Posted: April 2nd, 2022

Assessment of Expert Systems Third-party Logistics

Assessment of Expert Systems Third-party Logistics Companies Warehouse Management and Optimization

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Summary
Information technology systems play a fundamental role in businesses and organizations. Third-party logistics companies specialize in services, like distribution and warehousing. These types of companies manages the supply chain for other businesses and organizations both in the country and internationally. The core competency of these companies is logistics to facilitate warehouse management and optimization. However, to be able to achieve the goal of productivity and performance, there is a need for an innovative information system.
At third-party logistics companies, for instance, one of the problems that could be encountered concerns inaccurate predictions of future distribution patterns of goods. Currently, third-party logistics companies have implemented information systems that are based on predictions from previous distribution and storage records. However, the problem with such information system concerns how the data is static, in that only previous statistics are used for making future projections. Making predictions into the future cannot be accurate enough by using only previous records. There are many other factors that affect the distribution and sale of goods that need to be considered to make accurate predictions. To mitigate this challenge, third-party logistics companies require an innovative information system, one that is able to put into practice latest technologies, like artificial intelligence.
Accordingly, the focus of the proposed research is the assessment of an accurate tool that can be effectively used by the company to increase the productivity and performance of warehouse management and optimization. The proposed research will focus on an expert system that will have the capacity of conducting statistical analysis of distribution data to provide expert decisions and forecasts.

Background
At the point when businesses and companies are confronted with complex issues, they frequently utilize specialists in the field, also known as experts for counseling [1]. For the most part, these people have explicit information, experience, and involvement with the particular fields [2]. They perceive alternatives, the odds of achievement, and exchange advantages as well as potential misfortunes. In this manner, it can be said that companies and businesses hire experts for non-organized circumstances that may affect performance and productivity. In line with Kidd, expert systems are designed to endeavor to copy the services provided by human experts [3]. This way, expert systems are information systems that can be utilized to make decisions to the degree of a human expert, or even more, for solving problems in specific areas.
As per Korenevskiy, one of the principle highlights of the process of making decisions in companies depends on data and information [4]. Ideal and precise data and information are key components and an essential requirement during the decision making process for managing resources. Without the essential data, information and parameters, it is difficult to settle on decisions [4]. As such, a framework for supporting decision making is quite possibly the most present day tool utilized to assist in decision making. This is based on how the framework permits the coordination of information and experience of the management. In turn, the most generally utilized information systems for supporting this kind of decision making are expert systems [5]. They are utilized in every aspect of activity, particularly in offering logistics management.
Proposed work
The proposed research is the assessment of an expert system that can be implemented by third-party logistics companies to assist in warehouse management and optimization. In most businesses and organizations, expert systems can be applied in both managerial and technical aspects within a company [6]. Expert systems knowledge bases together with decision rules that are custom designed for analyzing problems and offering solutions and advice on the best steps to take. Judson states that expert systems are designed to maximize the virtues of human experts [7]. The systems do so by giving emphasis to reliability and performance. The systems are also easy to use and have the capacity to interface with other information systems.
In light of the identified primary issues, the proposed research intends to analyze an expert system, in light of how it can be utilized to assess input data and information together with a knowledge base collected from experts for predicting distribution of goods, in addition to warehouse management and optimization.
Aims and Objectives
The aim of the proposed research is to assess how expert systems can enhance productivity and performance of third-part logistics companies with regard to its warehousing operations and activities. The following are the objectives of the proposed work.
1. To establish how experts systems using machine learning are capable of making inventory management much easier, faster, and efficient for responding faster to customer orders and purchases.
2. To determine how expert systems add quality control and efficiency to warehouse processes and operations for improving the fulfillment of orders.
3. To identify how to reduce customer inquiries as a result of human errors during distribution and inventory, which will improve performance and productivity of the companies.
Rationale
As of now, there are numerous studies that have been carried out to study expert systems and their use of artificial intelligence and machine learning to make expert decisions [2]. Regardless, the field is far from being exhausted. To be specific, new research can be carried out in the assessment of expert systems for third-party logistics companies to analyze how warehouse management and optimization can enhanced. Provided with that, the rationale for the proposed research is to fill in the existing gap in literature with regards to the application of expert systems for warehouse management and optimization.
Methodology
This proposed research will utilize both a quantitative and qualitative research methodology since there will be both numeric data and information together with quantitative information to be used for analysis [8]. For the purpose of the proposed research, the methodology will utilize a mix of the exemplary sociologies research methods, i.e., interviews, surveys, and questionnaires [8, 9]. The questionnaires will be appropriated among managers of several third-party logistics companies, which have utilized information systems as a feature of their administration strategies, just as among chosen employees of similar organizations, who structure part of the group of similar administrators [9]. As a correlative technique, the proposed research will also utilize surveys and interviews with an equivalent number of delegates of the selected third-party logistics companies.
Program of Work
The proposed research will comprise of the following wok activities.
1. Preparation of proposal
2. Presentation of proposal
3. Preparation and submission of thesis application to ethics committee
4. Data collection
5. Data analysis
6. Report writing
7. Submission of thesis
8. Presentation of thesis
Relevance to beneficiaries
According to Rajabi, Hossani, and Dehghani, expert systems have been dependably utilized in the business world to acquire strategic favorable circumstances and gauge the economic situations [10]. In this globalization time where each choice made in the business world is basic for progress; the benefits that comes from adopting expert systems are without a doubt fundamental and exceptionally necessary for an association to succeed. One of the relevance of expert systems to third-party logistics companies concerns how they can be used for explaining possible trends and patterns of distribution and be the reason it considers as the most intelligent decision among different other options [10]. In the event that there are any questions in closing a specific issue; the systems will incite a few inquiries for clients to reply to deal with the best logical end result.
Another relevance to beneficiaries concerns how expert systems do not have human impediments and can work nonstop ceaselessly. Third-party logistics companies will have the option to as often as possible use it in looking for arrangements. The information on specialists is a priceless resource for these organizations. Consequently, the expert systems can be used by the companies to store warehouse data and information and use it as long as the association needs it [11]. Not at all like people who frequently experience difficulty adjusting to new conditions, the expert systems have high flexibility and can meet new necessities in a brief period. It additionally can catch new information from a specialist and use it as surmising rules to tackle new issues.
Conversely, one of the risk problem of the proposed research concerns how expert systems do not have human common senses required in some making decisions since all the choices made are based on the surmising rules set in the framework. Aside from that, expert systems additionally are not able to make imaginative and inventive reactions as human specialists would in irregular conditions [10]. This is a risk for the proposed research since the beneficiaries will still be lacking in some aspects to increase accuracy of predictions. Furthermore, the adoption of expert systems in companies will be a monetary burden since it has high improvement costs just as the resulting repeating expenses to overhaul the framework to adjust to the new business environment. Finally, another risk problem of the proposed research is that errors may happen in the collection of data and information [11].
Research management plan
For the proposed research, data and information will be collected and stored in multiple formats that will then be analyzed. The data and information collected will be preserved and archived by being stored in files and folders in the researcher’s personal computer. The data and information collected will be retained for a period of five years, following which it will be released into the public to assist in future research on the field.
Justification of resources
The proposed research will require are researcher who will be involved with collecting data and information as well as to perform analysis and assessments of the data collected. The proposed research will also necessitate funds that will be used for performing the research. Likewise, the proposed research will require the use of secondary sources of data and information to provide background into similar research studies carried out in the past.

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References
[1] Alma, Z., Mansiya, K., Torgyn, M., Marzhan, M., & Kanat, N. (2014). The methodology of expert systems. International Journal of Com puter Science and Network Security (IJCSNS), 14(2), 62-63.
[2] Puppe, F. (2012). Systematic introduction to expert systems: Knowledge representations and problem-solving methods. Springer Science & Business Media.
[3] Kidd, A. (Ed.). (2012). Knowledge acquisition for expert systems: A practical handbook. Springer Science & Business Media.
[4] Korenevskiy, N. A. (2015). Application of fuzzy logic for decision-making in medical expert systems. Biomedical Engineering, 49(1), 46-49.
[5] Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71.
[6] Chai, J., & Ngai, E. W. (2020). Decision-making techniques in supplier selection: Recent accomplishments and what lies ahead. Expert Systems with Applications, 140, 112903.
[7] Judson, P. (2019). Knowledge-based Expert Systems in Chemistry: Artificial Intelligence in Decision Making (Vol. 15). Royal Society of Chemistry.
[8] Barnham, C. (2015). Quantitative and qualitative research: Perceptual foundations. International Journal of Market Research, 57(6), 837-854.
[9] Mayer, I. (2015). Qualitative research with a focus on qualitative data analysis. International Journal of Sales, Retailing & Marketing, 4(9), 53-67.
[10] Rajabi, M., Hossani, S., & Dehghani, F. (2019). A literature review on current approaches and applications of fuzzy expert systems. arXiv preprint arXiv:1909.08794.
[11] Petrică, V. (2019). Enhanced Expert Systems. BoD – Books on Demand Publications.

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