By John Lewis, Dynamics 365 Solution Consultant, Xpedition.
It is vital to understand and keep pace with the moving tech landscape. Moreover, it is not only the type of changes, but the rate of change itself that is speeding up. Businesses that understand the role of technology in logistics, and are able to adapt are significantly more likely to survive the next decade of changes than those who don’t.
When it comes to finding focus in logistics tech trends, a lot of the focus has been on physical automation and innovation. It’s true that in the hardware space, startups such as Markforged use 3D printing to help minimise supplier lead times, while Tesla, Otto, Mercedes and others scrap in a hard-fought fight to make driverless shipping happen. Perhaps because the impact of such physical solutions is more visible and plays neatly into deep seated culture-driven thoughts on robotic automation, it’s easy for these to become the dominant talking points.
The reality is that while a number of major players are able to leverage their economies of scale to benefit from the bleeding edge of physical solutions, for the majority of small and medium-size enterprises the high hardware investment costs required result in an often insurmountable entry barrier. It’s for this reason that the bigger trends for the next phase of adoption will involve companies developing and capitalising on something that is already available within their organisations.
Oil, data and next month’s numbers
In the 20th century, Rockefeller, Getty and others amassed fortunes by drilling oil from land that the previous owners had either not been aware of, or had little or no use for. In the 21st century, data is the new oil, and business leaders need to be aware of the rich data insights available in their own backyard. Frontrunners who grasp this role of technology in logistics are bound to be the early winners. Already, the early ‘data barons’ such as Google, Amazon, Microsoft and Facebook have learned how to monetise data, while the next tier of businesses have to play catch up a decade or more behind. SMEs are now latching onto the fact that their data no longer simply gives them 20/20 hindsight, but when combined with machine learning, provides very real actionable foresight.
The first round of artificial intelligence and machine learning solutions involved large companies hiring rafts of data scientists straight out of Ivy League universities – innovators who were imagining use-cases and developing solutions. This was a costly exercise. Product suites now however, such as Microsoft Dynamics 365, are capitalising on that earlier work, and are providing finance and logistics solutions that are already integrated with their Azure Machine Learning platform. The large numbers of SMEs adopting the solution provide for the economies of scale, and the repeatable use cases mean that built-in machine learning is able to be simply ‘switched on’, rather than having bespoke design each time.
As a consequence, SMEs using systems that connect to machine learning are less reliant on analysing ‘what went wrong’, and are able to see information that illuminates the coming days and weeks. Execs more resistant to change have been reticent to trust machine insights over what a human could ascertain. The fact remains, however, that artificial intelligence systems are already better at identifying and predicting a number of patterns than humans, and their abilities are only improving exponentially with time and data. Furthermore, it is clear that the future role of technology in logistics will be centred on AI and how best to utilise it.
Smart algorithms, or artificial intelligence?
Tech companies love nothing more than buzzwords, and ‘Artificial Intelligence’ is definitely doing the rounds. However, businesses need to be aware that even though some solution providers might use the term to describe “something clever their software does”, in many cases this is just a decent algorithm or automation, not true artificial intelligence. Unfortunately, the distinction really matters, as true AI will continue learning and getting smarter and more accurate, while simpler algorithms will be left behind.
During engagements with companies now, in addition to identifying how functionality can meet known requirements, we are having to ask ourselves what foresight and insight AI could add. For an outdoor activities and equipment business whose sales were highly weather-dependent, we established a machine learning model that correlated temperature and precipitation against sales, so that moving forward, they could use weather forecasts to predict stock requirements at different shop locations.
Understanding the role of technology in logistics
Using out of the box Microsoft Dynamics 365 functionality, our SME clients in the logistics sector are increasingly using the standard machine learning models provided for forecasting their inventory levels, sales and cash flow, and speeding up the flow of their business. The most significant role of technology in logistics enables businesses to mine their already available data for foresight that is assisting them to react ahead of time to threats and opportunities. Self-driving shipping and the robotised warehouse may still be a few years out for many, but knowing what’s coming up via data-driven foresight is the single most important innovation available to switch on now.