Manufacturing is a more powerful and essential part of our industries and economies than ever. But setting these vital enterprises up for maximum success and unrivaled innovation takes information — and that means gathering data.
If you represent a manufacturing concern and you’re wondering about the benefits of capturing and analyzing operational data, you’ve come to the right place. Investing in analytics isn’t something to take lightly, but companies that do it well can set themselves up for success they didn’t even know was attainable.
Who’s Using Analytics in Manufacturing?
Broadly speaking, big data analytics is your company’s ticket to efficiency and productivity improvements. It helps bring actionable information to the surface — where before it was hidden or ignored — and puts those insights to work for you by recommending process improvements or more clearly identifying obstacles.
In recent research, 67 percent of executives from various manufacturing companies indicated that they had plans to invest in big data. That’s a resounding show of approval, especially when you consider the pressure modern enterprises are up against when it comes to cutting costs. What this tells us is that modern business leaders see big data analytics itself as a cost-saving measure with an attractive ROI.
Popularity is one thing, but is it justified? Let’s take a look at some of the major areas in which production line analytics can help you improve.
1. Monitoring Assets for Performance
Physical infrastructure and tangible assets can make or break manufacturing companies. Spending money on equipment upkeep and regular maintenance has always been a cost of doing business that’s impossible to avoid. But data analytics can ensure you get maximum life out of your assets and avoid breakdowns that cost you money in lost business.
Optimizing the performance and useful lifetime of any equipment begins with collecting machine logs. You’re probably familiar with the concept of the Industrial Internet of Things, but you may be less familiar with the selection of data-gathering equipment available — such as sensors — with which you can retrofit your existing material handling apparatus and even your vehicular assets to gather relevant performance data.
“Smart” equipment like this helps you prevent breakdowns and losses by measuring performance in real-time, with a granularity that includes excess vibration, unusual temperature readings and whether a machine is using more energy to accomplish the same amount of work.
2. Examination of Each Step in the Supply Chain
Manufacturing is a supply chain unto itself — but it’s also a part of a much larger chain, and one that oftentimes spans the globe. Think about how many processes and moving parts there are just under your own roof, not to mention across your other business locations, your suppliers and your freight partners. Now, consider the benefits of applying logistical analytics to the bigger picture, from top to bottom.
Think about what needs to happen in between sourcing raw materials and getting your products into the hands of your end-users. Your process is surely unique, but it likely includes the following:
- Transporting raw materials from the source to your factory floor for processing or assembly
- Employees moving finished and unfinished goods between process areas on the floor of your facility
- Teams and equipment stowing products for later sale or packaging for immediate dispatch
It’s all about discovering bottlenecks you didn’t know existed or even evaluating the benefit of bringing in additional personnel or equipment to bolster your most critical operations.
Even observing the delivery process through the impartial lens of data analytics could reveal opportunities to make changes and may even indicate when it’s time to choose a new and more efficient business partner for sourcing materials, improve last-mile delivery or any of the many steps in between.
3. Analysis of Customer Behavior to Revamp Products or Introduce New Ones
If there’s such a thing anymore as “traditional” manufacturing, it’s defined by producing identical products in great numbers, shipping those products to the point of sale, promoting them to prospective customers and then hoping for the best. Thanks to big data analytics, this paradigm is now decidedly in the rear-view. Companies that know how to collect meaningful customer data can put it to work developing new product lines and bringing impactful changes to existing ones.
Data analysis and customer relationship management platforms help modern manufacturers discover changes in customer buying behavior, such as finding out which products are becoming more or less popular over time and making proactive changes to the rate of production to address those trends.
More advanced forms of customer behavior analysis include things like:
- Observing which product pages have the greatest bounce rates
- Looking at which products are viewed or added to carts in tandem with others
- Finding out which products see the most frequent rates of exchange
- Making predictions for future customer purchases
- Analyzing customer sentiment and the use of keywords on social media
- Monitoring repair partners and the aftermarket to see how customers use, modify, repair and ultimately “live with” your products
The underlying goal here is one which has become universal among manufacturers: leaner manufacturing. Doing more with less has become the mission statement for companies all over the world and a matter of significant ecological importance.
Moreover, what business out there wants to risk having warehouses full of slow-moving or hard-to-sell merchandise? Gathering the right data, from the right sources, helps you get a handle on what your customers really want.
And on that note, customers don’t just want stock products. They want customization, which has become an increasingly important competitive advantage for companies that want to stake their claim on a global scale. Data can help you identify cost-effective places in your production lines where you can introduce variations or customization without reinventing your whole process from the ground up. A recent Deloitte report described this opportunity as “mass personalization” — and drove home the point that data is the key to offering greater product customization without greatly impacting operational efficiency.
Data in Everything
Manufacturers who want to optimize and even reinvent their production lines are spoiled for data partners these days. All of the major and minor technology companies — and an ever-wider variety of startups — have turned their attention to holistic data solutions that integrate with industrial control equipment and enterprise planning platforms. They’ve responded to the writing on the wall, which says data analytics aren’t optional these days, but rather an essential part of any forward-thinking organization.