The last few years have brought a broader perspective within the supply chain community. We are now more concerned about values and global issues—not just stuff, its number, when and where it should be placed, and how it should be moved. Rather, we often ask our customers what their concerns are, what kind of products should we develop to support human values, and what kind of company and team could we be. As we spend a few moments planning how we supply chainers will respond to our changing world, it seems fitting to begin this discussion with some contemplation.
In eastern philosophy, as well as in mathematics and science, the concept of duality is extremely prominent.1 Duality is living with pairs of opposites, of which both options may or may not really work for us. These are the ups and downs, the plenty vs. scarcity, and so on. Duality is considered a state of false or limited perception and to remove this we need knowledge—clarity about reality.
This duality is supply chainmanagement. We live in a world of duality, constantly making trade-offs between options which often are less than satisfactory with limited information in order to move forward with a plan—reduce cost, but decrease service level; manufacturing OEE vs. inventory/working capital; transportation costs vs. warehouse costs; and so on.
As we enter a new decade, we also want to contemplate the future of technology. Within the media, research and consulting community, there are the usual outsized proclamations declaring the impact of the new trend or technology. In the world of AI and machine learning, we have some predictions declaring close to $3 trillion of savings in 2021 (that’s right now) or a bleaker picture of a sheer lack of preparedness2 for the now and the future.3 Though these predictions may give us a sense of security that we are on the right path or, at least, not alone, they don’t provide the guidance we may need to make reality-based decisions to address our issues. Clearly, in selecting and investing in technology and process change, we want to remove duality, since so much is on the line.
Change. We all have to deal with it. Often, though, we are clinging to the past (seeking stability) which keeps us from seeing current things clearly.
Analytically, we might be missing definitive knowledge of what is going to happen next in the world, but we surely have a lot of questions—areas we want to explore so we can be better prepared to launch promising innovations. Two fundamentals—accepting change and doing discovery—need to form our present foundation so that we can be prepared to move forward. This seeking is where AI/ML really shines.
As supply chainers, we have learned and already codified much about every little grain of data about physical inventory and its dimensions, where it can be stored, and various routes to get it to the customer. But we still have limited data about the geo-social-economic-people world.
Change—Out with Stable and in with Resilience
We have been living through a series of dramatic economic, social, and global changes. There is no need to go into this further since it has been well written about. Suffice it to say, executives state that these dramatic swings are the “new normal,” and the challenges of globalization, where a seemingly small event in one corner of the globe can have life altering consequences for the whole planet, will continue to be the norm.
Discovering What Could Be
Our views—our data—as good as they were, were actually pretty limited. So, in order to “stabilize things,” we made a lot of compromises.
Since it was so hard to get the numbers right, many companies resorted to reducing the number of variables in the plan—that is, reducing product offering, suppliers, and triaging customer service. These were painful choices and companies always wondered what they were leaving behind.
Today, with the Internet and the entrepreneurship it enabled, we can often see what we have been leaving behind, as frustrated and innovative consumers decide to invent a product, from socks or pet products, to face creams (see sidebar examples). The cold numbers we used just never told the whole story. Conversely, some firms relied on the “‘instincts” of buyers who relied on their tastes, eschewing the numbers even when they pointed to declining customer interest and sales. That duality4 again—the pure numbers game or pure creativity—probably doesn’t put us on the best path to customer delight and profit.
Changing Channel Preferences
One area that is so familiar to us now is the channel changes—some have been evolutionary and some just wrenching as shoppers moved suddenly online. And it will change again.5
Of course, ecommerce shopping continues to grow and has become the preferred channel for a growing segment of the population. Post pandemic, it is unclear if shoppers will again flock to stores or just stay home. If it can be gotten right, a much more dynamic approach to cross-channel services and allocations can save millions in inventory and transportation costs while enhancing the customer experience.
In industrial settings, having the on-site installation has stalled out due to the pandemic. This, in turn, has affected companies’ abilities to accommodate the changing customer expectations of having on-site reliable and knowledgeable service personnel. Product and process designers are looking to replace some of the role of the service technician with really smart features, more remote installation capability, and/or augmented-reality, guided installation and simple repairs all done by the customer. Again, if done right, in the long run this will save an enormous amount of money in service expenses and hours of customer frustration.
These are just a few examples of what we hear across different industries. Supply chain’s role in determining the future customer preference in product and service is essential. Not only will the analytic systems have to explore demand from multiple dimensions, but also develop innovative and resilient processes that can respond to most eventualities. Thus, the motivation today to see what AI/machine learning can help us discover.
In Part Two of this series, we look at the potential use of AI/ML in enabling continuous planning and execution, and as a foundation for autonomous supply chains.