These are challenging times in the global auto industry. Constantly rising production costs and supply-chain delays brought on by the war in Ukraine have combined to lower automakers’ annual sales forecasts. Final vehicle delivery is at the heart of the problem.
In March, S&P Global Mobility cut its global light-vehicle production forecast by 2.6 million units for 2022 and 2023 to 81.6 million for 2022 and 88.5 million units for 2023 due to the Ukraine-Russia war. That’s a loss of some 5 million vehicles!
Consultants at LMC Automotive are even more pessimistic about auto sales in Western Europe. They noted recently that German automakers are shutting down plants due to the Russia-Ukraine war, to the tune of a loss of around 150,000 units in March alone. As a result, LMC drastically lowered its percentage of 2022 auto sales in Western Europe to 3.6%, from an earlier rate of 8.3%.
Such a sharp decrease in sales expectations has had a trickle-down effect on suppliers, partners, distributors and other parties connected across the global auto industry’s supply chain. Auto companies need to retool their operations in new ways. Enhancing supply-chain intelligence should be a top priority, using intelligent MRO optimization to elevate digital transformation.
Steps for Automotive Supply-Chain Companies
Auto manufacturers can make a substantial difference in their business outcomes by undertaking five Maintenance, Repair and Operations (MRO) inventory strategies to achieve cost reductions and do so without increasing risk or disturbing current operations.
Here are the key steps to get ahead:
Leverage decentralized cloud software reaching across all internal systems.
The first crucial step automakers should look at is integrating cloud services into their inventory management strategy. Centralizing data via the cloud allows review of MRO spending and supplies, enabling complete visibility and control of the flow of goods across the organization and interconnected global supply networks.
Cloud-based supply-chain intelligence offers a host of advantages over analog inventory management. For example, it provides visualization tools that provide enhanced analytics around production aspects such as item usage history, optimized ordering to prevent stockout, lower cash spending on standing inventory, improved access to vendor catalogs and performance metrics, and equipment management.
Use available raw data to gain actionable insights into MRO inventory data.
Data is only useful when it’s available and visible among other data it needs to connect with for decision making. For example, much of an auto manufacturer’s MRO inventory management traditionally has relied upon spreadsheets and much slower methods of communication. These systems may store relevant data, but they need to integrate with AI-enabled software that provides a more holistic view into the complex web of parts and components making the business run.
Implement cognitive computing technology with specific supply-chain intelligence to identify duplicated materials and components.
AI-integrated systems that identify and compare existing SKUs can provide methods of advanced inventory data creation and visibility with a significantly diminished probability of error.
AI-driven monitoring of MRO inventory items aids companies in locating and eliminating duplicates by providing increasingly accurate material visibility across the entire enterprise. Additionally, AI helps bridge the gap between past company processes and the present. Eliminating knowledge disparities ensures inventory data stays consistent during team member or company transition.
Use real-time reporting and analytics.
Introducing process automation into regular business processes enables better tracking, planning and management of inventory. These centralized supply-chain intelligence solutions facilitate more accurate organization of materials, data, equipment and supplies.
Using AI-driven automation to track MRO inventory items improves supply and materials plans, allowing the company to predict future needs better.
Optimize inventories on an ongoing basis with deep learning along with human augmentation.
Intelligent inventory decision-optimization adapts to current operational conditions, supplier trends and user inputs. In addition, the integration of deep learning capabilities through AI takes this a step further to predict MRO material needs so that production downtime is minimal.
Achieving MRO optimization requires real-time AI inventory management which enables the tracking of critical performance metrics, such as replenishment lead times and equipment age, to start preemptive maintenance that helps reduce downtime, preserve service levels and lower costs. Read more on how to improve MRO here.
Paul Noble is the founder and CEO of Verusen, an innovator in supply-chain data, inventory and procurement technology.
