selimhasagasioglu

Solution to Management Science Series #215:Real-world example: Cornell Endowment

Selim Hasagasioglu Academy and Consulting Services agreed with Cornell University’s Office of University Investments on increasing the average donation per Cornellian including alumni and current students.

Last year, average donation value was $200 per Cornellian. Selim’s company designed a new digital donation platform and was additionally tasked with assessing whether the new platform increases the average donation amount. The company investigated a random sample of 60 disparate donations and the sample’s average was calculated as $225 while the sample standard deviation was $100.

a. Help the company construct the Null and Alternative Hypotheses.
b. At a significance level of α=0.05, what should Cornell do, considering the results of this marketing experiment?
c. What kind of information is provided through this hypothesis testing project?

On E-commerce: Can every category be offered through e-commerce?

E-commerce and traditionally analog incumbents, especially those that are unspecialized, have been trying to penetrate into different industries and categories, thinking that many such offerings can be provided digitally.

Were they able to make it happen?

Of course, retail operations specific to some products have quickly utilized e-commerce practices, as well as omni-channel applications.
While computer software and video games have been moving into the “digital shelves” in a profitable way as they are innately digital, many other categories are lagging behind again because of their inherent analog nature.
There are only a few category killers and niche players that have been profitably disrupting innately analog businesses despite the presence of powerful, albeit unspecialized and broad-based, incumbents.

What are the categories that are/were tried and could not still be fully streamlined through e-commerce?

In this article, I draw on my experience at Amazon and elaborate on what it takes to be a profitable and viable e-commerce business. Furthermore, I also analyze companies such as 1stdibs, Etsy, and Wayfair and what kind of a future befall these companies, also expanding on what they were doing right and wrong. This article is intended to be another blueprint for those who plan to go online and launch their e-commerce business.

Solution to Management Science Series #214: Real-world example: Berserk Toy Company improving its QC processes

Berserk Toy Figures is a toy company producing its copyrighted Guts figures.

The manufacturing process owned by the company creates the head part of the figure that is designed to be 19 mm wide with a standard deviation of 1.5 mm.

Head part of the figure is an important feature of the figure and regularly goes through the Quality Control. QC sample size is 35 parts. A recent batch of head parts appeared to have an average width of 18mm.

1.What is the probability of getting an average width equal to or less than 18mm?

2. Is there a quality problem? Use your finding in 1.

3. Berserk Toys is a boutique toy maker that could not afford to stop production and run a root cause analysis. How can the company tackle this problem and increase the confidence?

Which company is this?

Which company is this?

Hints:
-It operates in a traditionally analog business that is tested by digital challengers.

-However, it continues to beat every one of them in terms of profitability.

-Operational excellence, efficient cash flow management, customer loyalty, and customer service are their hallmarks.

-It also started its digitization initiative, currently comprising 16% of its total net sales at maximum (does not disclose exact number)

-It has a positive book value, so do not mistake it for other companies that are technically accounting insolvent.

-Its stock is currently traded at a P/E of 45-50.

-Its stock is currently traded at a P/B of 15.
—
Note: This article is based on a social media post I wrote almost 4 years ago. In the article, I will unveil the company and dissect its operations and its stock. Moreover, we will try to look into whether much has changed since the first time I published my post.

Solution to Management Science Series #213: Real-world example: Marbella Call Center

Marbella Call Center is a contractor assessing the quality of calls to the customer services of top global brands. Recently, Marbella agreed with Eldorado, a global retailer, on assessing the quality of the customer service of Eldorado.

Marbella has been periodically recording a random sample of customer calls to Eldorado. Calls are evaluated on whether the employee taking the call abides by Eldorado’s customer support policies.

Historically, 80% of all calls conformed to the policy. A recent sample of 120 calls found that 70 adhered to the policy.

1. What is the sample proportion?

2. Is the normal distribution a good approximation?

3. What is the expected value and standard deviation of the sample proportion?

4. What is the probability of getting 70 or fewer calls that adhered to the company policy?

5. Do you see any problems concerning the adherence to the company’s customer support policies?

On Scaling Service Businesses and Digitization 💻

“Many businesses gain only limited advantages as they grow to large scale.
Service businesses especially are difficult to make monopolies.
If you own a yoga studio, for example, you’ll only be able to serve a certain number of customers.
You can hire more instructors and expand to more locations, but your margins will remain fairly low and you’ll never reach a point where a core group of talented people can provide something of value to millions of separate clients, as software engineers are able to do.”
➡️ Taken from Peter Thiel’s Zero to One
In this article, I try to disprove Thiel’s claim, providing a number of companies that are services businesses and have scaled through digitization successfully let alone have become monopolies despite inherently being service businesses. I elaborate on how these companies reinvented their business models and became successful while also sharing what they could have done better and some potential risks looming large in the future. I also demonstrate how these exceptional companies destroyed established trends and biases.

Solution to Management Science Series #212: Real-world example: Determining Portfolio Returns

An ETF created by the investment management firm, Gold Returns (GR) has a normally distributed expected monthly return with μ = 2% and σ = 8%.

a)What is the probability that the return is positive next month?

b)What is the probability that the portfolio’s return exceeds 10%?

c)What is the median return?

d)What is the first quartile of return?

Solution to Management Science Series #211: Real-world example: Kabuto Drinks forecasting production capacity and launching a loyalty program

A soft-drink company called Kabuto plans to model the production capacity for the year 2026.

The forecasters determined that the annual soft-drink consumption with the respect to Kabuto’s brands is normally distributed with a mean of 160 liters per customer of Kabuto per year and a standard deviation of 40 liters per customer per year.

a)What is the median per person annual consumption?

b)What is the mode for the per person annual consumption?

c)What percentage of Kabuto consumers do consume more than 160 liters per year?

d)What percentage of Kabuto consumers do consume more than 200 liters per year?

e)What percentage of Kabuto consumers do consume more than 230 liters per year?

f)What percentage of Kabuto consumers do consume fewer than 120 liters per year?

g)Your marketing department is proposing a loyalty program for the consumers who lie within the top 5% of Kabuto-branded soft-drink consumption. Find the threshold value of annual Kabuto drink consumption for these top consumers in liters.

h)In an alternative scenario, Kabuto will have customers within the top 3% of consumption join the loyalty program. Find the threshold value of annual Kabuto drink consumption for these top consumers in liters.

Career Advice 💡Focusing the numbers that do not add up 🧮

An on-campus recruiter came to your school and presented a slide deck showing some detailed metrics of the company.

You saw the revenues, profits, growth rates, and many other important financial metrics. Of course, such information is important for all prospective employees while they are trying to make a sound decision.

Then, you saw the slide, which was about the growth of the company in terms of headcount. The company had a cumulative annual growth rate of 25% for the last decade with respect to its net headcount growth.

What can you do to check whether this number is true?

You checked the company’s total headcount 10 years ago and found out that there were 500,000 employees working at the company at that time.

So, currently there should have been 3,725,290 employees now working at the company, which is more than the total population of your country!

If this trend were to continue for the next 44 years (which is of course very unlikely), almost every living human on earth would be working at this company.

This is an exaggerated story but I have witnessed similar stories in such company presentations.

TL;DR
Being on the lookout of such illogical trickery regarding numbers will serve you well in your career.
In this article, I will expand on the tactics used by some of the ‘prominent’ companies to lure you into their companies and what you need to do to prevent this from happening to you. They might be more desperate than you think. I also recommend a few books that unveil ‘the glamor’ of these companies and reveal the façade.

Solution to Management Science Series #210: Real-world example: Famous hotel chain, Mushroom Kingdom, bent on improving customer satisfaction

Real-world example: Famous hotel chain, Mushroom Kingdom, bent on improving customer satisfaction

You are the owner of the famous hotel chains called Mushroom Kingdom (MK). Historically, 75% of all the customers claimed that they were ‘completely satisfied’ with the services and accommodation offered by the MK.

You hired an analytics company, named Gotham Analytics (GA), that would conduct analyses to determine whether this still holds true. GA conducted a survey, by randomly sampling 35 customers out of a total of chain’s 250 customers. The number of customers in the sample who claimed that they were ‘completely satisfied’ is 25.

1) Calculate the sample proportion.

2) Could you assume that the distribution of the sample proportion is normal?

3) What is the standard deviation of the sample proportion using the finite population correction factor?

4) What is the standard deviation of the sample proportion ignoring the finite population correction factor?

5) Recalculate 3) and 4) assuming the chain has 110 customers.

6) Recalculate 3) and 4) assuming the chain has 1500 customers.

7) Assume again that the firm has 250 customers. Using the finite population correction factor to calculate the standard deviation of the sample proportion, what is the likelihood of finding 25 or fewer “completely satisfied” customers in a sample of 35 customers?