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Journal : IJIIS: International Journal of Informatics and Information Systems

Implementation of Knowledge Management in Different Industries Mukhtar Hanafi; Andi Widiyanto; Nuryanto Nuryanto; Hafidz Fahrizal Rahman; Mohammad Rahardian Adhitama
International Journal of Informatics and Information Systems Vol 4, No 2: September 2021
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v4i2.107

Abstract

Customer knowledge is a valuable asset, and gathering, managing, and sharing customer knowledge can be a useful competitive activity for organizations. Successful knowledge partnerships with essential and valuable customers can strengthen business performance and create an absolute competitive advantage that is difficult for competitors to emulate. In consumer knowledge management, companies often experience significant obstacles or constraints so that sometimes many companies prevent them from managing consumer knowledge. The company or organization depends on consumers who do not need to understand the industrial field of the company or organization. This paper discusses the comparison of the application of customer knowledge management in various industries. Research findings that all organizations must apply the three essential components of customer-related knowledge within the underlying conceptual framework. Three basic components of customer-related knowledge within a basic conceptual framework: knowledge for customers, knowledge of customers, and customer knowledge, cannot be eliminated. This proves the compatibility with previous studies. In fact, to succeed, they must be applied so that it is of value to the company. Implementation of the framework can present a great opportunity for the organization or company to make new products so that this will improve the performance of the company or organization and maintain the continuity of the company or organization.
A Data Mining Practical Approach to Inventory Management and Logistics Optimization Bambang Pujiarto; Mukhtar Hanafi; Arief Setyawan; Asti Nur Imani; Eky Rizky Prasetya
International Journal of Informatics and Information Systems Vol 4, No 2: September 2021
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v4i2.109

Abstract

The latent demand to optimize costs and customer service has been fostered in the current economic situations, characterized by high competitiveness and disruption in supply chains, placing inventories as a vital sector with significant potential to implement improvements in firms. Inventory management that is done correctly has a favorable impact on logistics performance indexes. Warehousing operations account for around 15% of logistics expenditures in terms of dollars. This article employs a method based on the Partitioning Around Medoids algorithm that incorporates, in a novel way, the application of a strategy for locating the optimal picking point based on cluster classification, taking into account the qualitative and quantitative factors that have the greatest impact or priority on inventory management in the company. The results obtained with this model improve the routes of distributed materials based on the identification of their characteristics such as the frequency of collection and handling of materials, allowing for the reorganization and expansion of storage capacity of the various SKUs, moving from a classification by families to a cluster classification. This article shows a suggestion for a warehouse distribution design using data mining techniques, which uses indicators and key qualities for operational success for a case study in a corporation, as well as an approach to improve inventory management decision-making.
With topological data analysis, predicting stock market crashes Nugroho Agung Prabowo; R Arri Widyanto; Mukhtar Hanafi; Bambang Pujiarto; Meidar Hadi Avizenna
International Journal of Informatics and Information Systems Vol 4, No 1: March 2021
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v4i1.78

Abstract

We are investigating the evolution of four big US stock market indexes' regular returns after the 2000 technology crash and the 2007-2009 financial crisis. Our approach is based on topological data processing (TDA). To identify and measure topological phenomena occurring in multidimensional time series, we use persistence homology. We obtain time-dependent point cloud data sets using a sliding window, which we connect a topological space for. Our research indicates that a new method of econometric analysis is offered by TDA, which complements the traditional statistical tests. The tool may be used to predict early warning signs of market declines that are inevitable.