Capacity planning is the process of determining the production capacity needed by an organization to meet changing demands for its products. In the context of capacity planning, "design capacity" is the maximum amount of work that an organization is capable of completing in a given period, "effective capacity" is the maximum amount of work that an organization is capable of completing in a given period due to constraints such as quality problems, delays, material handling, etc. The phrase is also used in business computing as a synonym for Capacity Management.
A discrepancy between the capacity of an organization and the demands of its customers results in inefficiency, either in under-utilized resources or unfulfilled customers. The goal of capacity planning is to minimize this discrepancy. Demand for an organization's capacity varies based on changes in production output, such as increasing or decreasing the production quantity of an existing product, or producing new products. Better utilization of existing capacity can be accomplished through improvements in overall equipment effectiveness (OEE). Capacity can be increased through introducing new techniques, equipment and materials, increasing the number of workers or machines, increasing the number of shifts, or acquiring additional production facilities.
Capacity is calculated: (number of machines or workers) × (number of shifts) × (utilization) × (efficiency).
The broad classes of capacity planning are lead strategy, lag strategy, and match strategy.
- Lead strategy is adding capacity in anticipation of an increase in demand. Lead strategy is an aggressive strategy with the goal of luring customers away from the company's competitors. The possible disadvantage to this strategy is that it often results in excess inventory, which is costly and often wasteful.
- Lag strategy refers to adding capacity only after the organization is running at full capacity or beyond due to increase in demand (North Carolina State University, 2006). This is a more conservative strategy. It decreases the risk of waste, but it may result in the loss of possible customers.
- Match strategy is adding capacity in small amounts in response to changing demand in the market. This is a more moderate strategy.
In the context of systems engineering, capacity planning is used during system design and system performance monitoring.
Capacity planning is long-term decision that establishes a firms' overall level of resources. It extends over time horizon long enough to obtain resources. Capacity decisions affect the production lead time, customer responsiveness, operating cost and company ability to compete. Inadequate capacity planning can lead to the loss of the customer and business. Excess capacity can drain the company's resources and prevent investments into more lucrative ventures. The question of when capacity should be increased and by how much are the critical decisions.
Capacity – Available or Required?
From a scheduling perspective it is very easy to determine how much capacity (or time) will be required to manufacture a quantity of parts. Simply multiply the Standard Cycle Time by the Number of Parts and divide by the part or process OEE %.
If production is scheduled to produce 500 pieces of product A on a machine having a cycle time of 30 seconds and the OEE for the process is 85%, then the time to produce the parts would be calculated as follows:
(500 Parts X 30 Seconds) / 85% = 17647.1 seconds The OEE index makes it easy to determine whether we have ample capacity to run the required production. In this example 4.2 hours at standard versus 4.9 hours based on the OEE index.
Repeating this process for all the parts that run through a given machine, it is possible to determine the total capacity required to run production.
If you are considering new work for a piece of equipment or machinery, knowing how much capacity is available to run the work will eventually become part of the overall process. Typically, an annual forecast is used to determine how many hours per year are required. It is also possible that seasonal influences exist within your machine requirements, so perhaps a quarterly or even monthly capacity report is required.
To calculate the total capacity available, we can use the formula from our earlier example and simply adjust or change the volume accordingly based on the period being considered. The available capacity is difference between the required capacity and planned operating capacity.
Some steps to get capacity planning in line with cloud services:
- Offer mixed deployments. Sometimes, it’s worth implementing a solution that proposes both internal and external solutions. What this does, in part, is allow you to offer pricing transparency to the application groups. Those business units can then make the decision between whether an internal cloud (and the higher cost that goes with it) is the right option, or whether they are willing to use a provider that may offer less in the way of service. Not only does this allow IT to be able to plan for capacity and pay for it, it puts the capacity choices where it should be – in the hands of the business units.
- Implement rationing. Your organization needs to understand that IT resources are not unlimited. You need to find ways to keep demand in check. Demand has to be relative to what’s actually available. Rationing isn’t usually an easy policy to implement, and many organizations will push back. Ideally, however, an application group should have to justify their access to resources. Just because it is possible to get a new virtual server online within a matter of minutes does not mean that you can implement an infinite number of such servers.
- Consider chargeback. Chargeback is controversial in most IT departments, but it’s undeniably effective in those instances where it’s implemented. Being able to demonstrate cost shows your business units that capacity really does require resources, and it put internal solutions into perspective with external ones.
Baselining is a method for analyzing computer network performance. The method is marked by comparing current performance to a historical metric, or "baseline". For example, if you measured the performance of a network switch over a period of time, you could use that performance figure as a comparative baseline if you made a configuration change to the switch.
Baselining is useful for many performance management tasks, including:
- Monitoring daily network performance
- Measuring trends in network performance
- Assessing whether network performance is meeting requirements laid out in a service agreement
Methodology to achieving baselining success is not strictly chronological: The data needs analysis, and instrumentation and reporting steps are typically iterative. Also, the control step may not be completed last. Thus with a particular emphasis on how service analysis and service-level metrics drive engineering decisions, as well as tools selection in the data needs analysis and instrumentation and reporting steps.
Service Analysis With the evolution of the network into an increasingly complex entity, the number of service alternatives has increased dramatically, both in terms of technology and contractual relationships. Your company may have relationships with several service providers for reasons of fault tolerance, geographic coverage, varied classes of service offerings from different providers (one carrier is used for the frame relay backbone, another for Internet access) or just to keep vendors on their toes. The all-important first step is to determine which services need to be baselined.
Although the variety of service types is broad, for the purposes of developing a baselining strategy, services can be divided into three metric categories:
· Bandwidth Services. These are used primarily for connecting sites into your corporate network. You own the equipment on either end, and the service provider charges you only for the pipes in between--whether they are leased lines, frame relay or ATM. Service-level metrics are easily defined and objective.
· Managed Services. The service provider owns and operates CPE (customer premises equipment) in addition to providing bandwidth. For example, the service provider might offer both the routers and the frame relay cloud that connects them. Service-provider performance depends on more than one link in a chain of technologies, so metric definition is a little more complex.
· Application Services. The service provider offers an application-level service, such as Web-hosting or messaging. The service provider has substantial control over end-user performance experience, so service-level metrics based on round-trip response time may be reasonable.
Service-Level Metrics For bandwidth services, service metrics are relatively easy to define. For leased lines, we're concerned primarily with availability (we want them to be available nearly 100 percent of the time--the actual percentage will depend on your business). For frame relay and ATM, we need to augment availability metrics with some performance metrics, such as CIR (committed information rate) or latency.
For managed services, we still are interested in availability and performance. However, the type of metric used for performance will be a little different; the service provider will control more than one element of the overall network solution, and the performance metric needs to be network response time-oriented. Unlike bandwidth services, where the metric is usually very specific and measurable, managed services may need to use a metric that doesn't quite measure actual performance, but rather approximates it.
to achieving baselining success (see "Baselining Your Operations," on page 174). This methodology is not strictly chronological: The data needs analysis, and instrumentation and reporting steps are typically iterative. Also, the control step may not be completed last. For example, management may present you with an pre-agreed SLA (service-level agreement) with which to measure performance.
That said, we'll follow the process, with a particular emphasis on how service analysis and service-level metrics drive engineering decisions, as well as tools selection in the data needs analysis and instrumentation and reporting steps.