yelp and give the place a terrible review.
It’s obvious to even the most untrained eye that this is a badly-run, inefficient restaurant and yet many of its would-be patrons are blissfully unaware that, while not as chronically horrendous, similar inefficiencies exist in their own companies.
In principle, business is a simple concept: one side provides a desired product or service, the other side provides the requisite compensation. The steps which take place between an order being placed and money being received are known as Order-to-Cash, or O2C.
Business operations are routinely hindered by factors outside the operators’ control. Why then are businesses also allowing themselves to be hindered by problems which are inside their control?
In this three-part series, we’re going to take a look at three common problems in the O2C process and what can be done to fix them, starting with on-time delivery (OTD).
It hardly needs explaining that customers expect delivery of goods and services to be on time. The problems with late delivery are obvious, but there are also some instances when early delivery can also be problematic, although these are rarer.
Not only late delivery, but also early delivery can potentially cause problems to clients.
The gold standard of OTD is the delivery of goods or services on the customer’s requested date. Businesses need to monitor this closely: not only is it a key factor in determining customer satisfaction, but contracts frequently impose penalties for late delivery. In some instances, clients may even seek to recover costs for losses they have suffered as a result of failure to deliver on time.
Customers who receive their orders late are much more likely to have a negative perception of their experience with your company. Regular failure to meet OTD targets is a guarantee of customer complaints and bad reviews.
Where such regular failure exists, it is a key indicator of problems elsewhere in the company operation, either in the supply chain or in the processes used to gather and fulfill customer orders.
The avoidance of delivery blocks is a key factor in OTD. Good processes are essential and in practice that generally means a high degree of automation. Whenever manual intervention is required, it negatively affects efficiency, as does any failure to automate a process which could be automated.
That said, automated blocks can also arise, most commonly in the form of arbitrary order limits and credit checks.
For example if a customer is allowed $1,000,000 worth of orders, and they order $1,000,000 worth of goods, everything after that point will be automatically blocked, even if they’re a reputable partner who always pays on time.
Likewise, a company may limit its exposure to bad debt by mandating credit checks on new clients. This is a sensible precaution but can cause problems of its own. An inefficient credit-check process can take so long that potential customers grow frustrated and take their business elsewhere.
Inventory issues are another common cause of delivery block: an item is out of stock or available in insufficient quantity, but lack of awareness meant that the order was accepted and now an explanation must be made to a disgruntled customer.
Efficient monitoring of inventory (and of supplies used in creating and maintaining inventory) will mean less unhappy customers receiving calls and emails explaining why their orders will be delivered late or not fulfilled at all.
The above problems are multi-faceted and can be complex, but they do have a common solution: Process Mining.
In the past, the best practice available was for businesses to retrospectively analyze their conduct and try to work out as best they could where something had gone wrong or could be improved.
But that approach has inaccuracies built-in: it depends on the perceptiveness of the people undertaking it and it practically guarantees that things will be missed. There is not a person on earth who can track and understand activities within hundreds of thousands of ongoing orders simultaneously and then afterwards provide completely accurate analysis of what worked well and what did not.
In contrast, Process Mining is entirely IT-based. All the organization’s relevant data is absorbed, reconstructed and analyzed for trends and patterns via algorithms. A root-cause analysis provides a clear image of where company processes are functioning correctly and where they are not.
Businesses then have a clear picture of their company operation as a series of processes. They are able to identify with complete certainty where changes should be implemented and where they have the biggest impact to improve overall performance and efficiency
Process Mining delivers results which positively impact on-time delivery. In any cost-focused enterprise, that’s a benefit which cannot be ignored.
Southard Jones is Celonis’ VP, Product Marketing. Prior to Celonis, Southard held various executive product and marketing roles at enterprise software companies in the Business Intelligence, Analytics, and Data Science market, including Domino Data Lab, Birst, Right 90, and Siebel Analytics.
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