On-time delivery (OTD) is the main metric to measure the efficiency of supply chain processes in your organization. It is an indicator of how capable your organization is to meet customer demand in terms of the requested delivery date (RDD). Many businesses create a monthly aggregated average of OTD that gives a false sense of security on how well their supply chain is genuinely performing in the eyes of the customer. Failing to meet your customers’ requests can lead to all sorts of negative outcomes. Worst case? You lose your customers to competitors.
In fact, throughout our daily work we experience a vast array of different interpretations and calculations of the on-time delivery metric. This indicates that customer-centric Outside-In™ thinking is not at the heart of every organisation. Over recent years we have made the following observations regarding the measurement of On-time delivery:
- OTD is simply measured as a ratio of the number of units delivered On-time divided by the number of total units shipped on a monthly base
- It is often not clear how the number of units On-time is calculated, e.g. what exactly is On-time
- OTD calculation reference points are unclear, e.g. are they based on the requested delivery date (RDD), first confirmation date or last confirmation date?
- The shipment dates are used for actual delivery date (ADD), not the delivery arrival date at the customer’s premises (ADD_C)
- Early deliveries without customer approval (e.g. month end pressure) are defined as delivered on time, although they are not!
- OTD is regularly calculated at an order level, not at a detailed order line level
- There is confusion about delivery windows at customer premises
All those observations are symptoms of a distorted view of On-time delivery as the metric of your supply chain performance. Frequently, we see surprised looks when we present OTD results at a transactional level that are not anywhere near the 90% On-time performance that was expected by the management.
Try to calculate your On-time (OTD) Baseline performance, using transactional order line data:
In order to get a clear understanding of your current On-time (OTD) delivery performance that your customers are truly experiencing, you need the shipping date (ADD) and the customer wish date (RDD) for every order position shipped in the time frame you are observing. Let us now build your OTD baseline performance:
- Choose a time frame of, ideally, 365 days
- Define OTD as the difference between shipping date (ADD) and the original customer wish date (RDD) for every order line in the time frame
- If you have the goods arrival date (ADD_C) at the customers premises use this instead
- Calculate your 95th and your 5th percentiles of this distribution or show it graphically on a Probability Plot
- Add up the absolute returned days of the percentiles to your On-time delivery (OTD) this is your Outside-In™ SPAN Metric
So, tell me, what are your percentages of order lines that are exactly on-time? How many are late and how many are early? What is your SPAN metric and what does it tell you? What might be possible root causes for your baseline performance and the SPAN you calculated? What would you do to reduce the SPAN?
You are invited to connect with me and share your thoughts, experience and comments on LinkedIn or firstname.lastname@example.org.
Mike Schmitt is a Partner at R&G Global Consultants in Germany