Paper Title
Decision Optimization and Information Management System for Production Industries
Author
Lillian Martina Ezugwu
Section
Arts, Language, and Communication
Abstract

This paper solely dealt with the decision optimization and information management system for production industries in Nigeria. The research design adopted was the descriptive research design. Two research objectives were generated to guide the study, to wit: design of an information management system for decision optimization in the industries, and accurate information support system for production optimization in industries. Findings showed that the valuation data requirement correspond to the respective graphical user interface of the design system used. Furthermore, the study provides better information management system for making decisions. Again, the results showed that production level of industries can be enhanced with optimized decisions. Based on the findings of the study, the following recommendations were made: (1) Industry stakeholders should be informed of the findings from this study through workshop, seminars and conferences; and (2) Improved decisions optimization should be the yearning of any industry that wants to optimize production. In conclusion, the researcher is of the view that if proper sensitization of the industry stakeholders about the findings of the study is carried out, production capacities of our industries shall be increased.

Keywords
Information management, decision optimization, software, hardware, architectural design.

Introduction

 

Background

The need for improvement in prediction behaviors in complex productions and supply-chain in manufacturing industries engendered the design of applications for making decisions to move the system towards desirable outcomes. The target of any industry is to eliminate chaos from the supply chain and to adjust to demand fluctuations. The disorders due to managerial uncertainty compounded by the increasing degree of information irregularities that exist in the supply chain or value network (suppliers, distributors, retailers, consumers) often contradict the objectives due to different goals of the parties (Shoumen, 2003). This situation creates barriers on the avenue to adaptive business networks of the future. The prime target of supply-chain is to secure large volume purchase commitments that consist of delivery flexibility from manufacturers whose defined goals are for mass production and to gain advantages of economies of scale. This strategic plan enables them to adapt to fluctuations even though resource utilization plans were based on demand forecast (Shoumen, 2003).

 

Ragan and Narayan (2011) opined that organizations have adopted information systems in handling repetitive tasks. They further argued that these are intelligent systems for managing data and providing guides for organizations. Similarly, Taylor (2012) states that these information management systems possess the qualities and capabilities to excel at repeatedly doing a monotonous task without variation and without making mistakes, from one transaction to the next. The use of these systems to predict risk, fraud, and opportunity as in decision management systems (DSS) has kept companies profitable despite the risks they face. These decision support systems have allowed companies to increase the value of their customer relationships through a laser focus on opportunity.

 

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Refrences

Brodsky A. & Wang X. S. (2008).Decision-Guidance Management Systems (DGMS): Seamless    integration of data acquisition, learning, prediction, and optimization. Proceedings of the 41st Hawaii International Conference on System Sciences – 2008. Retrieved October 12, 2019, fromwww.Decision-Guidance-Management-Systems.pdf.   

Hoffmann, J. P. (2010).Linear regression analysis: Applications and assumptions. Second edition, 23-46. Retrieved October 12, 2019, from www.Hoffmann Linear Regression Analysis_ second edition.pdf.

Rajan. V.,& Narayan, N. (2011).Intelligent decision support systems for admission management   in higher education institutes.International Journal of Artificial Intelligence & Applications (IJAIA), 2(4). Retrieved October 12, 2019, fromhttp://docplayer.net/14132669

Shoumen, D. (2003).Adapting decisions, optimizing facts and predicting figures: Can confluence of concepts, tools, technologies and standards catalyze innovation.Retrieved from          www.adapting_decisions_optimizing_facts_and_predicting_figures_10.1.1.201.8148.pdf.

Taylor, J. (2012).Decision management systems: A practical guide to using business rules and predictive analytics.Retrieved October 12, 2019, from www.ibmpressbooks.com.

 

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