CUE Theory

What is CUE Frame Work  &  What it is not 

CUE stands for Capacity Utilization Efficiency.    CUE Framework is to measure operational efficiency. of any deployed system.    The framework is similar to the "ROI" ( Return On Investment ) concept used in business process.   However,  CUE framework does not include business aspect ( financial value or viability ) by design.   The reasoning behind is that the framework assumes that the operation it is being used to measure is already business viable.  Thus CUE is not a business validation tool.   

The framework does not cover the remedy part as well.  The scope of the framework encompasses only monitoring and measuring operational efficiency.   Plan of action to be derived based on the measurement is subject to the context and left to the users.   

Fundamentals of CUE Framework 

Organizations deploy a specific resource that incurs a specific cost and provides value ( mostly through a set of services ).   The efficient utilization of the deployed resource is necessarily measured by the service availed from the resource.   

Deployed Capacity (C):   The actual amount (capacity) of resource that has been deployed.  The whole of deployed capacity incurs cost for organization.  No part of this capacity has zero cost bearing for the organization.  

Service Utilization (U):   The actual service utilized by organization through the deployed capacity.  This does not include the service that was available but not utilized.   In other words,  no part of this service is either unutilized or under utilized.  

Efficiency Factor (CUE):   The CUE factor is defined as the ratio of Utilized Service to the capacity deployed  i.e.   Efficiency Factor =  Service Utilization / Deployed Capacity 

Example for CUE measurement 

Let us suppose a library has a set of book-shelves that can hold 1000 books and there are 800 books stored in those shelves.   The number of books which are read by its visitors in a given day is 150. 

In this case,  the utilization efficiency factor for book-shelf is 150/1000 = 0.15 or 15% 

( In a conventional method,   utilization factor will be considered to be 80% based on the number of books placed in the shelf.  However,  given that only 150 books are used by readers,  650 books are not being utilized.  So these books will not be considered for service utilization. )  

Postulates : 

1.  CUE Framework is generic to any kind of operation.  The application of the framework is the choice of the user in given context and chosen KPI.  

2.  Higher the value of CUE factor, better is the efficiency. 

3. There is no standard or optimized value for "CUE" due to generic nature of the framework.   The optimal value for "CUE" should be evolved specific to the context and KPI (in the given domain).  

4. Operational process should align to increase the value of E.  The attempt should be to reduce the value of "denominator" rather than increasing the value of numerator.   This is because, the primary objective of the framework is to "minimize" the "Deployed capacity" as against justifying the deployed capacity.

5.  One important factor that influences "CUE" externally is "service availability".   

Service Availability =  Actual availability of services provided by Deployed Capacity / Required availability of services provided 

The service availability is calculated independent of capacity deployed.   Reduction of "Deployed capacity" to increase "CUE" should not adversely impact "Service Availability" .  

6.  Though "CUE" framework atomically represents a snapshot of given instance,  for effective use of the framework, the values should be captured continually on stipulated time intervals.  Thus time becomes an important factor.   

CUE =  Ut/Ct

Scenario 1:     If  Ct is constant during the given time interval,   CUE is proportional to Ut.   

This is applicable when the system is in the "launch" phase.  The service utilization is expected to increase gradually though the initial capacity deployed remains unchanged.    ( eg.  a metro rail segment is newly launched )

Implication =>  The capacity deployed should be modularized in a way that the deployment of additional capacity can be as minimum incremental as possible so that the curve for Ut to reach its maximum for the given capacity is minimal.   

Scenario 2:   If Ut is constant during the given time interval,  CUE is inversely proportional to Ct.    

This is applicable when the system is in a "stable" state with the level of service utilization reaching its maximum value.  ( eg.  a metro rail segment is reasonably stabilized )   

Implication =>  This stage demands further optimization resulting in a change (reduction) in deployed capacity for further improvement of CUE.  

Scenario 3:  If both Ut and Ct are varying continually, then CUE is dependent on both Ut and Ct.   

This is applicable when the system is in a dynamic state continually.   ( eg.  peak and off-peak hours of metro rail segments )

Implication =>  To achieve the higher CUE , it requires
a) the predictability of Utilization variation 

b) ability to deploy minimal capacity corresponding to the projected utilization

7.   While addressing the above scenarios,  an important parameter to be taken into consideration is the Service Availability.   Service Utilization for minor intervals within given interval period should be observed and analyzed.    Below measures will be of interest in respective aspects. 
a)  Minimum Service Utilization ->   Guarantees Service Availability 
b)  Maximum Service Utilization ->  Risk to service availability 
c)  Mode of Service Utilization    ->  Most likely scenario for service availability
d) Mean of Service Utilization     ->  Serves as a directional measure for CUE within the minor interval. (  if the difference between Minimum and Mean is very high,  CUE will become low.  

   if the difference between Mode and Mean provides higher 

   if the difference between Maximum and Mean is

) more frequentlThis may not be directly used, but can be corroborated between (b) & (c)