There are technologies that have been developed to manage warehouse inventory. For instance, relational database management systems provide a complete set of database management software technologies that offer a fundamental foundation for enterprises. They allow inventory managers to manage and mobilize information, especially from data centers to the right point of action. For most organizations, data is considered as a strategic asset that is used to drive smarter decisions coupled with enhanced operational efficiencies and improved customer service.
The traditional ideas concerning a centrally managed data in businesses have emerged. These have assisted managers to successfully overcome data management challenges, and are currently spanning a broad spectrum (Accorsi, Manzini, & Maranesi, 2014). In essence, data warehousing is deemed as a traditional domain of relational databases. There are basically three main reasons for this. First, data warehouses are mostly used in organizations that rely on large scale data sets that are developed in different legacy systems having relational data storages. Thus, the reports provide guidelines on how information will be captured, manipulated, managed, and shared (Accorsi, Manzini, & Maranesi, 2014). The report focuses on the value of the database what it will do on the organization. Also, it also focuses on the manner in which other industries; both domestic and international have successfully used a relational database to increase organizational efficiency.
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The main reason is to find a better balance between data warehouses entails the use relational database management systems and the opportunities provided. The concept of a data warehouse is basically an independent platform. The common application of relational databases in data warehousing mainly presumes the use of relational databases. A data warehouse is typically a relational database mainly designed to query the analysis instead for transaction processing (Accorsi, Manzini, & Maranesi, 2014). As such, data warehouses are relational data stores and are processed through the means of database management systems.
When using a relational database solution for warehouse inventory, the initial step is to define the business requirements, which is considered to be a more structured approach. Next, it is necessary to focus on making a major decision. The most important component is the inventory (Accorsi, Manzini, & Maranesi, 2014). However, this usually expands with various components that include purchasing, invoicing, payments, and the possibility of work orders. However, it is not the possibility of business users to understand that they probably need more than the inventory. Hence, the need to formulate the right question, determines the true requirements, and investigates the options and performs an options analysis.
The next step is to proceed with the design of the relational database. However, if the business requirements are mere statements, then it will be a challenging task to proceed. To design the database, data requirements will be necessary. This will be followed by refining the business requirements to a much greater level of detail (Accorsi, Manzini, & Maranesi, 2014). Essentially, it is appropriate, to begin with, what the users will need to get from the system. The next important element is to document the outputs, which is a sample report and other interfaces that will be appropriate to other systems; such s the general ledger.
Building a database required skills on data modeling where metadata and data integrity rules will be recorded. The most important practice when developing a relational database is to design primary keys and referential integrity. It is also necessary to design the tables for 3 to 4 values using prescribed value contents. These are rules that are easily generated through the use of data modeling tools. An experienced expert, who is familiar with data management concepts, will be needed to write the SQ, triggers, as well as constraint rules.
References
Accorsi, R., Manzini, R., & Maranesi, F. (2014). A decision-support system for the design and management of warehousing systems. Computers in Industry , 65 (1), 175-186. Retrieved from https://www.sciencedirect.com/science/article/pii/S0166361513001875.
Shirokova, S. V., & Iliashenko, O. Y. (2014). Decision-making support tools in databases to improve the efficiency of inventory management for small businesses. Recent advances in mathematical methods in applied sciences proceedings of the , 14 , 204. Retrieved from www.inase.org/library/2014/russia/bypaper/MEAS/MEAS-31.pdf.
