Biology chapter 18 assessment answers page 465

Case Study Question

W5: Case Studies

Graded Assignment: Case Studies - (Follow all steps below)

Carefully review and read both case studies found in your textbook from Pages 433 and 465-467

Sharda, R., Delen, D., & Turban, E. (2015) Business intelligence and analytics: Systems for decision support (10th ed.). Boston: Pearson.

Digital: ISBN-13: 978-0-13-340193-6 or Print: ISBN-13: 978-0-13-305090-5

When concluding the paper, expand your analytical and critical thinking skills to develop ideas as a process or operation of steps visually represented in a flow diagram or any other type of created illustration to support your idea which can be used as a proposal to the entity or organization in the cases to correct or improve any case related issues addressed. This is required for both cases.

When developing illustrations to support a process or operation of steps, Microsoft Word has a tool known as “Smart Art” which is ideal for the development of these types of illustrations or diagrams. To get acquainted with this tool, everyone can visit www.youtube.com using a keyword search “Microsoft Word Smart Art Tutorials” to find many video demonstrations in using this tool.

QUESTIONS FOR THE END-OF-CHAPTER from Page# 433

APPLICATION CASE

1. What were the main challenges encountered by CARE International before they created their warehouse prepositioning model?

2. How does the objective function relate to the organization's need to improve relief services to affected areas?

3. Conduct online research and suggest at least three other applications or types of software that could handle the magnitude of variable and constraints CARE International used in their MIP model.

4. Elaborate on some benefits CARE International stands to gain from implementing their pre-positioning model on a large scale in future.

QUESTIONS FOR THE END-OF-CHAPTER (Page NO#465-467)

APPLICATION CASE

1. Describe the problem that a large company such as HP might face in offering many product lines and options.

2. Why is there a possible conflict between marketing and operations?

3. Summarize your understanding of the models and the algorithms.

4. Perform an online search to find more details of the algorithms.

5. Why would there be a need for such a system in an organization?

6. What benefits did HP derive from implementation of the models?

Conclusion

QUESTIONS FOR THE END-OF-CHAPTER from Page# 433

APPLICATION CASE

1. What were the main challenges encountered by CARE International before they created their warehouse prepositioning model?

2. How does the objective function relate to the organization's need to improve relief services to affected areas?

3. Conduct online research and suggest at least three other applications or types of software that could handle the magnitude of variable and constraints CARE International used in their MIP model.

4. Elaborate on some benefits CARE International stands to gain from implementing their pre-positioning model on a large scale in future.

Screen Shot

QUESTIONS FOR THE END-OF-CHAPTER (Page NO#465-467)

APPLICATION CASE

1. Describe the problem that a large company such as HP might face in offering many product lines and options.

2. Why is there a possible conflict between marketing and operations?

3. Summarize your understanding of the models and the algorithms.

4. Perform an online search to find more details of the algorithms.

5. Why would there be a need for such a system in an organization?

6. What benefits did HP derive from implementation of the models?

Conclusion

Reference info. Minimum 3 or more.

End-of-Chapter Application Case

Pre-Positioning of Emergency Items for CARE International

Problem

CARE International is a humanitarian organization that provides relief aid to areas that are affected by natural disasters such as earthquakes and hurricanes. The organization has relief programs in over 65 countries worldwide. Just like other humanitarian organizations, CARE International faces challenges in offering the needed help to affected areas in the event of natural disasters. In the event of a disaster, CARE International identifies suppliers that could provide the needed relief items. Arrangements are then made regarding the acquisition of warehouses to transport the items. With respect to the transportation of the items, a third-party company transports the items by air to the affected country from where they are further transported by road to CARE International’s warehouse and distribution center. This mode of response to disasters could be slow, not to mention the unreliability of the transportation network used. Hitherto, CARE has preferred purchasing relief items from local suppliers since they are closer to the disaster areas and, also, it helps reinvigorate the local economy after a disaster. However, in the wake of a disaster, there are always issues with availability, price, and quality of needed items.

Specifically, CARE International’s challenges are twofold as identified by the authors of the research. First, the organization wanted the ability to gather supplies and relief items from both local and international suppliers in an agile manner so they could better serve people affected by disasters. Second, once the supplies are mobilized, they wanted to be able to effectively distribute them in the most timely and cost-efficient manner to affected regions.

Methodology/Solution

In collaboration with Georgia Institute of Technology, CARE developed a model in which relief items were placed in a pre-positioned network to serve as a complement to the existing mode of supplying relief items to disaster areas. Using a mixed-integer programming (MIP) inventory-location model, a pre-positioning network was designed based on two main factors. The first factor was up-front investment related to initial stocking of inventory and warehouse setup. The second factor was related to the average response time it takes to get relief items to affected regions. Basically, the main concern was to determine a configuration that would allow for the least response time given an up-front investment value. Demand data for the model was based on historical records of previous operations. Supply data was estimated hypothetically since historical data was not present. It was assumed that any supplier would be able to ship relief items within 2 weeks. The model for warehouse establishment was built based on 12 locations CARE considered as low or no-cost, as well as seven relief items necessary for most disaster relief operations. The object function was to reduce the total response time in moving items to affected areas. The capacity constraints employed were the number of warehouses to maintain and the amount of items to keep in them. The MIP model consisted of 470,000 variables and 56,000 constraints. It took the ILOG OPL Studio with CPLEX solver application about 4 hours to produce an optimal solution.

Results/Benefits

The main purpose of the model was to increase the capacity and swiftness to respond to sudden natural disasters like earthquakes, as opposed to other slow-occurring ones like famine. Based on up-front cost, the model is able to provide the best optimized configuration of where to locate a warehouse and how much inventory should be kept. It is able to provide an optimization result based on estimates of frequency, location, and level of potential demand that is generated by the model. Based on this model, CARE has established three warehouses in the warehouse pre-positioning system in Dubai, Panama, and Cambodia. In fact, during the Haiti earthquake crises in 2010, water purification kits were supplied to the victims from the Panama warehouse. In the future, the pre-positioning network is expected to be expanded.

Questions for the End-of-Chapter Application Case

1. What were the main challenges encountered by CARE International before they created their warehouse pre-positioning model?

2. How does the objective function relate to the organization’s need to improve relief services to affected areas?

3. Conduct online research and suggest at least three other applications or types of software that could handle the magnitude of variable and constraints CARE International used in their MIP model.

4. Elaborate on some benefits CARE International stands to gain from implementing their pre-positioning model on a large scale in future.

End-of-Chapter Application Case

HP Applies Management Science Modeling to Optimize Its Supply Chain and Wins a Major Award

HP’s groundbreaking use of operations research not only enabled the high-tech giant to successfully transform its product portfolio program and return $500 million to the bottom line over a 3-year period, but it also earned HP the coveted 2009 Edelman Award from INFORMS for outstanding achievement in operations research. “This is not the success of just one person or one team,” said Kathy Chou, vice president of Worldwide Commercial Sales at HP, in accepting the award on behalf of the winning team. “It’s the success of many people across HP who made this a reality, beginning several years ago with mathematics and imagination and what it might do for HP.”

To put HP’s product portfolio problem into perspective, consider these numbers: HP generates more than $135 billion annually from customers in 170 countries by offering tens of thousands of products supported by the largest supply chain in the industry. You want variety? How about 2,000 laser printers and more than 20,000 enterprise servers and storage products? Want more? HP offers more than 8 million configure-to-order combinations in its notebook and desktop product line alone.

The something-for-everyone approach drives sales, but at what cost? At what point does the price of designing, manufacturing, and introducing yet another new product, feature, or option exceed the additional revenue it is likely to generate? Just as important, what are the costs associated with too much or too little inventory for such a product, not to mention additional supply chain complexity, and how does all of that impact customer satisfaction? According to Chou, HP didn’t have good answers to any of those questions before the Edelman award–winning work.

“While revenue grew year over year, our profits were eroded due to unplanned operational costs,” Chou said in HP’s formal Edelman presentation. “As product variety grew, our forecasting accuracy suffered, and we ended up with excesses of some products and shortages of others. Our suppliers suffered due to our inventory issues and product design changes. I can personally testify to the pain our customers experienced because of these availability challenges.” Chou would know. In her role as VP of Worldwide Commercial Sales, she’s “responsible and on the hook” for driving sales, margins, and operational efficiency.

Constantly growing product variety to meet increasing customer needs was the HP way—after all, the company is nothing if not innovative—but the rising costs and inefficiency associated with managing millions of products and configurations “took their toll,” Chou said, “and we had no idea how to solve it.”

Compounding the problem, Chou added, was HP’s “organizational divide.” Marketing and sales always wanted more—more SKUs, more features, more configurations—and for good reason. Providing every possible product choice was considered an obvious way to satisfy more customers and generate more sales.

Supply chain managers, however, always wanted less. Less to forecast, less inventory, and less complexity to manage. “The drivers (on the supply chain side) were cost control,” Chou said. “Supply chain wanted fast and predictable order cycle times. With no fact-based, data-driven tools, decision making between different parts of the organization was time-consuming and complex due to these differing goals and objectives.”

By 2004, HP’s average order cycle times in North America were nearly twice that of its competition, making it tough for the company to be competitive despite its large variety of products. Extensive variety, once considered a plus, had become a liability.

It was then that the Edelman prize–winning team—drawn from various quarters both within the organization (HP Business Groups, HP Labs, and HP Strategic Planning and Modeling) and out (individuals from a handful of consultancies and universities) and armed with operations research thinking and methodology—went to work on the problem. Over the next few years, the team: (1) produced an analytically driven process for evaluating new products for introduction, (2) created a tool for prioritizing existing products in a portfolio, and (3) developed an algorithm that solves the problem many times faster than previous technologies, thereby advancing the theory and practice of network optimization.

The team tackled the product variety problem from two angles: prelaunch and postlaunch. “Before we bring a new product, feature, or option to market, we want to evaluate return on investment in order to drive the right investment decisions and maximize profits,” Chou said. To do that, HP’s Strategic Planning and Modeling Team (SPaM) developed “complexity return on investment screening calculators” that took into account downstream impacts across the HP product line and supply chain that were never properly accounted for before.

Once a product is launched, variety product management shifts from screening to managing a product portfolio as sales data become available. To do that, the Edelman award–winning team developed a tool called revenue coverage optimization (RCO) to analyze more systematically the importance of each new feature or option in the context of the overall portfolio.

The RCO algorithm and the complexity ROI calculators helped HP improve its operational focus on key products, while simultaneously reducing the complexity of its product offerings for customers. For example, HP implemented the RCO algorithm to rank its Personal Systems Group offerings based on the interrelationship between products and orders. It then identified the “core offering,” which is composed of the most critical products in each region. This core offering represented about 30 percent of the ranked product portfolio. All other products were classified as HP’s “extended offering.”

Based on these findings, HP adjusted its service level for each class of products. Core offering products are now stocked in higher inventory levels and are made available with shorter lead times, and extended offering products are offered with longer lead times and are either stocked at lower levels or not at all. The net result: lower costs, higher margins, and improved customer service.

The RCO software algorithm was developed as part of HP Labs’ “analytics” theme, which applies mathematics and scientific methodologies to help decision making and create better-run businesses. Analytics is one of eight major research themes of HP Labs, which last year refocused its efforts to address the most complex challenges facing technology customers in the next decade.

“Smart application of analytics is becoming increasingly important to businesses, especially in the areas of operational efficiency, risk management, and resource planning,” says Jaap Suermondt, director, Business Optimization Lab, HP Labs. “The RCO algorithm is a fantastic example of an innovation that helps drive efficiency with our businesses and our customers.”

In accepting the Edelman Award, Chou emphasized not only the company-wide effort in developing elegant technical solutions to incredibly complex problems, but also the buy-in and cooperation of managers and C-level executives and the wisdom and insight of the award-winning team to engage and share their vision with those managers and executives. “For some of you who have not been a part of a very large organization like HP, this might sound strange, but it required tenacity and skill to bring about major changes in the processes of a company of HP’s size,” Chou said. “In many of our business [units], project managers took the tools and turned them into new processes and programs that fundamentally changed the way HP manages its product portfolios and bridged the organizational divide.”

Questions for the End-of-Chapter Application Case

1. Describe the problem that a large company such as HP might face in offering many product lines and options.

2. Why is there a possible conflict between marketing and operations?

3. Summarize your understanding of the models and the algorithms.

4. Perform an online search to find more details of the algorithms.

5. Why would there be a need for such a system in an organization?

6. What benefits did HP derive from implementation of the models?