How to Unlock Connectivity for Holistic Digital Transformation of Manufacturing

How Neewee Streamlines Manufacturing Data Acquisition (DAQ)

“Connectivity” is the password for unlocking the holistic digital transformation of your manufacturing. There must be connectivity, not only between all the assets but also for all the processes in the entire manufacturing value chain. Neewee has always aimed at simplifying “connectivity” to streamline data acquisition and enhance customer experiences.

Different manufacturing systems require a different approach to collecting relevant production data. Several machines and processes are involved, and readiness of the equipment for connectivity differs.

At level zero, equipment resources such as sensors, actuators, and PLC transmit data. While at the next couple of manufacturing levels, Distributed Control System (DCS), Human Machine Interface (HMI), and the Supervisory Control and Data Acquisition (SCADA) or CNC machines for manufacturing systems act as the data sources.

Companies typically use an ERP (Enterprise Resources Planning) system for planning, supply chain, manufacturing, financials. While on shop floor or in plants manufacturers use information exchange standards such as OPC or MT Connect to streamline machine connectivity. In such cases, data acquisition can be simple by connecting to these systems.

In other cases, our Bodhee AI (Artificial Intelligence) apps are equipped to decipher a wide range of data sources. In addition, our range of IIOT devices called Rishee are used as data aggregators, smart controls, software connectors and gateways for continuous data communication.

“Information is in plenty, but intelligence is limited,

without seamless connectivity between machines and processes through all levels of manufacturing control systems.”

– Gucchu Gul Lalwani, Co-Founder & Chief Product Engineering, Neewee

Creating Seamless Connectivity in Processes and Machines

Harmonization of acquired data, in-depth diagnostic data analytics, and appropriate data visualization are essential for identifying and solving manufacturing problems.

Neewee’s Digital Twin technology makes the best use of the connectivity. Once multiple manufacturing assets and sub-processes data are linked together from end to end, a virtual replica gets created of every asset, component, and process in your entire manufacturing system. Digital Twin then facilitates the simulation of the LIVE production processes. Enhanced operational visibility provides the manufacturer with a clear understanding of the shop floor realities.

Integrated data analytics powered by AI and Machine learning algorithms then help identify the anomalies in the operations and assess improvement opportunities.

Monitor> Analyze> Control> Recommend

AI/ML-powered production monitoring and predictive analytics improve decision-making, enable process control, and provide actionable insights for process improvement.

 By aligning Neewee’s Digital Twin technology to the value you expect from your manufacturing processes, you can optimize production efficiency and enhance quality with a targeted approach.

To read in detail about the transformational impact Neewee’s Digital Twin technology can make on your manufacturing, you can read our previous post here (Back-to-Back Success for Neewee’s Digital Twin Technology. – Neewee)

Digital Transformation That Leads to Quantifiable Improvement

There will be a visible improvement in enterprise-wide performance. You can track and leverage key performance indicators (KPIs) to drive sustainable growth. Your manufacturing transformation is as efficient as the quantifiable factors indicate.

The Business Impact that Leads to $ Translation:

  • Reduced working capital by 8-10%
  • Enhanced product quality by 10-12%
  • Improved process yields by 8-10%
  • Improved production performance by 5-7%

Pick only the best holistic digital transformation strategy that guarantees quantifiable results.

Contact us. Let’s optimize!


[1] Neewee. (n.d.-b). Neewee – Enlighten your Manufacturing. Optimize Manufacturing with AI Apps That Deliver Integrated Analytics.

Data Experts Can Help Improve Manufacturing; Here’s How…

The way we manufacture anything today is drastically different than how our grandfathers did. Machines get connected to the web and are ripe for data analytics. The SMART machines are data-driven, enabling newer endless possibilities for manufacturing. In fact, realized intelligent factories depend on data science methodologies and collaborative manufacturing systems to make operations more flexible, adaptable and optimized. 

Data Science methodology is a set of techniques and processes used when solving manufacturing problems by utilizing data. This methodology has a significant impact on the practical application of data-driven products. Critical business data is analyzed with the objective of aiding enterprises better understand their business. The focus gets expanded from ‘what happened?’, ‘how often?’ and ‘where?’ to answer ‘why?’, ‘what if this trend continues?’, ‘what will happen in the future?’, and ‘what is the ideal scenario?’. Using subject-specific jargon, these questions correspond to analytical tasks widely known as statistical analysis, forecasting, predictive modeling, and optimization. [1]  

In other words, Data Analytics is about making the most of all data using a set of tools and technologies to deliver business value. The process begins with acquiring data from multiple sources (preferably clean and possibly in large amounts) in layman’s terms, the information you can leverage. Even the smallest of data can get integrated and analyzed to reveal whether or not a manufacturer is making profits, saving money, or over-spending to improve performance. Let experienced professionals or the right data analytics tools handle the data to get real value from your production processes.  

Aside from data analysts and statisticians, you can make the most out of available data by harnessing advanced AI apps, leading you to customer-centric goals and process efficiency. The AI apps work with the collected business data, bringing the numbers in the statistics to life, diagnosing, and correcting the manufacturing through informed decisions that drastically improve the production processes. 

“The goal is to turn data into information, and information into insight.” 

– Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co. 

A smart factory monitors the entire production process by linking physical and digital worlds. From building tools to individual operators in the workshop are rich data sources. It is an end-to-end connected and flexible production system, which uses a constant stream of data to provide learning from the machines. Based on the integrated data analytics, AI Apps provide real-time insights to adjust the process workflows, adapt to new needs, and optimize production. Intelligent factories thus achieve machine maintenance in advance, inventory management, and efficient production processes throughout the manufacturing network. 

Data can take many forms and fulfill many purposes in an intelligent factory environment. Data makes discrete information on manufacturing environmental conditions such as humidity, temperature, and pollutants accessible. It also helps self-optimize devices, addresses numerous manufacturing problems, improves process control, and guides while responding to new requirements. 

According to a recent study conducted by the World Economic Forum, nearly 75% of surveyed manufacturing executives consider advanced analytics to be critical for success. However, only a few companies capture the full value that data and analytics can unlock to help address the manufacturers’ most pressing challenges. [2] Establishing a strong technological backbone is a prerequisite to effectively scale data and analytics applications for manufacturing success in the post-pandemic world.  

Industry 4.0 is data-driven and when its technologies are growing exponentially, the frequency of data collection will also increase. Those organizations that are under-using data or are still not AI-ready run the risk of remaining on a lower rung of the business ladder.  

On a lighter note…. 

Although the D word is of BIG importance in the manufacturing world, we stand divided on the pronunciation. There is a valid reason behind the ‘proper’ pronunciation of the word Data. Ideally, it should be pronounced: day-taa, (not daa-taa) as data is the plural of datum (day-tum). 


[1] Omar, Y. M., Minoufekr, M., & Plapper, P. (2019). Business analytics in manufacturing: Current trends, challenges and pathway to market leadership. Operations Research Perspectives, 6, 100127.

[2] Weber, A. (2021, August 24). The Big Data Dilemma.