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

OM Podcast #12: Data Analytics and Operations Management

Our latest podcast is all about data analytics. Barry Render speaks with his son, Charlie Render, president of Render Analytics, about his real world experience using data analytics to solve problems around forecasting, sustainability, and quality. Charlie shares some recommendations for students who are considering studying or about to graduate in the field of data analytics.

Transcript

Word of this podcast will download by clicking on the word Transcript above.

Instructors, assignable auto-graded exercises using this podcast are available in MyLab OM. See our earlier blog post with a recording of author and user Chuck Munson to learn how to find these, or contact your Pearson rep to learn more! https://www.pearson.com/us/contact-us/find-your-rep.html

Guest Post: Using Data Analytics to Optimize Operations Management

Charlie Render is CEO of Render Analytics, a Florida-based data analytics consulting firm. He can be reached at https://www.renderanalytics.net/

Harnessing the power of data has emerged as a critical OM strategy for gaining competitive edge. Data-related technologies are being employed to elevate supply chain logistics, manufacturing efficiency, and overall operations.

Data analytics, the topic of Module G in your text, involves the systematic exploration of datasets to glean meaningful decision making insights. In the OM/SCM context, it encompasses the analysis of such diverse data points as order volumes, lead times, transportation costs, inventory levels, and customer behavior trends. This fusion is a natural convergence, given the voluminous data generated at each juncture of the supply chain.

Here are four real-world examples:

Amazon’s Demand Forecasting By meticulously analyzing historical sales data, seasonal patterns, macroeconomic indicators, and even external factors like weather events and cultural trends, Amazon employs advanced predictive models. This enables the firm to anticipate product demand with remarkable accuracy. As a result, it can adjust inventory levels dynamically, minimize excess stock, and ensure the timely availability of popular products. The outcome is not just optimized inventory costs but also a seamless customer shopping experience.

UPS’s Route Optimization UPS harnesses data analytics to refine its delivery routes. By integrating real-time traffic data, intricate delivery schedules, fuel costs, and even road closures, it constructs a comprehensive algorithmic approach. This approach identifies the shortest, most fuel-efficient routes for its fleets. The outcome is not just a reduction in fuel consumption and operational expenses, but also better on-time deliveries, positively impacting customer satisfaction.

Toyota’s Proactive Quality Assurance Toyota exemplifies how data analytics can revolutionize quality control within assembly lines. By tapping into data generated by sensors embedded within production equipment, Toyota has pioneered real-time quality assurance. This enables the detection of deviations from predefined quality benchmarks throughout the manufacturing process. Swift identification of potential defects lets Toyota rectify issues promptly. This means a reduction in defective units, lower warranty claims, and enhanced customer satisfaction.

Maersk Line – Transforming Container Shipping Maersk demonstrates the impactful combination of data analytics and sustainable supply chain practices. With the aim to minimize emissions and optimize routes, Maersk uses data analytics to study factors such as weather patterns, sea currents, and fuel efficiency. By leveraging these insights, it optimizes vessel routes, reducing fuel consumption, and subsequently decreasing greenhouse gas emissions. This data-driven approach not only aligns with a commitment to sustainable shipping, but also helps achieve substantial cost savings.

Guest Post: New Technology, Analytical Tools and Information Sharing Help Retailers with Sales Forecasts in a Post-COVID World

Dr. Misty Blessley, Associate Professor of Statistics, Operations, and Data Science at Temple University, shares her insights with our readers monthly.

The COVID-19 pandemic changed the way people live. When viewing the demand side of a business-to-consumer (B2C) transaction, our behavior as consumers and the new normal are inextricably linked and continue to evolve. A recent Wall Street Journal article states that “merchants veered between product shortages and overstuffed inventories in rapidly changing consumer markets” as a result of the COVID-19 pandemic. But now that inventories have been worked through, companies are looking for better ways to manage the flow of goods on the fly and make sure they have merchandise where it needs to be to boost sales and maintain margins. What does this mean for post-COVID sales forecasting?

That past behavior is a good predictor of future behavior is the premise on which time- series forecasts (the topic of Chapter 4 in your Heizer/Render/Munson text) stand. Forecasts were based on known patterns of seasonality and the economic outlook over the forecast horizon. However, retailers increasingly recognize that boosting sales and maintaining margins requires them to see and respond to changes in demand as it is happening. In other words, in customer driven markets, the objective is to match the supply of merchandise to consumer demand.

Macy’s has been upgrading its forecasting abilities over the past several years with new technology

Retailers are looking to new technology and analytics that are capable of capturing trends in data that predict future demand. For example, Dr. Martens (a $1.2 billion shoe manufacturer) is currently working to leverage a new order-management system containing a customer-data platform to provide better insight into customer spending. Macy’s Department Store has benefitted from a recent forecasting system upgrade, by recognizing and responding to increasing demand for work-wear over leisure-wear. Retailers are turning to sophisticated algorithms but are also increasing their communications with suppliers as part of having merchandise where it needs to be, in lockstep response to consumer demand.

Classroom discussion questions:
1. What “new normal” could have driven the change to work wear from leisure-wear for Macy’s?
2. Who should be included in a firm’s post-pandemic sales and operations planning (S&OP) process?
3. What is the role of operations and supply chain managers in creating new forecasting methods and then monitoring, controlling, and adapting the forecasts resulting from these new approaches?

OM in the News: Who Should Jack Up the Car in a Nascar Pit Stop?

The pit crew for Christopher Bell in action at Phoenix Raceway

“There are two ways to win a Nascar race,” writes The Wall Street Journal (March 10-11, 2023). The first is to go faster, when you’re in motion, than anyone else. The second is to spend less time at rest than your opponents, shaving away expensive tenths of seconds sacrificed in pit stops, as we illustrate in Chapter 10’s Global Company Profile.

Joe Gibbs Racing (JGR) has done it both ways—on the track, with star drivers like Denny Hamlin and Martin Truex Jr. In the pit, it has brought the business of data analytics to the greasy work of changing tires and refueling cars. JGR’s crews have been either the fastest or second-fastest in Nascar every year since 2014, a span during which the organization has won two Cup Series championships. A month into the 2023 season, three JGR drivers are among the top 10 point earners on the circuit, due largely to the roster of ex-football and baseball players assembled in the pit.

Their ranks include CJ Bailey, a former college running back who has become Nascar’s premier tire carrier, and Caleb Dirks, a former pitching prospect for the Atlanta Braves who now applies his length as a jackman, sprinting out with his hydraulic device and pumping the pitting car airborne. (An experienced pit crew member who works for a top-tier team, by the way, can make around $500,000 per year).

Affixing motion sensors and running JGR’s pit crew through a gamut of high-tech exercises. data analysts logged the fluidity with which they transitioned from one effort to another. A 4-tire pit stop is a frantic 5-man ballet—all tight corridors and heavy equipment, set at breakneck tempo. The difference between a 9.8-second and 10.8-second stop can decide a race and a season.

The analysts isolated biomechanical thresholds that, if met by a prospect, predicted success in a certain role. Prospective tire changers were valued for their baseball hitting background but also for their “arc of hip rotation.” Tire carriers had their relative eccentric force production gauged. One such uncovered gem was Bailey. Their data revealed that he had the precise proportions of upper-body might and nimble footspeed of the ideal carrier. Last season with JGR he was graded as 13.8% more efficient than any other carrier in the sport—the fastest hauler of metal and rubber alive.

Classroom discussion questions:

How do time and motion studies apply to Nascar pit stops?
What methods analysis tools in Chapter 10 can be used to examine pit stop efficiency?

OM in the News: A Safe Return to Manufacturing Productivity

COVID-19 is changing everything in manufacturing. Companies face a long journey to the “next normal,” one that will likely have far-reaching financial and operational implications, writes Industry Week (July 14, 2020). Immediate priorities include creating a safe work environment for production employees. Missteps could invoke legal or regulatory actions, something all companies want to avoid. As many manufacturers enter the Recover phase of COVID-19, one that is marked by restarting production at plants in regions that have been impacted differently by virus outbreaks, workforce safety becomes a critical priority. The restart/ramp-up should generate considerations across the work itself, the workforce, and the workplace.

Work: How will new physical distancing constraints and supply/demand variability be incorporated into operations? Are there opportunities to remove humans from processes through automation and/or robotics?

Workforce: How will workers “feel” safe and come back to work willingly? What new policies and procedures are required to protect employees, reduce risk of spread (e.g. personal protective equipment (PPE), break room policies)?

Workplace: What physical/operational changes are necessary to meet health and safety requirements? What technologies and solutions could create a safer work environment in plants and facilities?

A holistic approach toward the recovery phase should include solutions that address all three of these areas. It will likely blend strategy and process changes with advanced technologies, which can hold the key to a robust recovery for manufacturers. Some of the smart factory technologies that many manufacturers have already been piloting, such as data analytics–71% in a recent study, sensors–54% and wearables–29%, could dramatically accelerate the pathway to recovery.

Classroom discussion questions:

What other complications will operations managers face when reopening factories or service facilities?
What role can sensors play?

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: The Changing Technology in Supermarket Layout

Which products are placed where on shelves can move sales up or down significantly.

The biggest U.S. food makers are finding that supermarkets are taking away prime shelf space, writes The Wall Street Journal (Feb. 19, 2020).

Grocers are now relying on their own proprietary research to decide how and where to shelve certain products, rather than counting on companies that sell well-known brands to tell them what to put on what shelf at what price. Kroger and Walmart, for example, are using increasingly sophisticated software to decide where to place items and which products to shelve next to one another—factors that can move sales up or down several percentage points. As we note in Chapter 9, Layout Strategies, “the objective of retail layout is to maximize profitability per linear foot of shelf space.”

Large chains have invested in beefing up their ability to collect and analyze data from customers. That is changing the grocer’s relationships with suppliers and the way it lays out stores. The diminished power of “category captains”—the top sellers of products such as soup or cereal—is the biggest change to the way food is sold in the past 30 years. Retailers once relied on big consumer-goods companies when making decisions about allocating shelf space because the companies were the experts in their respective food categories. Grocers also didn’t want to invest in consumer insights, and they were happy to take the hefty slotting fees (also noted in Ch.9) big brands pay for prime space.

Now, retailers are more focused on doing what it takes to maximize sales growth even if it means giving up some of those fees by stocking more of their store-branded products. Supermarkets are also gaining leverage over retailers with generic products sold under their own brands at cheaper prices than name-brand goods. Kroger owns 33 manufacturing plants to make various store-branded products, which make up a growing share of its sales and shelf space.

Classroom discussion questions:

Explain the tradeoff between slotting fees and the new data analytics approach?
How does supermarket layout differ from department store layout?

OM in the News: Artificial Intelligence in the Next Decade

As a new decade approaches and firms move from artificial intelligence (AI) experimentation to implementation, new issues arise. How companies understand and apply this technology will play a pivotal role in how they accelerate efficiencies and growth in the next few years. Analytics News (Dec. 17, 2019) provides these 5 predictions for AI, machine learning and data analytics for 2020:

1.The move to “Transformation-as-a-Service.” Many large corporations realize they need to transform AI and machine learning operations and processes, but they can’t achieve this with speed and meaningful impact. The answer is Transformation-as-a-Service.

2. Customer experience is the main battle ground in digital. There will be two types of companies – those who do customer experience (CX) well and those who go out of business: the True North for digital transformation in 2020.

3. Human in the loop – the increasing value of judgment and reskilling. Humans will play a critical role in the last mile of AI and data analytics. While machines predict and analyze, humans are needed for their judgment, empathy and creative problem-solving. In 2020, the value of data decreasing while the value of human judgment increases.

4. The ethical governance of data, AI and digital. The rise of digital ethics officers, who will be responsible for implementing ethical frameworks to make decisions. This includes security, bias, intended use and built-in governance.

5. Increased modularity in the form of accelerators. Implementing AI is not enough; companies must expedite AI adoption through pretrained experts, or “accelerators.” How accelerators democratize AI will have huge implications given the prediction that by 2025 organizations that are AI leaders will be 10 times more efficient and hold twice the market share.

Classroom discussion questions:

Why is AI an important operations tool?
What is the role of the data analyst?

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.