In most industries, demand varies in the short, medium and long term. Such variability is why demand forecasts are inaccurate. Fluctuations in demand are notoriously difficult to predict and this has driven a trend towards shorter lead-times and “make-to-order” production. If demand in a factory was stable, its output could be set to guarantee satisfying that demand. Even when demand changes, such a factory can respond within its capacity if the forecasts are accurate. In both situations, the time it takes to process material or the interval between making batches would have no effect on the success of the factory in meeting demand.
However, in reality, demand does change, new opportunities for sales present themselves and existing customers change their minds about product mix.  These situations are impossible to forecast accurately. So, in real life, the factory must respond to those demand changes.  At those moments of unexpected changes in demand (and only then), it becomes apparent whether the factory responds like a supertanker or a jet-ski. Various aspects of product and process design influence responsiveness, but their effect can be reduced to two measurements: the interval in time between batches and the time it takes to process a batch.  “Response Time” is a measure of the ease with which manufacturing can change its output to meet changes in demand.

Definition
Response Time = Batch Interval + Process Time

An Analogy
It is helpful to think of it by analogy.  If you were planning to fly from A to B, how long should you allow to be sure of getting there?
 

Fig 1

i.e. Allowed Time = Interval between flights + Flight Time
 which equals 1 + 2 = 3 hours in this example.
If you arrive just in time to catch a flight, it will take you 2 hours, but if you just miss one, it will take 3.  In the absence of a flight timetable, you will need to allow 3 hours to be sure.
It is possible to talk about the response time for intermediate components too. However, it only makes sense if the intermediate is stocked: “planes must land at the airport”
 

Ways of Reducing It
It is clear from the definition that response time is reduced by:
• Making batches more frequently (reducing batch size)
• Reducing process lead time
However, the most common form of response time reduction is by the use of stocking points, in particular, finished goods.  Sales can then be in units (=batch size) of 1 and processing time is typically just the shipping time from distributor to customer.
Of course, buffering against sudden changes in demand by adding raw material, intermediate or finished goods reduces response time at the expense of inventory. So response time reduction should not be viewed as an end in itself. It needs to be done without increasing inventory or cost to be a credible improvement to the business.

 

Fig 2

A response time can be calculated for each set of processes which end in a buffer stock. The complete supply chain is thus an assembly of such links each with its own response time.
 
Thus there is generally more than one figure to be looked at, although the final one (nearest to the end customer) is the one most often scrutinized.

The Result
A more responsive factory:

• Is less likely to default on customer orders
• Has fewer disruptive changes to production plans
• Is in less of a panic

Fit with Management Strategies
Whilst not stated explicitly, Response Time reduction is the goal of many manufacturing strategies such as “Lean Manufacturing”.  However, there is nothing new about it as a concept.  Henry Ford can be thought of as an early advocate when he established a car production system that smelted 1500 tons of steel a day to turn out a car every 49 seconds in the shortest possible elapsed time.

It is a common sense concept. If you want to satisfy the customer, you must respond to their demands.

By James La Trobe-Bateman, reMODEL Consultants International Ltd

see Case Study for an Illustration of the Real Effect of Reducing Response Time