Supply chains are constantly having to adapt to the rapidly changing world around them, and operating models and best practices are often rendered invalid far quicker than desirable. Rod Johnson, a group vice president with Oracle, notes that factors such as changing customer preferences, commodity prices, global unrest, and weather patterns are all ’putting enormous pressure on supply chain leaders to make sure that they get the right product to customers at the right price and the right time.’ Technological advancement is also creating issues for supply chain leaders. While new technologies can solve all the problems mentioned, aside from exploiting them to improve processes, the disruption to the world around the chain inflicted by technology can actually create more challenges than they solve.
One of the most important tools for supply chain leaders to have arisen in recent years is data analytics. For many, supply chain analysis remains an untapped opportunity, with a wealth of data at their disposal but neither the tools or the knowledge to exploit it. Over the next decade, as IoT creates an explosion in the amount of data at their disposal, they are only going to lose further ground on their competitors if they are unable to properly analyze it.
The applications are many. For example, in inventory management, it enables supply and demand to be far better measured, and patterns discovered so that stock is where it needs to be, when it needs to be there. Metrics such as the weather can be applied to models alongside sentiment analysis from social media, among others, in order to better understand when consumers are more likely to buy a product. Just a subtle fall in heat can potentially cause customers to stop buying a product, while the unstructured data from social media can be analyzed to reveal insights into likely peaks and troughs in demand.
This is the most basic level of analytics in the supply chain. To take it to the next level, supply chains need to be analyzing their data analytics and using it to automate processes. Take a car repair firm as an example. If they were to install sensors on all of their equipment and add GPS tracking to their fleet of repair vehicles, they could collect all the data throughout the entire journey. By applying machine learning algorithms, which learn from the data by themselves without human programming, they can leverage insights from the data automatically, and when connected to other devices, action these to effectively allow the chain to run itself. As a result, the company saves money on both the staff needed to run the chain, and gas by being more efficient with scheduling repairs.
For supply chain professionals, this means significantly updating their technical skills to best understand the technologies, and recruiting those who already have them. In a recent Deloitte survey, just two-thirds of respondents said that having a technical competency in analytics will become more important for supply chain leaders in years to come, while just 46% see it as a strength today in their supply chain organizations. This has to change, and supply chain leaders have to appreciate its importance or they will likely fail.