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

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?

Good OM Reading: The Hard Work Behind Analytics Success

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

Five key findings came from the research:

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

Good OM Reading: Analytics–The Widening Divide

Why don’t more managers embrace the business analytic tools we use in so many aspects of our OM courses? A new report by MIT Sloan Management Review (Nov.8, 2011) answers the question with a survey of 4,500 executives regarding the integration of analytics in their enterprises. The report, Analytics: The Widening Divide, concludes that cultural biases, such as the need for new management competencies and organizational resistance to new ideas –more than technological hurdles–are the primary barriers.

First, a definition of business analytics: “the use of statistical, quantitative, predictive, and other models to drive fact-based planning, decisions, execution, management, measurement, and learning. Analytics may be descriptive, predictive. or prescriptive”.

The MIT Sloan report breaks companies down into 3 categories: Transformed (heavy users), Experienced (moderate users), and Aspirational (companies least experienced in the use of business analytics). The good news is that the number of firms in the 1st two categories, who use analytics for competitive advantage, has surged by 57% in the past year. The Aspirational group’s use of analytics actually declined by 5%. Transformed organizations have set the pace in expanding use of analytics and were found to be 2.2 times more likely to substantially outperform industry peers.

The Transformed group keenly appreciates the value of precise and real-time decisions, and is 3 times more likely to focus on speed of decision-making than Aspirational firms. This means managing operations and improving output levels based on real-time supply and demand management. Inventory replenishment processes, for example, are automated and production is optimized in these companies. As a case study, the report follows McKesson, which processes 2 million hospital orders per day. McKesson does so by embedding algorithms into customer orders to manage the inventory process without human intervention.

When students ask you why analytics are important in your OM course, this report provides a ready response.

Teaching Tip: Helping Your OM Students Find Jobs with Business Analytics

Just a day or two ago, I got a nice email from our colleague Barry Spraggins, who is Chair of the Managerial Sciences Department at the University of Nevada-Reno. Barry uses our text Operations Management, 10th ed., and noted that he teaches heavily from all the Quantitative Modules, including Decision Making Tools (Module A), LP (Mod. B), Transportation (Mod.C), Queuing (Mod.D), Learning Curves (Mod.E), and Simulation (Mod.F). He writes: “I still think these are relevant components”. Not by coincidence, we find that The Wall Street Journal (Aug. 4,2011), Jay and I, and IBM all agree.

The Journal reports that finding qualified graduates in business analytics has proven difficult–and colleges are finally stepping up to meet industry demand. IBM, which spent more than $14 billion since 2005 to buy a flock of analytics companies, has now teamed up with over 200 colleges to develop analytics courses. Fordham and Indiana U. are unveiling analytics curricula, as well as certificate and degree programs. Indiana, for example, is offering certificate programs in business analytics to both Deloitte and Booz Allen employees. Fordam has a required course called Marketing Analytics for MBAs. U. of Virginia, also working with IBM, is introducing an analytics track this fall.

“Analytics is certainly one of the top five things executives are worried about and investing in heavily”, says the president of Teradata. “Industry is going to demand it. Students are going to demand it”. As IBM and other big firms drive the software implementation process to the board room, we in academia may very well see a resurgence of demand for the very topics Prof. Spraggins has long espoused. In a tough job market, this is one way to help our students gain competitive advantage.