Skip to content
Ready to transform your business?
Our engineering experts will help you find a
tailored set of solutions.



Manufacturing CEOs face critical decisions about IoT & Machine Learning

by - Mike Galbraith | July 17, 2020 |

The numbers are in. The use of IoT and Machine Learning (ML)/Artificial Intelligence is exploding in the manufacturing market. Let’s look at the data:

  • The IoT segment is predicted to grow at a 29% Compound Annual Growth Rate (CAGR) to become a $45.3 billion market by 2022.
  • The machine learning market is expected to grow at an aggressive 44% CAGR, expanding from $1.4 billion in 2016 to $8.8 billion by 2022.
  • recent survey shows 61%of organizations see machine learning/artificial Intelligence as the most significant data initiative for their company in 2018, and 58% of those respondents have run models in production.


With the IoT market already at critical mass and the ML/AI market rapidly moving towards the mainstream, CEOs can either take action now to stay ahead of the curve or risk falling behind.



Why is AI & ML technology adoption so pivotal?
Many manufacturers are struggling with volatile demand as well as complex, differentiated products. These market factors make inventory planning and supply chain optimization a constant struggle. It leads to inefficient operations that result in a volatile bottom line.  IoT and ML technologies are disruptive to current manufacturing techniques, and companies that incorporate these technologies are dramatically changing their traditional cost and efficiency metrics.



AI & ML are revolutionizing manufacturing expectations
Incorporating IoT and ML increases productivity and efficiency, and the potential will only expand in the future. By providing integrated machine-to-machine communications, as well as gathering massive quantities of data for analytics, manufacturers are improving metrics throughout the supply chain and on the shop floor. The benefits of these technologies are myriad, assisting manufacturers by:

  • Increasing supply chain efficiency: The globalization of manufacturing has resulted in complex supply chains that include numerous providers, in increasingly diverse locations, so it is harder to react quickly to fluctuations in demand. Incorporating IoT devices in the supply chain provides precise information on current supply, demand, and the ability to track goods in transit. It provides real-time data analysis that enables organizations to react quickly to changes in demand and scale manufacturing in shorter timelines. In fact, McKinsey researched the German manufacturing market and posits that ML will reduce direct expenses by cutting transport and warehousing costs by 5 – 10% and reducing supply chain administration costs by 25 – 40%. In addition, it forecasts that ML predictive analytics will improve efficiency planning and lower supply chain forecasting errors by up to 50%, which will enable inventory reductions of 20 – 50%.
  • Improving inventory management and asset tracking: Machine learning algorithms and modeling are making it possible to optimize inventory across all distribution locations. It even enables companies to factor in variables, such as the weather and the economic climate that can impact demand and production.
  • Preventing downtime and reducing annual maintenance cost: IoT device sensors can identify and alert organizations of imminent machine failures, so floor managers can act quickly to order repairs during off-hours and avoid downtime. These devices can also track historical maintenance and performance information, sharing the data with ML-based predictive analytics tools to optimize your maintenance schedule. McKinsey posits that this process can reduce inspection costs by up to 25%, and lower overall maintenance costs by up to 10%.
  • Increase manufacturing productivity: Using IoT devices or programmable logic controllers, manufacturers can collate data from all machines to find efficiencies in the processes and equipment. In addition, organizations can increase yield by using IoT devices and sensors for early quality detection. By sensing quality issues sooner, organizations can drive down the costs of production and raw materials. McKinsey estimates that incorporating both IoT sensors and ML productivity tools can boost asset productivity by up to 20%.
  • Acquiring real-time data analytics: IoT devices can make the manufacturing floor more efficient by using real-time analytics from these interconnected devices. Instead of improving processes when a lag is noticed at the end of a shift, alerts can be triggered earlier in a shift, so the root cause can be identified and solved immediately.
  • Harnessing the power of predictive analytics: IoT devices can feed relevant information into machine learning technology to create an optimized, predictive and automated process.


As organizations compete in a globalized marketplace with increasingly tight margins, proper integration of IoT and ML into the manufacturing process can better predict customer demand and optimize inventory levels to ensure productivity without draining working capital.



How to re-define your business processes to incorporate IoT and ML
It is clear that AI and ML are critical for making manufacturing companies more productive and agile. The path to realizing the benefits of these technologies requires that you:

  1. Transform your business processes to incorporate these new digital technologies.
  2. Upgrade your enterprise resource planning software to incorporate AI and ML into your business processes.


The most effective way to redefine your business process is to work with a digital transformation consulting group. At ThoughtFocus, our team is composed of former CTOs, CIOs and VPs of operations, who know all of the best practices for creating innovative digital business processes that drive results across the entire value chain. We have established project methodologies that provide a clear vision for every step of your project, defining detailed business strategies, drafting innovative process designs and designing efficient implementations.


In addition to redefining your business processes, you need to update your business tools to integrate AI and ML. In good news, most manufacturing companies use SAP, and the new SAP S/4HANA platform has been completely re-designed to enable dramatically faster, more flexible, digital transformations. It provides the best platform to successfully incorporate IoT and ML into your manufacturing organization and maximize the value of your business. As an SAP Silver Partner, we have the expertise, proven methodologies and best practices to design and deploy innovative solutions that deliver value.


Ready to learn more? To see how ThoughtFocus can enhance the efficiency of your manufacturing process and supply chain, contact John Campanella, GM SAP Practice at or +1-310-427-7654.


  • Mike Galbraith

    Mike brings many years of experience as an IT executive and CIO for several Fortune 200 companies. Areas of expertise include global IT strategy, delivery and operations, Digital Transformation, ERP systems, IoT, Big Data and Analytics. At ThoughtFocus, Mike assists clients in developing capabilities to drive innovation and competitive differentiation.

Mike Galbraith

Former Vice President, Technology Strategy and Solutions

Mike brings many years of experience as an IT executive and CIO for several Fortune 200 companies. Areas of expertise include global IT strategy, delivery and operations, Digital Transformation, ERP systems, IoT, Big Data and Analytics. At ThoughtFocus, Mike assists clients in developing capabilities to drive innovation and competitive differentiation.