Big data analytics is being used all over the world to derive multiple benefits from the data being created. A variety of enormous data is being generated at an incredibly fast rate in various sectors, as a result, big data analytics has become extremely important and unavoidable.

Many manufacturing companies, especially in material handling and supply chain management, are actively applying data analytics and other data science tools for many of their business operations.

Shift emphasis from mechanical to computer-controlled equipment

Material handling is the science and art of receiving, packaging, storing, and transporting any kind of material.

The global material handling equipment market size was USD 211.76 billion in 2019 and is projected to reach USD 320.90 billion by 2027, exhibiting a CAGR of 5.4% during the forecast period.

To reap the benefits of the growth, the material handling industry must change its emphasis from mechanical to computer-controlled equipment and systems in order to build flexible supply chains. The use of Big Data is one of the most important aspects of this transition. These systems can make data-driven decisions and allocate resources accordingly based on real-time data.

To provide timely and customised deliveries, material handling systems would need to use data such as:

●      Customer position

●      Interests

●      Purchasing history

●      Possible future orders

●      The movement of products through the supply chain.

3PL companies will be able to make better staffing decisions and change job schedules thanks to data-driven material handling systems.

Similarly, Big Data would be crucial in monitoring production schedules and assessing the best maintenance downtime. As a result, predictive analytics based on Big Data will aid in the improvement of material handling equipment efficiency. It's a win-win situation for everyone because it eliminates business risk while also saving money on operations and maintenance.

Helping in decision making

Companies must concentrate their efforts on locating data that will assist them in making critical decisions and improvements that will improve the performance of their materials handling systems. This mission is made simpler by warehouse control systems (WCS).

The goal is to provide instant access and visibility in the process, rather than waiting one move, one day, or one week to analyse this form of data.

Companies that collect information can make quick decisions that improve efficiency and asset usage. When so many workers work on a single job, for example, productivity suffers. Using that information, a business will easily decide which workers will be better off doing something else

One of the key reasons why the need for big data in the distribution sector has risen so much—and so quickly—is that it allows businesses to access the right data to make smart decisions in the warehouse. They also want the data to be processed for a long period of time so they can view and evaluate it.

Companies now seek data that can provide real business intelligence—information that can be used to boost warehouse performance and productivity. Collecting big data on warehouse and materials handling activities is critical for businesses that work in a "pull" environment to help make better decisions on the fly.

By gathering data from our materials handling systems, we can say exactly what each piece of equipment, workstation, and employee can handle.

Post COVID scenario

In the post COVID-19 period, growth will be led by a re-doubled focus on modernization and technological advancements. Factors such as strong focus on automation, efficiency, data analysis and worker safety will play a paramount role in augmenting market growth.

The Combination of material handling machinery with big data and analytics is anticipated to positively change the market outlook.