The Race to Cost Savings: Why Part-Level Insights Are the Only Insights That Matter

When we think about the decision abyss and the struggle companies are facing to make sense of all of their data, more information isn’t always better. Information can be a bridge, but it can also be a canyon to get lost within. LevaData recently held an event with the Art of Procurement in which we discussed the importance of part-level insights and how they can improve cost savings and improve sourcing resiliency. Here are my key takeaways from our session on this timely topic.

In recent years, organizations have been gathering an increasing amount of data through different methods. Reports and subscriptions provided an easy way to get information, however this quickly became overwhelming. Subscriptions required multiple log ins, and still, the data remained siloed. All this siloed data created information overload because someone still had to read it all, figure out what it means, and decide what actions to take.

Business intelligence tools such as PowerBI or Tableau have helped, but analysts still had to understand how to pull the data together in a way that provided actionable insights. Even data science wasn’t a panacea because the statistical output was sometimes too scientific for practical applications. In-house software resources built custom applications from their data, but these initiatives still required iteration; ongoing maintenance; and large budgets to support, update, and improve them. None of these solutions were easily scalable or provided quick insights.

That’s where platforms can help, especially when you look at part-level data for direct material sourcing.

Every procurement team understands that part-level data is an important part of any negotiation strategy. Things are no longer as simple as selecting the lowest bidder. You also must consider sourceabilty factors like part lead time, inventory, sourceable alternatives, and the financial condition of the supplier.

Part-specific benchmarks offer a significant advantage over traditional benchmarking that takes a watered-down approach to pricing using averages without considering cost competitiveness or markups. The LevaData platform takes normalized community and market data and statistically compares percentage difference rather than directly comparing different prices. Our part specific benchmark is determined by applying the average percentage difference to a specific lowest distributor price. This level of accuracy and specificity isn’t available through traditional benchmarking procedures. When the process is built into a platform like LevaData does, it can help you quickly highlight groups of supplier parts that can be improved using more sophisticated negotiation techniques.

Additionally, traditional tools lack enough efficiency to enable procurement teams to manage more of their parts spend, so teams often focus on the big-spend items. As a result, cost-saving opportunities on lower-spend parts often get missed. If you’re not monitoring this portion of your spend, you might miss market shifts as prices decline and you will continue to pay higher prices. Also, if you can’t get your parts––even the lower-spend ones you’ve been neglecting–– you might have to shut down your entire line.

What about benchmarking custom parts that are “un”-benchmarkable? With a platform with advanced capabilities, drilling down to the attribute level lets you create a benchmark based on the components and materials that comprise the custom part. These are things you can compare, and this allows teams to optimize spend internally while also providing something that can be benchmarked to peers. This methodology even improves supplier relations because sharing this kind of information leads to discussions about fact based fair pricing which creates stronger partnerships. Ultimately, your suppliers can work with you to optimize part costs and resiliency.

How to get started with part-level insights:

Build better forecasts. With price, lead time, and inventory, you can predict how price is going to change over the next quarter. With data aggregation and contextualization, you can create better insight into your forecasts and avoid sourcing resiliency pitfalls like many have experienced over the past few years.

Focus on high-value sourcing resiliency attributes. Attributes such as cost benchmarks, lead times, inventory levels, and alternate parts will help you understand, compare, and secure the best suppliers to partner with.

Identify commodity parts for which there is lots of information. Chances are good you are not optimally sourcing commodity parts. Start here and once you have a methodology that works, expand to other parts about which there is data.