Data-driven decision-making: How analytics powers logistics control towers
From predicting the next storm to anticipating which books will be bestsellers, the role of data today has never been more important.
But while data has been the driving force for the winners of the digital economy, many companies have yet to fully embrace the data-driven culture. According to a survey of nearly 700 companies conducted by Business Intelligence, nearly 60 percent of executives still make at least half of their business decisions based on their experience and gut feelings.
This shift to a more data-driven decision-making model is crucial for logistics, given the complex supply chains many companies deal with. Many organizations have adopted logistics control towers to direct and crystallize the insights that data can bring.
Leveraging real-time data
Logistics control towers have been around for many years, acting as central hubs to give companies full visibility of their supply chain.
Pulling together real-time and historical data, control towers give full clarity into how supply chains function.
While companies make use of real-time shipping data and weather patterns to plan the best freight routes, control towers can take this one step further by merging the data collected with customer data to further optimize route planning.
Companies such as Tyson Foods, for instance, are integrating data from their Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems to glean valuable insights that bring down operation costs while boosting efficiency.
Elevating control towers with AI
But that’s not all that control towers can do. Apart from simply combining data, advanced analytics technologies are transforming the logistics control towers' operations. Powered by artificial intelligence and machine learning, control towers can predict trends and potential bottlenecks before they happen.
The pace of adoption is accelerating. In the 2020 MHI Annual Industry Survey, 30 percent of supply chain managers started using predictive analysis in 2019, nearly twice as many as compared to 17 percent in 2017.
Predictive analysis allows logistics professionals to anticipate consumer demand for goods and services beforehand, allowing them to plan logistics operations effectively. To comb through the large volumes of data from multiple channels, the control tower employs big data analytics – which examines massive amounts of data to draw a conclusion. The conclusions drawn from the vast amount of data help uncover hidden market trends, potential travel routes, and more across the supply chain.
Mega-chain fast food restaurant McDonald’s harvests a vast amount of information to make sense of consumer consumption habits. By doing so, they can push out vouchers and discounts for popular food items to generate repeat customers.
For mega giants like Shopee, Lazada, and more, collecting and organizing customer data across supply chains is key to their everyday business flow. By doing so, the platform can identify key areas of improvement in user experience and boost workplace productivity.
In other words, data-powered decision-making enables businesses to better allocate resources across the supply chain process, from freight forwarding to delivery manpower.
By drawing on physical data from manufacturing and warehousing operations, businesses can gain even sharper insights into their supply chain. And with IoT's growing importance, data use in logistics will only continue to grow. In 2019, the market size for global IoT in logistics was valued at US$34 billion. By 2026, the market size is forecasted to grow to US$63.7 billion.
And that is just the beginning of a new data-led era. With generative AI growing in sophistication, it is not impossible for the development of a chatbot that can develop a full operational plan without the need for the inspection of charts or numbers.
Despite the many reasons for adopting data in decision-making, a big reason why many businesses have not completely embraced data-driven decision-making is because of the challenges associated with setting it up.
While data is plentiful, it is still necessary to clean and sieve through the data before it can be used meaningfully. Doing so requires businesses to spend significant resources on building data pipelines and creating systems for proper collection and integration.
Similarly, good data security practices are key to maintaining data purity. Businesses must invest in data management and good cybersecurity software to prevent chances of a data breach.
Utilizing control towers can help simplify complex supply chain processes. Still, companies should be aware that they will ultimately need to be operated by human manpower and make the necessary arrangements to provide ample training for their workers to bridge the skill gaps.
The era of data-driven processes is here and companies, as well as their workforces, will do well to embrace it sooner rather than later.
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