The Need for Efficient Decision Support System
Questions:
1.Essay presenting ONE computational system for decision-making support, considerin?
2.Critical review of other computational systems for decision making support, stating the pros and cons of each?
1. Decision support system forms the basis of developing business intelligence architecture. Traditionally the business demission making has been managed manually and was heavily dependent on the human intelligence. However with the expansion of business requirements, there has been a major change in the way business used to operate. Today the business needs to deal with huge amount data due to expansion of customers as well as field of services. It is not only important to collect the data, but there should be proper analysis method for extracting useful information from the data set in order to make intelligent and effective business decision (Dssresources.com, 2018). With the advancement of technologies, business organizations are today experiencing the power of technologies. Organizations are recognizing the relevance of technology in the context of business decision making (Onlinecampus.bu.edu, 2018).
There are various approaches to design the decision support system and the process becomes easier when effective technology like Machine learning is used. It is one of the emerging technologies in the field of artificial intelligence, is already proving the efficiency and credibility in creating effective decision making for the various businesses organizations. One key area which needs to be considered while judging the efficiency of the computing based decision support system in the context of business analytics is the ability of the technology in bringing automation and speed in the decision making process. Decision support system is creating value for the organization by bringing acceleration in the decision making process itself. In order to store data that is huge in volume and also diverse in nature, efficient data storage techniques in necessary (Frank, 2016). Machine learning technologies are equipped with affordable data storage solution and advanced processing power. Due to this feature machine learning can process huge amount of data efficiently and it also helps to drive insightful information from the data set, thus helping in creating technology based decision support system.
Although companies are deploying technologies to bring automation in the workforce, however the design of efficient decision support system has always been a major problem for the organizations, which further affects the workflow process in the organization (Larson, 2014). Organizations often seem to be slow and inefficient in decision making. There could be various reasons behind that. It may be the work pressure that make the employees become less attentive to the decision making or perhaps involvement of too many decision maker in one approval step is what makes the decision process slow and inefficient which hampers the productivity and the profitability.
Designing of decision support system or DSS With machine learning has not only become hassle free but efficient as well. This makes the technology highly relevant in the context of business decision making. With effective decision support system in place today organization can focus on the information that matters the most to the business process (Sharma, 2015). All other aspect of the decision making like collecting data, analyze them to find insightful information from them are managed by the machine learning algorithms. Thus the decision making process becomes easier for organization and it is now possible to focus on the decision making process itself rather than managing others aspect what is required in making the decision process itself.
The Role of Machine Learning in Designing DSS
In order to understand the characteristics, there are six components that need to be addressed while using decision support system for business decision making. The nature of the data varies according to the requirements and also the information that needs to be extracted from the data set. Hence the requirements and the feature of the data play an important role in for making the data analytics useful and effective (Nagai, 2014).
Choice of designing principle is another important factor for the successful use of decision support systems. There are various kinds of designing principles depending on the nature of the data. Designing approach used for data prediction is not suitable for data classification or data clustering.. Naive Bayes, Support Vector Machines, Decision Trees, k-Means clustering are some popular designing principles that are commonly used.
Once the natures of the data and the expected features from the data set are clearly defined it is required to train the employees on basis of the architecture that is chosen for the system design. The training is required to train the employees so that they can learn by improving the experience. Once the training and evaluation of the system is completed, test is conducted to assess how the system performs when assigned with a new set of tasks that is different from the data used during the training of the employees. It is used to assess the overall performance of the system as well as the ability of the employees to use the new system (Goodfellow et al., 2016).
Decision support system improves the quality of resource management to a great extent. In order to improve decision making process it is important to identify and measure the credibility of the proposed solution and the desired solutions to minimize the error in the decision making process and optimize the output. However, it requires intensive case-by-case analysis when applying manual approach to find the errors in the measurement technique (Frank, 2016). Decision support system with the help of Machine learning makes these evaluations efficient and effective.. This feature makes decision support system effective in improving the quality of the decision making process by identifying and minimizing the errors in measurement.
Computing based decision support systems are being used by various leading organizations all over the world top make decision for various business related activities. Walmart uses HANA, a cloud platform by SAP which uses machine learning algorithm. It helps to make business and sales related decision. The platform stores replicated data in RAM rather than in disk which makes data access fast by the applications and analytics built on the HANA platform and makes the business decision making faster.
Domo, a software company which designs business management software, has designed a dashboard that uses AI and machine learning techniques to create decision support system for the organizations. It helps company to gather information and make effective decision based on that.
The eSales solution from Apptus, a software designing company, uses machine learning algorithm. It helps to predict the choice of the customer by analyzing their past purchase history and this information helps the retailer to serve their customers better and make effective sales related decision.
2. Big data analytics is another important toll for business decision making. Big data can be classified as a large set of data that is high in volume, velocity and variety. Volume refers to high amount of data, velocity defines the speed of incoming and outgoing data and variety means range of data sources from where data is coming. Data can be in structured, semi-structured or in raw format (De Mauro, Greco & Grimaldi, 2015).
Components of Decision Support System
Big data analytics, which make use of computer intelligence, plays an important role in decision making that drives business organization towards success (Gandomi & Haider, 2015). Big data analytics comprises of three key elements called data sourcing, data refining and exploitation of the collected data from various sources. In order to make data analytics work perfectly it is important to make sure that all of these key elements work in perfect coordination. Big data has an important role to play in the business decision (Wang et al., 2016). Big data analytics can be used to increase sales in the organization as well as bring optimization in the product price to increase return on investment. Big data with the combination of intelligent data sourcing and data analytics helps to determine the hidden pattern in the data sets and makes it possible to extract useful information that goes on making important business related decision. Big data analytics can be used to predict the future performance for the organization by analyzing previous data about sales, number of customer and data related to revenue as well.
However there are certain drawbacks that need to be considered as well. Due to the technologies that empower the data analytic projects is relatively new, there may be technical barriers that may affect the quality of the analytics. Big data enabled projects can have compensating results, yet regularly require specific assets and innovation so as to change information into applicable and noteworthy understanding. With this comes a level of budgetary duty that can make boundaries to hierarchical purchase.
ERP or the enterprise resource planning is another important tool for developing decision support system or DSS. The popularity of the ERP has only increased in the recent years due to its effectiveness and efficiency for decision making. The technique uses simulation model for predicting and allocating resources (Ptak & Schragenheim, 2016). ERP provides effective means to integrate the information flow with the business process, thus bringing more seamless approach in decision making. The ERP system helps to integrate different aspect of business decision making like production planning, purchasing, manufacturing, and sales, distribution, accounting and customer service (Nwankpa , 2015).
However the ERP system, instead of the popularity and the effectiveness in decision making is not effective for small and medium scale organization due to the complexity in the technological requirements and the high cost requirement which is a major concern for medium to small scale enterprise (Leyh & Sander,2015 ). However organizations that have already integrated the ERP system are now facing problem in incorporating new resources for decision support. Based on the information provided by the empirical research, it is found that the reporting capabilities provided by many ERP packages are not sufficient for making effective business related decisions for organizations that have implemented the ERP system.
References:
De Mauro, A., Greco, M., & Grimaldi, M. (2015, February). What is big data? A consensual definition and a review of decision support system . In AIP conference proceedings (Vol. 1644, No. 1, pp. 97-104). AIP.
Dssresources.com. (2018). A Brief History of Decision Support Systems. [online] Available at: https://dssresources.com/history/dsshistory.html [Accessed 13 Apr. 2018].
Frank, E. (2016). Data Mining: Practical machine learning tools and techniques for intelligent decision. Morgan Kaufmann.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics for business decision support. International Journal of Information Management, 35(2), 137-144.
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning for decision support system (Vol. 1). Cambridge: MIT press.
Larson, D. (2014). Machine learning, a perspective for intelligent decision support system.
Leyh, C., & Sander, P. (2015). Critical success factors for ERP system implementation projects: An update of literature reviews. In Enterprise Systems. Strategic, Organizational, and Technological Dimensions for decision strategy (pp. 45-67). Springer, Cham.
Ngai, E.W.T. (2014). From machine learning to machine reasoning to support decision. Machine learning, 94(2), 133-149.
Nwankpa, J. K. (2015). ERP system usage and benefit: A model of antecedents and outcomes. Computers in Human Behavior, 45, 335-344.
Onlinecampus.bu.edu. (2018). Management Information Systems and Decision-Making: An Overview. [online] Available at: https://onlinecampus.bu.edu/bbcswebdav/pid-843933-dt-content-rid-2221759_1/courses/13sprgmetad715_ol/module_03a/metad715_m03l02t02_managementinfosystems.html [Accessed 13 Apr. 2018].
Ptak, C. A., & Schragenheim, E. (2016). ERP: tools, techniques, and applications for integrating the supply chain. Crc Press.
Sharma, R.. (2015). Introduction to machine learning and intelligent decision support approach . MIT press.
Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics for sales decision in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.
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