MHI Solutions Improving Supply Chain Performance
Decision Intelligence Data Platforms Help Companies Optimize Operations
Supply chain executives are bullish on the ability of artificial intelligence (AI) to streamline operations and improve decision-making, but to fully unlock the power of AI, companies need to put a laser focus on their data.
It has been said that data is the lifeblood of AI, as it fuels the ability of AI systems to learn, adapt and make informed decisions. With unified data—the integration and consolidation of data from sources throughout the supply chain—companies can seize new revenue-boosting opportunities and improve key performance metrics. At present, however, most warehouses, distribution centers and manufacturing plants are struggling to achieve end-toend supply chain visibility, limiting their strategic options.
Those findings are from research papers published separately this year by InterSystems and MHI member Blue Yonder. The companies surveyed supply chain executives from around the world and found that most lack access to real-time information that could inform their decision-making.
InterSystems’ survey of 450 senior executives found that 44% rely upon data that’s older than one full day to make key decisions. Another 27% use data that’s up to one full day old, while only 12% have data from within the past hour and 16% use data from within the past five hours.
When asked about their biggest challenges with supply chain technology, 42% of respondents cited a lack of end-to-end visibility and reporting, while 37% said reliance on manual processes for data collection and analysis.
Other challenges included a lack of flexibility in existing internal processes (36%), an inability to sense and forecast demand and supply (31%), a lack of access to real-time data, including changes to customer demand (29%) and disparate or disjointed systems and applications (19%), according to InterSystems.
Without accurate, updated data, companies can’t optimize highly complex supply chains with interwoven dependencies, according to Mark Holmes, senior advisor for global supply chain at InterSystems. By upgrading their data-collection capabilities and investing in AI-powered decision intelligence data platforms (DIDPs), those companies could realize significant cost savings and improved margins, he said.
“We have heard from across vertical sectors that digital transformation efforts in distribution and warehousing are failing due to inaccurate, latent data,” Holmes said. “The ever-increasing number of disparate inventory locations, customer orders and applications in distribution centers, combined with first- and last-mile disruptions at the supplier level, has caused data to become siloed across their entire supply chain, making it impossible to maximize efficiency.”
DIDPs enable businesses to collaborate in real time across functions, supporting agile decision-making, improved customer satisfaction, profitable growth and resilient, sustainable supply chains.
“Unifying supply chain data will allow warehouses, distribution centers and manufacturing plants to consider [supply chain]disruptions and make real-time decisions using trusted data,” Holmes said. “Unified data will allow these organizations to fully implement AI capabilities, which will enable real-time, AI-assisted decision-making that can enhance productivity, ensure accurate delivery schedules, improve customer satisfaction and optimize operational efficiency.”
Technology Upgrades
Blue Yonder’s survey of 600 supply chain executives revealed heavy investment in data collection and AI. The poll found that 79% of companies increased their investments in supply chain operations this year, while only 4% reduced investments. In the United States, 49% of respondents are investing more than $10 million in supply chains this year, up from 38% last year and 24% in 2022.
Generative AI was cited by 45% of respondents as a top priority for supply chain investment, higher than any other technology, with supply chain visibility checking in second at 43%, according to Blue Yonder.
The poll found that 80% of global organizations have piloted or implemented generative AI in supply chain operations. Of those, 91% say it’s effective in optimizing supply chain processes and decision-making. According to the survey, 56% of global organizations are applying AI to supply chain planning, while 53% are applying it to transportation and 50% to order management.
“Companies are realizing the power of AI and generative AI in their supply chains,” said Andrea Morgan-Vandome, chief innovation officer at Blue Yonder. “This technology is changing the way companies plan their supply, react to changes in demand and pivot through disruptions. To truly digitally transform your supply chain means incorporating AI to stay ahead and meet your business objectives.”
In its report, Blue Yonder noted that 85% of U.S. businesses experienced supply chain disruptions in the past year, illustrating the need for greater supply chain visibility. The leading causes of disruptions included a lack of raw materials, cited by 48% of respondents, followed by extended delivery times (47%), a lack of labor (44%), a lack of shipping vessels (41%), changes to shipping routes (25%) and geopolitical unrest (16%).
DIDPs collect and analyze data about these factors and more, and they update their recommendations as soon as the data reflect changing dynamics. The sooner companies can identify possible supply chain disruptions, the easier it is to find a workaround, placing a premium on real-time data, Morgan-Vandome said.
“Supply chain disruptions are still a major challenge for businesses,” she said. “With a majority of global organizations reporting disruptions in the last year, it’s clearer than ever that we need innovative technology solutions to respond to those disruptions and enable businesses to adapt with lasting resiliency.”
A Comprehensive Overview
Unified data involves bringing data from inventory management, production processes, supply chain logistics, quality control, machinery performance, labor and other relevant areas into a single cohesive system. From there, DIDPs can analyze real-time data and make operational recommendations to managers.
Holmes, of InterSystems, said DIDPs can improve operations in myriad ways. For example, warehouses typically schedule more manpower when large shipments are incoming and when order volume is projected to be high. Without up-to-date information, a manager might have no way of knowing that inbound containers from a port are running several days behind schedule, so he should shift that labor to another day.
“Having visibility and accurate data about where products are is very important,” Holmes said. “If you have that data, you can plan for disruptions to your inbound put-away processes, for example.”
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