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1. #MEMBUAT APLIKASI STOK BARANG DENGAN EXCEL 2007 HOW TO#
2. #MEMBUAT APLIKASI STOK BARANG DENGAN EXCEL 2007 MANUAL#
3. #MEMBUAT APLIKASI STOK BARANG DENGAN EXCEL 2007 SOFTWARE#

This level of sharing of resources on expensive and large clusters requires new ways of determining how to run and execute data processing jobs so that we can meet the goals of each workload cost-effectively, and to deal with system failures, which occur more frequently as we operate on larger and larger clusters (that are required to deal with the rapid growth in data volumes).

These innovative platforms now aggregate multiple disparate workloads with varying performance goals (e.g., interactive services demand that the data processing engine return back an answer within a fixed response time cap) into very large clusters. With the advent of technologies like Hadoop distributions and cloud computing, we have the ability to store massive amounts of data at relatively low cost. Scale: Managing large and rapidly increasing volumes of data has been a challenging issue for many decades. This approach enables the predictive models to run on the entire data sets, thus providing more insights and at the same time reducing the cycle time to generate insights. The big data analytics platforms enables new compute and analysis paradigms such as effectively leveraging distributed processing techniques (Map-Reduce) and in-memory computing: in essence, taking the compute workload closer to the data.

This approach inherently demanded large computing horsepower and also heavy IO contentions. Traditionally, predictive analytics was done by running sophisticated algorithms on top of data sets kept in EDWs or analytical data marts. A big data analytics platform enables the bank with a scalable data ingestion and data storage platform that can keep pace with the volume and velocity of data. In the current scenario, especially in case of wealth management and capital market functions, these predictive models are fast becoming obsolete and need to be calibrated quite often because the volume and velocity of data are outrunning the usefulness of the predictive models. However, these predictive models were always run on sampled data. Notable examples are: correlations, back-testing strategies, Monte-Carlo simulations. Predictive Analytics: Banks have been pioneers in predictive analytics, applying statistical modeling techniques on historical data to predict what happens next. It is often difficult to accept that an item that was expensive to buy now has no value and should simply be scrapped. When a new machine is bought to replace an older one, the new spare parts are added to stock, but nobody remembers to remove the spares for the replaced machine. This is particularly common with inventories of spare parts. In reality, new items are often introduced without much planning, while old ones are left in stock on the off-chance that they are needed again. As an organization’s operations evolve, its requirements for stocks change, and it adds new items to replace older ones that it no longer needs. In part, it is a consequence of continually adding new items to the inventory. In part, this is a result of increasing stocks of existing items, based on arguments like, ‘we are succeeding because we give good customer service and this needs higher stocks’. In organizations that are not particularly short of money or storage space, this rise can be quite fast. Unless tightly controlled, there is a tendency for stock holdings to drift upwards.

#MEMBUAT APLIKASI STOK BARANG DENGAN EXCEL 2007 SOFTWARE#

With this software, the writer constructed an asset management software as a solution for asset management problems in Bank Banten Cabang Palembang. Currently, this electronic asset management system could be constructed by anyone using an application building software called VB.NET.

#MEMBUAT APLIKASI STOK BARANG DENGAN EXCEL 2007 MANUAL#

The lack of efficiency and effectiveness from the manual system which Bank Banten currently use, proved by some problems that discovered by the writer, such as the logbook that has been used to record owned assets in Bank Banten Cabang Palembang manually, decentralized assets recording makes it hard to find a specific asset, and that decentralized recording method often make the reporting of assets hampered. This system has been applied in Bank Banten Cabang Palembang to solve some issues in asset management which currently ineffective and inefficient.

In this report, the writer tries to construct and applies an electronic asset management system, which called Inventory Management System or in short IMS, in Bank Banten Cabang Palembang. Especially in this time and day where every operational aspect is already computerized, asset management can also be applied electronically. Every assets has its own benefits in supporting daily operational activities. Asset is an inseparable part in every operational activities of a company.