Question

Write your own fictional case study. (Yes, you can make-up the information). You are encouraged to be creative, however, your case study must be professional and focus on concepts from chapter 24 and/or chapter 25.

The case study must be a minimum of 4 paragraphs and provide enough depth and scope for solid analysis of the information. In addition to the case study, develop and list 3 discussion questions. You do not need to provide an answer for each question, however, you do need to identify the main course concept/theme and the page number for the answer. This information can be presented in bullet format under each question.

Text Used is OPERATIONS AND SUPPLY CHAIN MANAGEMENT fifteenth edition F. Robert Jacobs & Richard B. Chase

Topics Covered in Chapter 24: Health Care

The nature of health care operations

Capacity Planning

Workforce Planning

Quality Management and Process Improvement

Health Care Supply Chains

Inventory Management

Performance Measures

Trends in Health Care

Topics Covered In Chapter 25: Operations Consulting

The management consulting industry

Economics of consulting firms

When Operations Consulting is Needed

The operations consulting Process

Operations consulting tool kit

problem definition tools

Data analysis and solution development

Cost Impact and Payoff analysis

Business process reengineering

Answer 1

Hey!

Here is case study centered upon organizations's operations.

Data is the new gold

Companies like Google, Microsoft and Apple have already taken the lead in AI and ML by utilizing the data garnered by their businesses. Apple’s Siri, Google Assistant and Microsoft’s Cortana are a few examples of how the companies can utilize data to provide better facilities to their users. Facebook uses deep neural networks for targeted advertising. It is also using machine learning to develop a messenger Chabot. Big tech companies are using. Data intelligence is also creating a new high in the market. Data intelligence is the analysis of various forms of data in such a way that it can be used by companies to expand their services or investments. Data intelligence can also refer to companies' use of internal data to analyze their own operations or workforce to make better decisions in the future. Business performance, data mining, online analytics, and event processing are all types of data that companies gather and use for data intelligence purposes.

When it comes to AI, other tech giants like Apple, Facebook and Microsoft, have invested a lot in acquiring companies, with an aim to build the future. Even Google CEO Sundar Pichai, with his “AI First” policy, has been quite vocal about Google’s plans to work on machine learning-based systems. The companies have been trying to build upon its AI, mainly because there is so much data which gets accumulated when due to the huge number of users now. Features like Apple SIRI, Microsoft Cortana and Google Assistant are prime examples of how tech companies can use data to better its services. Even Facebook is known for using it for targeted ads. Due to this, any company which can lead the AI ecosystem will have an edge. Based on market intelligence research firm IDC’s data, the AI market currently stands at $8 billion, but experts believe that the number will surge multiple times in the next couple of years. In fact, according to research by CB Insights, the big companies have acquired as many as 34 AI startups in 2017 itself.

GizmoNxt (Assumed name) is an enthusiastic start-up based in New York, providing solutions using data intelligence. It wants to make data-driven decisions easier and more effective. They partner with governments, businesses, philanthropies and non-profits around the world. Their platform enables organizations to collect primary data, access secondary data, layer it, transform and merge it and visualize relevant insights through them. They break through conventional barriers of consultancy and provide effective and optimized solutions in the market.

Problems faced by the firm:

1. The firm ‘s whole world revolves around data. In order to provide effective solutions to its clients it needs to focus on major setbacks of data analysis.

a) Outdated Data: Public data is collected sporadically and after long gaps, such as during the Census which collects data from households once in 10 years. Researchers and policymakers struggle to make decisions in the absence of relevant data, leading to high loss in investments and poor decision making. Making public data in India searchable is a hard engineering problem to solve, much more so due to the inconsistencies in data. The data is in different formats, unstructured, and impossible to parse through one Python script. Yes, local language PDF files are the reason our engineers think PDF is evil.

b) Bad data: For instance, DISE is an annual data collection survey from schools in India. This is how the survey is undertaken: STEP 1: The central authority mails a paper form to the different schools in India. STEP 2: The headmaster takes on the responsibility of filling the paper form with basic details, like the number of toilets, ramps etc. STEP 3: The headmaster mails the completed form back to the central authority. An analysis of raw DISE data shows that a number of parameters are missing and unfilled. It doesn’t take a rocket scientist to figure out why.

c) Creating meaningful data: The last step in driving decisions from data involves being able to find stories in data.

2. As a new and emerging sector the firm needs to combat the failure in recruitment of good talent and hence develop a good strategy to retain the talent in its own circumference.

3. Competitive analysis of firms working on similar lines and getting backing from big names. 4. Conventional consultancy firms still in market.

Q1: To deal with current problems. Do a complete analysis of the entire situation in the current times. You may use any method of analysis SWOT, PESTLE etc.

Q2: To deal with the technical barriers of the firm identify their core problems and provide a technical solution to overcome them. Logic and approach matter more than the actual designing of the data collection tool.

Q3: Suggest practical methods to deal with problem no 2,3,4.

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