In this third guest post, LNS Research Marketing Analyst Mike Roberts shares 5 impacts the influx of Big Data and the Internet of Things (IoT) will have on quality management, and affords us solutions for managing them.
It seems like we can’t look anywhere lately without seeing some reference to the emergence of Big Data, and how the onset of the Internet of Things (IoT) will exponentially increase the vast amounts of data we’re already collecting. Many writers, thought leaders, and news outlets covering these game changers have positioned themselves as harbingers, warning the business world of the seismic effects the Big Data/IoT evolution will have on how we do things.
Much of the importance placed on this topic is just, for we are witnessing and will continue to witness radical changes in data collection and analysis. However, what is lost in a lot of the buzz surrounding this shake up is the practical advice we need to know to understand how Big Data will directly impact the way we manage quality.
“…a gradual—yet profound—transition…”
The onset of Big Data and IoT is often characterized as something that is going to “happen.” It is folly to think of it with this level of simplicity. Instead, this revolution has been percolating for years, and will be realized in the coming months and years through a gradual—yet profound—transition. At more mature organizations, the effects are more apparent while at others of lower maturity it has been slower to come.
We need to find ways to respond tactically in the present and to strategically prepare for the impacts of Big Data on quality management as we move forward.
Key impacts on quality management
Here are the 5 key impacts Big Data will have on quality management, and the opportunities they present:
1. Correlating performance metrics across multiple plants
While many different plants might generate the same product, use the same equipment, and collaborate with the same suppliers, quality performance can still vary greatly between plants. The onset of Big Data will challenge and enable us to better analyze structured and unstructured performance data across multiple plants.
Once we have derived intelligence from this data, we will be able to apply universal best practices across all plants to better align processes and improve quality performance. This aligns with our recent survey on Manufacturing Operations Management (MOM) in which 36% of respondents indicated that correlating performance across multiple plants would be a key impact of Big Data on manufacturing performance improvements.
2. Perform predictive modeling of manufacturing data
Behind the apparent conclusions our immediate metrics and key performance indicators (KPIs) present, there are often hidden correlations across collected data that can’t be identified without large data sets and sophisticated tools. When the right tools are applied, we can begin to unearth hidden meaning within our data. For example, we might identify that failure rates spike when a certain machine is activated above a certain temperature or when running a certain product.
The causation may not be immediately clear, but correlations can be tracked through robust analysis and we can eventually find evidence for causation—if we are looking for it in the right way with the right tools.
3. Better understanding of supplier network performance
If we implement the tools that can combine supplier test data with supplier manufacturing performance data and correlate this information to data associated with internal manufacturing operations, we will be able to gain insights into the true cost of a supplier non-conformance.
Once we have this intelligence, we will be able to push it back across the supplier network through requirements best practices to better drive long-term continuous improvement.
4. Faster customer service and support
The combined impact of IoT and Machine-to-Machine (M2M) communication means our smart, connected machinery will enable manufacturers to remotely monitor equipment in real time.
As a result we will be able to implement service-level agreements to drive added value for customers. Indeed, in the aforementioned MOM survey, 39% of respondents indicated faster customer service and support would be a key impact of Big Data on performance.
5. Real-time alerts based on manufacturing data
As any operations manager knows, it is critical to get the right information to the right decision maker at the right time. Currently, this is more about access to real-time data and our capacity to push that information to the right roles. However, as we move forward we will have more contextualized intelligence based on the specific past behavior of specific users and machine learning.
Just as we have seen how the search engine Google learns ”intelligently” what users have searched for and viewed online historically, tailoring personalized experiences based on past behavior, next-generation Big Data manufacturing software will feature algorithms that can achieve the same results.
New LNS Research Paper: “Big Data: Driving Quality Intelligence at the Speed of Manufacturing”
To learn more about the technology that’s transforming massive Big Data sets into quality intelligence, read LNS Research’s new paper, “Big Data: Driving Quality Intelligence at the Speed of Manufacturing.”
Click here to get the paper
