New technology enables us to design waste and pollution out of production, procurement and supply chain processes.
Imagine you’re sheltering at home in Southern California and have decided to kick off your Monday by ordering a case of healthy, flash-frozen King Salmon from a company in the great state of Alaska.
The vendor you’ve chosen is known for shipping high-quality fish packed on dry ice and says they can get it to you in just a few days. Sure enough, on Friday, just in time for the weekend, you receive a text saying your order is out for delivery. Great, you think. Nice customer service.
Well, it would have been, had it arrived. As the sun sets, you realize it’s not coming. Concerned, you call the fish company, but everyone seems to have gone home for the weekend. Sigh. Guess you’ll have to be patient and hope the seafood turns up on Monday.
Which it does. To your chagrin, however, it is not at all what you expected. Sitting there on your front porch in 100-degree weather is a soggy box containing warm (completely ruined) vacuum-packed salmon laying in a bed of dripping-wet bags that apparently once contained dry ice. Such a waste.
What happened? It turns out your fish took its normal, predetermined route from the ship to the docks to a truck to a plane to another truck and then to a local warehouse. But when a transport company picked up the seafood for final delivery early Friday morning, its driver ran into unexpected detours caused by several California wildfires. The driver ultimately ran out of time trying to get it to you, the fish sat thawing in the delivery truck over the weekend, and the vendor had to refund your money.
While wildfires do not disrupt supply lines every day, scenarios such as this are common for consumer-packaged goods (CPG) companies. Every year, in fact, the United States spends $218 billion – or 1.3 percent of its gross domestic product (GDP) – to grow, process, transport, and dispose of food that is never eaten, according to a report from ReFed. This waste doesn’t just occur during transportation; it can also happen at the point of production before any items even go out the door.
It’s a serious issue affecting every industry on the planet – as well as the planet itself - since all of that waste, including the plastic packaging it comes in, can end up in landfills. But it’s a challenge that can be overcome by understanding the root cause of the problem and addressing it with cognitive automation.
The main culprit behind this type of waste is the way manufacturers forecast customer demand, make products based on it and then ship batches of those items on timelines and routes that may have been established months beforehand. These materials resources planning (MRP) and distribution requirements planning (DRP) methods are meant to optimize production and enhance efficiency. More often, though, they do the opposite because they are rigid and don't easily adjust to the unexpected.
This is where cognitive automation can be valuable. Tapping artificial intelligence (AI) and machine learning (ML) technology, this software scours vast amounts of data and then indexes, analyzes, predicts outcomes and makes real-time recommendations to augment business-critical decision making.
It can also help companies automate many time-consuming repetitive tasks and decisions pertaining to sustainable manufacturing and supply chain processes, hastening corporate social responsibility efforts.
For example, dozens of companies, such as Nestle, Unilever and Procter & Gamble, have publicly pledged to make all of their plastic packaging recyclable sometime between 2025 and 2030. While it’s unlikely many have completely figured out how to do this, most acknowledge they’ll need to start by being sustainable by design. By committing to a circular economy where all products are engineered from the very beginning to be recycled and returned to production after they reach their end-of-life.
It sounds so simple. But there are hundreds of thousands of variables making it incredibly complex. You are required to precisely forecast demand to know how much inventory to stock and how many items to produce at any given time. You have to know and track the shelf life of those items so they do not spoil sitting on factory or warehouse shelves. And you need to address shifting social, political and environmental factors, and well as other issues affecting or disrupting your supply lines and transportation routes daily.
Let’s face it: human beings cannot accomplish all of this on their own. The data volume is too big, and there are too many decisions involved. This is why we continue to rely on inefficient MRP and DRP methods. But with cognitive automation, manufacturers gain a digital partner that can help them make smarter, more environmentally sound decisions.
Of course, people often worry anytime you begin talking about involving AI in decision making. It’s difficult for many of us to take our hands off the wheel when we’ve been calling the shots for so long.
Cognitive automation doesn’t require you to cede control, however. Rather, it quickly performs a lot of the grunt work we’d have to otherwise do ourselves. And it does this without the irrational emotions and biases that can corrupt our thought processes as human beings. If you determine there are repetitive decisions you want to offload, it can handle those as well. Similarly, if you decide you’d like it to automatically optimize production to eliminate waste and pollution, it can do that too.
Don’t take this sustainability opportunity lightly because it’s actually quite profound. More than 80 percent of consumers – your customers – feel strongly that companies should help improve the environment. A PwC UK study commissioned by Microsoft, meantime, found using AI for environmental applications could contribute as much as $5.2 trillion to the global economy by 2030, a 4.4 percent increase from business-as-usual. At the same time, the study indicated AI “levers” could reduce worldwide greenhouse gas (GHG) emissions by 4 percent in 2030, equivalent to the annual emissions of Australia, Canada and Japan combined.
The end goal should be to discard aging ERP and CRM systems and let computers shoulder most work, guided by you. To reach the point where, instead of forecasting and planning on monthly cycles, you’re able to do that weekly, daily or even hourly. Moreover, to design waste and pollution out of production, procurement and supply chain processes so it becomes ingrained in everything you do.
Nobody likes throwing anything away. With cognitive automation, manufacturers can not only produce the exact items that are really needed, thereby eliminating waste, but get them to their destinations as sustainably as possible without having to concoct a tale to explain why they didn’t.
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