The supply chain industry has undoubtedly learned a lot throughout the pandemic. If organizations had visibility of manufacturing parts and components, they could be managed and streamlined, but if they were invisible, not much could be accomplished.
Manufacturers today are paying close attention to their siloed data, looking to reap improved production and increased profits. One way to attain these benefits is through the power of artificial intelligence (AI).
There are several ways manufacturers can get a bead on 2023 supply chain challenges by moving forward with cloud-based AI platforms to better improve overall operational efficiencies.
Increase Resiliency: Chief among the critical targets in using AI technology for smart manufacturing is increasing resiliency for your supply chains. As a result, manufacturers can focus on their internal digital transformation and cost reduction, as well as growing their base of customers, to get goods faster and more reliably to customers.
Boost Procurement: In smart manufacturing, AI brings greater transparency to legacy costs with existing partners. AI tools can identify best-performing carriers, understand inventory needs, and stabilize the best available rates.
Optimize Supply Management: Using AI in smart manufacturing helps organizations with supply management. AI tools can provide accurate insights into inventory and operational processes, handle quantities of goods more efficiently, and meet ever-changing stock levels amid changing demand.
Analyze Transportation and Logistics: As fuel costs continue to rise and negatively impact shipping capacity, manufacturers must look to overcome these obstacles. AI can analyze immense data quantities and provide intelligence on how to secure the most optimal logistics and transportation avenues available at the time.
Across the supply and distribution channels, signs point to a gradual post-pandemic easing of pressures around manufacturing delay issues. Companies seeing improvements in goods and component delivery have been looking at one area in particular—available data about their respective inventories.
Various factors are responsible for this uptick in supply chain management. Consumers are increasing demand for physical goods, forcing manufacturers and retailers to catch up. Fitch Ratings noted that congestion at U.S. ports has dropped 80 percent since November 2021. As ports open, they will lead to faster delivery and reduced backlogged orders.
There may still be problematic areas, primarily due to erratic labor capacity and fuel prices across the supply chain. But these recent, positive signs are encouraging to those working in the supply chain management business.
Chief among reasons for easing supply chain issues is that more manufacturers and business leaders have begun using AI-enabled tools to create greater efficiencies in supply chain management. For example, AI and machine learning (ML) work on data to reveal important information, such as duplication or once unfound parts available for shipment. That often means significantly reduced time to production and shipment.
Senior executives are exploring how AI systems and processes can improve with stricter data management, tight automation, and more focused product visibility. For example, one North American industrial distributor, a subsidiary of a Fortune 500 company, faced challenges in managing massive amounts of data with its patchwork of legacy software systems. These systems had been installed in previous decades before the cloud and AI. Due to a recent acquisition by the company and alternate systems in play, the data revealed signs of inconsistency and error. As a result, its inventory data was not optimized to share with its customers.
The distributor needed a single catalog of data with related analytics to improve inventory data quality for its internal warehouse operations. Using an AI-based platform to ensure the integrity of its data catalog, the distributor efficiently managed over 11 million unique parts in under 12 months. As a result, the company experienced immediate business outcomes and better decision-support insights.
For manufacturers to win the supply chain battle, they need more accurate and efficient insights into their inventory and operational processes. A focused MRO (Maintenance, Repairs, Operation) strategy can help companies gain more decisive insights into their data catalogs and move toward a better cost balance and less chance of risks.
AI is cementing itself as an element that drives supply chain resiliency. What this means for manufacturers is that software intelligence through machine learning changes through digital automation, working to reduce material inventory and operations complexity.
A 2022 McKinsey study found that AI-enabled supply-chain management has helped early adopters reduce logistics costs by 15 percent, improve inventory levels, and impact service levels by 65 percent. In addition, a 2021 Gartner report, “Improve Asset Performance by Segmenting MRO Spare Parts Inventory,” shows how companies can segment spare parts inventories against critical business needs. Gaining these insights through an MRO strategy can help improve bottom-line asset performance.
Moving ahead with AI tools aligns with the digital transformation that many manufacturers are already undertaking. Companies that understand how this works will likely ease further supply chain pressures.
By leveraging MRO inventory data for spare parts, companies will achieve tangible and measurable goals in inventory management.
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