Raw Materials Procurement – Using Predictive OTIF Helps Stay Ahead of Price Increases
Raw materials procurement can get chaotic, so it’s vital for raw material procurement teams to find the best ways to procure raw materials while staying ahead of price increases.
One way they can do this is to use predictive on time in full (OTIF) to avoid unnecessary expenses on materials.
What is On Time in Full (OTIF) and Why Is It Important for New Materials Procurement?
On time in full is a metric that operations managers use to determine whether or not a supplier’s orders have been delivered on time (within the right time) and in full (the customer receives the quantity and specification ordered when they wanted to receive it).
The lead time is often printed on the order; suppliers must deliver the materials by this time. In addition, they must meet the exact specifications as reflected on the order form and the material safety data sheet. So, if a supplier has switched out a part for a different one that they perceive to be equivalent, the order is not considered delivered on time in full.
If the material arrives late, it doesn’t meet the requirements of a proper order because it did not arrive within the right time. Manufacturing processes, especially discrete manufacturing processes, rely on materials to show up on time. According to an ISM survey the manufacturing index rose to 61.2% in May 2021, but shortages of raw material and labor continue to be a problem.
If the order is delivered late, the manufacturing process most likely won’t work or may even shut down entirely. Therefore, lead time is critical for certain materials; consequently, OTIF is essential during raw materials procurement.
How Can OTIF Metrics Help Raw Material Procurement Teams Save More?
OTIF metrics help teams understand whether the quote they received is good or bad
If category managers and their teams can forecast that time in full with a high degree of consistency, they can learn a lot about supplier relationships and predict price increases and save money faster.
But, in many cases, the data that goes into price prediction is historical data related to an exact match with a similar line item. We can compare this to when stockbrokers predict changes in stock prices and how they relate to variance parameters associated with the swing and the price of stock over specific periods—you might have heard of five-year highs and five-year lows.
In raw materials procurement, there are similar durations of time where the purchase price varies for certain materials or commodities, for complex materials and finished goods, and for ingredients and packaging. They are statistically significant enough that if you notice the variance clusters together across certain sets of line items that had similar parameters, like similar quantities and lead times, you might be able to tell whether a price is high or low. You might then be able to, within a particular variance, predict the price.
People always ask, “How exactly do you do that if you don’t have an exact historical match for the item within our historical data?” Well, even if we did have a historical reference, that would not be a good indicator of the price prediction because the price prediction for any item is based on operational and lead time requirements, which might vary dramatically.
Remember, if the same order is delivered late, it’s not the same order at all. So, a price match for that order delivered with a different lead-time would, in many cases, cost a completely different amount.
OTIF Metrics Lead to Greater Customer Satisfaction and Retention
Attaining better OTIF doesn’t happen overnight, and it will definitely cost you time, effort and expenditure. There’s a bigger cost to not getting better OTIF performance however, and if you’re not working toward it, you may be losing more revenue than forecasted.
According to an article about Walmart from 8th and Walton, as of September 2020 a 98% OTIF score is the industry benchmark. When there is a supply disruption that figure could go down to 65%, which is enough to have a supplier removed from Walmart. Any supplier that cannot attain a 98% score would receive a penalty in the form of a charge.
OTIF Helps Predict Whether Supplier Costs Are Increasing So You Can Better Manage Your Strategic Categories
If a supplier has changed their OTIF delivery, you can use that to estimate whether their costs are increasing, especially logistics costs. If they are increasing, this might indicate a relationship between your purchase price variance and your OTIF.
This won’t be true for every category, but if you discover that it is true for one of your major or strategic categories, it can give you important clues on how to manage them. This is how the Arkestro model works when it does price prediction to benchmark quotes and tell you if a quote is high or low.
OTIF Helps You Decide Whether You Should Have More Inventory on Hand
The OTIF indicates whether that category should be ordered with more inventory on hand or whether you should adopt more of a just-in-time approach. If you have more inventory on hand for categories with a higher purchase price variance correlation with OTIF delivery metrics, you can see if their variances are increasing in advance.
In other words, if corresponding decreases in OTIF even out with higher purchase price variances, this means that the variance is increasing and indicates that you can buy more to get ahead of a future price increase. As we previously mentioned, this also helps you understand whether a quote is good or not. Whether you’re getting a good price depends on the price you paid last time and the price you would pay in the future if you waited.
Using predictive OTIF is a critical part of benchmarking savings, and it is often overlooked. The correspondence between purchase price variance and OTIF delivery can help you:
- predict whether there will be major price increases within your supply chain,
- save money, and
- make sure that your supply chain is showing up with all orders on time info.