Let’s say you and a coworker are attempting to find areas of waste in your supply chain. You have a large conference table on which you’ve laid a file that contains all of the transport plans utilized by the company for the past few years. When your coworker hypothesizes that a different grouping of goods would improve fuel efficiency, you need new documents with additional information, meaning that you have to leave the conference room and descend to the basement level where the files are kept. By the time you’ve returned, a new idea has occurred to your coworker, and you have to make a new trip to wherever your files are stored in order to retrieve the necessary information. The result of all this walking to and from the files? Some good cardio, but no plan to speak of.
Each year, topics like big data, advanced analytics, machine learning, and artificial intelligence dominate conversations about supply chain technology, becoming the focus of increasing amounts of speculation as the real power of these technologies becomes clearer and more apparent. As a result, it can be difficult to parse the jargon from the meaningful discourse and the pragmatic analyses from the wishful thinking when deciding how to set expectations for the evolving global supply stream. In the spirit of helping to put all of the hype into context and give supply chain managers the tools they need to look towards an ever-uncertain future, here are a few predictions for the future of advanced analytics:
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 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.
No matter how sophisticated your methods, or how intimate your knowledge of the field, no demand or sales forecast will ever be 100% accurate. Just as supply chain disruptions are simply a fact of life in the world of manufacturing, deviation from a your expected outcomes are unavoidable. Given this state of affairs, you may be wondering if it’s worth expending resources on improving forecast quality. This feeling is understandable, but while there will always be a gap between expectations and reality, the rise of Industry 4.0 has improved our ability to predict future outcomes. With modern IT solutions and business processes, it’s possible to escape the past-oriented planning models of yesteryear (which fail to account for future developments) and drive towards a more future-oriented approach.
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.
Every year, today’s manufacturing companies dedicate time, resources, and personnel to devising planning and production strategies designed to reduce the likelihood of disruptions across each touch point of the value chain. Whether we’re discussing integrated planning platforms, intelligent production sequences, or transport logistics, the ability to react and correct disruptions at the production, inventory, or transportation level depends largely on understanding the kinds of disruptions and how at-risk a manufacturing company is to experiencing each type. Given the interconnected nature of today’s global supply chain and expansive network of production facilities, warehouses, and transportation hubs, there is more opportunity than ever before for manufacturing companies to encounter disruptions or breakdowns at more touch points across their supply network.
However, this also means there is more information available for companies than ever before about the different breeds of supply chain disruptions and the methods companies can pursue to reduce the risk of these disruptions.
We’ve talked in a great length on this blog about the elements of effective global supply chain management and the implications thereof. But while these are important discussions to have as manufacturing companies work to expand their footprint and growth their customer base, at the end of the day the developments in supply chain management only really matter insofar as they add business value for these manufacturing companies. Advancements in procurement, production planning, job allocation, and transportation management must equal enhanced business value for each partner stage in a production network or else these aspects are simply window dressing designed to give the appearance of lean production principles.
One of the most valuable assets manufacturing companies can utilize to increase business value is the idea of sales and operations execution (S&OE). Though something of a recent concept in global supply chain logistics, S&OE is a powerful piece of planning capability planners and managers can deploy to increase the efficacy of their planning and production programs, as well as enhance a number of other critical functions across the value stream such as resource and material procurement, optimized inventory management, and even job shop scheduling and job allocation.