1 COIT 20253 Business Intelligence using Big Data Assessment item 1—Assignment 1Specifications Due date: Week 6 Monday (16 Apr XXXXXXXXXX:45 pm AEST. All students are to submit electronically – max...

Assignment title: Big data for Supply Chain and Operations Management
Name of Student:
Name of Professor:
Title of course:
Submission Date:
Content table
    Sr. No.
    Name of performed task
    Pg. No.
    1.
    Executive summary
    3
    2.
    Introduction
    3
    3.
    Performing collection with effective storage of data
    4
    4.
    Data for performing desired actions
    7
    5.
    Continuity of business through supply chain and big data
    10
    6.
    Conclusion and possible recommendations
    12
    7.
    References
    13
Executive summary
The given study work has five different topic listed but all of them are related to data collection and performing analysis for big data. Thus, to complete this assignment, the “Data for Supply Chain and Operations Management” is being decided to work on here. For performing the required data collection, the system named “PTC system” is being introduced here. The required processes and strategies for making data collection are described below. The processes required to perform the activities of data storage with their relative working importance are also described in proposed potential work of my study [Kim et al, 2014]. The customer centric design of selected products with probable suggestions for facing any occu
ed issues by any possible disaster for some business idea are also introduced here. The ideas to face the occu
ed issues are also represented here.
Introduction
The implementation of big data shows the use of complex natured large data set to any analysis work. In past, traditional databases are used for small and large size data sets, but with large size data set, their working capabilities are compromised. To overcome such issues and performing better and accurate results for consumers, BIG DATA is being introduced. The functions like searching required data element in large data set, sharing of developed database among different users, transfer of data set from one database to any other, use of virtualization on big data and performing updating of database status are main functions of big data [Zhong et al, 2016]. The developed report of my study work, show and describes the methods of data collection and data storage in any operational supply chain management system. These methods also show the importance of big data in any online e-business activity.
Performing collection with effective storage of data
Most of the organizations which are working in field of e-business and regularly perform data collection activities make use of “PTC system” for their desired task sets. The process used for performing data collection is very efficient to all data suppliers in PTC systems. The PTC system provides various data collection methods through which the organizations are able to perform automatic creation and submitting of all data from suppliers unswervingly in the dataset of manufacturer. The methods used in PTC system are developed according to possible standards of modern industry [Chen et al, 2014]. The fanatical data format which justifies the requirements of any individual is being used by this system. By help of this fanatical data format, only the specific data is being processed by organizations. This system can also be used by different organizations for the response aggregation and validation for any supplier data. The PTC system also make use of some third party applications for providing the functionalities of possible compliances, customer requested material, and to retrieve the detailed and specific information on any specific data portion. The design specifications developed by the supplier of the data are used for performing data collections through the PTC system. The organizations can also perform the normalization activities and data validation activities with help of PTC system. The reports generated by the help of PTC system can be used by organizations for tracking cu
ent and future trends of potential market. For generating such reports, the forecasting methods of data collection are being introduced by this system. The data collection methods of this system for implementing big data are explained here:
1. Advance analytics: It is most commonly used data collection method which use specific data transfer processes instead of some previously taken decisions. To observe the overlapping analytics occu
ed during any data collection, this method is best suited. The possible use of unusual analytics methods is main cause of these overlapping analytics.
2. Descriptive analytics: The business activities which are used in past by any organization and situations which may occur due to these past activities are majorly used to perform the data collection in this analytics method. The past trends, possible patterns and already predicted exceptional cases are being used in this data collection method. The developed standards for producing reports through dash board and packages for shelf are used to execute any customer query in this analytics method. This method provides data alerts, so that the organization can take required necessary steps for maintaining the supply chain stability [Dou et al, 2015].
3. Predictive analytics: The historical data is used for making required comparisons and to develop the possible analysis on real time bases in this analytics method. The predictions for probabilities which may occur in near future are defined by the future trends of this method. The SCM and predictive analytics collectively used for determining all the safety measures of collected data base. The implementation of statistical algorithms with effective use of regression process makes it most useful method for performing data analysis.
4. Prescriptive analytics: In this analysis method, the specific predictions developed on bases of data collections to take advantage from desired requirements of any consumer to avoid any issue are being utilized. The big data optimization and possible simulation of collected data are primary activities which are performed in prescriptive analytics method of data collection.
The maintenance of collected data set of large size and complex nature is always considered as the main precondition [Shi & Abdel-Aty, 2015]. The data growth maintenance in user specific manner of storage is most challenging analytics task for any big data implementation. The input/ output operations which are performed by the consumer are main responsible factors for affecting any...