Machine learning can help in uncovering layers of imperfect Internet of Things (IoT) data. ML also facilitated to provide powerful insights which helps in maximizing efficient supply chain operation. Implementing IoT into a supply chain is a very challenging task to perform. But, if executed well can yield us with extremely powerful insights. The constant stream of data from hundreds or even thousands of sources must be accurately captured and analyzed. The investment professionals believe that past performance will not guarantee future results. This is true in case of supply chain operations.
A supply chain planning and optimization solution needs to be capable of delivering robust planning, which also requires analysis of IoT data for predictive and prescriptive analytics. Once it is decided on how to handle the available data, considering the data quality issues that could arise, we shouldn't wait for IoT data to be perfect. Doing so can prevent the supply management team from moving forward. So, it is very important to be prepared to manage fuzzy or imperfect data.
Integrating IoT into the supply chain can also change how to examine and understand the data. Practitioners earlier used to develop a theory and set out to acquire data in an attempt to validate it. They used to look at the data expecting to find a certain link and use the data gathered from IoT to confirm their hypothesis. Once proven, they would move forward with planning based on the validated assumptions.
A new way to look at data is that every piece has value and should be proactively utilized, rather than be available for reactive analysis. If practitioners can embrace as much data as they can gather through IoT, it would provide with more opportunities to utilize ML algorithms in order to proactively mine the data for insights. The algorithms may highlight a relationship between pieces of data that were not previously connected.
Modern computers running machine learning algorithms have the capacity to consider and test millions of possibilities that the human brain could never consider in fractions of a second. Ultimately, today's supply chain professionals are still the gatekeepers for validating assumptions, but they need to be open to new ways of working to allow the proactive analysis and output of machine learning algorithms to fully maximize efficient supply chain operations.
The ability to take in this much data from the supply chain allows practitioners to widen their perspective. In general, there is quite a bit about interconnectivity of supply chain processes that we don't understand or have not connected because we haven't had the data or ability to do that. Before determining that machine learning is a boon for solving a supply chain puzzle, there needs to be some understanding of the data the practitioners is working with in order to know what machine learning approach to take.
Typically, there are three major categories of machine learning algorithms that are important to consider in supply chain planning.
i.Supervised learning algorithm. In this case, practitioners would have data with known inputs and known outputs. They use a supervised learning algorithm to determine the relationship between them and extract it to apply toward future planning.
ii.Unsupervised learning algorithm. This is used for data that is unlabeled, meaning there is some uncertainty surrounding what the data represents. Supply chain practitioners would use this algorithm to find hidden relationships in the data to highlight new patterns. This allows practitioners to take in as much data as possible and have the unsupervised learning algorithm organize it in a more meaningful way.
iii.Reinforcement learning algorithm. These types of algorithms were designed to optimize decisions where feedback is not provided immediately, but only at a future point in time. Bottlenecks in interconnected supply chain operations can be both difficult to identify and mitigate with planning changes. Reinforcement learning algorithms are perfectly suited to assist in both of these difficult tasks when provided the correct data.
For many supply chain puzzles, IoT data provides with the opportunity to run multiple types of machine learning algorithms. Practitioners may run an unsupervised algorithm as a preprocessing step to a supervised algorithm. In this case, the unsupervised learning algorithm can identify outliers quickly through clustering techniques and assign them a lower weighting so they do not influence the outcome as much as other data points. Unsupervised learning algorithms can also remove variables, and their associated noise, that do not influence the outcomes at all through dimensionality reduction techniques. After unsupervised learning runs, the amount of fuzzy or imperfect data is greatly reduced and higher quality data can be passed to a supervised learning algorithm for a higher quality result.
Navigating through data noise and improving data quality becomes more difficult when you have two pieces of data with all of the same inputs but different outputs. In this case, practitioners would need to determine the interaction with other data points that could have influenced a different outcome from the same inputs. The more complex results require a good understanding of machine learning algorithms and that they work with a strong, flexible solution.
With IoT, it doesn't require an understanding of all of the data collected because the algorithms will assist in determining what is important and what is not. IoT is very powerful, and layering machine learning algorithms to build complex layers of data enables practitioners to dig in and figure out what has happened and why, which ultimately results in continuous improvement in planning.