Tag: IOT

Guest Post: Merging OM Tradition with Digital Innovation

Dr J. Prince Vijai is Assistant Professor of Operations Management at IBS Hyderabad, in India.

The transition from traditional OM to digital operations is not a replacement but an evolution. Digital tools enhance the classical OM framework by adding intelligence, speed and adaptability.

1. Process Optimization and Automation In classical OM, process optimization involved detailed mapping and iterative improvements. With digital operations, AI can now identify inefficiencies, simulate improvements and automate decision-making without human intervention. Siemens has integrated sensors, cloud platforms and AI to create a digital thread across product design, manufacturing and logistics resulting in a 20% reduction in production time and a 30% reduction in energy consumption.

2. Inventory and Supply Chain Management Traditional inventory models rely on forecasts and safety stock assumptions. Digital operations use real-time data from IoT sensors and machine learning to predict demand, monitor inventory levels and automate replenishment. For instance, Walmart uses AI and IoT to streamline its vast supply chain, reducing stockouts and improving shelf availability.

3. Forecasting and Scheduling Operations managers have long used statistical tools for forecasting. Digital operations use advanced analytics and machine learning to provide more accurate, dynamic forecasts. Real-time analytics enables organizations to quickly adapt to market changes, weather disruptions or supply chain breakdowns.

4. Quality Management Traditional quality management emphasizes inspection and control charts. Digital quality management integrates data from machines, sensors and customer feedback for continuous, real-time quality assurance. Predictive maintenance, enabled by digital twins and IoT, reduces downtime and improves asset reliability. For example, GE developed digital twins to monitor the performance of jet engines in real time, enabling predictive maintenance and reducing unexpected failures.

The shift to digital operations is not without challenges. Employees accustomed to traditional processes may resist adopting new technologies. Data from different departments or legacy systems can be siloed, limiting visibility and coordination. Implementing AI, IoT and automation involves significant expenses. And digital operations increase exposure to cyber risks.  

Future trends include:

  • Hyperautomation that combines  AI and machine learning to automate increasingly complex tasks.
  • Cognitive operations that use AI not just to automate but to learn and adapt continuously.
  • Edge computing that enables data processing closer to the source (e.g., in factories or stores) for faster insights.
  • Green operations that leverage digital tools to track carbon footprints and support sustainable practices.

Embracing the synergy between OM and digital operations is a strategic imperative for long-term success.

OM in the News: Manufacturing Jobs are Looking Very Different

Digital transformation and Industry 4.0 are changing manufacturing, but the fact is that skilled operations talent is increasingly harder to come by. With projections that 2.4 million manufacturing jobs will be unfilled by 2028, the question becomes: What talent and skills do companies need in order to succeed in the factory of the future? Industry Week (Aug. 25, 2021) looks at four manufacturing jobs and how they are expected to evolve. 

Production Planners will shift from managing shop floor issues to more proactive roles in which they analyze data insights, manage exceptions and identify opportunities for continuous improvement. They will move from using manual processes for monitoring inventory to using predictive analytics and “digital twins” (virtual representation of a part or a process) to create optimized production schedules and proactively manage inventory issues. And they will need skills in lean and six sigma, data analysis and visualization.

Industrial Engineers will increasingly use digital twins and other cyber-physical systems, in addition to other methods of automation, to create greater connectivity between manufacturing processes and shop floor operations. They will need skills in the areas of design for manufacturability, data science, python and R  programing languages,  co-bots, IoT sensors, digital twins and wearables.

Machine Operators. Today’s operators tend to specialize in one machine or product line and rely on personal judgment in overseeing machines and processes, leaving room for human error. In the future, operators will use digital twins and AI to proactively identify and solve issues. They will be trained as generalists who can work across machines and product lines.

Quality Analysts. Today’s quality experts are often making changes to standards in reaction to customer complaints, bad yields, or defective products. In the future, they will be able to monitor processes in real time, predict quality issues before they occur, and quickly trace and diagnose any issues through the use of digital twins, advanced analytics and the ability to embed intelligence quality controls. This will require an understanding of big data, data science, and machine learning.

But beyond the clear need for a much higher level of digital acumen, there is also a critical need for human skills that machines cannot replicate such as conceptual thinking, decision-making, problem-solving, and innovation.

Classroom discussion questions:

  1. How many of your students will consider manufacturing jobs? Why?
  2. Explain the concept of a “digital twin.”

Good OM Reading: “The Toyota Way,” by Jeff Liker

In the 1990s Toyota’s principles of production equipment became “simple, slim, and flexible,” which some people might interpret as “go slow and be cautious in adopting new technology.”  In today’s age of lightning speed in the digital world, Jeff Liker’s new book, The Toyota Way (Oct., 2020) says that would be a mistake. His message is: “adapt technology that supports your people and processes.” Where are real needs that technology can address to help achieve corporate goals? This is a question of pulling technology based on the opportunity, instead of pushing the technology because it is the latest fad. The key issue, writes Liker, is to avoid the temptation to buy and implement the latest gee-whiz digital tools, and instead to thoughtfully integrate technology with highly developed people and processes.

Toyota’s largest supplier, Denso, in Japan, has made remarkable progress in adapting real time data collection, the Internet of Things (IOT), and data analytics to support lean systems and amplify kaizen. At the center of Denso’s approach is people, and their ability to sense reality and think creatively.  Denso demonstrates that technology has the greatest potential when there is a culture of continuous improvement and the people are highly developed. Denso operates on the belief that IOT does not cut people out of the loop, but rather provides superior information to people about the process. The power of big data and AI is to give the operator information just-in-time that they previously could only guess at. But Denso expects the operator to use that information creatively to find the root cause and solve the problem through kaizen. Denso calls this “collaborative creation and growth of human, things, and equipment.”

Toyota’s system, says Liker, is about forcing people to think deeply to solve problems. Will computer systems make us lazy thinkers?  How can we marry the powerful information coming out of the computers with the creativity of people in developing and testing ideas for improvement? This is a book worth sharing with your students when you cover Chapter 16, Lean Operations.

OM in the News: The Fourth Industrial Revolution–Industry 4.0

A recent IndustryWeek survey (Nov. 6, 2018) found that manufacturers are having trouble joining the Fourth Industrial Revolution, called Industry 4.0. And the World Economic Forum (WEF) found that 7 out of 10 manufacturers fail in pushing initiatives in big data analytics, A.I., and additive manufacturing.

But there is hope, the Forum asserts. They scoured the planet and after vetting 1,000 manufacturers, selected 9 “lighthouses” (listed below) with a solid Industry 4.0 strategy. “These pioneers have created factories that have 20-50% higher performance and create a competitive edge,” says a McKinsey exec. “They have agile teams with analytics, IoT and software development expertise that are rapidly innovating.” Industry 4.0 is expected to deliver productivity gains over $3.7 trillion.

Bayer Biopharmaceutical: Italy. Most companies use less than 1% of the data they generate. Bayer makes the most of its data, leading to a 25% drop in maintenance costs and while gaining 30-40% in operational efficiency.

Bosch Automotive: China. Bosch uses data analytics to deeply understand and eliminate output losses, simulate and optimize process settings, and predict machine interruptions before they occur.

Haier: China. Use of AI facilitates a “user-centric mass customization model” with electronic products made on-demand. Maintenance needs are predicted before incurring downtime via AI.

Johnson & Johnson: Ireland. This hip and knee joint factory implements IoT, leading to a 10% reduction in operating costs and 5% drop in machine downtime.

Phoenix Contact: Germany. The electronics manufacturer relies heavily on customer-specific clones to cut production time for repairs or replacements by 30%.

P&G: Czech Republic. Production lines, in a plant built in 1875, seamlessly change the product being manufactured with a push of a button, an innovation that reduced costs by 20% and upped output by 160%!

Schneider Electric: France. Sharing of best practices across its multinational force allows each site to reap the benefits of the others, saving 10% on energy and 30% on maintenance.

Siemens: China. Leveraging augmented reality to create 3D simulations, Siemens has optimized its production lines with reduced cycle time and 300% jump in output.

Fast Radius: U.S. The lone U.S. company uses real-time analytics and globally positioned distribution 3D printing farms to maintain rapid turnaround times to deliver prototypes and custom parts.

Classroom discussion questions:

  1. What is Industry 4.0?
  2. What do these 9 firms seem to have in common?

 

OM in the News: Making Sense of Supply Chain 4.0

McKinsey, Cap Gemini and the Boston Consulting Group all suggest Supply Chain 4.0, digital transformation, is about applying digital technologies– Artificial Intelligence (AI), Machine Learning (ML), the Internet of Things (IoT) and Blockchain– to operational processes and creating improvements.

 If digital transformation is to “transform” SCM, then it must as efficiently as possible match supply to real demand, writes IndustryWeek (Nov. 2, 2018). In SCM, there are 3 key factors that impact the ability to match supply to demand: (1) Demand uncertainty and the inability to accurately forecast demand; (2) Production uncertainties leading to changes in supply; and (3) Lack of synchronization among supply chain partners.

(1) Traditional forecasting methods can be impacted by one-time events (such as economic changes, special promotions, fashion trends, or a spike in social chatter) that affect the stability of historical sales patterns. Digital transformation can improve traditional forecasting methods in 2 ways. The first is to gather new data, such as sentiment information from social channels, weather inputs, economic performance or information from new IoT or Fog Computing sensors that can provide insights into customer demand. The second is to use ML to continuously “learn” from this data to determine the contributions of these factors in predicting demand.

(2) Digital transformation can use IoT to continuously monitor machines on the shop floor, track key performance metrics and then use predictive analytics to understand what these performance metrics mean for yield, quality or the likelihood of machine failure.

(3) At one end of the supply chain, a retailer may determine a particular demand based on what end consumers are buying. This demand signals the next tier in the supply chain, which sends its own demand signal to the next tier and so on. The end result is a view of demand a few tiers into the supply chain that is very different from the original demand requirement from the retailer. The supply chain, in effect, becomes unsynchronized.  Blockchain is a distributed ledger, with information instantly visible to all parties of the blockchain and ensures a single version of the truth – such as a single understanding of true end-customer demand – in the supply chain. This is what synchronizes all supply chain partners.

Classroom discussion questions:

  1. How does digital transformation differ from traditional forecasting?
  2. What is IoT? Give an example.
  3. What is blockchain and how can it help SCM?

 

OM in the News: The Pizza Technology War Wages in Asia

Pizza Hut's Pepper robot
Pizza Hut’s Pepper robot

“The next battle in the pizza technology wars has dawned as Pizza Hut Asia just announced that it will test an order-taking robot that uses artificial intelligence to interact with customers,” writes The Wall Street Journal (May 25, 2016). Rival Domino’s Pizza said that it is testing an autonomous vehicle for delivering pies to customers in Australia. Domino’s Robotic Unit interacts with customers through lights on a display and navigates around obstacles on footpaths and roads. The two pizza companies have long battled on the technology front, including promoting smart watches, connected cars and retinal scanning for order and delivery.

With Pizza Hut Asia, MasterCard announced Tuesday that it is has embedded its MasterPass mobile wallet into the robot, dubbed Pepper, that the pizza chain is testing in restaurants in Japan.  As customers approach and speak to Pepper, the robot can read facial expressions and understand natural language. Pepper, for example, can judge a customer’s mood and perhaps offer add-on products, trying to capitalize on how the customer is feeling. MasterCard plans to study the Pizza Hut transactions to see if there is an increase in order size for customers who interact with the robot.
Internet connected machines, including wearable devices and robots, can help companies personalize service in retail stores and other venues by accessing data about customers and applying algorithms to make tailored offers on the spot. By experimenting with robots, MasterCard hopes to learn how customers react to the technology and whether they begin to conduct financial transactions with the machines. The number of Internet of Things devices is expected to jump to 21 billion by 2020, from about 6.4 billion today.

 

Classroom discussion questions:

  1. What is the “Internet of Things” (IOT)?
  2. Why are restaurant chains exploring these new technologies?