Strategy Survival Guide

Forecasting
In practice 2: SU Waste Project
The Waste Project utilised a linear projection model that projected, on an annual basis to 2020, volumes
of municipal waste and waste management methods, volumes of recyclate, expenditure and facilities.
Prediction of the volume of arisings for the entire period was therefore a crucial part of the model.
However, the extent to which linear projection was used in the Waste Project, to estimate growth rates of
municipal waste, was limited due to lack of historical growth data. Detailed data was not collected until
about 5 years before the study, and even that data was incomplete. Furthermore the data that did exist
was controversial – industry sources questioned whether the numbers reported related solely to the
stream in question, given the unrecorded transfers between, for example, municipal and commercial
waste. To add further complication, there was no consensus over the growth drivers or trends, making
linear projection difficult to do and defend as the sole method.
This problem was partly resolved by using linear projection, in conjunction with estimation, to run two
different growth rates on top of each other. Firstly, a generic 3% growth rate, based on growth in the
previous period, was used, with the default growth rate becoming 2.5% from 2010 onwards. Secondly, a
set of waste minimisation programs in the scenario necessitated a separate growth rate for specific
targeted materials in the waste stream, hence, a more complex series of estimations, which were not
based on historical data, were overlaid the generic growth rate. These estimates were forward looking
and based on how waste minimisation programs, e.g. reducing household waste through producer
responsibility, home composting, disposable nappy reduction etc, would further reduce selected material
streams. The growth rates resulting from the waste minimisation program were determined using a
variety of sources.
In a modelling situation where there is uncertain data, or where more information is likely to emerge over
time, which will alter the growth rate and/or increase the confidence of the estimation, it is useful to allow
the model user to be able to change the questionable variable. The model must then be correctly linked
to the variable data to be able to reflect such changes.
Strategy Survival Guide – Strategy Skills
Page 157