IT Resource Capacity Planning via Stochastic Forecasting
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Solution Overview
Problem
Companies face challenges in ensuring optimal availability of IT resources in data centers, balancing overcapacity and undercapacity, due to uncertainties in demand and supply, leading to difficulties in forecasting and managing resource acquisition effectively.
Innovation Solution
A resource planning system (RP system) evaluates future demand and supply of IT resources, employing probability-based distribution samplings and optimization techniques like gradient descent to identify optimal order dates, minimizing costs associated with overcapacity and undercapacity, by generating demand and supply forecasts based on historical data and industry trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If IT resources are purchased early to ensure availability, then capacity availability is improved, but carrying costs and overcapacity increase
Solution Approach 1:
The system performs preliminary forecasting of IT resource demand using historical data and industry trends to predict future capacity needs. This allows companies to plan resource acquisition in advance without actually purchasing resources too early, optimizing the timing of purchases to meet demand while minimizing carrying costs and overcapacity.
2Loss of energy
If IT resources are purchased late to reduce carrying costs, then carrying costs are reduced, but capacity availability deteriorates
Solution Approach 1:
The system continuously monitors actual IT resource usage and demand patterns, comparing them against forecasts. This feedback loop allows the system to refine demand predictions and adjust purchase timing dynamically, ensuring resources are acquired late enough to minimize carrying costs but early enough to maintain capacity availability when needed.
3Productivity
If demand forecasting is made more accurate to optimize resource acquisition, then resource acquisition efficiency is improved, but forecasting complexity increases
Solution Approach 1:
The system uses a unified forecasting approach that leverages historical company data combined with industry-wide trends and patterns. This multi-functional methodology serves multiple purposes: it forecasts demand accurately, identifies procurement timing opportunities, and optimizes resource acquisition all through a single integrated process, reducing overall complexity while improving efficiency.
4Reliability
If lead time uncertainty is accounted for in resource planning, then capacity availability is improved, but planning complexity increases
Solution Approach 1:
The system accounts for lead time uncertainty by incorporating buffer periods and confidence intervals into the forecasting model. Rather than attempting to precisely calculate every variable, the system uses practical buffers that ensure capacity availability while keeping the planning process manageable. This partial action approach addresses the critical aspect of uncertainty without overwhelming complexity.
Data Source
AI summary
A system is provided that generates a capacity plan for a resource representing supply to meet demand based on minimizing a cost objective. The system generates demand scenarios by applying a stochastic process that factors in historical information, future goals, and uncertainty in demand. The system generates supply scenarios indicating supply over time for the resource by applying a stochastic process that factors in factors relating to quantity of supply units of the resource and uncertainty in supply. The system identifies a supply scenario that minimizes costs relating to delivery of supply at times other than the times at which supply is need to meet demand based on the demand scenarios. The supply scenario represents the capacity plan.