New technologies—from steam power to modern computers—have been the driving forces in supply chain management since the beginnings of industrialized society. Today, supply chain technology is changing at an exponential rate, providing supply chain planners with possibilities that would have seemed like science fiction even a few decades ago. If you follow the news and trends in SCM, you’ve no doubt noticed that machine learning (ML) is often touted as the next major innovation in this long line of technological evolutions—but what, exactly, is it, and how can supply chain managers put it to use?
Any production planner who has spent time working on non-clocked production processes can tell you that it presents challenges and hurdles that simply don’t exist on assembly lines or in other linear production processes. And yet, for some manufacturing outfits, non-timed production is the best way to maximize their machine and personnel resources while maintaining a relatively flexible and adaptable production environment. How do we reconcile the difficulty of scheduling production in a job shop with the obvious value that it presents for many businesses, and what can that tell us about the future of job shop scheduling?
In February of 2018, popular fast food brand KFC was in the midst of making some big changes to its UK supply chain. They were in the process of switching from Bidvest Logistics to DHL as their primary distributor, while simultaneously streamlining their warehouse system from six facilities serving the country to only one. Anyone who keeps current with supply chain management likely knows what happened next: the restaurant was forced to temporarily close more than 700 of its 900 locations in Great Britain. The reason? Chicken supplies were not reaching the stores.
Imagine you’re a trader on the floor of the New York Stock Exchange. Every morning, you check the prices of the stocks that you’re interested in, and you act on those numbers, not checking them again until the end of the day. Your competition, on the other hand, is using real-time information to inform their trading decisions. Which technique seems more likely to yield a profitable trading strategy? Your knee-jerk reaction is probably that you’re going to lose money virtually every day, because your competition has a more accurate picture of the real financial landscape while you’re using information that’s obsolete virtually as soon as you set foot on the trading floor.
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.
With a name like “intelligent planning,” it’s hard to imagine that many companies would express a strong preference to do the opposite. And yet, despite intelligent planning’s status as a potential value-added proposition with the ability to smooth out production and transport workflows, many businesses have been slow to implement smarter scheduling and operational planning processes. The reason for this is simple: many modern manufacturers are stuck in the past when it comes to data visibility and planning workflows. Production plans created with pen and ink or Excel spreadsheets can never provide the level of agility, flexibility, or transparency that a lean supply chain requires, but many companies’ planning workflows are unable to evolve do to widespread planning silos and shadow IT.
We discuss in great detail on this blog how integrated processes and optimized models result in enhanced operations, increased productivity, and more effective strategic vision. While these are certainly critical and worthy elements of discussion, they are part and parcel to a much larger concern we devote little conversation to: How these various planning and production techniques actually result in a more innovative way of doing business. Because, at the end of the day, a manufacturing company is a business, and a software solution or platform is only as valuable insofar as it helps a business develop and grow.
For example, take the idea of integrated production planning. Such a planning method is a core driver in helping today’s manufacturing companies (especially those in variant-rich industries such as automotive or packaging) not only reduce costs, but also create inroads for revenue generation and growth across the value stream.
It’s been said that we should think of scientific revolutions not as revolutions per se, but as paradigm shifts—meaning that, rather than thinking of the great breakthroughs in 20th century physics or medicine as groundbreaking seismic shifts, we should consider them in terms of reorientations of method and changing understandings of old knowledge. The same might well be said of new developments in industry. The rise of automation, for instance, didn’t do away with the use of manpower overnight. Instead, it led us to reconsider the way we utilize people as resources and the way that we structure processes around manual intervention.
What does this way of thinking mean for how we discuss “the fourth industrial revolution,” i.e. Industry 4.0? Simply put, the tremendous potential benefits of Industry 4.0 won’t happen on their own. Yes, manufacturing as a field will change drastically and factories will become smarter and more reliant on sensors and internet of things (IoT) devices, but companies need to make an active engagement with these changes by learning to rethink their processes and their use of resources across the supply chain. This raises an important question: how can companies make the most of this new paradigm shift?
In chess, players are taught to think at least three moves ahead. Every action in the game has a reaction, which can be predicted only to a certain extent, and each possible reaction must be planned for in order to efficiently execute a winning strategy. If each piece on the board represents mission critical resources and manpower, then your short- and mid-term planning must take a holistic account of the board and the structure of the game into account in order to be certain that time and resources are not wasted.