3 Manufacturing Technology Trends – CIS

Becoming an Industry Leader

The application of technology to manufacturing processes is not optional in a modern manufacturing business. Even companies performing in the middle of the pack employ technology to streamline their business practices. Industry leaders successfully merge business and IT strategies, often employing leading-edge technology solutions. A key aspect of a successful business IT strategy is to remain aware of and judiciously employ relevant technology solutions. This is no simple task, given the current pace of technology development. Below, we highlight three significant manufacturing technology trends, to provide insight into what industry leaders are doing.

AI / Automation / Cognitive Computing

A wide range of tech products are labeled Artificial Intelligence (AI) and/or Cognitive Computing. Based solely on marketing claims, it can be difficult to understand the difference between AI and Cognitive Computing. There is plenty of misinformation on this topic in mainstream marketing mediums. While both systems use similar computing tools, there are a few fundamental differences. According to Real-Time Insights:

  • Both AI and Cognitive Computing make use of tools such as machine learning, neural networks, natural language processing, and contextual awareness
  • AI uses pre-defined algorithms to resolve problems; it does not attempt to resolve problems in the same way as the human brain
  • Cognitive Computing does attempt to mimic human thought processes, in some cases by using sentiment analysis
  • AI takes autonomous action based on decisions it reaches using the fundamental tools
  • Cognitive Computing does not make decisions for humans, rather it makes recommendations to supplement human decision making

With this in mind, it is important to understand the differences in these definitions if you are shopping for AI / Cognitive Computing solutions for your business. Being clear about these differences will help you engage in more meaningful conversations as you develop your business IT strategy for your business needs.

How is AI and Cognitive Computing deployed in modern business?

  1. AI Example: According to Techemergence, Siemens latest gas turbines have over 500 sensors that continuously temperature, pressure, stress, and other variables. All this information is fed to their neural network-based AI. Siemens claims its system is learning how to continuously adjust fuel valves. Then, it creates the optimal conditions for combustion based on specific weather conditions and the current state of the equipment. More combustion results in fewer unwanted by-products.
  2. AI Example: In the same article, Techemergence describes how Siemens plans to use a product called Click2Make to create custom designs and start a bidding process among facilities that have the equipment and time to handle the order. The benefit is a significant reduction in cycle time to produce production plans and offer them to buyers, resulting in a quick turnaround from design to delivery.
  3. Cognitive Computing Examples: Siri, Alexa, and Google Assistant are good examples of cognitive computing platforms. These programs use machine learning, neural networks, natural language processing, and contextual awareness to understand the needs of the user and to generate solutions to meet those needs. The user is presented with solutions as recommendations.
  4. Cognitive Computing Example: Within the medical industry, cognitive computing is used to help medical staff with a review of available data and information relevant to a specific patient problem, and then to deliver recommendations for patient treatment and care. A doctor notably inputs data about their patients and cognitive computing algorithms explicitly analyze it using mimicked human problem-solving. The application then delivers some suggestions and information to help the doctor decide what to do next.

The range of potential applications for AI and Cognitive Computing is huge.

It is truly difficult to imagine a scenario where a business with even a moderate level of complexity could not benefit from an application of AI and/or Cognitive Computing. Whether or not AI/Cognitive Computing can give you a positive ROI surprisingly depends on many factors. It may even seem too complex and unknown to consider using it in your business. Regardless, considering the potential gains, a strong case could be made for investigating how this technology may benefit your business. Important to realize, a quality MSP can help you determine whether and how AI/Cognitive Solutions can benefit your business.

Industrial IoT (Internet of Things)

There seems to be a wide range of specific definitions for term IoT or Internet of Things. Moreover, the one that I prefer, from Business Insider, which fits well with the context of this article, suggests ‘The Internet of Things, commonly abbreviated as IoT, refers to the connection of devices (other than typical fares such as computers and smartphones) through(1) the Internet.’

Association of Equipment Manufacturers claims ‘roughly one-third of manufacturers today have an organizational strategy in place to apply IoT to their processes or embed the technology into their product offerings.’ Given the relatively new use of this technology, this bit of information clearly shows a growth trend for IoT in the manufacturing business. The use of this technology is diverse, and new applications continue to appear on the market.

Field Service

TGS, an Argentinian gas transmission company significantly improved employee utilization by 50% via the implementation of IoT, using tablet-based mobile solutions (ref. Field Technologies Magazine, July/August 2018, article ‘Tablet-Based Mobile Solution Improves Employee Utilization by 50%). With the heritage system, TGS field technicians spent a significant amount of their time completing the paperwork. In other words, this paperwork was necessary to accurately catalog information and track the movement of materials needed to effect maintenance and repairs. When moving to a digital system, the production efficiency of the field technicians improved remarkably. Which freed up the technicians to spend 50% more of their time performing business-critical tasks in lieu of the administrative tasks the old paper system required. While this is not a manufacturing application, these techniques are transferable to a manufacturing business, which significantly streamlines administrative procedures with the associated reduction in administrative time and cost.


IoT can certainly be a valuable tool for managing logistics. For one thing, consider how fleet management uses IoT. Delivery truck drivers, equipped with tablets connected to the internet, can indeed maintain logs of arrival times at key locations. The dispatcher and the customer (optional) are fed this information from a central database. Other information logged in to the central database includes truck and equipment maintenance schedules, load permits, fees and expenses charged during the trip, etc.

All of this information historically was submitted via paper. However, paper required significant administrative labor time and cost and required the resource for storage, retrieval, etc. Using IoT to manage the fleet, administrative costs and cycle time are significantly reduced. In addition, communication with customers is significantly improved, and maintenance schedules are more efficiently planned and executed. In general, real-time access to fleet information allows improved ability to manage the business proactively, resulting in better customer service and greater profit margins.


IoT significantly improves many aspects of distribution, resulting in greater operational efficiency and increased control of materials. Using RFID tags on inventory explicitly allows accurate remote tracking of material movement, giving management a clear view of warehouse inventory status, without needing to spend a lot of time on the floor. This can free up the managers’ time to allow more focus on business-critical tasks such as space management and material movement for increasing throughput volume and reducing delivery times. After all, operational efficiency and improved inventory accuracy are gained by scanning material for inventory control as opposed to manual counting. Additionally, monitoring of equipment utilization allows improved supervision and real-time adjustments to keep the equipment busy.

Does your manufacturing business, in fact, use IoT? Or rather, do you have an active plan to deploy IoT in your business? Regardless, if you are unsure how IoT may help your business, you may benefit from a conversation with a reliable and capable IT MSP to help you investigate the potential benefits your business might enjoy from the use of IoT.

Data Management and Analytics

A quality Data Management plan certainly ensures the collection of data needed to derive business-critical information. A viable business IT strategy explicitly provides for resources to implement a quality Data Management Plan. In order to remain competitive, the Data Management plan must utilize the best data collection and analytics systems.

There is a variety of options is available, especially for collecting and analyzing data. In fact, this includes data collection methods, data transfer methods, analytical software options, 3rd party managed analysis and reporting, results monitoring, etc.

Choosing the best data management and analytics system especially for your business is vital for ensuring optimum results. There are several factors to consider when making this investment decision.

Data Collection

More data sources may be available to you than of which you are aware if you carry a conventional data collection mindset. With significantly rapid advancement of data sorting and analysis techniques, the demand for expanding the range of input data is growing. Businesses are able to economically mine and analyze data at a fraction of historical cost. Consequently, as the range of data collection expands and as analytical techniques advance, companies are learning to leverage much more value from data collection and analysis than was possible before. Data Downpour is becoming more commonly used in many data-intensive business processes. It represents technology dedicated to collecting massive amounts of data related to a specific environment, for example, a manufacturing business. Input sources can vary widely, for example, data streams from sensors or marketing information.


For Data Downpour to bring value, analytical software explicitly processes the data and to reveal trends. Data analytics software can process much larger amounts of data than humans can. That is to say, humans have to make decisions based on the analysis provided by the software. For example, the software may include predictive capabilities, based on historical trends and outcomes. However, it cannot predict different outcomes from emerging markets or other factors that can change the path. In this context, data analytics is markedly the front end of Artificial Intelligence and Cognitive Computing.


You need to particularly understand how inputs and outputs transfer between existing hardware and software. As well as the newly installed hardware and software you choose to manage your data. When data is directly transferable between your enterprise system and your data collection and analysis system, you can adapt to conditions and make changes efficiently, to improve production efficiency.

About CIS

Seeking input from IT professionals who understand the options and your business is especially critical to formulating the best business IT strategy to manage and analyze your data. This will help you maximize the ROI on your IT investment. This ensures the systems you install are designed to run smoothly and profitably in the long run. A competent IT MSP, with the relevant experience in business strategy and IT systems development, can help you achieve these goals.

Click HERE for a managed services quote.

Published On: September 25, 2018Categories: Managed IT Services, Productivity, Strategy