Chapter 4 – The OM Blog by Heizer, Render, & Munson

Guest Post: Using Solver’s Nonlinear Programming Procedure for Operations Models

Prof. Howard Weiss shares his insights on the power of Excel’s Solver.

I previously have posted for The OM Blog that in the operations course it is important to help students develop their Excel skills. Today I will introduce students to nonlinear programming in Excel’s Solver for Trend Analysis models, a topic in Chapter 4 of your Heizer/Render/Munson text. It highlights to the students exactly what is being optimized – sum of squared errors.

Trend Analysis
Example 8 from Chapter 4 illustrates the Solver process. The initial intercept of 10 and slope of 10 yield the forecasts in column D. Errors and squared errors follow from the forecasts and demands with the sum of squared errors shown in cell F12. This is Solver’s objective. The changing cells are the intercept and slope, there are no constraints, and the method in Solver is GRG Nonlinear. In addition, for least squares the “Make Unconstrained Variables Non-Negative” needs to be unchecked since slopes/trends can be negative in forecasting– although not in this example.

After solving, the solution, not displayed here, appears as Intercept 56.71, slope=10.54 (as shown in the text) and the minimum sum of squares, not shown in the text or figure above, is 773.

Guest Post: Forecasting, Inventory Management, and “No-Buy 2025”

Professor Misty Blessley at Temple U. looks at the “No- Buy” movement.

Thanksgiving is just 3 months away, and Christmas only 4, but the holidays are long upon retail supply chains. At the same time, a growing number of consumers are pushing back against the pressure to spend, embracing a movement known as “No-Buy 2025,” which is gaining serious traction.

At its core the movement is a consumer mindset focused on refraining from non-essential purchases for a set period, for some an entire year. Trending on online communities are people sharing their No-Buy challenges and success stories. Some are motivated to cut debt or save for long-term goals, while others are concerned with sustainability, minimalism, or anti- consumption values. Participation is surging, especially among millennials and Gen Zs, who are juggling inflation, student debt, and climate anxiety.

Baby boomers, in contrast, are known to possess a large portion of total disposable income and to spend on luxury and leisure items. Participants are cutting back on categories often linked to impulse spending or excess and are instead using what they have:
 Apparel and accessories
 Home décor and seasonal items
 Beauty and skincare
 Toys and impulse gifts
 Functioning electronics

Why It Matters for Supply Chains
No-Buy 2025 has a ripple effect on retail supply chains. The holiday season typically drives massive retail volume, but with intentional non-buying, companies could face missed orders if underestimating demand or excess inventory if forecasts are too high.

Many demand forecasts rely on past trends, but for some generational cohorts demand is eliminated or potentially delayed. Retailers may need to reconsider their demand planning models, where inventory is held in the network, and be aware of consumer behavior (e.g., generational preferences, while remembering that generalizations are, by nature, generalizations). Supply chains that account for today’s values will be best positioned to respond.

Classroom discussion questions:
1. What are the shortcomings with traditional time-series and seasonality forecasting methodologies given the no-buy movement?

2. Do you think the product categories identified above should be forecasted differently when compared to one another? Why?

3. How does the purchasing power of various generational cohorts come into play?

Note: The Silent Generation (born 1928-1945), Baby Boomers (born 1946-1964), Generation X (born 1965-1980), Millennials (born 1981-1996), Generation Z (born 1997-2012), and Generation Alpha (born 2013-2024).

Teaching Tip: The 15th Edition Ties AI Into Your OM Class

Our new 15th edition, just released, brings the topic of artificial intelligence into the course with AI in Action boxes and new material throughout the text. But we have gone a step further through our Instructor’s Resource Manual, a fantastic teaching tool. If you are new at teaching the course, you will find this 400 page guide an invaluable resource. Each chapter now contains an AI in the Classroom section, created by Prof. Jon Jackson at Providence College, providing 15-20 minute exercises. Here is a sampling from 3 early chapters:

Chapter 1
All firms, regardless of industry, can use productivity measures to track process performance. This exercise is designed to explore relevant productivity measures for different industries with the help of an AI-powered chatbot. Student groups can explore the following types of
facilities/firms (each group will pick one):
 Manufacturing facility  Market research firm
 Warehouse facility  Accounting firm
 Retail store  Financial services firm
 Restaurant  HR department
Students can use the following AI prompt structure: ROLE: I am a manager in a [insert facility/firm here]. GOAL: I want to measure productivity (an output divided by an input). REQUEST: generate 5 measures of productivity.
In groups, students can compare AI responses, evaluate the validity of the productivity measures (connecting to Chapter 1 definitions), and identify the best productivity measures to implement.

Chapter 3
A work breakdown structure (WBS) can provide a hierarchical description of a project into more and more detailed components. This activity is designed to practice this process for fictional projects around campus with the help of an AI-powered chatbot. Student groups can explore the following projects (each group will pick one):
 College graduation party  Student art exhibition
 Charity 5K event  Entrepreneur shark tank
 Intramural sports tournament
Students can use the following AI prompt structure: ROLE: I am the project manager for an upcoming [insert event here]. GOAL: I want to create a work breakdown structure to break the project into more manageable components. REQUEST: generate a work breakdown structure with 4 main tasks, each with 2 subtasks. For each subtask, also provide a short description and an estimated duration to complete the subtask.
In groups, students can compare AI responses, identify if any main tasks are missing (or unnecessarily included), and evaluate the accuracy of duration estimates.

Chapter 4
AI-powered chatbots can be helpful to enhance our understanding of confusing topics, but it isn’t guaranteed to provide accurate information. This in-class activity (15-20 minutes) is designed to get us in the habit of being critical of AI output, and if necessary, re-prompting the AI-powered chatbot to give a better answer. Student groups will try to answer the following questions with the help of the AI-powered chatbot:
 When does a 2-period weighted moving average equal the Naïve approach?
 When does the exponential smoothing method equal the Naïve approach?
 When is it best to use MAD vs. MAPE?
In groups, students can critically assess the accuracy of the AI responses (referencing material in Chapter 4) and identify more effective ways to prompt AI-powered chatbots.

For a desk copy of the 15th edition, please click on this link.

Guest Post: The Orange Juice Supply Chain

Prof. Howard Weiss, creator of our free software packages, Excel OM and POM, shares his concerns as a part-time Floridian.

In simplest form, the OJ supply chain is very straightforward. It begins with planting orange trees, harvesting the oranges, preparing the oranges for processing, juicing the oranges, packing the orange juice, shipping the orange juice to distribution centers, storing the orange juice and then shipping the juice to retail outlets. The figure below is very similar to the supply chain illustrated in Figure 1.2 of your textbook.

There are, of course, additional aspects to the supply chain. For example, planting and maintaining trees involves supplying fertilizer and water. Packing the OJ requires the manufacturing of containers and, of course, shipping requires trucks, trains, ships and planes.

Supply Chain Risks: It is well-known that orange trees need to be protected from freezing temperatures and that Florida hurricanes can damage crops. There are other difficult problems facing OJ providers. One is the citrus greening disease. This disease causes the fruits to become inedible and eventually the tree dies. In addition, since many farmers have sold their farms to developers, production of oranges in Florida have dropped to just 8% of what production was 20 years ago! Tropicana now uses oranges grown in Brazil, which is the largest producer of oranges.

Your textbook (see Ch.11) notes that environment and natural catastrophes, such as the disease, can affect supply chain risk and suggests using multiple suppliers or alternative sourcing to offset the risk. This is precisely what OJ producers have done by using oranges from other countries, most notably Brazil and Mexico, which crops have not suffered as much damage due to disease as in Florida. In addition, OJ producers have created new products that mix oranges with other fruits such as apples and pears to offset the loss of oranges.

Declining Demand: OJ manufacturers are also facing a decline in demand due to increased prices to consumers, and consumers questioning the nutritional value of orange juice especially considering the large amount of sugar in OJ. Orange juice demand has dropped while sales of teas, coffees, seltzers, energy drinks and bottled water have increased. Consumption is expected to continue to decrease over the next 5 years.

Classroom discussion questions:
1. Over the past 10 years OJ consumption in thousands of metric tons has been 733, 700, 663, 631, 581, 572, 530, 556, 542, 527. Forecast consumption for the next 5 years.
2. At what stage of its life cycle (see Figure 2.5) is orange juice?

Guest Post: Forecasting Lessons Using PC Sales

Prof. Howard Weiss presents an interesting, real-world example of seasonality and forecasting.

If we examine PC unit shipments in the U.S. 2013-2023, by quarter, there are a couple of interesting lessons we can learn from the data.

The data are separated into pre-Covid and Covid time periods because it is obvious that the graph looks different before 2020 than at 2020 and beyond. If you look closely at the pre-Covid data, it is very easy to see the seasonality. Quarter 2 is higher than quarter 1, Quarter 3 is higher than Quarter 2 and Quarter 4 is higher than Quarter 3 in EVERY year from 2013 to 2019.

Chapter 4 of your Heizer/Render/Munson textbook discusses Seasonal Variation in Data. Using Excel OM for the method of Example 9 we find that the seasonal indices are as given in the table below for the pre-Covid period. In addition, using regression we find the line that fits the data best is:

Shipments (in millions) = 15.675 – .06*x

where x is the time period from 1 to 28.

Notice that shipments have been decreasing by 60,000 units per year. Using the regression equation, the forecasts for the next 4 periods in 2020 are given in the table

Pre-COVID	Percent of Demand	Seasonal Factors	X value (2020)	Forecasts 15.675 – .06*x	Actual (2020 data)
Quarter 1	21.6%	.862	29	13.935	10.83
Quarter 2	25.1%	1.003	30	13.875	15.70
Quarter 3	26.4%	1.057	31	13.815	23.62
Quarter 4	26.9%	1.076	32	13.755	19.03
			Total	55.38	69.28

Looking at the actual 2020 data, it is obvious that Covid caused a significant increase in PC shipments. The increase is even more pronounced in 2021. This is not surprising as more and more students and workers were working from home rather than in the office or university. Also, examining the graph, the seasonality for 2020-2023 is not as obvious as for the pre-Covid period.

During COVID	Percent of Demand	Seasonal Factors
Quarter 1	22.3%	.893
Quarter 2	26.3%	1.052
Quarter 3	26.8%	1.072
Quarter 4	24.5%	.982

When forecasts for 2020 were made in 2019 it was impossible to know that Covid would strike and affect shipments as much as it did. But by quarter 2 of 2020 it was clear that quantitative forecasts based on past shipments would have large errors. At this point it would be imperative to introduce a qualitative method into the forecasting process, as discussed in the chapter.

Guest Post: The Waffle House Index and Hurricane Milton

Professor Howard Weiss provides a timely example of qualitative forecasting.

Waffle House is a restaurant chain with over 2,000 locations in 25 states ranging from Florida as far north as Pennsylvania and as far west as Arizona, operating all day every day. When you think of a Waffle House, you think about eggs, bacon, and, of course, waffles. What you don’t think about is forecasting. The Waffle House Index is a map that Waffle House provides of the status of its restaurants.

 Red means the restaurant is closed likely due to severe damage or unsafe conditions
 Yellow indicates that the restaurant is open but only serving a partial menu. The restaurant is working off of a generator and may not have water but has the ability to cook the meals.
 Green means the restaurant is fully operational.

From the closings one can see the severity of an upcoming storm as indicated by this map captured one day before Hurricane Milton is scheduled to strike Florida. Residents can use the information to decide on their storm strategy. One can also see the damage caused after the storm based on the open or closed Waffle Houses. The use of the index is a qualitative forecasting method as discussed your textbook.

The government, including FEMA, uses different methods to track storms, including airplanes and satellite. But FEMA also began to use the Waffle House Index in 2011 to gauge the severity of any storm. Waffle House has a reputation for staying open during storms as long as or even longer than any other restaurant so that people can get a hot meal, charge cell phones or just warm up or cool down. The map is not only useful before a weather event but also afterwards since it indicates how the recovery is going in an area served by a Waffle House.

Waffle House has chosen a strategy based on keeping their restaurants open 24/7. This includes purchasing generators for their stores and using what they term as “Jump Teams”. The jump team consists of volunteers who go to the affected location by car or even plane in order to help the employees get the restaurant open as soon as possible. These teams are, of course, examples of varying the workforce and/or subcontracting as described in the Aggregate Planning chapter (Ch. 13).

Classroom discussion questions
1. What other organizations use subcontracting in the event of a storm?
2. What companies are essential to re-open as soon as possible after a storm?

Guest Post: Girl Scout Cookies and Operations Management

Prof. Howard Weiss always has an interesting view of OM to share with our readers

We are currently in the middle of the Girl Scout cookie season. Several operations issues discussed in your Heizer/Render/Munson textbook face the Girl Scouts.

Location Only two manufacturers bake Girl Scout cookies. ABC Bakers is in South Dakota and supplies 25% of the cookies while Little Brownie Bakers is in Kentucky and supplies 75%. Six of the nine types of cookies are baked at both bakeries but each bakery also bakes three flavors that the other does not bake. (See the map).

Transportation If the types of cookies baked at the two bakeries were identical then this would lead to a transportation model as explained in Module C of your textbook. The model would include 2 sources (bakeries) and over 100 destinations (Girl Scout Councils). However, each Council can select whichever bakery they want to use which means that there is no attempt to minimize shipping costs.

Forecasting Recently, the Girls Scouts put out a new cookie, Adventurefuls. Forecasting demand for Adventurefuls was difficult because there were no past sales available to help create the forecast, so the quantitative methods in the forecasting chapter (Ch. 4) could not be used. The forecast for the new cookies was considerably lower than the actual demand and meeting demand was compounded by a labor shortage due to COVID. The Aggregate Planning chapter (Ch. 13) lists five methods for handling differences between supply and demand. There was no inventory that could be used; increasing the workforce, using part-timers or subcontractors was not feasible– so the only method left was to influence the demand. The Scouts placed a cap on the amount of these cookies that each troop could order.

Supply Chain In 2023, another new cookie, Raspberry Rally, was introduced and, again, demand was underforecast. This time a major reason for the poor forecasting was that customers could order the cookies exclusively online rather than through a girl scout. The production could not be increased because the manufacturer needed a long lead-time to produce the cookies and there were power outages at the Kentucky plant. A new distribution channel opened as some people were offering up their Raspberry Rally cookies on eBay for $20, $50 or even $200 a box instead of the usual $6 per box.

Classroom Discussion Questions:
1. Suggest a method for forecasting the sale of new Girl Scout cookies.
2. If the Girl Scouts wanted to minimize costs would each council receive their cookies from the nearer of the two bakeries? Why or why not?

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?

Guest Post: What Does a Super Bowl Parade Cost?

Dr. Misty Blessley, Associate Professor of Statistics, Operations, and Data Science at Temple U., shares her sports preferences with us today.

Next week, the winners of Super Bowl LVII will be honored by their hometown fans in a Super Bowl Parade. This Sunday, the Kansas City Chiefs will face off against the Philadelphia Eagles. Everyone loves a victory parade, but how does a city plan for a parade that might not happen? As a faculty member at Temple University, upon seeing the Eagles clinch the NFL Conference Championship, on Sunday, January 29th, I looked into parade operations. In 2018, the parade celebrating the Eagles’ Super Bowl LII win was held the following Thursday. Public transit was halted to Temple’s campus, which disrupted a joint event with Institute for Supply Management.

If the parade were to be held on the Thursday following Super Bowl LVII, it would disrupt a Supply Chain Management consulting event. My first stop was to Google, When is the Super Bowl parade in Philadelphia?, and the response was,“omg, please stop Googling this until the big game actually happens.” (The Philadelphia Inquirer, January 30, 2023). As of earlier this week, Mayor Jim Kenney, “… doesn’t really want to talk about it.”(NBCSports.com, February 7, 2023).

The EA Madden game is going with an Eagles victory (Fortune, February 6, 2023), as are the legalized betting organizations. Still, we forge on with consulting event planning. I took a picture of a long line of portable toilets north of City Hall, which were in preparation for Pope Francis’ visit in 2015. It is to be food for thought about all that goes into planning a parade. “Kansas City officials are planning a multimillion-dollar parade for Feb. 15…,” (The Kansas City Star, February 2, 2023).

I’ll be cheering for the Eagles, but my heart belongs to the Pittsburgh Steelers. If you are like me, this Super Bowl commercial is for you – https://www.youtube.com/watch v=4taNFpPmZag . Still, enjoy the game!

Classroom discussion questions:
1. How can project management be used to plan a parade? What activities will most likely need to be crashed/require crashing cost payment?
2. What are the advantages and disadvantages of Philadelphia’s and Kansas City’s positions? Win or lose, what do they mean for city officials, planners and for workers employed in the hometown, in terms of productivity?
3. How can forecasting be used in the planning process?

OM in the News: Forecasting Fertilizer

Scotts Miracle-Gro’s warehouse at its Ohio headquarters this month.

Chapter 4 of your Heizer/Render/Munson text discusses many widely used forecasting techniques. And most companies use exactly these techniques. However, historical based techniques proved inadequate in a pandemic. Scotts Miracle-Gro fertilizer is a case in point. As The Wall Street Journal (Sept. 16, 2022) writes: “Never in the modern global economy have businesses seen such a rapid shift from shortage to glut.”

Scotts was in the middle of its selling season in 2020 when Covid-19 shut down much of the global economy. Scotts’ production had to respond, but like many firms, response was chaotic with production disruptions caused by sickness and an abundance of caution which eliminated entire shifts. It also soon became clear that homebound families were gardening more. Keeping stores stocked became a problem. And in spite of shortages, sales were up 20% in 2020 and another 10% in 2021.

So just months ago, Scotts was bracing for the biggest summer ever. After two years of struggling to fill store shelves, the company had ramped up production to catch up with consumer demand for lawn seed, fertilizer and other garden products. Massive investments in new manufacturing capacity were about to pay off as Scotts prepared for the usual rush of May orders from retailers looking to replenish their stocks. The CFO assured investors that Scotts was in a good place on inventory and that the firm was expecting banner 2022 sales.

But the orders never came. The pandemic was over, and inflation hit. Retailers cut orders. Scotts has cut 450 jobs and more layoffs are coming. Production schedules have been cut. Available cash is a fraction of what it was. Nobody is getting bonuses. Scotts was largely a casualty of bloated inventory at big retailers like Walmart, Target and Home Depot. Those companies didn’t foresee the sharp reversal in buying behavior that has taken place in recent months as shoppers, squeezed by inflation, cut back on furniture, electronics and other goods and shifted spending to travel, food and fuel.

Classroom discussion questions:
1. Chapter 4 discusses seasonal adjustments to forecasts. How much would seasonal adjustments have helped Scotts in the past two years?
2. Your text (see page 138) suggests a forecasting technique know as Stagger Charts. How might Scotts implement this technique?

OM in the News: Amazon’s Forecasting System Misfires

Amazon expanded operations and staff during the pandemic, but demand hasn’t kept pace.

As Covid-19 spread in 2020, homebound customers turned to Amazon at an unprecedented clip. Orders neared that of the holiday season and the company was short-staffed and often out of stock on key items, pushing delivery windows from 2 days to weeks on some items. Founder Jeff Bezos greenlighted a strategy, guided by a revered internal forecasting tool, that overshot the long-term projections for demand. Instead of a permanent shift in consumer behavior, the pandemic-fueled growth in online shopping has slowed as in-person shopping has bounced back.

Early in the pandemic, Amazon opened hundreds of new warehouses, sorting centers and other logistics facilities, and doubled its workforce from 2020 to 2022, to more than 1.6 million people. But demand hasn’t kept pace with that planned capacity. Now new CEO Andy Jassy is cutting back the excesses. He is subleasing at least 10 million square feet of warehouse space, deferring construction of new facilities, and finding ways to end leases with outside warehouse owners. Jassy has also abruptly closed down the company’s bricks-and-mortar retail operation—68 stores—and is paring back its bloated head count.

Part of Amazon’s e-commerce challenges today, writes The Wall Street Journal (June 17, 2022), stem from a piece of technology long prized as a secret weapon, an internal forecasting system called Supply Chain Optimization Technologies, or SCOT. It was designed to incorporate a multitude of factors and spit out projections for product demand and the growth in logistics needed to fulfill it.

SCOT forecasts produced low, medium and high estimates. Because of unprecedented volume in the early days of the pandemic, Amazon repeatedly chose the higher end of SCOT’s estimates. Those estimates meant that the company needed many more fulfillment centers and other infrastructure to keep up. So Amazon aggressively built out new warehouses and transportation hubs, and went on a hiring spree to get customers their packages. But the forecasting technology wasn’t equipped to process an unforeseeable event like the pandemic and caused the company to commit to building infrastructure early in the pandemic that take 18 months to 2 years to come online. When the virus receded, Amazon was left with more planned capacity than orders. After being understaffed for 2 years, the company was suddenly overstaffed.

Classroom discussion questions:

Using Chapter 4 terminology, what type of forecasting system did Amazon employ?
What could the company have done differently, in hindsight?

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: Smoking– Forecasting and Product Life Cycle

Today’s Guest Post comes from Prof. Howard Weiss, the developer of the Excel OM and POM software that comes free with our text.

Forecasting: For the past 40 years, cigarette smoking has been declining at a rate of 3% to 4%. The
drop can be seen in the figure below and it clearly is following an almost straight line, which makes
forecasting very easy using the trend projection method discussed in Chapter 4.

Some of the more recent decline can be attributed to the introduction of e-cigarettes and vapes. However, smoking is on the rise again during this current pandemic which means that time-series forecasting methods, which rely on past data, would not be very useful for forecasting sales of cigarettes in the foreseeable future.

Product Life Cycle: Below is a figure that displays sales of cigarettes from 1900 to 2015 for 8 different countries on 3 different continents.

What is interesting about the figure is that while smoking started and peaked at different years, for all of
these countries, the pattern is identical for each country to Figure 2.5 in the text, which displays the 4 phases of the life cycle – Introduction, Growth, Maturity, and Decline. It is also interesting to note that the life cycle for cigarettes has been over 100 years.

Classroom Discussion Questions:

Cite another product or service with a life cycle as long as a century.
Do you think you can trust all of the data in the figure?

OM in the News: Fighting Pandemic Stockouts

Plenty of retailers and supply chains are still suffering from stockouts. From cleaning products, to kettlebells, to appliances, to meat, retailers, manufacturers and suppliers are coming up short. Forecasts need fixing in a period of severe economic upheaval such as a pandemic–and this article in Supply Chain Dive (Sept. 8, 2020), has 4 ideas for making the changes.

Supply chain managers should plan more often. Companies normally planning on a monthly cycle should switch to every 2 weeks; those on a 2-week cycle should shift to weekly. As demands related to coronavirus mash up with those of the upcoming holiday season, it is critical to have more data points and more sophisticated methods around demand planning to account for some of that volatility and surprises.
Focus on core products and customers. Companies can learn from how home improvement retailers operate during natural disasters. Instead of trying to keep everything in stock, they focus on the most in-demand items. Several large brands and retailers have employed the core product tactic during the pandemic, cutting back SKUs and, in some cases, entire product lines.
Shift more manufacturing to the U.S. A domestic U.S. industrial company with 2-3 factories with 4-5 distribution centers has a lot more ability to flex up and add shifts, even with the new social distancing and safety measures that are required because of COVID.
Give something new a shot. If items are in short supply, and not coming back on board anytime soon, companies can try a new brand or a new product — maybe one they have considered bringing in. Those items may be easier to get, and customers are more likely to be receptive to having an alternative option rather than nothing at all.

Classroom discussion questions:

Which of these would be most critical to a sports equipment manufacturer today?
How must forecast methods change?

OM in the News: A Forecasting Nightmare for Farmers

In early July, S. Carolina farmer Jeremy Storey dropped off an order of eggs at a restaurant’s back door as planned and continued on his way. But 6 hours later, he got a call that the eggs were never collected — the restaurant had suddenly closed because a staff member tested positive for Covid-19, and nobody canceled the order. After half a day in the hot sun, the eggs could no longer be eaten. “Half the restaurants we’re going to now, we find out upon delivering to them that they’re closed,” he said. He’s now sitting on a surplus of about 24,000 eggs, with no idea when, or if, things will stabilize.

The unpredictability is a major problem for farmers. If they can’t forecast what demand for eggs (and other products) will look like tomorrow, much less months out, they run the risk of overproducing — which would leave them with expensive surpluses — or underproducing, which would prevent them from having enough product on hand to meet demand.

Now, just as restaurants began to resume dine-in service, a new surge of coronavirus cases has paused reopening plans and put farmers back in limbo — and made it even more difficult to forecast. Planning is essential to farmers because they have to anticipate their customers’ demand months, and sometimes years, ahead of when they deliver so that they have enough time to grow crops or raise animals, writes CNN Business (July 16,2020).

Uncertainty “is really what’s causing the problem,” said a union exec. “There’s no end in sight. That makes it “really hard to plan for the future.” If the uncertainty drags out for another year or two, he said, some farms and restaurants will go out of business. It also means that some restaurants that make it through this dark period won’t have a steady source of supply at the other end.

Classroom discussion questions:

1 Should demand during the pandemic be excluded from future time-series forecasts?

2. With so much uncertainty, is it better to raise or lower production for the next agricultural cycle?