In 2014, the owner of a food truck based in Hamilton, Ontario, was looking over the first year of her operations. In addition to working in Hamilton, she had tried to maximize her revenues by driving to several other cities and charging various prices for each burger, depending partly on the fresh ingredients available in each city. Besides location, the owner had collected data on a few other factors-the weather, the day of the week, the city's population, and whether a festival was going on-that had had an impact on the demand for her product. She wondered whether analytics could help her decide where to sell and how much to charge on a daily basis. The owner also wondered whether this decision-making and data-collection process could be automated since she would be using it every day.
In July 2014, the managing director of Lakshmi Projects in Delhi, India, finds himself struggling with the marketing and sales strategy for the year ahead. Founded in 1997, the company specializes in offering turnkey solutions for bulk material handling systems for industries in the fast-growing infrastructure segment of the Indian economy; its two main product categories are elevator and conveyor systems. Yet, the company was failing to meet its sales targets, largely due to an overextended and underachieving salesforce. What was the right structure for the sales, after-sales and quality teams in the organization? An additional concern was that a sales strategy for the company's new product, set to launch in October 2014, had not yet been decided. Fluctuating industry dynamics, financial strains, field sales and service requirements meant that this was a complex decision that held larger consequences for the company's future.
Hyrule Cinemas is losing money quickly and its owner must take steps to rectify the problem. Using survey data and general information about the business, three types of analysis can be completed: Van Westendorp, conjoint, and a decision tree. These analyses will enable Hyrule Cinemas to make the best decision possible about price points and location, thereby helping the company to become profitable.
Whenever an investment is made, there are always costs and benefits. This case explores, based on quantitative and qualitative factors, a decision that a typical student at university might face. The decision to rent a house or purchase one in order to rent out the additional rooms is a difficult one. All of the pros and cons of each option need to be carefully considered.
Mistral Energy is looking to build a $40 million power plant in close proximity to both the Alberta and Saskatchewan power markets. The Alberta market is deregulated and the price fluctuates hourly with supply and demand. The Saskatchewan market, on the other hand, is a regulated monopoly. Mistral Energy needs to understand into which market they should sell their power. Because the prices available in Saskatchewan are unknown, Mistral is particularly interested in what power price would make the company indifferent between markets. Additionally, because the power plant is roughly equidistant between Alberta and Saskatchewan transmission lines, it might be possible to choose between markets on an hourly basis. Mistral is interested in investigating the value of this inter-market connection. Unfortunately, for technical reasons, this switch is not instantaneous, and the plant must be shut down for 30 minutes before supplying power into the other market. Another challenge is predicting when the power price in Alberta will be greater than the contract price available in Saskatchewan. Because the future Alberta price is unknown and highly variable, the risk exists that high prices might not be sustained long enough for Mistral to realize any value.
Sinofert Holdings Limited, the largest comprehensive fertilizer enterprise in China, is trying to improve the profitability of its urea business. The company has invested a great deal of time and money but still reported losses in 2007 and 2009 and only a small profit in 2008. Sinofert both manufactures urea and purchases it from external suppliers, as well as distributing it to the provinces. Manufacturing costs, transportation costs, market prices, demand forecasts and manufacturing constraints are all known. An optimal distribution plan using linear programming can be compared to the plan derived by Sinofert management. Substantial profitability increases are shown to be possible, although the optimization reveals some issues with contract constraints. If the company is to make its urea business profitable, it needs a fresh look and a change in the way of doing business. The company's chief analytics officer has been asked to look at the urea business and to provide recommendations to increase profitability.
This note is an introduction to simulation in Excel and VBA. The note demonstrates different ways of doing simulation in Excel and Excel VBA with examples. The concepts used and explained in the note are random number, probability distributions, data tables, loops and arrays, histograms and descriptive statistics.
This note is designed to give readers an overview of some useful tools within Excel and VBA. These include Offset, Vlookup, Dynamic Charts, Form Controls and Pivot Tables. The note contains explanations followed by examples for each tool.
This case presents a situation in which a large financial institution has acquired a sizable portfolio of new clients of travel (corporate expense) cards. The bank must decide on the optimal mix of clients to retain in order to achieve their goals of maximizing profitability, entering a new product market successfully and maintaining reputation. The optimal mix depends on a number of different factors, including annual account spend level, complexity of serving the account, the number of cards in each account, account risk and account retention level. The selection and number of clients chosen will affect the bank's future profitability and long term strategy. The bank is limited by attempting to achieve a three-year payback, and facing costs that can vary significantly (and which are not in the bank's control).