Case Study In December 2016, Arshad Ali joined Imperial Computers Ltd. (ICL) as a Senior Programmer, with a handsome pay. Prior to this job, he worked successfully as an assistant programmer in Gem Computers (Gem). Arshad felt that ICL offered better career prospects, as it was growing much faster than Gem, which was a relatively small company. Although Arshad had enjoyed working there (at Gem), he realized that to grow further in his field, he would have to join a bigger company, and preferable one that handled international projects. He was sure he would excel in his position at ICL, just as he had done in his old job at Gem. ICL had international operations and there was more than a slim chance that he would be sent to USA or the UK on a project. Knowing that this would give him a lot of exposure, besides looking good on his resume, Arshad was quite excited about his new job. Arshad joined Gunjan’s five-member team at ICL. He had met Gunjan during the orientation sessions, and was looking forward to working under her. His team members seemed warm and friendly, and comfortable with their work. He introduced himself to the team members and got to know more about each of them. Wanting to know more about his boss, he casually asked Rehman, one of the team members, about Gunjan. Rehman said, “Gunjan does not interfere with our work. In fact, you could even say that she tries to ignore us as much as she can.” Arshad was surprised by the comment but decided that Gunjan was probably leaving them alone to do their work without any guidance, in order to allow them to realize their full potential. At Gem, Arshad had worked under Sultan and had looked up to him as a guide and mentor – always guiding, but never interfering. Sultan had let Arshad make his own mistakes and learn from them. He had always encouraged individual ideas, and let the team discover the flaws, if any, through discussion and experience. He rarely held an individual member of his team responsible if the team as a whole failed to deliver – for him the responsibility for any failure was collective. Arshad remembered telling his colleagues at Gem that the ideal boss would be someone who did not interfere with his/her subordinate’s work. Arshad wanted to believe that Gunjan too was the non-interfering type. If that was the case, surely her non-interference would only help him to grow. In his first week at work, Arshad found the atmosphere at the office a bit dull. However, he was quite excited. His team had been assigned a new project and was facing a few glitches with the new software. He had thought about the problem till late in the night and had come up with several possible solutions. He could not wait to discuss them with his team and Gunjan. He smiled to himself when he thought of how Gunjan would react when he will tell her that he had come up with several possible solutions to the problem. He was sure she would be happy with his having put in so much effort into the project, right from day one. He was daydreaming about all the praise that he was going to get when Gunjan walked into the office. Arshad waited for her to go into her cabin, and after five minutes, called her up, asking to see her. She asked him to come in after tem minutes. When he went in, she looked at him blankly and asked, “Yes?” Not sure whether she had recognized him, Arshad introduced himself. She said, “Ok, but why did you want to meet me?” Arshad started to tell her about the problems they were having with the software. But before he could even finish, she told him that she was busy with other things, and that she would send an email with the solution to all the members of the team by the end of the day, and that they could then implement it immediately. Arshad was somewhat taken aback. However, ever the optimist, he thought that she had perhaps already discussed the matter with the team. Arshad came out of Gunjan’s cabin and went straight to where his team members sat. He thought it would still be nice to bounce ideas off them and also to see what solutions others might come up with. He told them of all the solutions he had in mind. He waited for the others to come up with their suggestions but not one of them spoke up. He was surprised, and asked them point-blank why they were so disinterested. Aftab, one of the team members, said, “What is the point in our discussing these things? Gunjan is not going to have time to listen to us on discuss anything. She will just give us the solution she thinks is best, and we will just do what she tells us to do; why waste everyone’s time?” Arshad felt his heart sink. Was this the way things worked over here? However, he refused to lose heart and thought that maybe, he could change things a little. But as the days went by, Arshad realized that Gunjan was the complete opposite of his old boss. While she was efficient at what she did and extremely intelligent, she had neither the time nor the inclination to groom her subordinates. Her solutions to problem were always correct, but she was not willing to discuss or debate the merits of any other ideas that her team might have. She did not hold the team down to their deadlines not did she ever interfere. In fact, she rarely said anything at all. If work did not get finished on time, she would just blame her team, and totally disassociate herself from them. Time and again, Arshad found himself thinking of Sultan his old boss, and of how he had been such a positive influence. Gunjan, on the other hand, even without actively doing anything, had managed to significantly lower his motivation levels. Arshad gradually began to lose interest in his work – it had become too mechanical for his taste. He didn’t really need to think; his boss had all the answers. He was learning nothing new, and he felt his career was going nowhere. As he became more and more discouraged, his performance suffered. From being someone with immense promise and potential Arshad was now in danger of becoming just another mediocre techie.

Question2: What should Arshad do to resolve his situation?

If you were a COE of a rebuilding organization what steps would you take to rebuild it?

Round 7 in Capsim Simulation... my Low End product, Acre, did not sale well at all ...see below for positioning. Resulted in large inventory carrrying cost I need some guidance on what to do Round 8.

Units available for sale 3008

Sold 463

pfmn 3.5 size 16.5

MTBF 12000

age Dec 31 ...was 5.9

Automation 10

JUST LIGHTING CASE STUDY ( Domestic and International Sourcing)

Just lighting is an enterprise in London Ontario which currently sells light fitting from various manufactures to a growing market of contractors and developers. It has grown from a $1 million turnover to a $5 million turnover organization. It does not have any borrowing or loans as it has a sound cash flow. Just lighting owns a facility comprising of an office block, a small warehouse and a large packing area for future expansion. The owner of Just Lighting has always been recognized as an honest and religious man and has run his business the same way. However, he retired a year ago and his son-in-law has taken over the CEO position on the board.

One product which is very price sensitive in the market is a fluorescent surface mounted light fitting called the Con fitting, which makes up 30% of their turnover. The largest volume comes from the 4 foot, 2 lamp (36 watt) fluorescent range.

The marketing Manager, Sparky has recently reported that one of their largest customers has just landed a five year contract to refurbish a number of large low cost commercial buildings which need to upgrade their lighting, to meet the new code. The order is pending subject to Just lighting giving a firm price on 500,000 units. The requirements for this project are the same for each year

As Just lighting wants to grow its market share but sees this order as strategic, it has decided to set up a cross functional team made up of the Marketing Manager, Accountant, Inventory and Logistics Manager and the Purchasing Manager, to come up with a plan. The Purchasing Manager, Chris is responsible for compiling a proposal for the CEO recommending outsourcing or insourcing the additional volume of Con fittings.

Sparky has recommended that Just Lighting should continue to purchase the fitting from the existing supplier, Strip Lighting Manufactures. See exhibit for pricing history. Strip Lighting has been the only supplier of the Con fitting range and the relationship has been a good one although the Accountant, Bookie, has been expressed his concerns about the dinners the supplier lays on for the Just Lighitng customers and some senior staff at expensive restaurants. Sparky responded that is how business is done and the new CEO really enjoys his parties. He added, “Besides, if you don’t then you will lose your customers.”

Chris, did some research to ‘feel out the market’. One option, which showed potential, was to import the Con fitting from INCO Lighting in South Africa (See exhibit 2)

INCO Lighting had a good reputation but it would mean having to employ a person to manage the shipments and logistics, not mentioning a potential problem with the existing warehouse space if too many containers come all at once.

The Inventory and Logistics Manager, Sharon, suggested Just Lighting make their own 2 lamp 4 foot Con fittings. Bookie did some calculations with input from Chris to analyze the cost of setting up a fabricating and assembling lines at Just Lighting (See exhibit 3). Sharon became the concerned about how she would cope with all the addition components, volumes as well as the increased value tied up in inventory. However, she remembered about ABC analysis from her Fanshawe College days and she decided to look it up in her textbook.

Strip Lighting had just put on a large Christmas party for all of Just Lighting customers and senior staff and this year gave each wife a Zumba robotic vacuum cleaner. They were surprise that Just Lighting was even considering other options!

Exhibit 1

Details of Strip Lighting are as follows:

Quoted Price: $50.00 FOB Destination (Canadian Dollars)

Quantity Discount Structure: See chart below

Warranty: covers the free replacement of nay faulty product.

Inventory Costs: Strip Lighting uses a Third Party Logistics Provider, which operates a JIT (Just-in-time) process to the customers

Exhibit 2

Details of INCO Lighting are as follows:

Quoted Price: USD 28.00 CIF INCO Terms 2010

Exchange Rate: 1 Canadian Dollar = 0.80 USD

Additional importing costs including customs clearing, forwarding agents fees and inbound transport is estimated at $4.50 per fitting.

Quantity Discount Structure: $200 per faulty fitting

Supplier’s Defective level: 5000ppm

Inventory Costs: Assume one month’s average stock level, which costs 15% per annum on the value of Inventory.

The additional workload will require one imports controller costing $50,000 per annum plus 30% benefit costs.

Exhibit 3

The information on the direct material is as follows:

Steel: Pre-Painted Cold Rolled 0.8mm coil from the United States supplier Steel Coils

Price: USD 520/MT FOB Shipping Point (MT = Metric Ton)

Exchange Rate: 1 Canadian Dollar = 0.80 USD

Steel Mass for each fitting: 5kgs

Inbound Transport: $100/MT

Electrical direct materials (Ballasts, Wiring harness, lamp holders and starters) $20 per fitting

Assembly line runs 320 units per day at a cost of $5.20 per unit.

Factory Overheads are estimated at $5.20 per unit

No degreasing or painting is required

Tooling Costs: All tooling costs = $500,000 and will last for 500,000 units.

Depreciation cost: 8 x CNC Punching and bending machines with de-coilers. Life expectancy for each CNC machine is five years. Each CNC machine cost $200,000.

Finance costs were considered not necessary or important to the decision by the team. However, Bookie protested.

Learning Curve

Questions:

1. Identify the immediate issues and other issues concerns.

2. Perform the situational Analysis (SWOT, PESTLE and POTERS 5 forces)

3. Determine the all total cost analysis (insourcing & Outsourcing) in briefly manner with all calculation steps.

4. Identify at least three alternatives which are well described and clearly related to the organizational goals and developed from the situational analysis.

5. Make a final recommendation. Identify the risk management processes and its matrix.

6. Make an implementation timeline chart and conclusion.

For application case 4.6 – Data Mining Goes to Hollywood, describe the research study, the methodology, the results and the conclusion.

Data Mining Goes to Hollywood: Predicting Financial Success of Movies

Predicting box-office receipts (i.e., financial success) of a particular motion picture is an interesting and challenging problem. According to some domain experts, the movie industry is the “land of hunches and wild guesses” due to the difficulty associated with forecasting product demand, making the movie business in Hollywood a risky endeavor. In support of such observations, Jack Valenti (the longtime president and CEO of the Motion Picture Association of America) once mentioned that “…no one can tell you how a movie is going to do in the marketplace…not until the film opens in darkened theatre and sparks fly up between the screen and the audience.” Entertainment industry trade journals and magazines have been full of examples, statements, and experiences that support such a claim. Like many other researchers who have attempted to shed light on this challenging real-world problem, Ramesh Sharda and Dursun Delen have been exploring the use of data mining to predict the financial performance of a motion picture at the box office before it even enters production (while the movie is nothing more than a conceptual idea). In their highly publicized prediction models, they convert the forecasting (or regression) problem into a classification problem; that is, rather than forecasting the point estimate of box-office receipts, they classify a movie based on its box-office receipts in one of nine categories, ranging from “flop” to “blockbuster,” making the problem a multinomial classification problem. Table 5.4 illustrates the definition of the nine classes in terms of the range of box-office receipts.

Data

Data was collected from variety of movie-related databases (e.g., ShowBiz, IMDb, IMSDb, AllMovie, etc.) and consolidated into a single data set. The data set for the most recently developed models contained 2,632 movies released between 1998 and 2006. A summary of the independent variables along with their specifications is provided in Table 5.5. For more descriptive details and justification for inclusion of these independent variables, the reader is referred to Sharda and Delen (2007). Business Intelligence Spring 2017

Methodology

Using a variety of data mining methods, including neural networks, decision trees, support vector machines, and three types of ensembles, Sharda and Delen developed the prediction models. The data from 1998 to 2005 were used as training data to build the prediction models, and the data from 2006 was used as the test data to assess and compare the models’ prediction accuracy. Figure 5.15 shows a screenshot of IBM SPSS Modeler (formerly Clementine data mining tool) depicting the process map employed for the prediction problem. The upper-left side of the process map shows the model development process, and the lower-right corner of the process map shows the model assessment (i.e., testing or scoring) process (more details on IBM SPSS Modeler tool and its usage can be found on the book’s Web site).

Results

Table 5.6 provides the prediction results of all three data mining methods as well as the results of the three different ensembles. The first performance measure is the percent correct classification rate, which is called bingo. Also reported in the table is the 1-Away correct classification rate (i.e., within one category). The results indicate that SVM performed the best among the individual prediction models, followed by ANN; the worst of the three was the CART decision tree algorithm. In general, the ensemble models performed better than the individual predictions models, of which the fusion algorithm performed the best. What is probably more important to decision makers, and standing out in the results table, is the significantly low standard deviation obtained from the ensembles compared to the individual models. Business Intelligence Spring 2017

Conclusion

The researchers claim that these prediction results are better than any reported in the published literature for this problem domain. Beyond the attractive accuracy of their prediction results of the box-office receipts, these models could also be used to further analyze (and potentially optimize) the decision variables in order to maximize the financial return. Specifically, the parameters used for modeling could be altered using the already trained prediction models in order to better understand the impact of different parameters on the end results. During this process, which is commonly referred to as sensitivity analysis, the decision maker of a given entertainment firm could find out, with a fairly high accuracy level, how much value a specific actor (or a specific release date, or the addition of more technical effects, etc.) brings to the financial success of a film, making the underlying system an invaluable decision aid.

Two industries (Industry X and Industry Y) are run by labor unions. Even though the unions overseeing these industries are considered honest and conscientious, we have seen a large disparity in pay between the industries. In fact, the wages in Industry X are now three times the rate as those of Industry Y. What factors account for the differences in these two industries?

Research Lincoln Electric and their Aligning for Global Growth What challenges do you see for Lincoln moving forward, and why? How strategic suggestions do you have for them, and why?

Leadership Behaviors

Research a leader that you believe uses either directive leadership, supportive leadership, participative leadership, or achievement-oriented leadership. Describe the leader, and explain why you think the leader uses either directive leadership, supportive leadership, participative leadership, or achievement-oriented leadership by describing the leader’s history of behavior and decision-making.

Answer in 7-10 sentences. For each website you visit to find information, if you use that information be sure to put it in your own words and include that website as a reference in APA format.

SMART Goals

Research a job description that you would like to have (it cannot be a job description you have used for previous exercises). Evaluate the job description based on Goal Expectancy Theory. Describe the job description, and explain how the individual who accepts that position could be motivated by Specific, Measurable, Achievable, Results-Based, and Time Specific (SMART) goals, using chapter concepts and terms to defend your position.

Answer in 7-10 sentences. For each website you visit to find information, if you use that information be sure to put it in your own words and include that website as a reference in APA format.

list some of facebooks operations with examples

how it is important to have a knowledge about gender, race and class in today's society.

2. At a hydrocarbon processing factory, process control involves periodic analysis of samples for a certain process quality parameter. The analytic procedure currently used is costly and time consuming. A faster and more economical alternative procedure has been proposed. However, the numbers for the quality parameter given by the alternative procedure are somewhat different from those given by the current procedure, not because of any inherent errors but because of changes in the nature of the chemical analysis. Management believes that if the numbers from the new procedure can be used to forecast reliably the corresponding numbers from the current procedure, switching to the new procedure would be reasonable and cost effective. The following data were obtained for the quality parameter by analyzing samples using both procedures:

a. Use linear regression to find a relation to forecast Y, which is the quality parameter from the current procedure, using the values from the proposed procedure, X.

b. Is there a strong relationship between Y and X? Explain.

What are your thoughts about Google and Facebook mining your personal data for advertisers and mostly doing it without you knowing. Are you, people ready to quit Facebook, stop using Google? What are some of the solutions to this problem?

Daddy Warbucks, a very wealthy investor, built his fortune through his legendary investing knowledge. At present, he has been offered three investments from which he would like to choose one.

The first is a conservative investment that would perform quite well in an expanding economy and only suffer a small loss in a worsening economy. The second is a speculative investment that would perform extremely well in an expanding economy, but do quite poorly in a worsening economy. The last alternative is a countercyclical investment that would suffer some loss in an expanding economy, but perform well in a worsening economy.

Warbucks believes that there are three possible scenarios during the lives of these investments as follows:

· An Expanding Economy

· A Stable Economy

· A Worsening Economy

He is somewhat pessimistic about where the economy is headed, and so has assigned probabilities of 0.1, 0.5, and 0.4 respectively to these three scenarios. He also estimates that his profits under these respective scenarios are shown in the following payoff table.

1. Considering this data, which investment should he make based on an Expected Monetary Value (EMV) criterion?

2. Upon reflection, Daddy Warbucks doesn't have a great deal of confidence in the accuracy of his probability estimates. Which investment should he make under each of the following criteria?

a) Maximax

b) Maximin

c) Realism Criterion with indices of 0.25, 0.65, and 0.85

d) Equally Likely States of Nature e) Minimax Regret

3. Briefly describe how Warbucks might leverage Bayes' Theorem (Bayes' Decision Rule) to improve his confidence about his probability estimates if he believes that the 10% estimate for an expanding economy is accurate, but is unsure about the odds of the other two scenarios.