big data – The OM Blog by Heizer, Render, & Munson

Good OM Reading: Top 10 Supply Chain Trends for 2025

In its new report, Top 10 Supply Chain Trends, the Association for Supply Chain Management states the supply chain landscape will continue to evolve at an unprecedented pace. To be competitive, companies will consider technological advancements and innovation, geopolitical shifts, and evolving consumer expectations.

Here are the top ten trends:

Artificial Intelligence. AI will be employed for better decision-making, optimized transportation routes, prediction of demand fluctuations and automated quality control inspections. Smart robots work alongside humans to perform packaging and assembly, while automation tools such as computer vision systems identify product defects.
Global Trade Dynamics and Geopolitical Policies. Supply chain organizations will prioritize diversification and contingency planning to address challenges related to global trade dynamics and geopolitics. These companies will spread supply sources across multiple regions and develop backup plans.
Big Data and Advanced Analytics. Tapping into vast amounts of supply chain data, businesses will improve inventory management, supply chain visibility, forecasting of demand and production, transportation and logistics processes, and decision-making. Big data and analytics will also enable better predictive maintenance, digital twin modeling and AI-powered insights.
Cybersecurity. Supply chains will prioritize cybersecurity to protect sensitive data and critical operations.
Agility and Resilience. Organizations will prioritize agility and resilience to adapt to rapidly changing market conditions by implementing flexible manufacturing systems and advanced technologies including robotics and AI. Real-time visibility, diversified supplier bases and robust contingency plans will further enhance their resilience.
Visibility and Traceability. By implementing real-time tracking systems, tapping into IOT-enabled devices and leveraging blockchain technology, companies will better monitor the movement of goods, identify potential disruptions and improve supply chain efficiency.
Digital Integration and Connectivity. To improve efficiency, transparency and resilience, supply chains will implement the latest technologies — particularly AI, robotics and automation, cloud computing, and the IOT, making it possible to streamline operations and reduce costs.
Strategic Sourcing and Supplier Management. Advanced analytics and AI-powered tools will help identify and assess potential risks, such as geopolitical events and natural disasters. By tracking and analyzing key metrics, organizations will be able to select suppliers that align with sustainability goals.
Workforce Evolution. By upskilling and reskilling employees, businesses will ensure their workforces are equipped to handle the demands of an increasingly automated and digital supply chain.
Risk Management. By mapping networks, evaluating suppliers, forecasting demand and simulating scenarios, organizations will be able to handle potential disruptions.

OM in the News: Data Analytics for Factories

A Norsk Hydro aluminium plant in Norway. The company’s CIO, called the availability of data during the pandemic “a clear game-changer.”

Manufacturers will be spending far more on data management and analytics tools in the aftermath of the coronavirus outbreak, and will be using those tools for deeper insight into operations, sales and supply chain disruptions, reports The Wall Street Journal (June 3, 2020).

Data—produced by shop-floor scanners and other hardware tools—can now be used to more accurately measure and improve the performance of production-line machinery. Such benefits are expected to spur annual spending by global manufacturers on data management and analytics to nearly $20 billion by 2026, up from $5 billion this year.

Advanced data tools will give factories a clearer view of operations and equipment performance, allowing them to speed up production, reduce waste, improve their product quality and avoid downtime by more quickly identifying maintenance issues, among other things. Factories will also be able to identify and extract relevant data sets to feed into artificial intelligence software designed to predict production and supply chain problems. “It’s a case of going from reactive analytics, reporting on what happened, to proactively analyzing what might happen and the suggested actions to take,” said one industry expert.

The pandemic has made manufacturers aware of the need for more sophisticated ways to monitor operations, especially when plants are accessible to only a handful of workers. “We’re working with clients on taking unprecedented amounts of data and deriving insights that can shift decision-making,” said the CIO of NTT Data Services, referring to streams coming from shop-floor sensors, machinery, supply-chain fleets and other systems. Manufacturers are using that data to get a better view of equipment performance and maintenance needs, quality control and workplace safety.

Classroom discussion questions:

What is the difference between descriptive, predictive, and prescriptive analytics (see Module G in your Heizer/Render/Munson OM text)?
Which of these methods is discussed in this article? Why?

Guest Post: Storytelling Using Data Analytics During Coronavirus Outbreak

Today’s Guest Post comes from Charles Render, whose analytics firm (render-consulting.com) works with client organizations of all sizes, leveraging big data to optimize their businesses.

During these unprecedented times, data analytics and data visualization experts have a unique opportunity to measure the magnitude of the Coronavirus’ impact on the business world. The “big data” trend that has taken off the last decade has not been around long enough to experience a world-defining event such as this, and this storytelling opportunity is too good to pass up for data scientists. Data visualization is not just about giving answers; it is also about presenting the opportunity to ask new questions we have not yet thought to ask.

For example, which companies/industries are benefiting from quarantining and people staying home? Uber Eats, a food delivery service, has experienced a spike in Google searches while OpenTable, a restaurant reservation service, has dropped to almost no traffic.

Which stocks could rise while the rest of the market is struggling? Are there any companies that may get a free increase in stock share as a result of having the right name? After many investors mistook Zoom Technologies (stock ticker ZOOM) for Zoom Video Communications (stock ticker ZM) during a rise in the latter’s popularity amidst COVID-19, Zoom Technologies’ stock price surged about 318% in 8 days.

These are just two examples of the data visualization tools that are discussed in Module G (Applying Analytics to Big Data in Operations Management) of your OM text.

OM in the News: Where OM Data Analytics Meets Chocolate

Todd Ferris uses advanced analytics to find solution to problems like ways to route peanuts.

Todd Ferris is a principal data scientist for Hershey Chocolates. He can track a cocoa bean from harvest to chocolate bar on a store shelf. Here are some excerpts from The Wall Street Journal (March 29, 2019) interview that you might use in class when you cover our new chapter, Module G, Applying Analytics to Big Data in Operations Management:

Our team goes after complex problems across the supply chain. Can we see our products from sourcing a cocoa bean in Western Africa all the way to manufacturing, shipping and getting it to the customers? It’s very difficult to keep track of all that data.

We are always fighting this bullwhip effect, the phenomenon of small changes at one end of the supply chain creating huge issues once you get back to manufacturing. If customer demand varies by 100 chocolate bars at retail, by the time that information gets back to us at manufacturing that signal may be 1,000 bars. This creates a lot of inefficiencies.

We use a programming language called R, a counterpart to Python. You’ll do your modeling inside one of those programs; we’ll use SQL to access, manipulate and filter data before we bring it into analytical tools.

You can do procurement, like forecasting the health of crops or their availability. I’ve worked on manpower analysis—how we shape our manpower in our manufacturing plants in the best manner possible. We just worked on the best way to route peanuts from our suppliers to our plants. On one side we are forecasting crops and on the other we’re at the store level trying to determine if we have too much inventory or too little. We try to predict what’s going to happen and then make sense of how we need to respond.

Classroom discussion questions:

Describe the “bullwhip effect” and its importance in OM.
How would you describe “data analytics” to an executive and explain its role?

Our New Chapter, Applying Analytics to Big Data in Operations Management

The marriage of business analytics, big data, and operations/supply chain management is a revolutionary change in our field. We are the first text to include a chapter (Module G) on this subject, which includes sections on data management, data visualization, and predictive and prescriptive business analytics tools. The topics include heat maps, conditional formatting for cleaning data, and pivot tables. The module includes numerous exercises that will use students’ Excel skills and show them the power of Excel in Big Data. This is a topic instructors have asked for and students will really appreciate!

The new edition is now available, so contact your personal Pearson rep for your copy at:

http://www.pearsonhighered.com/educator/replocator/

Here is the first page of the new Module G.

Good OM Reading: Supply Chains and Data Analytics

The OM field will soon face a major change in the way we make decisions. Big data, data analytics, and business intelligence are all skill sets our OM students will need. The Gartner Group has just issued an interesting report on these concepts. Gartner identifies 4 core skill sets to support the successful adoption of analytics: Data engineers who make the appropriate data accessible and available for data scientists. Supply chain expert analysts who understand supply chain requirements and priorities to ensure the right tools are used. Data scientists who create predictive and prescriptive models. Citizen data scientists who are lighter versions of a data scientist who can build or choose models, but within a platform.

There is, of course, a shortage of data scientists. This is compounded for supply chain, which might not be viewed as attractive as finance, sales and marketing. But analytical platforms can alleviate this shortage. This is because within the platform environment, “citizen data scientists” can build new apps and solutions.

As the line between the physical and digital world blurs in business, the algorithmic supply chain affords companies the ability to leverage massive data from increasing connections among people, businesses and things. This allows them to respond quickly and profitably to changes in market demand. In an algorithmic supply chain, decision-making relies on the company’s intellectual property (IP) that captures data and encapsulates it into reusable, unique and optimized information assets. Embedding this IP in supply chain processes, the company can solve large-scale, dynamic problems and create competitive advantage.

UPS provides a powerful example of using analytical platforms to build On the Road Integrated Optimization and Navigation (ORION) to support its core business processes. ORION generates daily routing manifests to 55,000 UPS drivers. The platform incorporates optimization, heuristics, predictive analytics and custom mapping. It generates $300-$400 million in annual benefits, based on reducing fuel consumption by 10 million gallons, carbon emissions by 100,000 metric tons and driven miles by 100 million, annually.

Teaching Tip: Why Students Should Major in OM and IT

You might want to point your OM students (who typically want to major in Accounting, Finance, or Marketing) to a fascinating USA Today (Jan. 24, 2017) article on the “50 Best Jobs in America.” Jobs that require a range of STEM skills (science, technology, engineering and math) claimed 14 spots in Glassdoor’s new survey. This includes the top-seeded position: data scientist, a job in which employs math and computer programming skills to wrestle huge amounts of raw data into intelligible and useful data sets. That job took the crown with a leading Glassdoor score that reflected the number of openings for the position (currently 4,184), a top company satisfaction rating (reflective of culture and values) and a healthy median base salary ($110,000).

Here are some others that share skills we teach in operations management:

#2 DevOps Engineer (2725 openings, $110,000 salary); #3 Data Engineer (2599, $106,000); #5 Analytics Manager (1958, $112,000); #7 Data Base Administrator (2977, $93,000); #18 Supply Chain Manager (1270, $100,000); #22 Quality Control Manager (2531, $92,000); #42 Operations Manager (1009, $93,000); #45 Supplier Quality Manager (862, $80,000); #50 Construction Project Manager (1944, $85,000).

The proliferation of technology-related jobs is due to those skills now being needed at businesses that don’t consider themselves traditional tech companies. These days, almost every company is in some way a tech company, requiring workers who are able to create and maintain a firm’s technological infrastructure. “Any company with data today is trying to get these people,” says Glassdoor’s chief economist. “The problem in filling these positions is that generally employees’ skills have not kept up with the demand.”

Good OM Reading: The Hard Work Behind Analytics Success

The hype around business analytics, our topics in Modules A-F, has reached a fever pitch. From baseball to biomedical advances, stories abound about data scientists applying their wizard like talents to find untapped markets, make millions, or save lives. Data has been described as the new oil, the new soil, the next big thing, and the force behind a new management revolution. Despite the hype, the reality is that many companies still struggle to figure out how to use analytics to take advantage of their data. The experience of managers grappling, sometimes unsuccessfully, with ever-increasing amounts of data and sophisticated analytics is often more the rule than the exception, concludes a new MIT Sloan Management Review study (March, 2016).

Five key findings came from the research:

Competitive advantage with analytics is waning. The percentage of companies that report obtaining a competitive advantage with analytics has declined significantly as increased market adoption of analytics levels the playing field and makes it more difficult for companies to keep their edge.
Optimism about the potential of analytics remains strong, despite the decline in competitive advantage. Most managers are still quite positive about its potential. They’ve seen increased interest in analytics over the past few years, and they expect its use to continue to grow.
Achieving competitive advantage with analytics requires a sustained commitment to changing the role of data in decision making. This commitment touches many organizational aspects, from revamping information management to adapting cultural norms.
Companies that are successful with analytics are much more likely to have a strategic plan for analytics, and this plan is usually aligned with the organization’s overall corporate strategy.
Most companies are not prepared for the investment and cultural change that are required to achieve sustained success with analytics, including expanding the skill set of managers who use data and broadening the types of decisions influenced by data.