Module G – The OM Blog by Heizer, Render, & Munson

OM Podcast #31: The Impact of AI on Jobs and on the Environment

In our latest podcast, Barry Render interviews Charlie Render, President of Render Analytics, which helps businesses of all kinds implement AI. Charlie is also the creator of the popular job-search engine, Apply Genie (ApplyGenie.ai). In this episode, Barry and Charlie discuss the impact of AI on the environment and on jobs.

Heizer Render OM Podcast · OM Podcast #31: The Impact of AI

Transcript

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OM in the News: AI and Warehouse Supply Chain Disruptions

The ability to react quickly to supply chain disruptions is critical, and companies are under increasing pressure to predict and prevent them before they occur. Instead of managing reactively, firms are turning to artificial intelligence (AI) and predictive analytics to revolutionize operations, writes Material Handling & Logistics (Feb. 13, 2025). AI provides the tools and insights to anticipate disruptions and optimize processes in real-time.

By analyzing vast amounts of operational data, AI can identify patterns and trends that may indicate potential bottlenecks. This allows companies to foresee bottleneck issues such as labor shortages, equipment breakdowns, or delayed shipments before they occur, giving them time to adjust and implement preventive strategies.

At the heart of this proactive approach is predictive analytics, our topic in Module G. Predictive analytics uses historical data, machine learning algorithms and statistical models to forecast future events and behaviors. For example, if a shortage is predicted, the system can recommend adjusting staffing levels or reallocating resources to avoid delays. Similarly, predictive analytics can predict when certain equipment may require maintenance or inventory levels are likely to drop below critical thresholds, allowing a business to take preventive actions and avoid disruptions.

Bottlenecks are among the most significant threats to warehouse efficiency. These disruptions can lead to delays, increased costs and missed deadlines, impacting customer satisfaction and profitability. Predictive analytics allows businesses to foresee bottlenecks before they become critical. For example, suppose analytics indicate that a certain shipping lane will be delayed due to increased demand or reduced capacity. In that case, a warehouse can reroute goods to avoid congestion.

To summarize, there are four key advantages of using AI in warehouse operations: (1) Improved Resource Allocation, (2) Increased Labor Efficiency, (3) Reduced Downtime and Delays, and (4) Enhanced Decision-Making.

With real-time data and forward-looking forecasts, operations managers can make better, more informed decisions about handling day-to-day operations and long-term strategies. This leads to better outcomes and improved performance across the entire supply chain.

Classroom discussion questions:

How can AI be used to improve warehouse operations?
What is the difference between descriptive analytics and predictive analytics? (See Module G of your Heizer/Render/Munson text)

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: Navigating Supply Chain Disruptions

“Historically, supply chain teams react to crises only after they have already begun,” writes Material Handling & Logistics (Dec. 19, 2024). A crisis starts and the team goes into fire-fighting mode. After the situation is remedied, teams return to business as usual, only to await the next crisis. Balancing strategic imperatives with solving these short-term crises is the key to effective supply management.

For years, supply chain professionals have been forced to play defense, constantly reacting to minimize disruptions as they arise. This approach not only diminishes employee productivity by forcing them to constantly switch between projects and contexts, but it also undermines the perception of the function’s strategic importance.

The Panama Canal is no stranger to challenges and complexities brought about by natural disasters, geopolitical tensions, or technical failures

But new technology is transforming the way supply chains are managed. Instead of addressing problems as they arise, procurement professionals can identify opportunities for strategic value early and often, developing proactive response plans for dealing with predictable disruption events. While the specific timing and severity of disruption events like hurricanes, port closures, labor strikes, or country shutdowns are difficult or perhaps even impossible to predict, there are a finite number of event types each year that can disrupt supply chains, and thus a finite number of response plans that can assure resilient continuity of supply.

With the advent of new predictive procurement tools like those we discuss in Module G (Applying Analytics to Big Data), supply planners and purchasing teams now have the capacity to reduce the chaos of unexpected disruptions. AI-driven tools are now helping to streamline and automate labor-intensive tasks, allowing procurement teams to quickly identify alternative suppliers and manage spot-market opportunities when unexpected challenges arise. By analyzing data trends, such as historical supplier performance metrics and environmental factors, these predictive procurement systems enable businesses to make more informed decisions proactively.

Identifying alternative sources of supply within a company’s existing supplier base is key, since qualifying new suppliers can be time-consuming, and expanding the total number of suppliers may introduce unnecessary complexity. Also, securing carriers with secondary capacity is equally important, as logistical challenges often arise when transport routes are disrupted.

Classroom discussion questions:

What tools do AI provide supply chain planners?
What canal issues have companies faced the past two years, and how have they dealt with them?

OM in the News: Manufacturing and Early AI Adoption

Manufacturers are betting artificial intelligence (AI) can help address pressing challenges, from supply chain volatility to the shortage of skilled workers. Three-quarters of manufacturing executives say that adopting emerging technologies such as AI is their top priority in engineering and R&D, says Industry Week (Oct. 4, 2024)

AI is a broad term that encompasses basic data analytics (Module G in our text), machine learning, deep learning, and generative AI. Adopters are using AI to solve key problems in procurement, assembly, maintenance, quality control, and warehouse logistics. Some are deploying generative AI to synthesize huge volumes of unstructured data. Others are experimenting with AI service bots that partner with field technicians, for instance, to recognize more quickly when maintenance is required and to improve the quality of that work.

AI can also report supply chain bottlenecks in real time and predict potential disruptions in advance. In manufacturing it can include: minimizing assembly defects and improving quality control; boosting productivity; and streamlining warehouse management.

For example, one manufacturer adopted AI-based video processing to track manual assembly activities and automate quality checks of those activities,. This reduced failures in the assembly process by 70%, while also cutting down efforts for quality checks by 50%.

Another firm adopted an AI-powered industrial copilot that converts natural language into code and translates old programming languages into natural language, completing both tasks faster and better than human developers. Engineers using this AI solution were 5% more productive.

AI can also help ensure that warehouses operate at top efficiency, carrying items that meet demand and minimizing extra inventory. One company adopted an AI-based inventory management system that helped it minimize overstock while still fulfilling all orders. AI also provides more flexible job production planning so that companies can allocate specific assembly activities to the most relevant assembly expert at a given time to maximize productivity.

As a growing number of companies experiment with and deploy new AI solutions, they are raising the industry bar for productivity and performance. The article suggests that companies that defer investing will need to run twice as fast to keep pace.

Classroom discussion questions:

Summarize the AI advantages noted in the Industry Week article.
Provide additional examples of potential AI use in manufacturing. In services.

Guest Post: Using AI to Decrease Food Waste and Combat Food Insecurity

Temple University Professor Misty Blessley raises an interesting issue in her Guest Post today.

The Supplemental Nutrition Assistance Program (SNAP), overseen by the U.S. Department of Agriculture (USDA), plays a crucial role in combating food insecurity across the U.S. SNAP offers monthly benefits through an Electronic Benefit Transfer card, enabling food- insecure individuals and families to purchase food at authorized retailers.

Food insecurity, defined as a lack of consistent access to adequate and safe nutrition, affects about 13% of the U.S. population. Delaware recently became the first state to pilot an AI-powered app aimed at linking surplus food with SNAP demand. With over 30% of food wasted during production and distribution, food insecurity is increasingly viewed as a supply chain challenge.

This new AI-powered app is instrumental in combatting food insecurity by addressing the potential for waste along the supply chain.

How it works: The app, called the Smart Shopper app, allows producers and retailers to offer SNAP-approved items at discounted prices in locations where surplus inventories and unmet SNAP demand overlap. SNAP recipients can download digital coupons to purchase food that would otherwise be wasted. Developed by the creators of Priceline, the app operates similarly by offering discounted goods, much like Priceline offers unused hotel rooms and airplane seats. One key advantage is its ability to predict food surpluses at their source, rather than merely reacting to food approaching its expiration date.

The benefit: The app extends the value of SNAP benefits, helping recipients make fewer compromises when deciding which foods to purchase. It also creates a win-win situation.
Delaware noted that “when we help our most vulnerable buy locally grown products, they receive the most nutritious, freshest food Delaware has to offer, and we support small farms, boosting and growing the local economy.” Additionally, the app addresses a $250 billion food waste issue at the retail level. The app is expected to become available nationwide.

Classroom discussion questions:
1. Refer to Introduction to Big Data and Business Analytics in Module G of your Heizer/Render/Munson text. In what ways does the Smart Shopper app move decision-makers from information to optimization?
2. Why is inventory record accuracy important to the proper functioning of the Smart Shopper app?
3. How are agricultural products the same as/different than hotel rooms and airplane seats?

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.

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: ChatGPT’s Tech Boom

Charles Render is founder and CEO of a Florida-based data analytics firm. He can be reached at RenderAnalytics.net

In a world rapidly advancing in technology and artificial intelligence, the business landscape is continually evolving to adapt to these changes. One of the most remarkable developments is the rise of ChatGPT, a language model developed by OpenAI that has sparked a wave of innovation across various industries. From assisting with data analytics to revolutionizing customer interaction, ChatGPT has enabled businesses to reimagine their operations and service offerings.

ChatGPT, short for “Chat Generative Pre-trained Transformer,” is an AI language model designed to generate human-like text based on the input it receives. Its ability to comprehend and generate natural language has opened up exciting avenues for businesses seeking innovative solutions in data analytics, application development, and customer engagement. By harnessing the power of AI, businesses can streamline processes, enhance customer experiences, and create new revenue streams.

Here are five examples:

Automat combines the capabilities of ChatGPT with robotic process automation (RPA). By capturing process descriptions or videos from customers, the technology automates workflows through ChatGPT-like interfaces and RPA tools. This innovation optimizes efficiency by automating repetitive tasks, enhancing productivity, and enabling businesses to focus on strategic initiatives.

Freshworks has witnessed remarkable improvements in application development with ChatGPT by integrating it into its coding workflows. Freshworks’ developers have reduced the time required to create complex software applications from 10 weeks to less than a week.

Udacity, an online course provider, has tapped into the potential of GPT-4 to develop an intelligent virtual tutor. This tutor delivers personalized guidance, feedback, and explanations to students, helping them navigate challenging problems.

Amto, a player in the legal process outsourcing (LPO) sector, has harnessed ChatGPT to revolutionize legal services. The firm employs a combination of AI and human reinforcement learning by actual lawyers to process legal content.

Baselit offers a unique solution for database queries and data analytics. By harnessing GPT-3’s text-understanding capabilities, Baselit allows users to perform complex database queries using plain English, eliminating the need for coding expertise.

OM in the News: Trying to Forecast Demand for COVID-19 Tests

Just as stores are discounting once scarce bottles of hand sanitizer, many people have more than enough rapid tests filling up their drawers. That includes the free tests shipped directly to homes by the U.S. government. And now insurance companies will pay for 8 tests per person per month.

Supply chain difficulties have become standard for nearly all businesses during the pandemic. But one of the bigger issues with the COVID-19 testing supply chain is determining demand, especially for at-home test makers, writes Supply Chain Dive (April 14, 2022). Manufacturers would shutter factories, only to scramble to reopen them when new waves of the virus pushed up demand.

Longhorn Vaccines and Diagnostics received an FDA request to massively scale up demand for at-home tests in 2020. The company historically produced at most 100,000 units a year, and the FDA was requesting 3 million units a week. Longhorn was able to make 50 million units that year after ordering large quantities of chemicals from China and working with tube manufacturers to scale up production. In early 2021, the company decided to stock up even more on chemicals to ensure it could last through 2 years of peak COVID. Storage costs were high, but the company couldn’t afford to run short on the chemicals. Then, in February 2021, testing slowed. One week Longhorn was selling 10 million units, and the next week, people were canceling orders. So when orders stopped, the supply chain was already full of tests.

At-home testing is challenging because it’s essentially a new market, as we note in Chapter 4, and it’s hard to find similar products for comparison in forecasting. There is no historical data as a comparison. There is also volatility over what is driving demand. It can be the community infection rate, office or school requirements, requirements for travel, policy issues, port availability, and cost. Predictive analytics (our topic of Module G) can be used to anticipate demand, but these analytics must be taken with a grain of salt due to the volatility.

There are costs of having too many and too few products: If a company estimates a demand for 1 million tests per week, does it expect variability between 900,000 and 1.1 million, or between 500,000 and 1.5 million? That affects production and logistics, with supply procurement, warehousing, and trucking reservations.

Classroom discussion questions:

What forecasting techniques in Chapter 4 might be used in this case?
What are the 3 categories of analytics noted in Module G?

Guest Post: The Future of Operations Management–Analytics Takeover

Our Guest Post today comes from Charles H. Render. He is Owner and Lead Consultant at Render Analytics, and also Co-Founder and COO of AnalyticsBox

Operations management and supply chain practices are increasingly becoming optimized and even automated by data analytics, the topic of Module G in your text. The days of old-school human calculations are over. Decisions are being made based on machine learning algorithms, and processes are being improved through complex AI-generated statistical analyses. Let’s look at three operations management practices that are now being entirely managed through data:

Predictive Inventory Management

Large corporations no longer use unsophisticated inventory management practices that involve human decision-making. Companies like Amazon can determine what you are going to order before you do. Amazon’s “Anticipatory Shipping” program uses a predictive analytics model created using customer data such as prior purchases, order frequency, cart contents, and search history. The algorithm ensures the relevant products are shipped to the closest hub in anticipation of the next customer order. The result: Customers enjoy the benefits of faster shipping times at a lower cost, increasing their brand loyalty and providing Amazon with an increase in sales.

Forecasting

Forecasting models are a common OM tool to predict future outcomes based on historical information. But creating the optimal model that does the best possible job in projecting future outcomes correctly is not a simple exercise. Most data-driven organizations now use machine learning to create these models. AnalyticsBox, a tech start-up that helps businesses grow their online presence faster and more efficiently, uses the Holt-Winters seasonal methodology (which is a time series model that incorporates exponential smoothing–see Chapter 4) to provide users with a high-accuracy prediction of their future web traffic. Their model is constantly improving based on new data, and the algorithm’s constant enhancements are entirely automated requiring no human intervention.

Location Strategy

Choosing where to open a new retail store, factory, distribution center, or corporate office is a critical decision for businesses. Location strategy is a complex problem as there are so many factors that go into the optimal decision. as seen in Chapter 8. For a new retail location, for example, a business should consider the traits of their customers, local traffic trends, population density, labor costs, and much more. For an international giant like Starbucks, the scale of their growth simply makes it too complex to rely on traditional location-determining methodologies. Instead, they use an in-house mapping and business intelligence platform to evaluate nearby neighborhood demographics, retail centers, and public transportation access points.

It is nearly impossible nowadays to work in OM without relying on analytics in some capacity. We are processing and converting data into actionable, profit-driving insights at an ever-accelerating rate, and soon enough, no decisions will be made without the use of data science.

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: 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: Fiat Chrysler and 40,000 Unordered Vehicles

Fiat Chrysler has been manufacturing more cars and trucks than its U.S. dealers are willing to accept, at one point creating a nationwide stock of about 40,000 unordered vehicles and stoking tension with some of its retailers. Dealers claim the company has revived what’s known in industry circles as a “sales bank,” writes Bloomberg (Nov. 12, 2019). The practice is decades old and frowned upon by investors because it can obscure an automaker’s inventory figures. Dealers don’t like it because it can amp up the pressure companies place on them to take delivery of vehicles they don’t want.

Fiat Chrysler denies the sales bank claim. The company says it put a predictive analytics system in place early this year that aims to better align its supply chain and manufacturing plans with anticipated dealer orders. But it recently paid a $40 million penalty related to filing years of sales reports the SEC said were fraudulent. One way the company inflated figures was by paying dealers to report fake sales. The predictive analytics strategy was implemented this year and has already increased the required lead time for dealers to order cars, saving the automaker $441 million.

Some dealers were looking to pare back inventory after being burned by rising interest rates that increased the cost of holding cars, and a lack of incentive support from the company to boost sales of older models. Just last week, Fiat Chrysler told dealers it would allocate them vehicles for both November and December all at once, and that it may restrict orders for certain models. Dealers viewed this as a bid by the company to work through inventory by prodding dealers to order cars that remain in the sales bank. Fiat Chrysler said the restrictions apply only to certain vehicle configurations where demand exceeds production capacity.

Classroom discussion questions:

Is there an ethical issue in this story?
Point out the forecasting (Ch.4), supply chain (Ch.11), analytics (Mod. G), and inventory (Ch.12) OM implications.