The growing momentum for advanced technologies, such as machine learning, deep learning and IIoT in manufacturing is undeniable. At the same time, we all know that just because technologies hold promise for improving processes or productivity, it doesn’t make them a good investment for your business. And figuring out if these types of solutions make business sense is challenging.
First you need to track down reliable information about how much value you could realistically expect a solution to deliver and weigh that against the potential costs.
Based on our research, you are likely to find that many machine learning and IIoT solutions aren’t driving investment-worthy value… yet.
The reason is not because the solutions don’t deliver improvements. Rather, it’s that the costs or challenges associated with them are currently prohibitive and/or the improvements are often statistically insignificant compared to more traditional methods and solutions. For example, our review of several machine learning papers shows that you might only get something like a 2% improvement for demand forecasting using machine learning compared to multiple linear regression analysis. Clearly, that improvement could be of value to a large and complex enterprise. But for most organizations, there are usually far less expensive ways to make gains.
Of course, there are also many different applications for machine learning and IIoT solutions, and some areas hold more promise than others. Let’s take a look at what some research has shown as well as a few key things to consider as you look for ways to drive continuous improvements in your operations.
Breakthrough or incremental improvements? Research insights at a glance.
This report, Application of machine learning techniques for supply chain demand forecasting, explores the potential of machine learning to forecasting “distorted demand at the end of a supply chain (bullwhip effect),” comparing machine learning against several other methods. The researchers used a data set from a simulated supply chain as well as a data set from Canadian Foundries orders as the basis for their research. They discovered that although the advanced techniques did lead to more accurate forecasts, the improvements were not significant enough to justify the additional complexity and cost compared to a simpler linear regression model.
This report, Deep learning for smart manufacturing: methods and applications, provides an overview of deep learning techniques and brief history of machine learning. It includes a summary of some of the different ways advanced analytics methods have been used for smart manufacturing as well as a comparison of traditional machine learning and deep learning techniques. The paper, which is a good resource for understanding what it takes to make a machine learning algorithm succeed, discusses many uses of advanced techniques throughout the manufacturing process, from machine failure to product failure. Interestingly, it highlights the importance of good data (not just lots of data) and well-defined use cases to success with these technologies.
This report, Applications of Machine Learning Algorithms to Predictive Manufacturing, looks at trends related to machine learning in manufacturing and includes a case study about analyzing tool wear in a small automotive shaft manufacturer using regression algorithms. The research highlighted in the paper makes a good case for the potential of machine learning to predict tool wear quickly and accurately, which could lead to quality improvements. The costs per machine, however, would be very high, making the business case hard to justify in many cases.
Analysts are certainly more bullish on the potential of the combination of sensors and cognitive models to improve supply chain performance. For example, IDC predicts that analytics-driven cognitive capabilities could help one-third of manufacturing supply chains improve cost efficiencies and service performance by 10 and 5% respectively by the end of 2020.
It’s also worth considering that although the sensors may be cheap, and getting more data may be easy, these solutions will still add significant costs. Just consider that data scientists are in high demand, and new graduates are earning more than $100,000 out of school. In other words, in most cases the incremental value a solution could create versus the cost you will add to your business to analyze all of the data and do something useful with it is up in the air.
These simple examples are not meant as an indictment of or an argument for the technologies. But they are a good reminder for why it’s worth proceeding with caution and a well thought out plan.
The tipping point for advanced technology
Fortunately, you don’t need a huge budget or vast resources to start your journey toward IIoT and machine learning. Given the uncertainty of the benefits with machine learning, as well as security risks and other challenges with IIoT, however, most companies will benefit from a phased approach. And before you start seriously thinking about either technology, it’s important to consider how well your company is currently able to use its data from across key systems to keep operational performance humming. For example:
- Do you have capabilities for collecting and integrating data from across the organization?
- What capabilities and methods are you currently using for data analysis and how do your related processes help drive continuous improvements?
- How well do your teams track and manage improvement projects?
Depending on your answer to these questions, it might make sense to pursue a more advanced technology solution. But even then, finding a way to run a contained pilot that will allow you to accurately gauge the value a solution should be a top priority. And to that point, it’s important to ask:
- What exactly do you want to do or achieve?
- What capabilities are needed to do that and why?
- Do you have the process rigor and necessary resources to handle a potentially complex project and all of the additional data it will produce?
It all starts with integration
If you struggled with the first set of questions above, then it’s crucial to consider how you start improving performance across the organization and building a foundation for advanced technologies. The right approach could help drive double-digit operational performance improvements of 15% or more while setting you up for greater success with future initiatives.
Integration is a key first step because knowing what is happening at all critical points of your business in real time has become essential to staying competitive. But data without context will get your business nowhere. Consider a platform, such as our Dploy Solutions suite of operational excellence tools, that makes it easy to harness data from critical disparate systems and visualize it so your operations can consistently hit performance goals. Dploy integrates your data in a central location, giving people at all levels of your organization the ability to track and monitor goals and KPIs and take timely and appropriate actions based on what they’re seeing. Leaders are even alerted about critical misses that will negatively impact the day and then ultimately the week, month and year.
Once you integrate your data and can really see what’s happening in the business and why, you can continually fine tune operational performance and processes. And in the meantime, you’ll gain a much better idea for what other types of technologies you really need to continue improving.
No matter what type of technology solution you think you need, our team can help. With more than 500 years of combined operations experience, we can work with you to more accurately determine the potential benefits of a project to ensure the anticipated ROI is realistic.
If you are considering a machine learning or artificial intelligence initiative but need help getting started, contact us.
 Application of machine learning techniques for supply chain demand forecasting, European Journal of Operational Research, 2007.
 Deep learning for smart manufacturing: methods and applications, Journal of Manufacturing Systems, January 2018.
 Applications of Machine Learning Algorithms to Predictive Manufacturing: Trends and Application of Tool Wear Compensation Parameter Recommendation, ACM, 2015.
 Let’s Get Digital: Supply Chains are Going to Get Smarter in 2018, Material Handling & Logistics, December 2017.