Capacity Management

Capacity Management Definition

Capacity management refers to the wide variety of planning actions used to ensure that a business infrastructure has adequate resources to maximize its potential activities and production output under any condition.

Diagram shows the general life cycle of capacity management, and the components that are involved to making it operational.
FAQs

What is Capacity Management?

Capacity management theory consists of the planning, IT monitoring, and administration actions undertaken to ensure that information technology resources have the capacity to handle data processing requirements across the entire service lifecycle.

The goal of capacity planning management is to ultimately balance costs incurred against resources required, and balance supply against demand. The capacity management procedure concerns performance, memory, and physical space, and should cover both the operational and development environment, including hardware, human resources, networking equipment, peripherals, and software. 

The main objectives of project management capacity planning include: 

  • Identify IT capacity requirements to meet current and future projected workloads
  • Develop and maintain a capacity management plan
  • Ensure performance goals are met on time and within budget
  • Monitor capacity continuously to support the service level management
  • Assist in diagnosing and resolving incidents
  • Analyze the impact of variances on capacity and take proactive measures to improve performance where it is most cost-effective

Types of Capacity Planning in Operations Management

There are three main types of capacity planning and control in operations management that ensure there are adequate resources for both the short- and long-term.

  • Product capacity planning -- ensures that there are adequate products or ingredients for deliverables
  • Workforce capacity planning -- helps estimate the most efficient number of team members and hours required to complete jobs, and the most ideal time frame in which to start recruiting new employees, including consideration of the onboarding process
  • Tool capacity planning -- ensures that there is always adequate equipment to complete jobs, e.g. assembly line components, manufacturing machinery, and transport vehicles for delivery of products

Strategies for Managing Capacity

Capacity management tools and methodologies vary, ranging from manually compiled performance spreadsheets to specially compiled hardware or software that is designed to produce detailed insights on the functioning of computing components. These tools examine the operation of hardware and software, and monitor and measure the volume and speeds at which an organization’s applications move data through the IT infrastructure. 

The software and hardware elements that should be monitored include: cloud services, end-user devices, networks and related communications devices, servers, and storage systems and storage network devices. Information on internal processes of individual components and data movement metrics are extracted from these IT elements. Using this information, an administrator can run a software utility program to measure the transfer rate of data during processing.

Some proactive capacity management and planning activities include: utilize network capacity management, production capacity management and storage capacity management tools to predict network, production, and storage needs; implement pre-emptive, corrective actions; identify trends to estimate future utilization requirements; build models based on estimated changes; ensure upgrades are budgeted in a timely fashion; and develop and maintain a capacity plan to optimize the performance of services and increase efficiency.

What is the Primary Focus of Business Capacity Management?

Capacity planning decisions in operations management for businesses focus on measuring how much a company can achieve, produce, or sell within a given period of time. This includes: 

  • Management and prediction of the performance and capacity of individual elements of IT technology
  • Management and prediction of the performance and capacity of live, operational IT services
  • Analysis of capacity supplier agreements and supplier management contracts by a capacity management analyst
  • The timely quantification, design, and implementation of future business requirements for IT services

Capacity and Performance Management Best Practices

The following best practices should be adopted to help monitor the intelligence and adaptability of existing IT systems:

  • Develop a comprehensive view of available resources in order to ensure that resources are distributed to the appropriate people and projects 
  • Run a variety of test scenarios with different variables and analyze the impact of the changes in order to identify project risks proactively.   
  • Derive insights from historical data with the predictive capacity management process in order to predict the likelihood of success.
  • Prioritize tasks and assign resources effectively with continuous planning and monitoring.
  • Avoid overestimating or underestimating resource utilization needs by generating accurate capacity versus demand capacity management ratios.

Advantages of Capacity Planning in Operations Management

Strategizing capacity in operations management ensures that systems are operating at adequate levels to achieve company goals without over-provisioning resources. By identifying and eliminating extraneous activities, companies can reduce costs and increase efficiency. Accurately anticipating resource needs encourages more effective purchasing to accommodate future growth. Production obstacles such as bottlenecks and equipment failures can be predicted and avoided altogether with constant monitoring of hardware and software operations.

Does OmniSci Offer a Capacity Management Solution?

In planning hardware and infrastructure sizing, it is crucial for a capacity planning manager to manage and monitor daily data ingestion volume, data volume for one-time historical load, the data retention period, multi-data center deployment, and the time period for which the cluster is sized. It is imperative to incorporate the computing power of GPUs for processing and extracting insights from these enormous datasets with accuracy and at real-time speeds. As the pioneer in GPU-accelerated analytics, the OmniSci platform is used to find insights in data beyond the limits of mainstream CPU-based analytics tools.