In the past, I have often heard people say, “If you can’t track it, you can’t measure.” However, in todays connected, IoT-enabled environment, every “thing” has sensors on it - from the shoes that I wear, to the car that I drive - so clearly tracking and capturing data is not the problem. We are deluged with data. The main challenge is knowing what to do with that data sensors provide, and how to make actionable sense of it.
It may now be a case of “I can track it, but now how do I measure it?
From big data to intelligent analysis
In my previous article, I discussed how “customer centricity is the new mantra of supply chains.” In order to improve demand visibility, impress the customer and run a sustainable supply chain, you need enough information to make the right decisions.
With the prevalence of Internet of Things (IoT) and sensor technology, we now have access to unimaginable amounts of data. We have smart products sending terabytes of data about how the assets are performing, how much they are being used, and where they physically are located.
We also have sentiment analysis that allows us to monitor what products are trending, where they are trending and who they are being “liked” by.
We have more “big data” than we know what to do with, but it is just data with no real value, until we can provide the business context. The key to deriving true value is analyzing the data and determining the best ways to leverage the information to streamline the business process for both the provider and the customer, ultimately elevating performance.
For example, a supply chain planner needs different types of information, in different levels of granularity than other occupations, like the plant manager, a transportation planner, or a maintenance supervisor.
Once you have the data in a format that is relevant to your industry and job title, then you can make informed decisions to best analyze the situation.
From responsive actions to predictive analytics
Now, with access to real time data, your organization can take responsiveness the next level, moving from reactive to predictive. Today we are often dealing with timely crises that could be prevented if organizations have access to the right information at the right time: machinery malfunction, late deliveries or issues around product quality.
To help combat these road blocks, we leverage technologies, such as machine learning, artificial intelligence and analytics, to help uspredict what will happen based on real-time insights into the assets in the field, the trucks on the road and the weather forecast. This data can help mitigate these common issues when it comes to logistics, especially when it comes to cutting down on supply chain disruptions and rerouting late deliveries.
From linear supply chains to an agile ecosystem
A desired level of customer service and value requires much collaboration, not only within all the different functions of an organization (sales, marketing, research and development, planning, manufacturing, logistics etc.), but also, across partners, including as customers, suppliers, logistics service providers, and contract manufacturers.
As assets and products get “smarter,” we are also seeing the growth in networks of assets (things) who are sharing information about their state, location and performance with each other and the business systems that leverage this data, enabling certain activities on the edge of the enterprise through machine learning and predictive analysis.