Disadvantages Continuous Inventory System †Myassignmenthelp.Com

Question: Discuss About The Disadvantages Continuous Inventory System? Answer: Introducation Currently I am working as the enterprise architect of computerized shipping and tracking in a multinational corporation named Carrefour company. We are working on improving the efficiency of this system. The importance of improving the working of this system is to make the process of shipping merchandize more efficient by minimizing shipping errors and making the shipping process much easier. There are different modern web based software and technologies that are used in this process such as transport management system and Flash View (FlashGlobal, 2017). These systems are used by business people who are savvy in the consolidation of all their supply chain aspects into one place which s in the single dashboard which have highly proactive alerts. The softwares enable the business people to digitally and in real time; organize inventory, manage the shipping process and monitor it, track information on the merchandize, and be able to create bill of landing electronically without being physically present. This brings about cost reduction and saves time. Customers are also provided with the ability to track their orders continually until they get to them. Shipping errors are also very easily noted and addressed (Robbins, 2017). Today, technology in any organization is essential in ensuring business efficiency and customer satisfaction (FlashGobal, 2017). It is therefore imperative for my organization to streamline our supply chains in a bid to ensure optimal productivity. SWOT Analysis of Computerized Shipping and Tracking Strengths Weaknesses Improved efficiency of operations in the shipping sector by reducing shipping errors and assisting in the identification of the existing errors. Reduced the time spent on shipment, receipt, tracking, and compiling data related to given orders. Save money and increase revenue for the company due to the high levels of efficiency and reduced costs to the company. Increase the levels of customer satisfaction by enabling them to watch the shipment of their packages and finally receive them in good time. These systems have ease of use and they are easily integrated with the platforms that customers use in their daily operations. It may be expensive to continually upgrade the systems. The initial cost of installing the systems may be very high and therefore reduce the benefits enjoyed by the organization in the short or also in the long run. In some situations, the recorded inventory showing in the dashboard might not be the actual or intended inventory and this may go unnoticed until the end of the process (Zaheer Raja, 2013). The organization may need to teach employees about this technology due to complexity in some systems which could consequently lead to additional costs. Moreover due to the presence of new employees, there could be errors being made before they are conversant with the system. Opportunities Threats Could lead to market expansion due to efficiency of the systems and the ease of use. Could aid the organization to have a global reach. Since the data is saved in databases, it can be easily retrieved and manipulated to draw insights that could enable the business to draw insights on matters such as customer preference, marketability of products in various regions, and identify the best suppliers. High likelihood of loyal customers due to timely deliveries. New technologies could come up therefore leading to high costs of upgrading (Robertson, 2017). Due to the use of technology, some very productive employees may have to be let go due to reduced work. Summary of the SWOT Analysis in terms of goals and objectives Short term goals The short term goal is to increase efficiency in how the process is run by ensuring timely delivery of shipments. Mid-term goals The mid-term goal is to increase revenues, save time and costs. Long term goals The long term goal is to enter new markets both locally and internationally. Recommendations It is paramount that organizations continually improve their information and communication technologies to be able to keep up with market demands and reduce competition. I recommend that all companies find out how they can optimize their activities by taking advantage of ICT and incorporating new technologies in their processes. Data Mining Data mining is an important technology that has been developing over the years. This report contains information concerning data mining. It contains background information on the history of data mining and names the three steps that are involved in data mining. The steps are; exploration of the data, building models, and using the results to create predictions. The report also contains information on the process of discovery of knowledge and data mining systems. Moreover, it contains information concerning stores for data mining and the functionalities in data mining. Data Mining Today our society is very computerized. This has in turn increased our abilities to generate and collect data from different sources substantially. Almost all aspects of human life have had a tremendous overflow of data. This increase in available data has consequently led to a need for automated tools and better techniques to assist people to intelligently transform the large amounts of data into actionable insights and knowledge. This has in turn led to the generation of data mining. Data mining is the automated process by which patterns are developed from information stored in databases, the web, data warehouses, or data streams. The process of data mining occurs in three stages. There is the initial exploration of the data, followed by the building of models and the identification of patterns, then applications of the models to come up with predictions. The processes in data mining include; time series analysis, class description, classification, association, clustering, and pred iction. History of Data Mining Data mining came by as a result of the evolution of information technology. Data mining began in the late 1980s, continued through the 90s and is increasingly being used in the twenty first century which has been called the information age characterized by vast amounts of data. The field of data mining is very extensive and rapidly developing. In the 1960s, data collection and analysis involved primitive file processing systems. In the 1970s there was the presence of network, relational, and hierarchical database systems. Moreover, there was data modelling, use of querry languages such as SQL, and online transaction processes. The database systems became advanced in the 1980s and the advanced data models were able to manage complex data. The 1980s were characterized by advanced data analysis techniques and the mining of complex data which was applied in retail, society, business, among other fields (Witten, Frank, Hall, 2011). Today, the systems have become more complex and the data in existence has increased massively. The Data Warehouse One data repository architecture which is emerging is the data warehouse. This consists of numerous heterogeneous data sources that have been organized at one site in a scheme that is unified to aid in the decision making process. This technology of data warehouse includes cleaning and integration of the data as well as online analytical processing (OLAP). Online analytical processing involves the use of different functionalities such as aggregation and the ability to see information from various points of view. OLAP tools are vital for decision making by making use of multidimensional analysis (Adedoyin-Olowe, Gaber, Stahl, 2014). However, for more in-depth analysis, there is a need for additional data mining tools to ensure; classification of data, clustering, detection of anomalies, and the identification of how data changes with time. Knowledge Discovery Process from Data The steps followed in the extraction of knowledge from data are as follows (Clifton, 2017); Data cleaning. This is the process in which irrelevant data is removed in the data before the beginning of the analysis process. Data integration. It is the process whereby different data sources are combined to come up with one dataset. Data selection. It involves the retrieval of data that is relevant to the task of analysis being conducted form the database. Data transformation. The process where the retrieved data is transformed into appropriate forms for mining by performing different functions on the data. Data mining. It is the process where methods are used on the data in a bid to extract patterns from it. Pattern evaluation. It involves an identification of interesting patterns from the data that represent knowledge founded on some measures of interestingness. Knowledge presentation. This involves the use of knowledge representation and visualization techniques in the presentation of the knowledge mined to a given user. The System of Data Mining On average the architecture of a system in data mining has these significant elements; First, it has a database and data warehouse or any other information repositories. This may require a single database or a number of them, spreadsheets, data warehouses, or other forms of information repositories. After retrieval this is when the processes of data cleaning and integration can be carried out on the data (Han Kamber, 2012). The second component is the data warehouse or database server. This is very significant because it follows the request of the data miner and fetches relevant data. Third, there is a knowledge base which is the knowledge that guides the search or used in the evaluation of the level of the interestingness of the patterns. This domain knowledge could include concept hierarchies, metadata, interestingness thresholds, and user beliefs that are used in the organization of attribute values or attributes into various abstraction levels. Fourth, there is a data mining engine. This component is very important in the system of data mining and it is made up of functional modules for different tasks. These tasks include; classification, association analysis, characterization, deviation analysis, and evolution (Jiawei, Kamber, Pei, 2015). The fifth component of the data mining architecture is the module for pattern evaluation. It is a component that makes use of interesting measures. It interacts with the modules used in the data mining process and focusses the search on interesting patterns. This module may become integrated to the module of mining, depending on the applied method of data mining. Alternatively, it could access thresholds of interestingness that are stored in the knowledge base (Han, Kamber, Pei, 2012). Efficient data mining requires the evaluation of interestingness in patterns to be carried out in the final stages of data mining process to ensure that the it is only confined to the patterns that are interesting. Finally, the graphic user interface (GUI) is a module that is used in the communication process between the data mining system and the users (Maurizio, 2011). It enables the users in their systems interactions activities by providing a given data mining task, providing the information to aid in completion of the task, and conducting exploratory mining of data relying on the initial results of data mining. Moreover, the graphic user interface aids users to browse data structures, evaluate already mined patterns, and it helps in the presentation process by visualizing the patterns in various forms. From the perspective of a data warehouse, we can view data mining as more advanced and complex online analytical processing (OLAP). Data mining however extends beyond data warehouse systems analytical processing by using techniques of data understanding that are more complex. Real data mining requires a system of analysis that can be able to deal with large data amounts (Han, Kamber, Pei, 2011). Otherwise, if the data analysis system is not capable of handling very large data amounts, it is categorized as a tool for statistical analysis of data or a machine learning system. Data mining involves an integration of various techniques from different disciplines such as machine learning, statistics, high performance computing, database technology, neural networks, pattern recognition, spatial analysis of data, data visualization, and information retrieval (oracle, 2017); Data Mining Stores There are various data stores on which mining can be carried out. Fundamentally, it should be possible to carry out mining in any kind of information repository. These include; data warehouses, relational databases, advanced database systems, relational databases, the world wide web, and flat files (Hastie, Tibshirani, Friedman, 2017). The techniques and challenges associated with mining may be different for each repository system. A relational database also referred to as a database management system (DBMS) is made up groups of data that is interrelated, called a database, as well as a number of software programs to aid in the management and access of the data. This type of database has many tables with each table having a unique name. Every table has different attributes in columns and tuples in rows. Each tuple is a representation of a given object that is known because it has a unique key. It is also described by various attributes (Witten et al., 2011). Data in relational databases is accessed using database queries that have been provided in a given query language. This could include SQL or the use of graphic user interfaces (GUI). A data warehouse is a repository of different information that has been gathered from various places. This information is then stored in a unified schema, and resides at one site (Han Kamber, 2012). The warehouses are created through the process of cleansing of the data, data transformation and integration as well as loading it and refreshing it periodically. To ensure efficient decision making, data i warehouses are arranged around topics that are major such as customer, item, and supplier among others. Transaction databases are made of a file which has records that represent transactions. This uses a unique transaction ID and the items in the transaction. Systems of relational database are applied extensively in businesses. Due to the advancement of database technology, there is an emergence of various types of advanced systems of database. These new applications include; spatial data, multimedia and hypertext data, engineering design data among others (Clifton, 2017). Data Mining Functionalities There are two main categories of data mining tasks. These are prescriptive and descriptive modelling. Descriptive tasks provide the general properties of the data by highlighting similarities in data to determine reasons for various situations while the prescriptive tasks are used in providing inference on data which is then used in making predictions. This is the technique used in uncovering insights on matters such as credit faults and campaign response (Jiawei et al., 2015). Another type of modelling is the prescriptive modelling. Due to the presence of massive unstructured data from books, PDFs, emails, comment fields and audios, text mining has grown significantly. This unstructured data needs to be parsed, filtered and transformed to use it in predictive models to ensure accuracy in predictions Once the stage of data modelling is completed, the deployment stage then occurs. This is where the model that is chosen as the most favorable is used and new data is brought in to bring about predictions or estimate the outcome that is expected. Data mining has become very useful as a tool in business information management (Clifton, 2017). Though data mining is based on exploration data analysis and modelling (EDA), it is oriented towards the applications than with determining the relationships that exist between the variables involved. Conclusion Efficient and effective data mining is very important in this information and data age. There are challenges that developers may experience in the process but these can be solved because the methodology of data mining is being continually improved. Data mining is very important in different organizations for them to be aware of the situations on the ground and be able to predict future occurrences for them to plan for the future in advance. It is also important for people who carry out data mining to do so in ways that are scalable and efficient for large databases. This is in the use of big data to be able to make comprehensive decisions. References Adedoyin-Olowe, M., Gaber, M. M., Stahl, F. (2014). A Survey of Data Mining Techniques for Social Network Analysis. Clifton, C. (2017). data mining. Retrieved from https://www.britannica.com/technology/data-mining FlashGlobal. (2017). Uses of Technology to Improve Supply Chain Management. Retrieved from https://flashglobal.com/blog/supply-chain-management/ FlashGobal. (2017). Supply Chain Management Technology. Retrieved from https://flashglobal.com/what-we-do/supply-chain-technology-systems/ Han, J., Kamber, M. (2012). Data mining?: concepts and techniques. Elsevier. Han, J., Kamber, M., Pei, J. (2011). Data Mining. Concepts and Techniques, 3rd Edition (The Morgan Kaufmann Series in Data Management Systems). Han, J., Kamber, M., Pei, J. (Computer scientist). (2012). Data mining?: concepts and techniques. Elsevier/Morgan Kaufmann. Hastie, T., Tibshirani, R., Friedman, J. H. (Jerome H. . (2017). The elements of statistical learning?: data mining, inference, and prediction. Jiawei, H., Kamber, M., Pei, J. (2015). data-mining. Retrieved from https://www.ebook-daraz.com/wp-content/uploads/2015/11/data-mining.png Maurizio, M. (2011). Data Mining Concepts and Techniques. oracle. (2017). What Is Data Mining? Retrieved from https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/process.htm#DMCON002 Robbins, K. (2017). 7 Advantages of Order Processing Software - Now Commerce. Retrieved from https://www.nowcommerce.com/blog/7-advantages-of-order-processing-software/ Robertson, T. (2017). The Disadvantages of the Continuous Inventory System. Retrieved from https://smallbusiness.chron.com/disadvantages-continuous-inventory-system-20849.html Witten, I. H. (Ian H. ., Frank, E., Hall, M. A. (Mark A. (2011). Data mining?: practical machine learning tools and techniques. Morgan Kaufmann. Zaheer Raja. (2013). disadvantages of inventory management; current and future trends of inventory management. Retrieved from https://rajazaheer87.wordpress.com/tag/disadvantages-of-inventory-management