In one sense, transitioning to a full-fledged Logistics 4.0 system is always going to be more of a challenge than making Industry 4.0 a reality. Yes, Industry 4.0 requires big investments in carefully-chosen IT infrastructure and a strong commitment to visibility and connectivity, but the purview of this technological revolution is mostly limited to the factory floor—i.e. a physical space that you, as a manufacturer, have access to and control over. With logistics, this limit doesn’t exist—instead, you’re trying to gather data from objects that are in motion all around the world, from shipping containers to trucks to the forklifts in your partners’ warehouses.

Let's say you’re a chef at a fancy farm-to-table restaurant. You don’t have a set daily menu for your dinner service, focusing instead whatever fresh produce and other ingredients you can lay your hands on. This gives you a lot of room for creativity, but it also puts a lot of pressure on your supplier relationships. While the chain restaurant down the block places the same order (with small adjustments) through the same produce supplier every week, you need to consider seasonality, brainstorm potential dishes, and have a frank discussion with your supplier about which items in a given week are going to best meet the needs of your culinary mission.

The two most basic concepts in business are supply and demand, but as they play out in something as complex as the modern industrial supply chain they’re anything but basic. To wit, about 70% of supply chain businesses have adopted some kind of S&OP (sales and operations planning) workflow in order to more effectively match demand projections and production/operational plans on a quarterly or yearly basis. Though processes like these are a good start, even they aren’t the be-all-end-all. To wit, nearly two thirds of respondents to a recent survey said they wanted to take steps to improve their S&OP processes.

Studies show that, as of 2018, 73% of companies had at least one application in the cloud. This includes the 47% of companies who are using on-premise servers to handle their ERP, but using APIs to connect to the cloud elsewhere. And it’s not hard to imagine why companies are gravitating away from on-premise and towards more flexible cloud-based options—after all, the upfront costs are lower and the risk of obsolescence or costly maintenance is greatly reduced. Even if you’re still hosting critical functions like your ERP in-house, there are still benefits to be gained from cloud-connectivity in terms of flexibility, analytics integration, and visibility.

Let’s say you’re a kid, and you’re trying to set up a lemonade stand in front of your parents’ house. You go to the store (possibly with parental supervision) to get lemons and sugar, you come home and mix the two into a pitcher, and you set up a little folding table near the sidewalk. Since children are notoriously bad at big-picture thinking, you probably think of the lemons and the sugar as your only real costs, and you price the cups of lemonade (which are set out by the pitcher) accordingly in order to achieve a worthwhile ROI.

In supply chain management, as with anything else, it can often be difficult to see past the hype and figure out which technologies are worthwhile and which aren’t. With things like cloud-based ERP, blockchain-based tracking, AI, and other buzzy new technologies flooding the SCM technology market in recent years, you might find yourself wondering what features and capabilities to prioritize when choosing a software vendor. Do you really need neural nets to analyze vendor performance in the cloud in real-time, or would you be better off leveraging your resources elsewhere?

According to Gartner, by 2023 cloud-based supply chain technology will be worth $11B. And from the looks of it, that might not be far off. Optimism about the cloud is guiding decisions that companies are making with regard to their SCM and ERP technology—with investment in cloud infrastructure continuing to rise across industries. As with any new technology, though, it’s hard to separate the hype from the real value adds. Is the cloud the right choice for your value stream, or is good old fashioned on-premise hosting a safe bet?

As the Harvard Business Review points out, the 2011 Fukushima disaster had a large and unexpected impact on the global supply chain. While most large supply chain players didn’t expect their sourcing workflows to be impacted (based on the locations of their first tier suppliers), they quickly realized that a tremendous number of second and third tier suppliers were being hit hard by the incident. The result was that planners had to scramble to find new sources for raw materials, or risk shortages, outages, and late deliveries.

By all accounts, advanced analytics are becoming a more important part of the manufacturing sector than ever—and it’s easy to see why. By McKinsey’s estimates, the combined effects of using advanced predictive algorithms to proactively schedule machine downtime and prescriptive analytics to optimize machine yields can add up to a whopping 10% increase in gross earnings. How is this possible? Well, for starters, predictive analytics trained to predict machine breakdowns can help reduce machine downtime by up to 50%, which not only improves your production plant’s throughput, it also extends the lifespan of each machine, saving additional costs down the road.

Though the manufacturing sector has traditionally not been the fastest adopter of cloud technology, there’s no denying that the demand for cloud-based apps and services in the industrial world is increasing. One estimate suggests that the cloud manufacturing market could nearly triple in just a few short years, from just shy of $40 billion in 2018 to more than $110 billion by 2024. Based on the way people talk about cloud technology, it’s easy to see why: it’s often cited among the most important drivers of the Fourth Industrial Revolution, and insiders often suggest that it has the power to revolutionize ERP, power improved demand forecasts, and improve operational visibility.