Businesses around the world have discovered high technology through digital transformation. Such significant cross-functions as purchases and logistics are no exception. The efficiency of the business largely depends on how well the procurement and distribution process runs, how logistics operate with material flows. Companies can best set these processes up through digital transformation, but only if they learn how to benefit from Big Data.
The volume of information accumulated by organizations is increasing and the Big Data analytics market is going to reach $103 billion
by 2023. However, companies are still learning how to extract useful information from their dataflow. According to IBM
, poor data quality costs the US economy up to $3.1 trillion yearly. Due to Big Data methods, processing massive amounts of data for specific tasks and goals, the capabilities of analytics greatly expand and bring valuable statistics. Using Big Data
, companies can get a lot of useful information about their internal functioning, competitors, partners, and customers.
Logistics of the future
Advanced retailers and transport companies have a variety of IoT sensors and RFID tags to identify and track goods and receive massive amounts of data: current location, size and weight, traffic congestion, weather conditions, and even driver behavior.
Information on the performance of transport applications allows to manage and assess and warehouses, to identify peak loading and unloading hours, and more effective human resources and time management. Real-time arrival data will enable managers to consider the actual timing for ratings and further planning.
If delivery is associated with specific requirements, IoT systems
will keep records of the compliance and will be an additional reminder to warehouse staff, drivers, staff about existing requirements. It can be data on special temperature mode, CO2, protective clothing, maximum load, etc. Supervisors will receive statistics of compliance with the established requirements and any divergences that may affect the condition of the cargo or the time of delivery. Thus, the company can avoid accidents and prevent delays and losses in the future.
Predict the Future Through an Analysis of the Past
Big Data collection reveals the full history of logistics processes and an opportunity to plan the way forward. The Big Data architecture is a base for machine learning and regression analysis algorithms. With the help of regression models, analysis can dig into past events and identify the essential factors influencing the result. The daily assessment of these factors estimates the probability of the outcome of the same event in the future. The interpretation of these factors brings the ability to make informed decisions based on data-driven decisions.
In turn, collecting data on decisions and results is the basis for building solutions based on artificial intelligence.
Artificial intelligence and Big Data enable companies to interact with their customers in a targeted and accurate way, focusing on the principle of "unique consumer - unique service."
To meet customer expectations, retailers, logistics companies, and suppliers are actively implementing IT solutions and analytics tools that collect, structure, and analyze customer data. It helps to understand both partners and consumers and bring interaction to a personal level. Data analysis not only helps to make the most economical and fast route track in real-time but also provides transparency
of the delivery process for suppliers and customers who can track the movements of their orders.
Most companies now realize that they have resources and advanced tools and platforms
to use their data and analytics and gain a competitive position. As McKinsey
director Tim McGuire remarks, "Analytics will define the difference between the losers and winners going forward."
It can take a long time to list the benefits of Big Data analytics. In the final account, digital transformation for procurement and logistics begins with extracting value from data and make the core business quickly adjust in response to the changing market with Big Data tools.