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Machine learning has begun to transform the world as we know it. Everywhere around you, you are ‘connected’ to something. Now with Google Echo and Alexa, you are ‘connected’ even when you are relaxing on your couch. An average person is connected to at least 3 devices and multiple accounts through them, at any given time. This network of connectivity is denser than ever, and is doubling every year.
Imagine more than 7.5 billion people on the planet, of which, around 50% are connected to the internet. A total of 3.7 billion people are online. Furthermore, total active social media users are a staggering 2.7 billion.
If each of these individuals were connected to at least 3 devices, as stated above, then the number of connections in the world might easily cross an amazing 8 billion.
These numbers are worth more than just ‘sitting-up-and-taking-notice’, there numbers are jumping-out-shaking-head-to-believe. However, how does these numbers affect you? What do these number mean to a person working to optimize their logistics and field workforce management system?
Enter Machine Learning! The Keystone of the Internet of Things!
People have talked about the Internet of Things (IoT) in diverse terms, but what is it? IoT is the interaction and exchange of data amongst various connected devices and entities over the Internet, which then function towards singular goals. It’s like having a virtual connected universe, where you customize almost all your electronic interactions. And machine learning is the process followed by these devices and entities to interpret your current actions, and predict your future interactions.
You can consider IoT as the Holy Grail of the tech industry.
Machine learning and automation are the steps towards this fabled end-goal. But still, how does a field workforce management professional utilize IoT to boost their business process quality and efficiency.
Field Workforce Management and Machine Learning
Machine Learned Field Workforce Automation
For a professional in the logistics management industry, cost reduction and overall performance enhancement are the key performance indicators. Effective field workforce management can bring the cost down. However, to manage the cost reduction without affecting the quality of the delivery process is a big task.
Machine learning coupled with automation can solve this problem and strike an immovable equilibrium for the entire field workforce management system.
The way this happens is:
- The connected devices and vehicles gather performance data.
- This data is run through an intricate but comprehensive planning and analytics engine.
- This engine incorporates historical and current data patterns emerging from various sources such as traffic, weather, customer preferences, etc.
- Crunching multiple petabytes of information, the engine permutates the most optimal route for the deliveries.
- This information is sent to the field service agents’ devices and vehicles.
- The field service agent follows the suggested route to fulfill all service requests or deliveries efficiently.
- Every successful fulfilment is captured by the on-field devices and fed back into the system.
- The engine, built on stacks of extensive self-learning algorithms, puts the latest factors to its historical database.
- This historical database then works to enhance future analysis and optimization.
Essentially, within the field workforce management system, the efficiency, accountability, and consistency would increase with every service request, delivery, or task fulfilled.
Machine Learning Today, Predictive Automation Tomorrow!
Even though machine learning as a part of field workforce management seems futuristic, it is very much a part of today. LogiNext has built its optimization engine around several self-learning algorithms. These algorithms auto-develop data points for the company which are then utilized by its comprehensive planning engine to optimize routes and capacity management for its clients.
About 10 million data points are created by the engine every week. Machine learning is a reality that we are just waking up to.
The eventual evolution of machine learning is predictive automation. Predictive automation is when the machine learning is applied to predict process patterns and pre-plan an optimized layout for the entire field workforce management system of a company. Predictive automation might be a reality in the next couple of years itself. If IoT is the destination then we are on the right track.
