AI supply: Logistics could look to AI for big data
Rapid growth in the logistics and supply chain segment is creating opportunities, but at the same time the talent is drying up
The UAE logistics sector contributed close to $29bn to UAE’s GDP in 2015, grew an estimated 4% in 2016 and is expected to grow at a compound annual growth rate of 5.7% between 2015 and 2020, according to a report by Frost & Sullivan.
The growth of the UAE’s logistics industry is being supported by ongoing economic diversification, growing domestic demand and the development of multimodal transportation. Airport expansion and the planned GCC rail network will strengthen the alternates for freight and cargo transportation.
As a tangible sign, Aramex is preparing for the anticipated growth in logistics with the construction of its first fully automated facility, at a cost of a $20.4m, in the Umm Ramool area in Dubai. Exciting times clearly lie ahead for logistics in the region, where strong growth in the sector will provide broad employment opportunities.
However, where opportunities are present challenges also lie, and in the case of the logistics sector sourcing quality personnel is becoming increasingly problematic.
According to Gulf News Employment, the trade and logistics growth in hiring in the UAE was 55% year on year in 2015. But at the same time, as reported in The Leadership Network, 25% to 33% of the supply chain workforce is either at or already passed the retirement age and there is currently a 61% talent gap in middle management supply chain. Ultimately, for every supply chain manager entering the work force two (or more) are retiring. In the USA, more than 60 million employees will retire by 2025, but only 40 million are expected to enter the supply chain and logistics sector.
Supply Chain Insights also reports that there is currently a 15% turnover in supply chain personnel. This is roughly the average across all industries, which indicates that talent is both hard is hard to find and equally not particularly easy to retain. The report also indicates that the turnover rate is expected to increase in the future. According to a further report published by Deloitte, only 14% of companies believe that they are doing better than their peer groups in managing supply chain talent, and 43% believe they are doing worse. Most CEOs are aware of this talent shortage and know they’re not doing enough to fix this problem.
Growth in any industry often results in process problems which results in the need to hire more personnel to fix these issues. Due to the talent shortage, most companies end up hiring personnel unfit for the supply chain role in the belief that they can train them up to lead supply chain/logistics in the future.
According to our research, it costs organisations close to $350,000 on average to train up a supply chain employee in order to make a significant impact on cost savings. But 15% of supply chain employees leave their organisations due to job dissatisfaction — so just imagine spending close to $350,000 only to lose that employee. The organisation has just lost a tremendous amount of knowledge and experience apart from their investment.
At other times, the training is rushed so the employee takes a very long time to make any significant cost saving impact. Hence, profits tend to drop instead of rise with growth.
At present, organisations try to preserve the knowledge base through training courses, design books, best practice books, mentoring sessions and standard work instructions.
While these are great for the preservation of knowledge, it requires a substantial amount of time to go through these and understand the supply chain principles in detail. Instead, let’s see how we might be able to solve this problem using technology.
Modern technology, especially in the field of big data and artificial intelligence (AI) allows us to make better use of this knowledge by integrating it into a single platform that gives insights to the end-user to highlight bottlenecks in supply chain processes.
We live in an age where a tonne of data is being generated every millisecond. Automating the data analysis process can not only accelerate decision making, but significantly reduce training costs. According to a study by McAfee and Brynjolfsson, enterprises that embraced big data analytics were on average 5% more productive and 6% more profitable than their competitors.
The problem here is that only about 20% of the supply chain talent actually possess the analytical skillsets needed to integrate big data. But, according to a survey by XPlenty, on average a minimum of 70% of the total time for supply chain strategy projects was spent on data cleaning and analysis.
A sophisticated IT or AI solution can not only reduce the talent gap problem, but make it easier for people in supply chain, especially in transportation and logistics, to focus on implementing key strategies instead of wasting precious time on data analysis.
AI functions like machine learning can help organisations not only predict future outcomes of processes, but guide the user to the optimal solution and come up with improvement strategies. They can identify process bottlenecks in real-time and give the end-user actionable insights into which areas of their operations to focus on without needing to manually churn all that data. E-commerce companies like Amazon and EBay already use machine learning to suggest products to customers — resulting in significant revenue improvements. The time is now ripe and right for the global supply chain sector embraces this technology to reap these benefits.
At RigBasket, we have taken this step by automating the role of a supply chain analyst to allow organisations to spend more time on implementing effective supply chain strategies and less time on data analysis. Through proper utilisation of technology, logistics organisations can not only overcome problems as a result of the growth, but also make higher profit margins compared to their competitors and become an industry leader in logistics.
Khizer Hayat, chief technology officer and co-founder RigBasket.