Savvy businesses have been experimenting with multiple disruptive technologies

Two disruptive technologies simply stand out–Machine and

IoT-enabled devices have been increasing at a fast pace and are forecasted to reach up to 30 Bn by 2020

The need to deliver on rapidly evolving customer expectations and simultaneously ensure profitability are driving businesses to rethink the way they have been executing chain and logistics operations. Achieving this twin objective is not easy.

To address this, savvy businesses have been experimenting with multiple disruptive technologies for quite some now, hence opening the doors to solutions that are driven by technologies like machine learning, the Internet of Things (IoT), automation, data analytics and more.

From enabling -time monitoring and visibility of ground-level activities, improving productivity to reducing operational expenses, these technologies are making core supply chain and logistics processes more efficient. With regards to the world of supply chain and logistics, two disruptive technologies simply stand out–Machine Learning and IoT.

Internet of Things (IoT)

IoT powered devices empower supply chain logistics stakeholders to drastically boost the visibility of delivery processes

The world of logistics has now been exposed to a vast pool of physical devices, those which are built with supremely high-definition technology and are well-designed to communicate with each other over the Internet. Interestingly, the number of these IoT-enabled devices has been increasing at a fast pace and is forecasted to reach up to 30 Bn by 2020.

The deployment of more and more IoT solutions has been continuing to transform and modernize the supply chains by bringing in more operational efficiency to the system.

These smart devices are built-in with sensors, which facilitate easy monitoring of goods through each leg of the supply chain. These sensors generate massive volumes of data like shipment locations, the status of the inventory, etc, which is transmitted back to the software applications.

With advanced algorithms running on real-time and historical data, insightful trends and information can be generated and used by the supply chain managers, making it easy for them to analyse risks, gain predictive intelligence and take data-driven decisions, thus establishing greater control over the supply chain.

Apart from being able to track the location and overall health of goods during the process of delivery, the use of IoT solutions also simplifies the process of fleet management. The condition of the vehicles at any given point of time during delivery, the fuel consumption patterns, any expected delays due to traffic conditions, etc could be instantly highlighted, so that steps could be taken to ensure faster and timely delivery of goods to the consumers.

No matter how tightly the processes in the chain are controlled, there always remains a level of uncertainty in the order fulfilment process, which could be difficult to predict or control, Such uncertain variables include an unexpected increase in demand, stock shortages, sudden changes in temperature, etc, which could be instantly alerted the moment they occur, helping the personnel take necessary corrective steps.

Warehouse management is another area which IoT aims to simplify. The use of highly advanced, innovative devices enables supply managers to take complete stock of inventory from all warehouses and identify any kind of damage done. It also helps them ensure that the warehouse space is being effectively utilized.

IoT-powered supply chains are lending increased transparency and visibility to all the stakeholders involved in the process. As the technology continues to evolve and IoT’s full potential get harnessed, many new trends are going emerge which are going to create a ripple effect on the supply chains worldwide.

Machine Learning (ML)

Machine Learning is a rich form of Artificial Intelligence (AI), by way of which all the supply chain data generated by sensors and various IT systems is quickly absorbed and made good sense of to automatically identify and derive meaningful patterns in the supply chain. These patterns serve as great insights to the supply chain managers who could use this intelligence to optimize their decision making, strengthen deliveries and fulfil consumer expectations.

Daily production planning could be streamlined with the use of insightful patterns in customer demands. The process of inventory management could be controlled and strengthened as the use of Machine Learning allows accurate forecasting of demand and sales.

The use of Machine Learning also provides powerful patterns on supplier’s quality level throughout each stage of operation, which ensures that the products delivered would of superior quality. Besides this, machine learning can also identify any serious issues in the supply chain which could hamper the overall functioning of the organization. Such risks could be minimized, wastage could be reduced, costs could be controlled which would bring about an improvement in the business processes.

Machine learning algorithms have been exposing these new patterns every day by continuing to analyse large sets of data on a daily basis.  It is an exciting technology the use of which is set to double in times to come. Companies which had adopted Machine Learning at early stages of their operations have been able to make accurate predictions and have therefore experienced significant improvements in their outcomes, earning gross margin growth of up to 9%.

The use of these two powerful technologies has the utmost potential to reshape the dynamics of the logistics industry. Clearly, we foresee a yet brighter future of the businesses worldwide, with supply chains taking a stronger grip.

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