Solving LP Problem And Understanding Big Data

The LP problem and its mathematical model

Questions :

1. The general manager of bicycle manufacturing company would like to find out how many bicycles and scooters that the production department can build next month. The decision making is based on the following information below.

The production of each bicycle consumes 20 days of labour and $1000 of materials. The production of each scooter consumes 50 days of labour and $1500 of materials. Each bicycle brings $800 profit to the company, while each scooter brings $1200 profit to the company. The company has a capacity of 3000 working days per month, and the budget for the materials is $250000 per month. The sales department requires that at least 50 units of bicycles and 20 units of scooters be produced per month.

2. Explain the main difference between big data and big data analytics. Give a real life example on how big data can be useful for supporting business operations.

3. What is the importance of a collaborative technology? Provide the name of a software that is used for collaboration and discuss how this technology is used.

1. Since, the general manager of bicycle manufacturing company would like to find out how many bicycles and scooters that the production department can build next month to attain maximum profit; here the decision variables are number of bicycle (X1) and number of scooters (X2).

As the profit from one unit of bicycle is $800 and profit from one unit of scooter is $1200, here the objective function will be

Max Z = 800 *X1 + 1200 * X2

There were labour constraint, material constraint and production unit constraints as follows:

Labour constraint: 20 * X1 + 50 * X2 ≤ 3000

Material constraint: 1000 * X1 + 1500 * X2 ≤ 250000

Production constraints:

X1≥ 50

X2≥ 20

Non negativity constraints;

X1, X2 ≥ 0

Thus the LP problem looks like:

Max Z = 800 *X1 + 1200 * X2

Subject to,

20 * X1 + 50 * X2 ≤ 3000

1000 * X1 + 1500 * X2 ≤ 250000

X1≥ 50

X2≥ 20

And X1, X2 ≥ 0

Using Microsoft Excel solver, the LP is solved. The details of the solution are given as below:

Mathematical model for solving LP:

	Variables	X1	X2
	Coefficients	800	1200
	Solutions	100	20
	Z	104000
	Cons1	20	50	≤	3000
	Cons2	1000	1500	≤	250000
	Cons3	1		≥	50
	Cons4		1	≥	20
		LHS	RHS
	Cons1	3000	3000
	Cons2	130000	250000
	Cons3	100	50
	Cons4	20	20

Excel output:

Microsoft Excel 12.0 Answer Report
Worksheet: [KHA 49323.xlsx]Sheet1
Report Created: 1/20/2015 2:55:47 PM
Target Cell (Max)
	Cell	Name	Original Value	Final Value
	$E$7	Z X1	0	104000
Adjustable Cells
	Cell	Name	Original Value	Final Value
	$E$6	Solutions X1	0	100
	$F$6	Solutions X2	0	20
Constraints
	Cell	Name	Cell Value	Formula	Status	Slack
	$E$17	Cons1 LHS	3000	$E$17<=$F$17	Binding	0
	$E$18	Cons2 LHS	130000	$E$18<=$F$18	Not Binding	120000
	$E$19	Cons3 LHS	100	$E$19>=$F$19	Not Binding	50
	$E$20	Cons4 LHS	20	$E$20>=$F$20	Binding	0

From this above solution, it can be said that maximum number of bicycle is 100 units and maximum number of scooters is 20 units. Here, the profit for the next month will be $104000.

Big data is different from big data analytics. The three major aspects of difference are discussed below:

Volume:

The analysts firm, International Data Corp (IDC), has undertaken a research and indicated that the amount of digital data is growing across the globe and it is expected to be 40,000 Exabyte by 2020. On the other hand, big data analytics is the procedure of scrutinizing huge data set containing different type of data or big data. Hence, the volume of big data analytics is not growing in the same manner as big data. Big data analytics is consisted of some tools which are important for extracting the relevant information (Sathi, 2012).

Understanding big data

Velocity:

The speed of data is considered to be more important than its volume. It is impossible to deal with the big data in a specific period of time. It will be very difficult and a slow activity to access the big data for specific purpose. The raw data is not useful to the consumers.  On the other hand big data analytics have made enables the organization to access real time and non-real time information for making decision in a quicker method. It also helps in implementation of the moves in a faster way in comparison to its competitors (Baesens, 2014).

Variety:

It has been found that big data comes in different forms. For example, if someone posts a photograph on face book or sends via e-mail, tweeter or using other social media networks, it will generate data. Different forms of data are available in an unstructured manner. On the other hand, data analytics is analyzing the data and uses some specific elements of computing such as memory, storage, bandwidth etc (Baesens, 2014).

Real example of application of big data

The researchers of MIT Media Lab had used the location data from the cell phones and they had successfully determined number of people in the parking lot of Macy on Black Friday. Additionally, the initiation of Christmas shopping in US was forecasted by using big data (Arellano, 2015).

3. In the present situation the companies requires the workers to perform faster and more productively and achievement of that goal is possible with the introduction of the collaborative technologies in addressing the collaborative behaviors of the employees. The use of the collaborative technologies not only gives the employees an opportunity to discuss their work but also creates new ways for employees to deliver a particular job. The following are some of the advantages of implementing collaborative technology (Ding, Yu and Sun, 2012) .

Initiates new process of performing jobs

The organizations by embedding the collaborative technologies into the daily work schedule can effectively make it a natural and acceptable part of the job. For instance by sharing ideas via online platforms like Face book, Twitter and Myblogs, employees at CEMEX were able to reduce the CO2 measures by 1.8 million metric tons per year.  Similarly the engineers at GE aviations were able to solve problems in short span of time by sharing documents (Ding, Yu and Sun, 2012).

Guidance about process performance

Ding, Yu and Sun (2012) opined that the collaborative technologies can also provide guidance about the best ways in which a particular process may be performed in order to increase the quality and th productivity of the process. For instance the Social workflow platforms can provide standardized group work plans with detailed tasks, and instructions. The group members gets the opportunity to use this platform as a source of sharing updates, submit reports, review check lists and approvals.Tan, (2012) opined that the sharing of the ideas and documents helps the group members to ask for any kind of support or help.

The three major aspects of big data

Shaping of the collaborative behavior

The collaborative technologies aim in shaping, encouraging and motivating of desired collaborative behaviors within the organization. The organizations in this respect have introduced various incentives like collaborative participation game and social network using reward. The companies in order to encourage the act of collaboration introduced rewards and corporate recognitions for the employees

Employee talent management

An effective collaboration technology supports the present work capabilities of the employees as well as creates opportunities for future working opportunities (Tan, 2012). Majority of the companies are adopting new operating models in which the organization will have to deal with vendors, outsourcers, partners, suppliers and customers hence implementing a collaborative technology will help in organizing all the related stakeholders.

Timely completion of projects

The projects are completed before the schedule because of the use of project collaborative management software. The organizations are able to collaboratively employ all the necessary groups and team members with the help of collaborative software like Wrike and on stream meetings. This software enables around 100 participants to enter into virtual digital meetings at the same time (Ding, Yu and Sun, 2012). The face to face interaction of the team members helps in ascertaining the loopholes of the project and also helps to judge the feasibility of the project. Hence the companies suffer no financial loss due to delay of projects. The opinions of the different team embers of different sectors are also prioritized (Tan, 2012).

Some of the major and most popular collaborative software used in small as well as large organizations are namely LiquidPlanner, Wrike, Confluence, On stream meetings, XaitPorter,Webex meeting centre, Proof hub etc.

Among them Wrike is a collaborative software that provides a virtual real time work space where different teams of an organization can collaborate in order to complete a project successfully with quality. The organizations feel it as user friendly software because the collaboration is easy and the group members of different groups are able to get a clear view of the progress of the organization.

Moreover the process of open communication between the group members helps the organization to manage the project completion schedule and also the resources effectively. The real time decision making increases the pace of the project. Organizations like PayPal, Google, Hootsuite, Ecco, Adobe, HTC and EMC prefer using this software for the purpose of critical project completion. Tan (2012) opined that the collaboration of the teams like the sales team, marketing, communication, product and Human resource make the decisions faster for the organization and also helps in completion of the project without any flaw. The avoidance in the delay of the project schedule also makes the project financially feasible for the organizations.

References

Arellano, N. (2015). 3 key differences between big data and analytics. [online] IT World Canada.

Baesens, B. (2014). Analytics in a big data world. Hoboken, N.J.: Wiley.

Ding, B., Yu, X. and Sun, L. (2012). A Cloud-Based Collaborative Manufacturing Resource Sharing Services. Information Technology J., 11(9), pp.1258-1264.

Sathi, A. (2012). Big data analytics. Boise: MC Press.

Tan, X. (2012). A Contextual Item-Based Collaborative Filtering Technology.Intelligent Information Management, 04(03), pp.85-88.