There’s a growing interest in Supply Chain 4.0 technology, especially as logistics professionals cope with stock shortages, port delays, and other challenges. Advanced tech can boost productivity in warehouses and all other points of a product’s journey to its destination. Here’s a closer look at what supply chain management can do for those who invest in it.

Facilitating Productivity and Reducing Worker Strain

Hard physical labor is a regular occurrence for people who have many supply chain jobs. Getting relevant training about how to bend, lift, and avoid strain from repetitive tasks can pay off in helping them stay injury-free and able to give maximum output. However, some companies are also investing in robots to help even more.

In one case, a distributor of adult incontinence products pursued robotic palletizing to streamline its receiving process. An associate begins by scanning a label on a carton to tell the robot a product is on the way that must be unloaded soon. The robot then refers to 20 patterns stored in its memory to decide how to build a pallet based on the incoming items. Once the robot creates the pallet, the goods go to a picking location or storage area.

There are many other opportunities to incorporate robots into Supply Chain 4.0, too. Some autonomous mobile robots bring goods to warehouse workers so those employees don’t have to leave their workstations and take the extra time and energy to replenish what they need.

Other robots work beside supply chain employees, saving them from some of the more laborious or error-prone tasks. Robotic machines excel at duties that require them to do the same movements for hours on end. They don’t get tired and, as a result, can prevent fatigue in humans.

Minimizing Packaging Waste

Supply chain management technology can ensure that each product shipped out of a warehouse has just enough packaging to protect it for the rest of its journey. Packaging has seen numerous user-friendly improvements over time. Creating perforations in materials lets people tear off pieces of cardboard or bubble wrap without using scissors.

Often, these perforations create a clean opening, helping people use the package for other reasons rather than throwing it away. Plus, many food wrap packages have integrated blades that let users cut the foil or parchment at the desired length. These examples show how smart packaging decisions can reduce waste, thereby pleasing consumers and helping manufacturers conserve resources.

However, there’s still room for improvement. Most people can recall occasions where they ordered a small item online and received it in a gigantic amount of packaging. That’s an unwanted outcome for everyone involved. However, Supply Chain 4.0 could make such situations happen less frequently.

Amazon developed a system that uses computer vision and machine learning to determine the type of packaging a particular item needs. The model can detect an object’s size, plus packaging details, such as whether an item is inside a plastic bag or a glass bottle. It also recognizes perforated parts of the package.

When the model has sufficient confidence in the ideal package for a given item, it can select it automatically, which increases efficiency. However, when the confidence level is lower, the system can flag that instance. In such cases, a human reviews the specifics and makes a judgment call. This approach helps Amazon meet its aims to cut down on packaging used. However, it also means items should arrive well-protected, but not overly so.

Achieving Better Visibility With Supply Chain 4.0

Supply chain management can get tricky because it often involves predicting demand based on known factors and making educated guesses about the unknown ones. What makes a certain product highly desirable worldwide while seemingly similar items don’t sell nearly as well? Which steps should supply chain professionals take to avoid long-term outages? Technology can help address those all-important questions.

One study found that artificial intelligence-driven demand planning caused a 50% drop in the product volume affected by extreme forecasting errors. Then, overall forecasting mistakes went down by a third. Those outcomes likely occurred because artificial intelligence can efficiently process large amounts of data and pick up on things humans would miss without technological help.

Decision-makers at computing brand Dell created a digital model of the company’s supply chain to help it cope with the ongoing semiconductor shortage. That tool enables running various simulations so that leaders can plan how to best handle the most likely scenarios.

One way Dell uses the simulated situations is to determine which products will probably become increasingly difficult to source. The company compensates by designing many items with interchangeable or reusable parts as one practical strategy for dealing with current and near-future conditions.

In another case, Unilever unveiled a digital twin that found the optimal batch time by calculating how long it took to produce the necessary quantities of shampoo. Having that data enables consistent production output and helps managers spot bottlenecks within a factory or elsewhere that could cause supply chain strain if left unaddressed.

Measuring Outcomes With Data and Metrics

Supply Chain 4.0 technologies typically don’t give optimized outcomes immediately after implementation. Instead, people in authority must examine the available data and make relevant tweaks accordingly.

Fortunately, that’s becoming easier to do with data analysis tools and sensors that automatically gather data for future review. Perhaps a factory leader hoped to increase weekly output by at least 25% after installing several logistics robots. A platform that collects and analyzes real-time data could show how close the facility is to meeting that goal.

Alternatively, a company may deal with a persistent problem of machines breaking down unexpectedly and significantly hindering the workflow. Connecting smart sensors to the problematic equipment could make it easier for maintenance workers to identify issues before they cause factory shutdowns.

Many decision-makers are understandably hesitant to invest in a lot of Supply Chain 4.0 technology at once. They’d prefer to see evidence of the positive effects of such spending first. Luckily, it’s progressively easier to get it.

A manager could start by calculating the money lost due to equipment failures. They could then measure how much smart sensors save by alerting people to issues before those machines become inoperable. Since so many connected technologies can gather data, they prove whether certain investments provided the efficiency gains people hoped for at the outset.

Supply Chain Management Technology Is Undoubtedly Valuable

These examples show how moving ahead with Supply Chain 4.0 plans could generate impressive results. However, that doesn’t mean people will get those advantages in all cases. They can massively raise their chances of success by considering the biggest supply chain obstacles affecting a business and how advanced technologies could help resolve them.

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Emily Newton is an industrial journalist. As Editor-in-Chief of Revolutionized, she regularly covers how technology is changing the industry.