Manufacturering supply chains involve multiple moving parts making them substantially hard to monitor and manage. This challenge is exponentially harder when applied to MRO materials, and businesses are realizing how costly MRO can be, even if “properly” managed.
As manufacturing operations become digitized, tools such as artificial intelligence (AI) and machine learning (ML) are now important instruments for organizations to tackle supply chain sourcing and MRO management challenges
This article explores how AI and machine learning tools in procurement can optimize MRO sourcing with examples.
Utilizing AI and machine learning for procurement and optimizing MRO
Enable real-time MRO inventory visibility
Most businesses today are still reliant on disparate and outdated systems, restricting the ability to establish real-time visibility of MRO inventory data. Siloed data and slow, manual systems are not flexible enough to respond to operational and procurement needs, making the system rife with inefficiencies.
An AI-powered MRO platform can provide a enterprise-wide view of the entire supply network instead of just a keyhole view of a subset of it. With this in hand, manufacturers can access accurate, MRO analytics in a single platform.
For instance, this unified view provides information regarding how much inventory sits in each storeroom, which location has overages that can be shared, and how many spare parts have critical shortages. All of which help organizations fully understand, prioritize, and resolve critical issues in real-time to avoid unplanned downtime or wasted spend.
Supply forecasting and tail spend reduction
Artificial intelligence and machine learning models can analyze large data sets to yield valuable insights. Historical MRO data such as consumption metrics, BOM details, and price variances can find patterns rife for optimization. This information is then used to construct accurate simulation models to predict stocking policies for MRO materials across the network.
Predictive analytics can detect precise min/max levels to help determine how much inventory you should hold. Instead of holding idle or excess stock, you can not only prevent overstock and out-of-stock situations, but you can also save on costs through spend avoidance or network sharing opportunities, which can now be used in sister plantas facilities.
Advanced analytics shed light on parts that are purchased more often than the others, which allow manufacturers to identify hidden opportunities to consolidate suppliers in order to avoid contract leakage and reduce tail spend.
Based on this data, manufacturers can make informed business decisions instead of guesswork. For instance, these parts can be strategically aggregated to certain plants in order to incorporate a more centralized procurement strategy for MRO.
AI in supply chain sourcing offers data insights through natural language processing
Natural Language Processing (NLP) is an interdisciplinary field that integrates computer science, linguistics, and artificial intelligence to process, understand, and analyze textual data. NLP derives meaning from variated textual data to identify concepts such as duplicate materials, critical shortages, contract leakage, outdated stocking policies and more, all while avoiding outdated data cleanse projects.
A useful application of NLP in MRO materials is to compare SKU IDs, part names, and material pricing. NLP can be used to review critical information regarding key supplier metrics around pricing and lead times. Supply chain sourcing teams can use this information to build strategies for supplier consolidation, contract negotiations, and broader tail spend reduction.
For instance, during the COVID-19 pandemic, many operations and procurement teams were buying up extra inventory due to lead time uncertainty. This resulted in a bloated supplier catelog and unsustainable inventory levels. NLP analyzes existing SKUs to identify excess materials or suppliers they may no longer need to work with. As more supply chain sourcing and procurement teams are tasked with cost reduction initiatives, adopting a solution with equipped with NLP will help them meet company targets much faster than manual efforts ever could.
Manufacturers can’t afford to avoid prioritizing MRO
The MRO supply network is inundated with complexities that are hard to track and monitor. Maintaining optimal inventory levels, tracking lead times, meeting operational needs, and preventing out-of-stock and overstock situations are just a few of the challenging tasks that a maintenance teams and their procurement counterparts must endure.
Cloud-based solutions come with a range of features such as end-to-end visibility across your entire MRO supply network, integrated artificial intelligence and machine learning algorithms that provide valuable insights, and NLP capabilities that can help tackle your MRO problems.
To learn more about how AI-powered cloud-based software can aid in supply chain sourcing, take the self-guided tour today.