December 10, 2024 Reducing the Fear of Artificial Intelligence

Why AI skepticism might be holding your company back from improving your bottom line.

By Michael Miller, CEO of SourceDay

Current estimates show there are more than 600,000 manufacturing job vacancies in the US, creating an obvious strain at the industry and company level. And regardless of training and reskilling efforts, that level of labor shortage is nearly impossible to overcome in today’s market.

So how do manufacturing companies, and those similar, like distribution companies, close the labor gap? One rapidly emerging option is utilizing Artificial Intelligence (AI) and Machine Learning (ML). These technologies have now advanced to the point that they can accurately accelerate data analysis and human decision-making, paving the way for a leaner and more productive workforce.

A recent McKinsey report shows that nearly three in four organizations have integrated AI tools into at least one of their business functions since 2017. Despite this, many manufacturing and distribution companies remain hesitant to fully embrace AI and ML due in part to a lack of exposure to where these technologies are most safely and effectively deployed. As this is an area I know well, I’m going to dive into the benefits of AI and ML with the goal of dispelling concerns and highlighting the operational and financial benefits they deliver, specifically within the supply chain.

Current hesitancy surrounding these technologies puts manufacturers and distributors at an operational disadvantage. For example, let’s focus on the purchase order (PO) ecosystem. We’ve processed more than $59B in direct PO spend through our platform and the data shows that 52% of PO lines require some level of manual intervention, with 25% of those tasks being high risk. Incorporating the right AI and ML solution has a profound impact on this process. It not only consolidates the POs requiring attention, but enables the prioritization of risk. High risk items are prioritized for attention and low risk items can be automated by the use of configurable, rules-based workflows, generated by historical decision making.

While that is a very real and specific example, below I’ve included three broader supply chain benefits AI and ML bring to the manufacturing and distribution industries.

Advantage #1: Reduce Supply Chain Risks

Supply chains are the heart of manufacturing and distribution and when one facet is disrupted, it creates a domino effect that impacts the operational flow of the business ecosystem. Supplier reliability is a great example of this. When PO on-time-delivery (OTD) percentage drops or fluctuates, production scheduling, inventory, and even revenue forecasting are affected, creating less stability and driving up costs.

Manufacturers and distributors can mitigate these sometimes significant shortfalls by leveraging AI and ML to uncover and detect risks like supplier reliability in real-time. If a supplier’s OTD has been declining or if their deliveries have been historically longer than the required lead time, manufacturers and distributors are alerted, with customer-level recommendations for resolution. Automated capabilities use advanced algorithms that not only monitor for potential issues, they also recommend preemptive resolutions to high-level risks that make their way to the balance sheet, including increased costs of inbound supply orders (POs), excess inventory, and production delays. These are critically important because late shipments are more often caused by issues in the first mile of the supply chain rather than the last.

When it comes to managing supply chain operations, AI and ML put manufacturers and distributors in the driver’s seat as they also flag inaccuracies within purchase orders and out of date information, empowering more human decision-making. Users are quickly exposed to supply chain blindspots, which can wreak havoc on a company’s bottom line when left unaddressed. Through risk mitigation and prediction, AI and ML ensure manufacturers and distributors securely maintain sustainable operations and minimize disruptions.

Advantage #2: Maximize Sustainability Across the Supply Chain

A key component to Industry 4.0 – the fourth industrial revolution – is sustainability. The National Association of Manufacturers reports that 68% of executives within the industry are implementing corporate-wide sustainability strategies within their organization, a statistic that has nearly doubled since 2019. This effort is compounded by a growing list of government ESG-focused policies that require manufacturers to report their output of material waste. And yet, many companies within these industries still struggle to reach their sustainability goals and adhere to policies because of the excessive waste due to overproduction. Kearney, a global management consulting firm, found that top tech manufacturers hold over $250 billion in excess inventory, most of which runs the risk of becoming obsolete.

Manufacturers and distributors can break the cycle of wasting materials and valuable warehouse space on unused inventory by harnessing the power of AI and ML in their supply chain operations. The proper application of these tools is revolutionizing forecasting by utilizing real-time data, previously inaccessible due to manual processes. This advancement leads to accurate demand predictions, boosting supplier delivery performance and reducing overbuying and material waste. As companies of all sizes prioritize sustainability, AI and ML can help them take one giant leap toward reaching their organization goals and complying with government sustainability mandates.

Advantage #3: Boost Operational Efficiencies

Let’s face it – many processes in traditional supply chains are now antiquated and in desperate need of an overhaul. Manually managing inbound supply reliability has a significant impact not only on demand forecasting but sustainability initiatives and the bottom line. After all, companies spend far too much time combing through purchase orders and manually managing supplier inbound supply. The reality is that no matter how diligent a company’s procurement team might be, outdated processes are no longer sustainable or practical. Weaving AI and ML into the supply chain lowers operational costs by turning reactive risk mitigation into proactive risk management, enabling teams to automate low-risk, routine tasks.

In addition, the data insights that AI and ML render enable companies to receive real-time updates on the status of materials in addition to identifying waste that often flies beneath the radar. Manufacturers and distributors can use this information to address potential discrepancies within the supply chain before they impact the entire production schedule and contribute to excess inventory. Every advantage counts these days, which is why leveraging AI and ML in the strategic areas of the supply chain (such as first mile) is becoming a critical factor for manufacturers and distributors in maintaining profitability in the current business market.

Supply chains impact the economy on a global scale and as a result, the margin for error has become non-existent. Manufacturers and distributors are working to deliver their end products faster and more efficiently while also eliminating material waste, which is why implementing AI and ML into their workflows is key to reaping these benefits.

For more information, please visit https://sourceday.com/

About the Author:
Michael Miller is the CEO of SourceDay and has held multiple C-level roles at early stage and emerging supply chain technology companies. Most notably, Michael was COO at fellow Silverton Partners portfolio company Convey Inc for 4 years, overseeing all delivery functions during a period of high growth and through its acquisition by project44.

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