Logistics Startups Bank On AI and ML To Up Efficiency
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With the advent of Industry 4.0, the global economy is fast embracing a series of smart new-age technologies that promise to revolutionise every field down to the last sector: from manufacturing, services to supply chain. Be it healthcare, retail, e-commerce or even legal processes, there is hardly any segment left untouched by the revolutionary impact of artificial intelligence (AI), machine learning (ML) and Big Data. The logistics sector that in many ways forms the backbone of the economy by ensuring seamless movement of goods and supplies is also experiencing sweeping changes with the advent of these smart digital technologies. Evidently, Industry 4.0 is not feasible without smart logistics and supply chain management which is crucial to building a better connected ecosystem across sectors.
Retail bigwigs such as Walmart and Amazon are already investing heavily in supply chain automation starting with deployment of robots in fulfilment centres as well as warehouses. However, intelligent robotic logistics is just one example of how AI and automation can revolutionize the logistics space. Logistics startups today are banking heavily on AI and ML to improve efficiency and management of a series of processes, optimizing delivery times, managing high order volumes effectively and predicting consumer behaviour.
Here are a few spheres through which AI and ML are helping the logistics space augment efficiency and productivity and revolutionise supply chain management:
Accurate prediction of demand and other variables across the supply chain is a very critical element of effective logistics management. Not only does forecasting models take into account the history of consumer behaviour and demand of products and services over multiple seasons, they also need to take stock of potentially disruptive episodes or circumstances that may affect the demand graph. Having close to real predictions is crucial for effective logistics management and maintenance of adequate supply and inventory as also to maintaining timely delivery models.
The use of AI and Big Data has in recent years made this extremely complex process of logistics forecasting more accurate and reliable for logistics providers. Availability of mounds of digital data contains hidden clues to historical consumer behaviour and demand patterns as well as environmental or economic disruptions. AI-based tools can effectively make sense of this numerous data quickly and with high degree of efficiency. Logistics startups as well as established providers are today increasingly using AI-based algorithms to set up effective forecasting models that are automated as well as dynamic i.e. they keep adapting to additional information and data sets with accuracy and alacrity.
Effective prediction enables logistics service providers to ensure deployment of adequate supplies at adequate place and time. At the same time, it allows last mile delivery providers to deploy effective planning and streamlining of resources to meet the rising demand volumes at different points of time. All this results in better optimisation of resources and lowering of costs across the supply chain right to the last mile delivery.
An interesting 2016 report found that in the US traffic bottlenecks cost the trucking industry a whopping USD 74.5 billion annually and 1.2 billion hours in lost productivity. While we do not have a similar ready analysis for India, evidence suggests our logistics industry also faces heavy losses due to road congestions and other obstructions. This is where the need for effective route optimisation using AI comes into play. With increasing order volumes particularly during last mile delivery, service providers need to ensure they optimise their travel routes and make effective use of their time, particularly when there is a capacity crunch in times of fast delivery requirements.
AI based tools not only keep a ready track of traffic congestion but also suggest potential new routes and ensure trucks and delivery agents follow the fastest possible routes to reach their multiple destinations. Cost effectiveness, efficiency and increased productivity are a natural corollary of effective route optimisation processes.
Efficient warehouse management
As discussed above, use of robotics is increasingly becoming common in warehouses where manual management has traditionally resulted in higher costs and lower efficiency. Robotic logistics helps automate a series of manual warehouse management processes ranging from picking, packaging, re-arranging, processing and loading orders as well as transporting. The outcome is improved productivity, huge slashing of warehouse labour costs and better optimisation of resources. Use of automated guided vehicles and aerial drones to monitor inventories is another area that is emerging as the next automation target for the sector.
Peak hour volume management
When it comes to ensuring efficient last mile delivery, tackling peak hours with high order volumes is one of the most important requisites. Express deliveries are becoming the norm with customer today increasingly demanding next day and even same day deliveries for a multitude of products. Ensuring their express delivery requirement is met is critical to customer satisfaction and retention. AI based tools are a major help in optimising resources during peak hours. By predicting order volumes during peak hours in advance along with forecasting traffic and other variables such as availability of delivery agents at any given area, AI and machine learning algorithms help optimise resources to the fullest, saving costs, time and ensuring customer satisfaction.
ConclusionHigh logistics costs have been one of the most pressing concerns for the logistics sector in India. It is estimated that logistics costs account for 14 per cent of India’s GDP which is much higher as compared to BRICS countries (10-11 per cent of the GDP). A report by CII & Arthur D Little India suggested that India’s supply chain industry needs to halve logistics costs from the current 14 per cent of GDP to 7 per cent to make the sector globally competitive. Smart digital technologies are today increasingly enabling logistics startups towards bridging this optimisation gap as new age logistics providers bank heavily on AI, Big Data and ML tools to augment their efficiency.