Browse Prior Art Database

Applying Retail Supply Chain Analytics and Forecasting on Storage Systems

IP.com Disclosure Number: IPCOM000243098D
Publication Date: 2015-Sep-15
Document File: 4 page(s) / 56K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed are a method and system to utilize retail-world forecasting algorithms and methods to perform storage space analysis and prediction of depletion.

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Applying Retail Supply Chain Analytics and Forecasting on Storage Systems

Storage administrators need to predict and prepare for upcoming storage or performance issues because storage issues affect the users. The most basic performance storage space alert, which an administrator sets, is triggered when storage space reaches below a set threshold. To properly address an issue of depleted storage, an administrator needs early notification of the decreased storage levels. With adequate notification of a problem, the administrator can identify the best solution (e.g., purchase a new storage machine, release data on a used storage machine, etc.).

Moreover, current cloud systems can contain a pool of a large variety of storage systems, each enabling a different set of capabilities, all of which the storage administrator must manage. If space on one system becomes depleted, it may not be replaced by space on another system. The storage administrator needs to understand which storage capabilities are about to run out of space and how to replenish that space.

The novel solution is a method and system to utilize retail-world forecasting algorithms and methods to perform storage

space analysis and prediction of depletion. Although several applications for forecasting storage space are available, none declares derivation from the retail forecasting world.

Assuming that a storage system of certain capabilities is a product that has trends and seasonality like any other

products, the same algorithms are applicable in order to:

• Perform historical demand analysis for trends, seasonality, and cycles • Forecast short-term and long-term demand
• Recommend actions (e.g., replenishment, etc.)

Historical and future analysis may also assist in calculating quality of service (QOS) vs. given Key Performance Indicators (KPIs), as well as Returns on Investment (ROIs).

The analysis can answer questions such as: Were users turned down on specific capability storage space. Did users

wait? How much did the client pay for the storage, and how much of it was used?

In the retail and supply chain space, data analytics achieves cost effectiveness. A retailer can wisely plan inventory by

using smart analytics of a product's historical demand and future product demand estimations. This process usually consists of three main steps:


1. Historical demand data analysis


2. Future demand forecasting

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3. Recommendations

These processes are applicable and useful in the storage systems space.

Translation of Retail Concepts

Product forecasting methods use mathematical formulas to predict future demand based on historical demand. These models assume that past occurrences can assist in predicting future occurrences. Each of these methods considers trends, cycles, seasons, and random events that may impact the demand, either historical or future.

Step 0 - Defining a storage system product

Storage systems differ from one another by vendor, capabilities, releas...