How Preventive Business Models in Logistics Can Add Value to Companies

Tags: Logistics I.T., Logistics, Technology

In business, it is always better to be proactive than reactive. In logistics, this is even more vital, as unanticipated reactions quickly become costly outcomes—making or breaking supply chains in today’s fast-moving world. Let’s take a look at how preventive business models on the front end and back end of the supply chain can help to add value to companies in the business of shipping.

Key Preventative Measures in the Warehouse

One of the most intuitive ways to be prepared on the back end of shipping is a simple one: Be sure to have the supply to satiate the needed demand by having more stock available than expected in the warehouse.

With the use of anticipatory logistics, a company can be ahead of the curve. By obtaining the data of what geographical or demographical areas order which kind of products, companies can predict times of need by location and stock accordingly. This allows warehouses to prepare for “peak days”—ensuring that they have the space needed for inventory when high demand occurs.

Artificial intelligence (AI) is needed to translate these metrics as they apply to your business, as this information is not freely available. A well-tailored AI system comes from investing in software that can be implemented to help provide the framework where the data can pour in, and then the AI can start providing valuable analytics.

Preventive measures for maintenance are also essential because, in order to keep up, companies need to be able to fire on all cylinders. With IoT sensors ascertaining data from the warehouse, AI can determine the cause of maintenance issues before sending technicians in.

This results in faster and more efficient solutions as technicians are equipped with the information they need to complete the job. In addition, by allowing AI to consistently monitor warehouse assets, it can optimize each component of maintenance intervals, and minimize downtime when it occurs.

In general, there is a trend of warehouses moving to robotics over humans for efficiency. This is an investment but can drastically improve the margin for mistakes, making for what is known as augmented intelligence—the next logical step for AI. This is where the algorithms provide humans with understandable insights and humans apply years of their industry expertise. Together, AI provides all available information in real-time, and workers can translate that information, modifying the decisions made by AI to achieve better results.

Key Preventive Measures on the Road

IoT sensors on physical assets can also provide preventive data from the road, giving companies sophisticated analytics of their resources in real-time.

When in combination with AI, IoT innovations and telematics essentially provide a roadmap to enhanced feasibility and productivity through both predictive analytics and demand forecasting. This increases a company's visibility and gives logistics professionals the ability to better predict demand and optimize utilization management—improving efficiency in routes, mitigating fuel consumption, and enhancing performance in drivers.

AI in combination with IoT gives companies intel on the state of operations up to weeks or months in advance, helping logistics professionals consider the many variables that are involved in the shipping industry. It is an impossible task for the human brain to understand how to optimally transport a single shipment from point A to point B. An AI-powered solution can monitor all these activities and even forecast more relevant factors, ensuring the accuracy of the predicted demand. With this preventive insight, a multitude of advantages can be brought to every stage of supply and demand planning—ultimately cutting costs.

Companies must embody a mindset that challenges the status quo and is constantly looking to improve on multiple levels in order to keep pace with today’s escalating demand. Preventive technological approaches can give companies a competitive edge in essential deciding factors. This starts with being proactive, and not reactive, and blazing a new trail for logistics and shipping for the better.

 

ABOUT THE AUTHOR

Marc Meyer is CCO at Transmetrics, a company that optimizes cargo transport planning by leveraging machine learning and predictive analytics.






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