More from less: Making resources more productive
For industrial manufacturers, resources remain a huge financial and
managerial cost. A change in perspective can lead to real breakthroughs in
reducing resource consumption.
The struggle to make the most of the world’s resources has
many fronts—something worth remembering even as headlines trumpet the supposed
end of the “commodity supercycle.” In fact, the vast majority of the world’s
manufacturers have a wealth of opportunities to make more money and increase
returns to shareholders by using fewer resources. Their full range of options
includes maximizing the use of raw materials, minimizing harmful emissions,
cutting water loss, and reducing or avoiding waste streams through recycling
and energy recovery.
Our experience shows that details count. We
hope that by presenting some vivid examples of these concepts in action, this
article will stir the imaginations of senior leaders about the possibilities
for using resources more productively.
Think lean
The lean ideas first
advanced in the Toyota production system gave organizations a new way to
recognize and root out waste. Applying that same rigor to a specific form of
it—energy and materials—lies at the center of resource productivity.
In practice, these methods often involve
following a product through a factory or service operation. That’s known as
value-stream mapping, which can be illustrated by a Sankey diagram that
highlights streams of resource waste—in this case, the analysis of a familiar
process: baking cakes for a school fund-raiser. Exhibit 1 tracks inputs, such
as ingredients and electricity for running the oven, as well as losses, such as
heat leakage from the oven. Currently only one loss is recovered, and that only
partially: apple cores are used to feed chickens. Could the oven lose less heat
in baking? Could eggshells be added to garden compost? What if the oven ran on
gas instead of electricity or the electricity came from a solar panel whose
cost has already been paid?
The starting point for most
operational-improvement efforts is incremental change: taking an existing
process as a baseline and seeing what improvements are possible from that
point. For example, an organization might begin with actual consumption and
identify ideas to reduce it. As Exhibit 2 suggests, an aggressive approach to
resource productivity makes almost the opposite assumption. For any process,
the baseline is the theoretical limit: the level of resource efficiency that
the process could achieve under perfect conditions, such as a hypothetical
state in which it produces zero emissions or if the heat it generates can be
recovered.
As the bottom part of the exhibit shows, the
difference between the theoretical limit and actual consumption is labeled as
what it truly is: a loss. Most people, and most organizations, are far more
motivated to avoid losses than to reduce consumption. Reframing the problem in
this way is therefore more likely to produce major improvement opportunities.
An iron and steel manufacturer in China, for instance, followed this exercise
and increased the power it generated from waste heat by 25 percent—which alone
reduced its production costs by more than $1 per ton.
Think profits per hour
To choose among competing
resource-productivity initiatives, companies need a common language for
evaluating each idea’s impact and the trade-offs involved. Ideally, an
organization would quantify potential savings by using the one metric companies
generally care about most: profit. But until recently, inadequate data and
limited analytic tools meant that many manufacturers could measure
profitability only by the amount of product they generated—euros per ton, for
example.
The problem is that profit per ton ignores an
essential resource: time. If the same equipment can produce two different
products with two different margins, using it to make the low-margin product
reduces the time it’s available for the high-margin one. That loss cannot be
recovered.
Now that companies can generate the needed
analysis, the results are revealing. Exhibit 3 tracks a typical portfolio of
products by profitability as a percentage of a company’s highest-margin
offering. The x-axis shows each product’s margin on a traditional per-kilogram
basis, while the y-axis shows the same product’s margin measured per hour. Most
products end up near the same point on both measures. But two of the
highest-volume products, shown at the center of the diagram in blue, are less
profitable per hour than per ton, while several lower-volume products, shown in
orange, are more profitable by the new metric.
That sort of
comparison can help companies make crucial resource-productivity choices. For
example, in the chemical industry, increasing a product’s yield usually reduces
environmental waste but requires longer reaction times and leaves less capacity
for other products. If, however, the product’s profit per hour increases by
running the reaction longer and improving the yield, the decision to do so is
an easy one.
Embrace state-of-the-art analytics
Advanced analytic techniques can multiply the
power of profit per hour, helping companies sort through millions of possible
interdependencies among variables such as the quality of raw materials, the
configuration of equipment, or process changes. Exhibit 4 illustrates how a
precious-metals company solved an especially thorny set of questions as it
sought to increase yields from its processes. Initially, it found that the
optimum yield came from a fairly narrow range of ore grades, but when it
examined grades in more detail, it found no discernible patterns.
To understand what was
at play, the mining company turned to neural networks to isolate specific days
and events when the yield should have been higher. The gray line shows the
actual yield, while the green line suggests what the yield should have been.
(Arrows indicate points where the deviation was significant and required
further investigation.) The analysis showed that increasing the concentration
of oxygen in the process offsets the yield loss resulting from a decline of ore
grades over the previous year. Thanks to the changed process parameter, the
company increased yields (and therefore production) by 8 percent in three
months.
Go beyond tools
Approaches such as the ones we’ve described
here are only part of the story, of course. Resource productivity also requires
a comprehensive change-management effort. Many organizations whose resource
projects falter over time rely too much on teaching their employees specific resource-productivity
tools and analyses. Success stories, however, change people’s underlying
mind-sets so that they “think holistically” (Exhibit 5). Equally important,
exceptional organizations support the new mind-sets with revised metrics and
more frequent performance dialogues as part of a new management infrastructure.
At these companies, resource productivity informs almost every aspect of
operations, ensuring that people keep finding new opportunities to create more
value from less.
Together, these shifts move organizations away
from the traditional take–make–dispose logic: take raw materials out of the
ground, assemble them into finished products, and then throw them away. A more
sustainable logic is to “think circular,” creating new value for companies and
society by looping products, components, and materials back into the production
process after they have fulfilled their initial use.
byMarkus Hammer and Ken
Somers
http://www.mckinsey.com/insights/operations/More_from_less_Making_resources_more_productive?cid=other-eml-alt-mip-mck-oth-1508
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