Imagine for a moment that you’re at an antiques auction. You’ve scoped out a handful of items that might meet your needs, and you have a strong but flexible sense of how much money you would be willing to spend on any given item. But when the first of your lots is on the auction block, instead of sitting in the auction house, you’re situated at a remote location, watching a live video feed of the proceedings. When you want to place a bid, you have to instruct your representative at the auction house to raise her paddle. Naturally, by the time you’ve done this, the price that you’re acting on is already out of date. As a result, you wind up with none of the items you had hoped for, even in cases where you might have been willing to spend more on them than the price that they ultimately went for.
Imagine a world in which trillions of individual pieces of information are gathered each day to create complex predictions about future supply chain disruptions and events. Extremely granular data on trade markets turns information about the movement of goods and services throughout the globe into cognitive systems with the power to illuminate new possibilities and intelligently predict changes in demand before they occur. While this may sound like science fiction, it’s increasingly becoming a reality as supply chains become more and more integrated with machine learning, artificial intelligence, and big data analytics. By 2020, IDC predicts that 50% of supply chains will utilize advanced analytics and artificial intelligence, and the effects on the global supply chain are sure to be widespread.
Let’s say you’re driving down a winding country road to some remote destination. At first, navigating is easy, but as the sun goes down and your headlights come on it becomes more and more difficult to make sure that you’re driving safely down the correct course. Eventually, night has fallen completely and your headlights provide the only illumination—you have to slow your driving speed, so that if an animal or other unexpected nocturnal being wanders into the path of your headlights you’ll have enough time to stop the car. If there’s stormy weather, the visibility becomes even more limited, and the possibility of an unexpected snafu increases.
According to McKinsey’s estimates, the rise of the Internet-of-Things (IoT) will have more than a $11 trillion economic impact within the next 7 years. Much of this value will come in the rapidly evolving world of connected consumer goods, such as the internet-enabled products that make up the modern smart home, but the impact will also be felt widely in a number of industries, from health care to natural gas production to, of course, automotive manufacturing. We’ve spoken briefly on this blog about the application of IoT devices for tracking inventory usage and traffic patterns, but what impact will this explosion of connected devices have on factory production processes themselves? More to the point, how can you leverage them into meaningful value propositions within your business’ existing workflows.
Advanced analytics continues to be one of the most talked about new advances in supply chain technology. Also known as big data analytics, this increasingly-important tool can increase the power and accuracy of a given company’s predictive forecasts and suggest prescriptive process improvements by analyzing mountains of information that would be impossible to comprehend if they had to be analyzed by hand. While integrating this new technology remains a significant pain point for many manufacturers, it also represents a unique chance (especially in the early days of its widespread adoption) for businesses to gain a competitive advantage and increase revenue. Here are some surprising facts about it:
Let’s say you’re tasked with designing a factory. You need to decide how to integrate various production lines, where to locate specific resources, how to organize space in a way that maximizes efficiency within and between processes, and how to leave room for potential future process changes. The interplay of a complex series of elements and structures will ultimately determine the success or failure of many planned production programs, so your grasp of the interrelations between these elements must be excellent. Once the factory has been established, things become even trickier. If you want to reposition a piece of machinery, for instance, you should know in great detail what processes involve that machine and how those processes will be affected. In short, these are tasks that you wouldn’t undertake without a carefully devised strategic plans that accounts for a variety of modalities.
Imagine you’re living in a smart home. One evening after work you decide to drive to the grocery store to pick up snacks and drinks for an upcoming party that you’re hosting. When you take the road to the market rather than your usual route home, your car sends a notification to the appliances awaiting you at your house. The refrigerator, sensing that you’re low on milk, sends a reminder to your phone to pick some more up. Your dishwasher, tracking your detergent usage over time, estimates that you will run out before you next go shopping, because you usually wait at least a week between trips. Lastly, your car sends a text alert to your spouse, who might remind you of your guests' snack food preferences or other salient details.
It’s long been an open question in the world of business: which is a bigger hurdle, planning or execution? As the global supply chain has become more sophisticated, however, we’ve gotten a wealth of evidence that for the majority of companies, execution is the more frequent stumbling block. In an informal poll a few years ago, Dick Ruhe at Blanchard found that 76% of the more than 300 respondents said that the most common experience at their company was "good planning and poor execution" (compared to just 4% who said "good planning and good execution", 8% who said "bad planning and bad execution", and 13% who said "bad planning and good execution"). Though these statistics don’t speak to supply chain management in particular, they do give an accurate sense of how difficult it can be to put even a well-conceived business or production plan into action.
Imagine for a moment that you’re planning a camping trip with your friends. There are several of you, and the trip will last a few days, meaning that you’re going to have to take two cars and considerable volume of supplies. How do you decide how each car will be filled? Let’s say your friend already has tent poles and fire starting material, so it might fall to you to procure and transport sleeping bags and food supplies. If one car is more fuel efficient than the other, does that change your plans? How will you go about choosing the right route to your destination in order to find the right balance between toll roads and potentially less direct pathways?
In industrial, shipping, and freight forwarding sectors, equipment breakdowns are simply a fact of life. That said, unplanned machine outages or vehicle breakdowns can have wide-ranging impacts throughout a given company’s entire value stream, negatively impacting production schedules, transport routing, and capacity management. IndustryWeek estimates that across the world of manufacturing, as much as $55 billion is lost annually to unplanned maintenance time, with some businesses losing up to $22 thousand per minute of machine downtime—meaning that any solution that can decrease the number of unplanned outages represents a significant value added proposition with the ability to decrease overall supply chain volatility.