Role of Analytics in Setting Up a Smart Plant

Role of Analytics in Setting Up a Smart Plant

Role of Analytics in Setting Up a Smart Plant


The modern era of technology has enhanced our lives in many different ways. From smartphones, smart cars, and smart homes; the trend is now shifting towards smart factories and smart plants.

Since the onset of the industrial revolution, manufacturing companies have been constantly adopting new processes and methodologies to improve their equipment efficiency, maximize yield, reduce the scrap, and optimize their energy consumption. In the last few decades, with the use of sensor technologies and the implementation of computer-based systems, industries have achieved high levels of automation in the production lines and lowered their manufacturing costs.


Pathway to a Smart Plant

From digitization to digitalization, to total digital transformation, manufacturing industries have come a long way from managing their operations manually to being largely run by robots and controlled by smart devices. Digital Transformation integrates digital technology into all areas of business, fundamentally changing the way you deliver value to your customer. In this blog, I will describe a few things to consider on your journey to a Smart manufacturing plant:

Figure 1: Components of Smart Manufacturing Plant


Data Generation

In the present times, enterprises are moving towards the complete digital transformation of their end-to-end operations and business processes. Traditional PLCs are being gradually supplanted with highly sophisticated integrated Industrial Internet of Things (IIoT) and Internet of Things (IoT) systems. In place of traditional maintenance activities, the plants are gearing towards alert based and predictive maintenance that aids in saving costs caused due to unexpected breakdown of key machinery that could lead to unplanned outages. IoT & IIoT enabled devices & equipment to generate many data points and the use of this information drives the success of a smart plant.

Integrated systems with models that have machine learning capabilities allow operators and plant supervisors to make decisions much early on before any catastrophic event occurs.

All of this and much more has largely been possible with the use of smart gadgets and extensive use of advanced predictive analytics. Smart analytics empowers the plant operators with insightful information that immensely supports them in making crucial decisions in supply chain management.


Digital Infrastructure Platform

Most organizations going through the Digital Transformation of their manufacturing plants will require an upgrade of their existing network infrastructure to a highly efficient digital network. An advanced digital infrastructure will enable fast movement of massive amounts of data from IoT enabled devices to analytics platforms and support real-time processing and reporting across different geographical locations.

The Plethora of data that IoT devices generate is sensitive to the security breach. This calls for heightened security around the advanced digital network. Technologies such as distributed network or software-defined networks should be considered to achieve the level of security required to ensure both personal and proprietary data is safely transmitted over the company’s network.



Data Acquisition, Integration & Enrichment

Collection and acquisition of data from a range of sources such as PLCs, Sensors, IoT/IIoT enabled devices, External & Internal systems is of utmost importance in the making of a Smart Plant.

There is a range of Data Acquisition (DAQs) tools available in the market to choose from. Selection of the right tool should be made keeping in mind the data generation capability of the tool, as well as its ease of integration with:

  • Equipment & machinery used in the plant
  • Databases & upstream systems
  • Analytics applications

Additionally, some advanced data integrators offer an out-of-the-box reporting suite that is based on the sensor data being generated directly from the machinery used in the manufacturing plant. This data is time-series and is primarily good for real-time operational reporting.

Figure 2: Real Time Data Acquisition and Visualization


Data Analytics and Visualization

The Journey to a smart plant traverses through various maturity levels of data analytics. From the simplest reporting platform to advanced Analytics, a smart plant benefits with the use of the highest level of data analytics to optimize the business of any manufacturing organization.

One of the key advantages of the digital transformation of any manufacturing plant lies in combing time-series sensor data with data from MES (Manufacturing Execution) systems and other ERP systems to create near real-time analytics reporting.

Setting-up Data lakes and building big-data analytics solutions with state-of-the-art visualization is key to the success of a Smart Plant. From mobile devices to tablets, to large display screens on the plant floor & meeting rooms, the Analytics reports provide insights into the operations and help businesses with running the manufacturing process more efficiently.

Digital Dashboards with metrics from different operational areas in the manufacturing plant provides one-stop-shop for the overall functioning of the plant to managers and leadership of the company.

From an inward movement of raw materials to the production & transportation of finished goods to the customer, as well as other areas such as EHS, Quality, Maintenance, Sanitation, and Work-force management, analytics reporting offers visualizations into all of this and supports operation and preventive maintenance of the plant and its management very efficiently.


Analytics-Driven Decision-making

The Analytics maturity of a manufacturing plant goes higher as the overall approach and use of reporting applications move from Reactive to Pro-active.

A Smart Plant runs advance analytics tools and applications that are geared towards providing optimization and predictive insights into different functions and operations of the business processes. Advanced Analytical applications bring awareness to the users, helps them with decision-making, allow timely creation and escalation of actions, and promptly resolve the problems for the smooth operation of the plant.

Figure 3: Pathway to a Smart Plant


Smart Plant Analytics: Shift from ‘Descriptive’ to ‘Prescriptive’

The approach to analytics that is largely based on the Enterprise Data Warehouse (EDW) is getting outdated rapidly. Primarily due to the nature of analytics (or reporting) is descriptive in nature that makes the business process run in a reactive mode to data insights. Another challenge to the EDW approach is the speed to insights, i.e. the time taken to reach the insights from the time the data was generated.  The expectation is nearing to real-time in case of operational intelligence.

Figure 4: Type of Data Analytics


Predictive Analytics allows statistical models to be built that correlate data from various manufacturing processes and empower the business to derive insightful information and make intelligent decisions proactively.


Illustration of data-driven decision-making

To understand how data analytics plays a fundamental role in establishing and sustaining a smart plant, let us consider a typical challenge faced by any manufacturing company around inbound supply chain visibility.


A pressing issue faced by the plant supervisors is to manage the incoming supply of its raw ingredients and the allocation of deliveries to the right tanks. More often than not, either the bulk tanks are overfilled, causing spills on the floor, or, remain undersupplied, disrupting to the processing.


To resolve this, the plant can install State-of-the-art laser fill level digital sensors on the Silos and tanks for the raw ingredients. Utilizing the data from the new fill sensors, a model can be developed that identifies upcoming material deliveries and provide routing for delivery of the materials to the correct silo and tank.

Visualization can be developed that will provide a recommendation for silo or group of silos that have the open capacity so that the incoming delivery can be routed to those Silos/tanks.

The plant operators know the demand for raw ingredients for the next few days, correlate it to their current consumption and able to successfully route the incoming deliveries to the right silos and tanks. This smart solution not only allows for effective operation in the plant but also streamlines the supply chain process.

Figure 5: Visualization showing the allocation of inbound delivery of raw materials to the silos based on available capacity with split logic




Traditional analysis frameworks based on batch processing and passive data querying are unable to effectively support continuously running equipment and operations in a plant. Therefore, data analytics platforms must provide near real-time streamlined data for reporting and analysis and help businesses in making pro-active decisions. Smart production is an on-going process with continuous enhancements and improvements. One of the key factors in implementing a smart manufacturing plant is having the analysis results for a piece of running machinery or a process, returned quickly.

Smart Plants are nothing, but a result of a successful Digital Transformation combined with advanced Data Analytics. With the innovation in technologies, enterprise leadership is deeply focused on implementing higher degrees of data analytics and undergoing total digital transformation to stay ahead in the competition for achieving an intelligent enterprise status.

Industrial Internet of Things (IIoT) and smart manufacturing technologies are developing and evolving at a fast pace, however, the journey to a Smart Factory can be quite challenging.

It is of significant importance that companies make the right choices and get collaboration from multiple parties and process owners who are responsible for running the operations of the manufacturing plant.

Here are some guidelines that should be considered in the journey of a smart plant:

  • Choice of the right digital platform (cloud vs. on-premises) to build high-performance technical infrastructure with the highest levels of security.
  • Use of suitable sensors and data acquisition tools that are robust, accurate and provide an easy interface with both upstream and downstream applications. Data integrators with reporting capabilities offer a greater advantage over those that do not.
  • Implement the right set and combination of operational and analytical applications to overcome the complexities and challenges in business processes.
  • Engage the resources with the right set of skill sets in engineering as well as IT analytics teams to implement a digital solution that works well and offers the best value to your business.
  • Train the workforce (bottom-up approach) at different levels and different stages of the implementation.


– By Amit Tandon

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