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Rapid Product Development

Rapid Product Development is what I like to call the process developed by Don Reinertsen

In his first books 'Developing Products in Half the Time' & 'Managing the Design Factory' Don Reinertsen introduced us to the importance of Rapid Product Development, and in particular the concept of 'Cost of Delay' - a method to put a price on programme delays, by factoring in the lost opportunity cost.

In Manufacturing Lean, (and also Theory of Constraints) the concept of Flow is an important one.  Don Reinertsen has incorporated this idea into his theory of Lean Product Development, by utilising Queueing Theory, and Batch Sizes amongst other things to show us how to obtain maximum flow thorough the Product Development process, and hence increase efficiency and minimise the cost of delay.

Key aspects of Rapid Product Development are

bullet
Economic Decision Making
bulletCost of Delay
bullet
Queues
bullet
Queuing Theory
bullet
Focus on queue size; know it & reduce it
bullet
Variability
bulletCope with variability
bullet
Batch Size
bulletReducing batch sizes reduces queues
bullet
WIP Constraints
bulletReducing WIP reduces queues
bullet
Cadence, Synchronisation & Flow
bulletMaintaining flow
bullet
Fast Feedback
bulletLearn quickly & adjust
bullet
Decentralized Control
bullet
Empower teams to make decisions
bullet
Know the commander’s intent

Queueing Theory

Agner Krarup Erlang, a Danish engineer who worked for the Copenhagen Telephone Exchange, published the first paper on what would now be called queueing theory in 1909. In Queueing Theory, different types of queues are described by a notation such as M/M/k

bulletM stands for Markov or memoryless and the first M means arrivals occur according to a Poisson process. The second M describes the service time distribution and and means the service requirements are exponentially distributed
bulletD, if used, stands for deterministic and means jobs arriving at the queue require a fixed amount of service
bulletk describes the number of servers at the queueing node (k = 1, 2,...). If there are more jobs at the node than there are servers then jobs will queue and wait for service.

For an M/M/1 Queue (i.e. 1 server), the waiting time goes up as the utilisation goes up. (See below). A similarly shaped result (though with different absolute values) is obtained for other numbers of servers.

What this means is that if you aim for high utilisation of your resources, you are bound to suffer longer waiting times.

The queue size doubles:-
Going from 60-80%
Again from 80-90%
Again 90-95%
95% Capacity Utilisation = 95% Queue Time !
 

Project delays are often due to waiting time due to overloaded resources in one or more areas, so increase those resources, or reduce the number of projects using them , and because projects do not usually provide a steady flow, have some slack to be able to cope with the peaks.

Note Little's Law for a stable, i.e. steady state system says

Wait Time = Length of Queue/Processing Rate

 

 

 

 

 

 

 
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