It’s a popularly quoted statistic that supply chain inefficiencies can waste as much as 25% of operating costs, which only goes to show how much an impact you can have on your bottom line by working to reduce waste. This is, of course, easier said than done: supply chain waste comes in myriad forms and is notoriously difficult to root out. Why? Because every decision you make across the entire value stream has the potential to introduce unforeseen costs down the road.

Remember when you were in school? No matter the class, every term you got a syllabus for each class that laid out when exams, quizzes, and term papers were all due? Then you set about working each syllabus into your own personal calendar, with short-term items like quizzes, mid-range ones like exams, and the long-game term papers for each class. There’s a similar way to look at your production planning schedule, with short-, mid-, and long-range goals and KPIs. Long-range is handled by your annual strategic plan, mid-range duties fall to S&OP, and today we’re going to dig into the short-range process of S&OE. Specifically, we’re looking at how to know if your sales & operations execution process is successful or not.

We all have different ways of getting a handle on our supply chain activity. Some folks might check a series of KPIs every morning to see what small fluctuations in supply and demand have occurred overnight, while others might be more interested in the big picture, seeking out a comprehensive visualization of the supply chain at the end of every month. However you like to think about and analyze your supply chain data, your routine probably revolves around a dashboard.

Industry 4.0 technology is making its impact felt all along the supply chain as we enter the third decade of the 21st-century. Alongside IoT sensors, GPS trackers, smart pallets, and robotic picking technology, the progress made in supply chain management software has been unstoppable. Whereas once Excel sufficed to layout a strategic plan and track forecasting, today this method is becoming increasingly outdated and outpaced by more collaborative options. These new systems allow for real-time updates and enable real-time collaboration on planning documents by multiple stakeholders at the same time.

We’re surrounded by redundant expressions every day. Close proximity and basic fundamentals spring immediately to mind. Unintended mistake, past history, and plan ahead follow close behind. When hearing the phrase “advanced analytics,” many people jump to the conclusion that this is just another business-speak example of redundant word use. Aren’t all analytics advanced? In truth, the expression has a specific use, particularly in a discussion of data use in supply chain management.

From 2018 to 2019, Gartner’s outlook on Industry 4.0 adoption seemingly became a little less sanguine. It’s certainly not the case that their opinion of the potential of this massive industrial paradigm shift has lessened in any way, but the focus of their Industry 4.0 predictions for 2018 was how CIOs could find useful models of successful digitization, while for 2019 their focus was on dealing with the gap between expectations and reality that numerous industrial businesses are encountering with new technology. Again, it’s not that the outlook on Industry 4.0 itself is any less rosy than it was a year ago, but it seems like we’re reaching the point where real implementation hurdles are beginning to show themselves.

They say that those who don’t learn from history are doomed to repeat it—but in point of fact, relying too heavily on historical knowledge can often be just as bad. History tells us a particular new innovation will never work, or a new strategy will never succeed, and as a result we’re often blindsided when something truly innovative or unusual comes around. This is particularly true in the logistics industry, where changes in the global economy and the nature of supply chain technology are causing an exponential increase in the number of paths that any given cargo might take from producer to consumer.

Is your boss starting to ask uncomfortable questions? Like what your average order cycle time is? Or what the latest shrinkage numbers are? Sounds like it’s time to line up your metrics and develop a solid plan for tracking and reporting to management. 

As the era of Industry 4.0 approaches in earnest, production managers will soon have access to more data and information than ever before. Internet of things (IoT) sensors and RFID chips throughout the production chain will offer real-time monitoring for your planned production programs, just as robust software integration will help you to better understand what’s happening at various other touchpoints on the supply chain. This is exciting, but it can also be a bit daunting. After all, what exactly are you supposed to do with all of that data?

Artificial Intelligence (AI) can refer to a number of things, from machine learning to computer vision, but in general the phrase is used to indicate computer programs that can reason, learn, and problem-solve from data in a way that’s reminiscent of human intelligence. This takes any number of forms, from digital personal assistants like Siri and Alexa to competitive chess playing to autonomous vehicles—each of which involves a slightly different understanding of what AI is and does. For manufacturers, the most pertinent uses of AI are likely going to be the ones that are most heavily focused on gathering insights from large quantities of data—simply because of the sheer amount of information collected and stored by most industrial and supply chain planning platforms. The question still remains, however, of what manufacturers in general and production planners in particular should expect from AI in the coming months and years.