At the onset of 2016, we identified key trends to watch for in manufacturing, and as the year draws to a close, it’s time for an evaluation of our predictions to discover the state of manufacturing. In a sense, we are looking back at our previous selves to see how we can improve our future. This is the fundamental concept behind most supply chain processes, so let’s take a closer look at what was expected versus what happened.
The State of Manufacturing: eCommerce, Analytics, and Robotics
E-Commerce Grew More Important in Manufacturing.
E-commerce was expected to become the dominant player for manufacturers in 2016 through the following:
- Increasing share of aftermarket parts sales.
- Custom e-commerce solutions.
- Integration of e-commerce with IoT-based technologies.
- Mandated integration of dealer-manufacturer systems.
- Greater sales of parts directly to consumers.
Since e-commerce can be best analyzed by reviewing Cyber Monday 2016, consider the record-breaking sales achieved. Per Fortune Magazine, this was the most successful shopping holiday in history. Major retailers, including Walmart, Target and Kohl’s among others, dramatically improved their online shopping portals in anticipation of the holiday. Cumulatively, shoppers spent $3.45 billion on Cyber Monday, and total online sales in the U.S. have surpassed $40 billion, as of November 29, 2016.
In other words, e-commerce has clearly defined its path with the state of manufacturing. More importantly, the named retailers have also expanded online sales options to include products direct from manufacturers and various dealers or vendors. Of course, none of this would be possible without improved self-service features, as explained by “The Impact of B2B E-Commerce on Manufacturers and Distributors,” and comprehensive, easy-to-integrate platforms. Meanwhile, 79 percent of manufacturers and distributors, surveyed by Handshake, reported increasing use of e-commerce to meet customer demand, and the role of business-to-business e-commerce has become more of concern for up to 63 percent of companies.
Advanced Analytics Became Slightly More Complicated.
Our prediction for the state of manufacturing in analytics were supposed to become more advanced, giving manufacturers more power to predict and accommodate changes in production on a real-time basis. Unfortunately, manufacturers may have grown too preoccupied with ideals to understand how analytics played into production in 2016. While 41 percent of manufacturers are beginning to embrace analytics, approximately 50 percent do not understand key difference between business intelligence, big data analytics and predictive analytics, reports Johnny Williamson of The Manufacturer.
True, all analytics do provide insight, but the different types describe different ways data can be used.
For example, big data is all the data compiled and analyzed. Predictive analytics provides insight-based predictions on how production and distribution may change, and business intelligence uses this information to boil down to the fact-based, immediate decision making.
There are a few reasons for this delay and confusion. Manufacturers have simply not yet fully integrated systems or applied them to changing current processes. Today, most analytics capabilities are being used to design next-generation products, not improve current standards. Thus, analytics has grown more complicated, but it does not yet provide the key benefits of improving productivity, reducing machine downtime and improving quality. The argument can be made that next-generation products improve quality, but how can a manufacturer improve the quality of today’s products without understanding how they can manufactured faster, correctly or more efficiently before moving on to the next product? In other words, manufacturers need to fix today’s issues before working on the problems of tomorrow.
Meanwhile, the use of analytics is starting to become isolated. Only 40 percent of workers are considered knowledge workers, giving them access to data insights. This is the result of manufacturers experiencing trouble creating a “single point of access” for data, reports Industry Week. Ultimately, most manufacturers continue to operate on a safety-stock forecast, not a real-time planning process, giving rise to a “higher level of product quality [reducing] costs and improving customer satisfaction,” asserts Sam Pearson of Deloitte.
Robotics in Manufacturing.
The use of robotics in manufacturing was expected to continue growing. By 2018, 1.3 million new industrial robotics were expected to become key parts of global manufacturing. However, there appears to be a greater acceleration than anticipated.
In a recent blog post, “Robotics in Manufacturing: Creating More Efficient Operations & The Future of Manufacturing Jobs,” we discussed how many Americans view foreign manufacturing as the killer of American jobs. Most jobs lost in the U.S. this year were due to robotics. In fact, the value of robotics in the U.S. will climb to more than $1 billion by 2020, and globally, the value is expected to reach $3.1 billion, explains Jim Lawton of Rethink Robotics. Of course, the addition of new robotics in collaborative manufacturing means more people will be needed to repair them and complete the tasks robots are not currently doing.
Robotics were also primarily linked to mass manufacturing in our prediction. However, the use of robotics is becoming more acceptable in small to medium-sized businesses. Per China Briefing, the costs associated with integrating collaborative robotics into existing systems are much smaller now than previously seen. More importantly, these costs are going to drop more than 20 percent over the next decade, making them even more affordable.
Stay Tuned For The State of Manufacturing: Part II As We Trek Back Through Our Predictions and How They Fared.
We made six bold predictions, and many of them do retain their fundamental benefits and growth. However, our prediction surrounding analytics went a bit further than actual implementation and usefulness in the industry throughout the year. While we would like to see more manufacturers take advantage of analytics, we must also look at how they did in relation to our other predictions, which will be discussed in Part II.