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:

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

Guest Post: How Will Artificial Intelligence Impact ERP Systems?

Katie Decker is Marketing Manager at Account Mate, a California software firm with over 150,000 clients. She regularly shares her ERP expertise with our readers.

The integration of AI into Enterprise Resource Planning (ERP) systems (the topic of Ch. 14 in your Heizer/Render/Munson text) may revolutionize how businesses manage their operations. There is a lot of buzz around how AI will impact all businesses, and ERP software is not exempt. AI can transform ERP systems from mere transactional platforms to intelligent systems capable of predictive analytics, process automation, and enhanced decision-making.

Benefits of AI-Enhanced ERP Systems

  1. Increased Efficiency: Automation of routine tasks and processes reduces manual effort, speeds up operations, and increases overall efficiency.
  2. Cost Savings: AI-driven optimizations lead to cost savings in various areas, including inventory management, supply chain operations, and customer service.
  3. Better Decision-Making: Enhanced analytics and predictive capabilities provide more accurate and timely information, enabling better decision-making.
  4. Improved Customer Satisfaction: AI-powered customer service tools and personalized experiences lead to higher customer satisfaction and loyalty.
  5. Scalability: AI-enhanced ERP systems can scale easily to handle growing data volumes and business complexity, making them suitable for businesses of all sizes.

Challenges and Considerations

  1. Data Quality: The effectiveness of AI depends on the quality of data. Businesses must ensure their data is accurate, clean, and well-organized.
  2. Integration: Integrating AI with existing ERP systems can be complex and may require significant changes to infrastructure and processes.
  3. Change Management: Implementing AI requires changes in workflows and employee roles. Effective change management and training are essential for successful adoption.
  4. Security and Privacy: AI systems handle sensitive data, making robust security measures and compliance with data privacy regulations crucial.

AI is poised to have a profound impact on ERP systems, transforming them into intelligent platforms that can predict, automate, and optimize various business processes. By leveraging AI, businesses can achieve greater efficiency, cost savings, and enhanced decision-making. However, successful implementation requires careful planning, quality data, and a focus on change management.

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:

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

OM in the News: UPS Forecasting Project Will Improve Logistics Planning

Packages at the new UPS hub in Paris

United Parcel Service is working on an ambitious analytics and machine learning project to gather and consolidate data from various applications within the company’s logistics network to better predict package flow, volume and delivery status, writes The Wall Street Journal (July 17, 2018). The predictive analytics tool will gather and analyze more than 1 billion data points per day at full-scale, including data about package weight, shape and size, as well as forecast, capacity and customer data. This allows UPS to know exactly what’s going where, and when it’s going to arrive, much more accurately than before.

The project is an example of how UPS is upgrading technology systems as it faces heavy competition from rivals including FedEx and Amazon as well as ever-growing e-commerce shopping demands. The company still relies on some outdated equipment and manual processes, but it’s opening new automated facilities and working on technology upgrades, such as this one, as part of a $20 billion capital spending plan.

It will give staff more accurate forecasts about the package volume that needs to be processed at UPS facilities on any given day. That will give employees enough lead time to determine whether they need more resources at package and sorting facilities in the event of a higher-volume day. Predictive analytics also could help eliminate bottlenecks in the supply chain because of unforeseen weather or emergency situations. Knowing how upcoming inclement weather will impact the supply chain days in advance will result in better planning.

Developing the tool was an ambitious feat because of how many hundreds of millions of data points needed to be consolidated into one single platform. Until now, forecast, capacity, customer and package data was housed in different applications. The tool is expected to be available to UPS employees by the end of the year via a smartphone, desktop and tablet application.

Classroom discussion questions:

  1. Why is this project so important to UPS?
  2. Why is the forecasting system so complex?