Most companies didn’t need August’s interest rate reduction to tell them that
times are tough. The mood on Britain’s high streets is the worst for a decade,
with like-for-like sales 1.9% lower than in July 2004 – the biggest fall in
almost a decade.
Throw in a manufacturing sector in recession, weak travel bookings, and an
oil price stuck comfortably above $60 a barrel, and it should come as no
surprise that the mood in many boardrooms comes close to matching the gloom on
the high street. In short, sales have stopped growing, and the surge in profit
warnings could turn into a torrent.
Yet the real difference between now and, say, 1991, or even 1980, is that
today’s FDs have tools and techniques that let them act as much more than just
scorekeepers, recording in their p&ls and balance sheets the success (or
otherwise) achieved by others in the organisation. Through better forecasting,
FDs can provide not only a warning of troubles ahead, but one that comes early
enough to avert some or all of the impact, turning 20/20 hindsight into 20/20
The first thing to realise is that the problem isn’t lack of data, says
Robert Bittlestone, chairman of consultancy and software group Metapraxis. “In
principle, the data is all there,” he says. Instead, the problem lies in the
reporting systems, and the way those reports are interpreted. In the wake of
sales slumps and other reversals, he says, hindsight usually reveals ample
warning of impending difficulty in the entrails of the relevance figures – had
anyone been inclined to look.
“Very few conventional management reports are designed to warn executive
teams about a sudden hiatus or reversal of growth,” says Bittlestone. “What’s
needed is a way of detecting early warning signs – being able to switch
instantly from straightforward charts of monthly data to growth rates, for
example, or to moving averages and comparable data visualisations. Operational
problems such as ‘Unfortunately we are below budget this month’ need to be
interpreted differently, in strategic rather than tactical terms. What’s
important isn’t the latest month but whether the news indicates a fundamental
shift.” But a shift from what, precisely? Just as management reports can betray
a worrying lack of focus, it’s far from unusual to find businesses with two or
even three separate forecasts of sales. If the concern is a departure from
forecast, it’s important to be clear about which forecast, and to be doubly
certain that the forecast is the one that really matters.
“It’s all too easy to get caught in a classic bind, where salespeople give
you one forecast of what they would like to sell – which is high, to make sure
that capacity is available – but also provide a lower forecast, which is what
they would like to be measured against,” says Rob Lucas, finance director of
Headland Foods, a Welsh manufacturer of own-brand frozen meals for large retail
Headland has worked hard to stamp that dichotomy out, Lucas says.
“Essentially we’ve one forecast within the business, built up from a detailed
understanding of what we’re likely to sell: a combination of item-level
forecasts overlaid by a view of opportunistic business that we may be able to
win.” Clever algorithms in the forecasting module of Headland’s Ross Systems
iRenaissance enterprise system generate detailed-level forecasts, factoring in
such crucial influences as seasonality and weather, while a judgement on the
balance of probabilities takes care of the opportunistic sales.
“If we’ve six potential pieces of opportunistic business, each with a 50%
chance of winning, then we don’t want to treat them as item-level forecasts,
reserving capacity and ordering raw materials, but neither do we want to exclude
them altogether,” says Lucas.
It seems to work. In a market that has been growing at around 10% a year,
Headland’s sales have risen 60% over the last five years.
But the real challenge lies in improving profitability: when production
exceeds demand, the excess goes into storage, and that’s an expensive business
for frozen food, equating to 2%-3% of the cost of sales.
The margin of error is tight. “We’re an industry with an inherently low
return on sales,” says Lucas. “Most own-label businesses struggle to make a 5%
return on sales, and storage costs can easily consume half of that.” Constant
vigilance, it transpires, is the watchword, with a steady stream of improvements
in forecast accuracy proving the best way of growing profits.
Yet in one sense, Headland has it easy.
Britain’s high streets contain just a handful of large retail chains, and
keeping tabs on their likely buying intentions is a matter of keeping in close
touch with a few well-chosen individuals.
When selling to consumers, those same high streets contain not just a
handful, but millions of customers, which makes forecasting buying intentions
far more difficult.
The past, of course, is no guarantee of what will happen in the future. But
the past is a slippery notion: last month’s sales figures may be a poor guide to
next month’s, but that doesn’t mean that yesterday’s sales won’t be a decent
indicator of tomorrow’s. A lot depends on granularity – that is, the level of
detail – and an emphasis on granularity is what underpins sales forecasting at
mobile phone retailer Carphone Warehouse.
Thanks to an Oracle data warehouse, and data consolidation and validation
technology from Informatica, Carphone Warehouse can update its financial
position daily, says David Parfett, head of relational technologies at the
company. Overnight, information on the day’s sales is pulled from each of the
company’s 1,200 stores in 10 separate markets, compared with forecast, and fed
back to the stores to drive any corrective actions.
“The number one benefit is visibility,” says Parfett. “At a salesperson
level, we’re giving the people making the sales the information they need. But
we can react at a market level too, seeing a trend, and responding to it.
Working on a monthly basis, we couldn’t do either; working on a daily basis,
we can.” But while greater forecasting granularity can help, it isn’t the whole
solution. Large retail groups have hundreds and even thousands of outlets. An
outlet in a prime location will outsell an outlet in a poor location. An outlet
close to competition can expect to be outsold by one that isn’t. And an outlet
with good management and motivated staff will be more successful than one with
Increasingly, businesses are reflecting such considerations in the way they
set and monitor sales forecasts. Last year financial institution Abbey applied
sales outlet prediction techniques to its entire branch network, using
specialist firm CACI. “The aim was to establish on a branch-by-branch basis that
our distribution strategy was correct,” says Abbey network planning manager Jack
“Are we in the places we should be? And do our branches represent us in the
right way, with a big enough footprint, and in precisely the right location?”
The review modelled the branch network in some detail. According to John Rae,
CACI’s director of business planning, around half a dozen variables were
included in the model, ranging from quality of pitch to the number of
competitors within a certain distance, and distance from other Abbey branches.
Some variables are more difficult to get a handle on than others, says Rae:
assessing the quality of branch management, for example, involves an element of
Such approaches provide an indicator of how a given retail outlet should be
doing, based on intrinsic factors other than historic sales. It has allowed
Abbey to implement strategies such as moving underperforming branches in
affluent towns to better locations or larger premises.
Better still, such techniques provide a calibrated early warning system of
the form that Metapraxis’ Bittlestone regards as so important.
If a dozen or so top-performing branches – geographically dispersed, and with
comparable management and demographic characteristics – all see a downturn, then
it should ring alarm bells sooner rather than later.
Something is happening, and it’s unlikely to be a one-month blip.
It’s certainly a lesson that paid dividends for corporate travel agency Hogg
Robinson, which saw its new status as a leveraged buyout coincide with one of
the biggest shocks that the travel business has ever experienced – the “perfect
storm” of Sars, 9/11 and the Iraq War. Revenues dropped by 10%-15%, recalls
group financial director John Kennerley – a worrying development for a new
buyout with banking covenants to meet.
Fortunately, says Kennerley, the business was already moving away from its
reliance on 24- month rolling forecasts and towards a more predictive style of
The early warning paid off. When revenues fell off the cliff, Kennerley took
pre-emptive action: employees form 75% of the cost base, and a policy of
encouraging sabbaticals and reducing headcount kept overall costs roughly in
line with forecast revenues. “In the event, our profit figures were just 1% or
2% away from our original budgeted EBITA,” he says.
That’s a remarkable achievement in the circumstances and proof indeed that
forewarned is forearmed.
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