Two years into the pandemic, businesses around the world have come to realize just how delicate supply chains can be. Numerous tech contenders have responded with the promise of solving problems for manufacturers and distributors that are contending with these logistical nightmares.
Robots driven by artificial intelligence are perhaps the most popular example of this trend. They’re alluring and easy for the public to visualize. But that doesn’t mean they’re the answer for all operations within the supply chain.
It's software analytics, based on AI, that will prove the most effective solution for factories and warehouses. Their current state of chaos can’t be solved simply by throwing more bodies (or machines) at the problem. AI software designed to see patterns and determine inefficiencies must become commonplace. While not as sexy as an R2D2-like robot performing tasks in the warehouse, the technology will prove to be a force multiplier for the currently stretched-thin workforce.
Deploying AI is also the way forward to maximizing efficiencies across the supply chain, through faster data analysis, continual process optimization, and supply and demand forecasting capabilities. These goals are achieved via two key strategies:
To bring about better outcomes for both individual workers and companies, it’s crucial that companies deploy models based on predictive analytics that are user-accessible. High license fees and complex analytical tooling designed for PhD data scientists create barriers and put the value of AI out of reach for many teams. Now, however, data science software is becoming democratized and universally accessible, tearing down the barrier to entry.
Predictive models reduce the knowledge gap and create a more efficient decision-making process. As a result, both technical and non-technical managers can approach their daily supply chain objectives differently, with a more critical eye on what transpires along the way, as well as previously undiscovered patterns and inefficiencies. By bringing these realities to the forefront, they can combat them with an array of solutions, by brainstorming with teams or utilizing the AI platform to run alternative simulations.
The way forward for supply chains is through more intelligent, data-driven decision-making. Companies can maximize productivity across the operation and acquire a greater sense of the optimal processes. It’s one thing to have a general understanding that a supply chain practice is inefficient. It’s another to have clear-cut analytics to back up those suspicions and drive substantive change.
Tuncay Isik is co-founder and chief executive officer of Prevision.io.
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