Manufacturing Analytics – How can big data help manufacturers make informed decisions.
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” ― Geoffrey Moore, Author of Crossing the Chasm & Inside the Tornado
It is statements like these that inspire fear and uncertainty amongst businesses today. Industries like media, retailing, banking, telecom, travel have already been substantially disrupted by technology. Today, as the Internet moves to its next frontier; Machine-to-Machine (M2M) communication, some of the biggest disruptions are happening in manufacturing.
Challenges for manufacturers
Change is happening in a hyper-competitive world, beset with global trade tensions and emergence of disruptive technologies. While manufacturers understand that they have to invest in data and analytics, they’re struggling with their own challenges and are unclear about the usefulness of technology.
Let’s step back for a moment and look at the top priorities for manufacturers today:
We researched a number of companies in the manufacturing sector to understand their prioities and the key challenges they face. This was then grouped into five major priorities.
||Efficiency & Cost Reduction:
|Consistency In Quality:
||Customer Service Enhancement:
|Safety & Security:
Manufacturing analytics helps answer all these questions
Today, sensors allow us to capture large amounts of manufacturing data. This can be integrated with existing data sets from ERPs, CRMs, customer surveys, and manufacturing systems. Using analytics and AI tools, this data can provide immense insight which can address many of the key priorities of manufacturers.
Let’s take a closer look at some of the applications of analytics:
|Growth||Analytics on customer buying patterns:
How are customers responding to newer business models
How do customers feel about new features – should we continue to innovate on those lines, or pivot?
Are new geographies and new verticals opening up for our products? What could be the revenue potential?
|Prediction of when and what products customers are likely to buy
Carrying out data-driven, customer-focused innovation, instead of merely replicating what is in vogue
|Efficiency & Cost Reduction||Real time monitoring of machines for predictive maintenance
Analytics on real time data to optimise supply chains and inventory
Energy and utilities monitoring
Algorithms for production planning
|Reduced machine downtime and increased machine life
Significantly reduced transportation and inventory costs
Reduced power and utility costs
Real-time creation and updation of the optimum production plan
|Quality||Automated quality measurement using sensors and big data analytics||Faster and more accurate quality checks
Ability to test 100% of parts/ products as opposed to mere sampling
|Customer Service||Real time feedback via sensors on the product||Anticipate problems before the customer does
Innovate around un-met needs
|Safety & Security||Sensors on moving parts, pollutants, temperature etc.||Automated alerts improve worker safety substantially|
This is just a sample of the power of analytics and big data. Applications and use cases are multiplying with the advent of machine learning . As we go along this journey, more powerful benefits will accrue.
While analytics alone may not solve all manufacturing problems, these tools can provide significant benefits aligned with the top priorities of manufacturers. These benefits accrue across the value chain, right from improving responsiveness to the market & anticipating customer needs, to optimising supply chains & delivering the best quality.
Moreover, many of the benefits we see are just the tip of an iceberg! Remember, these are ‘smart factories’. Thanks to AI and machine learning, there is an inherent predictive capability which learns as it evolves. So the system keeps getting smarter!
In today’s world, analytics is not optional. It is essential if one wants to stay competitive.
Mandar Gadre | Director of Engineering – IoT
Mandar Gadre serves as Director of Engineering – IoT for GS Lab. Mandar holds B.Tech from IIT Bombay, and a Ph.D. in engineering from Arizona State University, USA. He brings deep expertise and experience in crafting industrial solutions, leading technology teams, while contributing technically to sensor technology, hardware and control solutions, and data analytics. Mandar has helped numerous organizations implement IIoT and delivered results that have shaped new business models for those organizations.