To keep up with the element of uncertainty in the hyper-competitive logistics industry, supply chains need to be adept at optimising their performance in light of the disruption brought about by the digital era. As consumers desire personalised goods and services delivered at a low cost, supply chains are feeling the pressure of increasing demands.

Logistics firms thus need to leverage data analytics and machine learning capabilities for the creation of reliable and dynamic supply chains capable of anticipating and handling uncertainty.

In-House Suppliers

One way supply chain firms can optimise their performance is by reducing the time required to build equipment . 3D printing (otherwise known as additive manufacturing) is slowly changing the face of manufacturing – they are capable of building larger parts in-house at a much higher precision and lower production cost. 

Implementing this technology will collapse the supply chain into much simpler parts. On top of reducing the costs incurred in production, distribution and improving assembly cycle time, consumers can also expect much faster deliveries. On the whole, reducing production costs will consequently lower costs absorbed by consumers. 

Autonomous Equipment

Supply Chain Automation and Robotics (SCAR) revolutionizes the traditional approach in logistics by equipping companies with the abilities to improve process efficiencies and to react dynamically to business fluctuations. Such enhancements can be manifested throughout the entirety of the companies’ workflow - from the inbound processes of unloading and sortation, to the outbound process of routing.

Specifically, autonomous equipment in the form of vehicles, drones and robots are introducing innovation and value-added efficiencies to supply chain operations. According to Deloitte, autonomous robots help supply chain firms to improve labour utilisation, reduce frequency of checks and balances as well as optimise the packing and sorting process. Additionally, autonomous robots can improve worker productivity, effectively lowering an estimated $4.3 billion lost in labour. 

Allowing for the continued growth and development of autonomous entities in production work can lead to more strategic and seamless operations in supply chain networks. 

Machine Learning

Machine learning models are capable of finding anomalies, predicting patterns and analysing big data sets in a very small fraction of time. According to a study done by McKinsey, machine learning models will provide significant insight as they anticipate anomalies in logistics costs and performance before they occur. 

Using the algorithms that they are based on can isolate damaged goods and make recommendations throughout the entire network. For instance, IBM’s Watson platform demonstrates machine learning capabilities in using both visual and systems-based data to track, report and make recommendations to products and services in real-time. 

By improving demand forecasting accuracy, supply chain firms can streamline their operations and act in accordance to the changes that occur based on prediction – effectively reducing costs incurred in sudden demands and volatile markets. 

Here at Roadbull, we are dedicated to improving our processes with the incorporation of technology at every step of the supply chain. Making investments into digitalised services and automation can put supply chains on the fast track towards offering streamlined, efficient and capable services.