Top Applications of AI in the Distribution Industry
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Top Applications of AI in the Distribution Industry
McKinsey says that the distribution industry should go digital entirely if it wants to secure its future, but do we have the resources to help achieve that?
Joher Aurangabadwala
Joher Aurangabadwala

Global Practice Director

Infor

Birlasoft

Bradley Albers
Bradley Albers

Program Director

Birlasoft

 
The colossal rise of AI (artificial intelligence) in the supply chain and distribution has been one of the key pillars behind the unprecedented growth of cross-sector distribution businesses since the pandemic hit. The higher transaction volumes coupled with superior AI and ML-powered processing have propelled the rate of change.
AI and ML (machine learning) have been the cynosure for every industry in the last few years and are the most impactful and transformative technologies in modern history.
When the going got tough for the supply chain in the last few years, new-age AI technologies, such as digital twin supply chain simulations, IIoT (Industrial Internet of Things), and others have played their part. They helped build the necessary resilience to counter the highly uncanny situations.
This article discusses the applications of AI in the distribution industry and how it holds the power to reshape the way we see the distribution landscape.
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Some interesting applications of artificial intelligence in the distribution industry
The digital transformation of the distribution industry today is largely because of the growing application of AI amongst businesses with a multi-channel supply chain and a well-spread-out distribution fabric. Here are some of its exciting use cases -
The omnichannel congregation made easier
As per Gartner, by 2023, 50% of product-centric organizations are expected to invest in platforms that offer real-time transportation visibility.
The omnichannel logistics that is personalized, efficient, and transparent with agility to back it is expected to reshape the network infrastructure dynamics that we see today in the distribution management landscape. But the primary issue hindering such a shift is the presence of redundant IT infrastructure, warehousing systems, and fleet management.
These issues are a significant roadblock in growth and innovation and are also slowing down the speed at which these brands digitally transform themselves. Additionally, data remains highly distributed between systems incapable of coming together, and working as one is not helping the cause either.
The adoption of intelligent infrastructure that inculcates modern phenomena like autonomous driverless supply chain fleet and superior vehicle technology for optimized routing is expected to solve these bottlenecks.
The inculcation of IIoT and new-age techs, such as intelligent signage-backed intelligent roads and smart vehicles, is expected to cater to the lingering issue of eclectic data being available only in their native formats across sources. In addition, AI technology solutions will revamp internal connectivity, which will lead to the development of new business concepts that are not necessarily linear (natural) extensions of historically understood business operations. As a result, they will significantly impact how your fleet moves.
RPAs and APAs will rule the roost
Today, supply chains have broken the shackles and transformed themselves into data-gathering machines. With sensors finding usage across every nook and corner of distribution management, it has generated tons of specific and general data. But using them was always a challenge because of organizational silos, such as different stakeholders having specific proprietary knowledge.
Top Applications of AI in the Distribution Industry
While APIs have helped the cause, the need for continual data cleaning and formatting before preparation did not go well with AI companies. However, with businesses becoming more synchronous, granular, and self-actualizing, AI tools and automation are expected to aid and contribute to probabilistic synchronization for keeping pace with rapidly evolving market dynamics.
RPAs (robotic process automation) solved many of these hurdles with innovative, solution-driven use cases. These were able to automate repetitive processes via bots, extract data from a plethora of data sources, cleanse them, and send them back to the requisite files and database. But that was not why RPAs were built.
In a recent McKinsey Global Survey on Digital Transformation, it was reported that four-fifths of respondents had begun their transformational journey, but only one-third of them succeeded.
It led to the development of APA (Analytical Process Automation), which is designed to work in collaboration with RPAs in most cases. APAs are ML-powered, self-learning robots that can receive inputs from RPAs and other sources in real-time, handle structured and unstructured data simultaneously, and bring about data-driven automation. The combination of APA and RPA has helped in hyper-automation and data federation by automating high-volume tasks and bringing complex data sources under the periphery of analytic processes not available before.
Superior autonomous planning will take center stage
Today, the increase in the number of federated business components and their accelerating lifecycles are constantly posing challenges to the traditional mindset regarding the lifecycle of a process. It has stemmed from the progressive inculcation of artificial intelligence capabilities to complement traditional distribution center applications.
In addition, the rapid shifts in demand have led to traditional supply chain planning is insufficient to cater to the changing times. It has led to logistic companies bringing together the several elements of AI by imbibing autonomous planning on a large scale – a closed-loop planning process, fully automated and designed to improve supply chain processes in real-time. It combines AI technology mainstay – Big Data, with advanced analytics capabilities throughout the supply chain to drive holistic improvements.
Autonomous planning leverages AI in a never-seen-before avatar and requires more than hardware and software. It seeks to reduce human involvement and instead relies on advanced integrated analytics to create a link between demand forecasts and the company’s internal logistics and other processes. As a result, it plays an integral part in improving planning efficiency and real-time reporting of anomalies throughout the supply chain.
AI and ML are integral to the growth of distribution and supply chain management
AI and ML are all set to help distribution businesses deliver a robust and more effective customer experience while improving productivity and the quality of operations. By deploying the right AI technologies within their ecosystem, companies can seamlessly pivot between efficiency and resilience. With AI helping to improve data and process alignment, there is little reason to believe that we have only scratched the surface, and there is a lot to explore.
 
 
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