Can Advanced Analytics for IoT Drive Significant Business Value for Manufacturers?

Advanced analytics for Manufacturing

In a recent survey by Tata Communications, Industrial manufacturers reported a 29% increase in revenue from the previous year and predicted IoT initiatives will increase revenue faster than any other market segment from 2015 to 2018.  

Of the thirteen industries included in the study, industrial manufacturing is by far the most optimistic with regard to IoT’s ability to drive increased revenues.

The operations of a modern manufacturing environment involve thousands of sensors and machines that collect massive amounts of data.  Leveraging all this data for increased business performance is the obvious goal.

But how and where to begin are difficult questions to answer.  Fortunately, new ideas and tools in analytics for IoT are emerging that can help manufacturers make sense of it all and make decisions to improve their operational efficiency and overall business results.

Let’s review some of the major use cases for Analytics in IoT as a start to answering the where and how questions.

Use Cases and Applications for Advanced Analytics

The breadth of data in manufacturing scenarios means that there are a number of potential areas of leverage to gain significant business value.  Sorting through these opportunities to determine priorities itself requires some analysis and study.

In a recent survey in the Economist, respondents indicated which use cases and application they thought large volumes of IoT data would yield significant gains for their business:

IoT use cases data from Economist survey

Figure 1. IoT Use Cases Data (source: The Economist)

This data leads to a discussion of a few applications that are good candidates when searching for strong ROI on your IoT analytics projects in manufacturing.

While the appropriate starting block to address the maturity model for manufacturing will vary,  three of the most popular and impactful applications include:


Maintaining high value equipment at maximum production capacity is the single most important goal for any manufacturer. Predictive maintenance that ensures minimum unplanned downtime is critical to maximizing return on equipment investments.

IoT sensors on critical assembly equipment can deliver data in real-time that enable managers to make decisions rapidly and trigger actions to maintain production lines at maximum capacity.

Preventing hiccups of any type in a manufacturing process is crucial, and systems that can predict and suggest preventative action BEFORE anything happens to the production line are very valuable.


Ensuring maximum throughput through an operating line is another area where IoT analytics applications can add significant value. Complex assembly operations rely on a reliable stream of sub-components and a consistent supply chain flow.

IoT sensors and devices can provide early indicators of supply chain imbalances. Analyzing this data flow can yield many points of insight and potential actions for streamlining the flow of components, processes, and human resources applied to a particular production application.

Carefully balancing component supply with operational throughput can reduce excess inventory, accelerate the assembly process, and reduce capital requirements. All of these benefits deliver increased profits and improved customer service.


The devices and machines on a typical manufacturing line in today’s environments are major financial assets for any manufacturing organization. They are typically the most significant capital assets for these companies.

Maximizing the benefits on investments of this scale is one of the most important topics for management and is a core topic of the ongoing dialogue between the COO and CFO. The ROI on these investments is often the difference between success and failure – sometimes more than the economics of the product itself.

Manufacturers that have superior processes and asset utilization often gain competitive advantage with pricing power and flexibility that their less efficient competitors lack. IoT sensors and systems are key tools for companies looking to lead in maximizing asset utilization in both discrete and process manufacturing.

The Analytics Value Chain™ – The Key Tool for Generating Value in IoT Analytics

The irony of the IoT era for manufacturers is that while it offers great promise because of the ability to leverage the high volume of data and interactions, it is also difficult to put it all in context and take meaningful action that will have an economic impact.

The applications outlined above often strong potential for analytics applications, but a methodology is needed to sort through and prioritize the projects. This blog began by asking if advanced analytics can drive business value.

The answer is a qualified yes. Driving value depends on having a systematic methodology and approach to drive increased value. It does not happen automatically simply because “advanced analytics” are used.

To create significant value, there is a need to execute analytics in real-time across the Analytics Value Chain™ (streaming, historical, predictive, and prescriptive) with relevant contextual and situational data that addresses the critical last step for timely outcomes.

Let’s step through the process in the figure below.

Analytics Value Chain for Manufacturing

Figure 2. The Analytics Value Chain™

  • Fast Data Ingestion – Ingesting data at speed and volume throughout the factory sets the stage for additional processing
  • Real-time Streaming Analytics – Real-time Streaming Analytics processes incoming streams of data from sensors and devices in the factory environment
  • Historical Analytics – This refined data is then correlated with contextual and historical data to provide a baseline for advanced analytics. Contextual data can include information like the physical environment of the factory or historical performance of suppliers.
  • Predictive Analytics – The next step is to predict failures, anomalies, or patterns using predictive analytics that are based on machine learning over situational data such as external events like the current plant utilization rate or the condition of production equipment.
  • Prescriptive Analytics + Intelligent Actions – The final step in the analytics value chain is to apply prescriptive analytics to determine the next best intelligent action to take. This next best action could be a wide variety of actions associated with lowering risks, addressing an outage on the assembly line, or other timely actions that enable more efficient assembly line operations.

As shown in the final figure below, it is this final step that creates the greatest value.

Analytics Value Graph in Manufacturing

Figure 3. analytics value graph

The increasing value chain shows how each step in the process refines the data and adds more value and context.

The important point is that specific actions based on a rich understanding of history and context must be taken now in order to capture that value.

Final Thoughts

IoT in manufacturing offers numerous points of opportunity, but the massive data volumes and complexity make it difficult to know where to begin. Using the analytics value chain as a guiding tool, manufacturers can pick and choose which applications to build first to capture value quickly. There is no time to waste in manufacturing.

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advanced analytics for manufacturers white paperTo learn more about how the many types of data in manufacturing can be leveraged as well as how the sales and channel environments are rapidly changing, check out our Advanced Analytics for Manufacturing white paper.

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