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This note explains the fundamentals of neural networks that are used for classification and prediction systems, with a primary focus on feed-forward back-propagation neural networks.

In March 2017, the director of operations at Namratha Oil Refineries Pvt. Ltd., a leading manufacturer and distributor of high-quality packaged coconut oil in southwest India, faced a challenge. Because of inefficiencies in the company’s packaging division, pouches, jars, and bottles were being overfilled with coconut oil, leading to an “oil giveaway” that had weakened the company’s bottom line. The director analyzed the current processes and discovered improper data recording, frequent breakdowns, poor allocation of the maintenance crew, and an improper inventory management system. His experience told him that the existing production facilities were sufficient to meet the company’s requirements, and therefore, the existing processes needed to be streamlined to ensure minimal or no loss of oil. What strategy should he recommend to reduce losses and restore the company’s profitability?

Teach For Lebanon (TFL) was a non-profit organization (NFP) founded in response to the national problem of low quality public education. The case examines the sustainability challenges that TFL faced in a developing country, covering the financial, legal, and social dilemmas that NFPs struggled with. The case tracks TFL’s innovative solutions to enhance the organization’s financial sustainability and considers scaling-up and initiating a new business to generate additional income. Using benchmarking, students can invent and list their own suggestions for enhancing NFPs’ financial sustainability.

Excel spreadsheet for students.

Spreadsheet to accompany product 8B19E019.

A management science professor had an unpleasant experience with a hotel she stayed at in New York City. Consequently, she wanted to figure out if hotel ratings were enough to recommend a hotel, or if customers’ text reviews could be used as more important and accurate indicators of customers’ hotel experiences. The exercise serves as an introduction to the topic of text analytics—specifically, sentiment analysis—and introduces the concept of text mining and the importance of dealing with unstructured datasets. Much of the exercise focuses on the method and rationale behind document indexing and the subsequent weighting of the indexed terms through term frequency–inverse document frequency. Textual data from customers’ hotel reviews are provided to apply the text mining techniques and to provide insight for a better decision-making process that would help the professor in her next hotel booking.

Queues (or waiting lines) are common in modern life. We wait in lines at the campus cafeteria, to board an airplane, in the emergency room, and when we call a company for customer service. We even wait for applications to be initiated and processed by our smartphone processors, and our online orders wait to be fulfilled at companies’ warehouses before being shipped to us. How long we wait in line depends on several factors and parameters. It may depend on the number of servers working and the amount of time it takes to serve each individual customer (the system’s capacity) or on how many customers arrive at any given time (the system’s demand or flow rate). Most importantly, queues form due to random variations in arrival patterns and service times.

Hospitals frequently deal with congestion and blockage that affects patient flow and increased costs. In 2018, at University Hospital, part of South West Health Centre, patient flow in the medical surgical intensive care unit seemed to be highly susceptible to congestion, and this was creating ripple effects throughout the hospital and leading to increased costs. The complexity of patient flow presented an opportunity to build a discrete-event simulation model to provide insights regarding the patient flow, costs, and opportunities for improvement. But how could the hospital’s management use a simulation model of patient flow to determine alternatives to optimize both patient flow and costs?

Spreadsheet for product 8B19E007.

Spreadsheet for product 9B19E007.

Student Spreadsheet to accompany product 9B19E004.

The manager of Water Operations at the City of London wanted to reduce the cost of pumping water by optimizing energy use. Water Operations had been consistently experiencing higher operational costs, and electricity bills accounted for up to 30 per cent of its direct costs. If the manager could predict the future cost of energy, she could schedule the operation of the water pumps to benefit from periods of low energy prices, potentially saving up to 25 per cent of the energy costs. The manager obtained the hourly Ontario electricity prices from 2003 to 2015, and wanted to use that data to evaluate how accurately she could predict future electricity prices. How should she proceed?

In 2017, wait times for kidney transplants in Ontario were getting out of hand. While patients from London Health Sciences Centre’s kidney transplant program in London, Ontario, had a reasonable wait of approximately one year, patients in Toronto's kidney transplant program waited almost four years. In an attempt to improve the overall wait times for all Ontario patients, the provincial Ministry of Health intended to merge the two currently independent programs and create a unified wait-list. Two doctors at London Health Sciences Centre were concerned about the effects of the merger for their patients in London, and asked an analytics specialist to determine the effects of the merger. Would the merger have the adverse outcome they expected for their patients’ wait times?

Spreadsheet to accompany product 8B18E020.

The continued spread of AI has stoked fears of catastrophic job losses in which machines and algorithms run the world, but research by Accenture paints a more encouraging picture. Many old jobs are changing and new ones are emerging in the “missing middle,” where humans and machines work together. The authors see evidence of at least eight novel skills that draw on the fusion of human and machine talent within a business process to create better outcomes than could be achieved when working independently: 1) taking back your time and doing something positive with it (with AI systems taking over many rote tasks, workers must redirect their time to more distinctly human tasks like interpersonal interactions, creativity, and decision-making; 2) anticipating unintended consequences and normalizing the use of technology and AI; 3) utilizing human judgment (AI can get many things right, but it still doesn’t know how to read situations and people well enough); 4) asking the right questions of AI; 5) helping bots help workers extend their human capabilities, business processes, and even careers; 6) understanding how machines work and learn and adapting accordingly (i.e., melding); 7) being a willing learner from AI and teacher to AI; and 8) reimagining just about everything. For years, the dream was to create an AI that could rival the intelligence of people. Today, we’re seeing that practical AI is becoming a tool to extend our own human capabilities at work.

The purpose of this note is to explain the application areas of the chi-square goodness-of-fit test and how to run the test in Excel.

In 2015, the assistant general manager at JSW Steel Ltd. (JSW), one of India’s largest steelmakers, faced a dilemma. Should JSW continue to transport the company’s end products to clients, or should JSW instead outsource the transportation to a third-party provider? Outsourcing would ensure timely delivery but would increase the cost. Another option was to pay an agency a premium for sharing information on the availability of Class I and Class II barge vendors. Class I barges were more reliable in terms of on-time and damage-free delivery, whereas Class II barges had a smaller capacity, were less reliable, and had a greater risk of goods being damaged, for which JSW could face both monetary and non-monetary losses. Although the Class I barges led to higher payoffs, the outsourcing option offered a fixed and relatively lower payoff. The assistant general manager’s objective was twofold: to meet the customer requirements in time and to benefit JSW financially. How should he decide which option to pursue?

Instructor Spreadsheet for product 8B18E011.

Instructor Spreadsheet to accompany product 8B18E015.

Spreadsheet to accompany product 9B18E015.