1. business and industrial
2. iron and steel industry

Perspective
Hans Bohnen
Nils Naujok
Joachim Rotering
Peter von Hochberg
Emerging Steel Giants:
Are All Sources of Value
Explored?
How to Generate All
Top- and Bottom-Line
Contributions from
Ever-Growing
Production Networks
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CONTACT INFORMATION
Joachim Rotering is a partner based in Düsseldorf.
He focuses on market strategy, postmerger integration,
restructuring, transformation, and sourcing. His industry
experience is in process industries, especially steel and
chemical. He can be reached at +49-211-3890-250 or
[email protected].
Hans Bohnen is a principal based in Düsseldorf.
His areas of focus are cost management, optimizing
production networks in process industries, Lean
production, and Six Sigma deployments in the chemical
and steel industries. He can be reached at
+49-211-3890-112 or [email protected].
Peter von Hochberg is a partner based in Düsseldorf.
He has broad experience in integrated restructuring, cost
management in operations, manufacturing diagnostics,
and transformation. His industry experience is in the
automotive, steel, and high-tech industries. He can be
reached at +49-211-3890-170 or
[email protected].
Nils Naujok is a principal based in Berlin. He has broad
experience in manufacturing, supply chains, postmerger
integration, restructuring, and sourcing, especially in the
chemical, steel, and industrial gas industries. He can be
reached at +49-30-88705-855 or [email protected].
Originally published as:
Emerging Steel Giants: Are All Sources of Value Explored? How to Generate All Top- and Bottom-Line Contributions from Ever-Growing Production
Networks, by Joachim Rotering, Peter von Hochberg, Hans Bohnen, and Nils Naujok, Booz Allen Hamilton, 2008.
Emerging Steel Giants: Are All Sources of
Value Explored?
How to Generate All Top- and Bottom-Line Contributions from Ever-Growing
Production Networks
In recent years, observers have seen the steel
industry rapidly consolidate. In just the past
five years, more than 800 steel producers have
changed hands. Consolidation activities in
the mid-1990s focused on alliances between
domestic steel producers. In Europe, for
example, Krupp acquired Hoesch and then
merged with Thyssen to form ThyssenKrupp
Steel, and British Steel merged with the Dutch
metals producer, Koninklijke Hoogovens. When
Japan’s Kawasaki Steel merged with NKK, the
result was JFE Steel—the world’s fifth-largest
steel producer and Japan’s second-largest after
Nippon Steel. This type of consolidation wave
can also be expected in the Chinese market;
the extremely fragmented steel industry in that
country has more than 40 steel producers with
a total production capacity of less than 5 million
tons of crude steel.
During the past five years, global alliances have also
become strategically relevant. Domestic steelmakers
have allied with those in different regions and with
foreign companies. Mittal Steel was among the first
to put a global spin on this M&A trend with its most
recent coup—the acquisition of main competitor
Arcelor. Mittal’s move was soon followed by Tata
Steel’s unexpected acquisition of Corus. Today’s steel
landscape indicates that both regional and global
consolidation will continue in the upcoming years.
Although the trend among U.S. and European players
has been to merge with companies in their respective
markets, they might need to take a global approach
in the future. U.S. Steel and Nucor, for example,
are big players in their home countries; each has
an annual output of about 20 million metric tons.
Globally, however, these production levels place
them in a secondary tier, and they have only a minor
international presence for serving their globalizing
customer accounts. For an overview of steel industry
consolidation activities since 1992, see Exhibit 1,
page 2.
Ultimately, the goal of consolidation in the steel
industry is simple: become more flexible and thereby
better able to manage the cyclical nature of the
market to ensure price stability. Because these
cycles will never completely disappear, consolidation
is a good way of matching supply with demand.
Emerging steel giants will manage the balance of
supply and demand by leveraging their increased
bargaining power over consolidating raw material (iron
ore, coal) and customer (automotive) industries. But
consolidated industries at the beginning and end of
the steel value chain can create a “consolidation
trap,” the negative effects of which numerous large
steel companies have experienced. Such companies,
2
Exhibit 1
Consolidation Process in the Steel Industry since 1992
1992–1997
2007 Rank, in MTPA
(metric tons per annum)
1998–2006
LNM Group
Ispat
LTV
Acme
Bethlehem
Weirton
Georgetown Steel
Cockerill Sambre
Usinor
Arbed
Aceralia
1
2004
66
Arcelor
51
Nippon Steel
33
JFE Steel
32
POSCO
30
Corus
18
117
2002–2004
2
1998
3
2001
4
NKK
Kawasaki Steel
2002
5
1999
British Steel
Koninklijke Hoogovens
Tata
2006
2005
Anshan Steel
Benxi Steel
U.S. Steel
National Steel
Kosice
Sartid
9
2001–2002
2005
Wuhan Steel
Liuzhou Steel
1993
1997
6
8
2002–2003
Nucor
Trico
Birmingham
25
7
1998
Shangahai Steel Systems
Meishansteel
Hoesch
Krupp
Thyssen
Mittal Steel
ISG
10
11
Tata Steel
7
Anben
23
Baosteel
23
U.S. Steel
21
Nucor
20
Wuhan
19
ThyssenKrupp
17
Source: Booz Allen Hamilton
however, now can leverage their combined market
knowledge to a greater extent, and this enables them
to make better price and production decisions.
complexities of cultural integration. This is especially
true for those mergers that bring together very
different cultural heritages.
These strategic objectives alone do not make steel
industry mergers and acquisitions successful; other
enabling prerequisites must be achieved during postmerger integration. From the beginning, the new
enterprise has to establish a stable organization
and a joint knowledge base for dealing with the
In addition to creating a well-functioning
organizational network, it is almost as important to
create a newly balanced and harmonized production
network. This “network perspective” on production
yields significant and sustainable top- and bottomline synergies in addition to the more common ones
3
from SG&A (selling, general, and administrative
expenses) and procurement. But despite the
ongoing consolidation activities and increasing joint
production capacities of the merged companies,
little focus has been placed on optimizing the newly
established production networks. Many companies
still view production as the sum of individual plants
and not the product of an integrated production
network, even though a well-optimized production
network offers significant potential for cost reduction
and throughput increases. To react to cyclicality and
remain competitive, steel companies will have to
increase production efficiency by making better use
of their existing or newly created networks’ capacity.
Booz Allen Hamilton has established a structured
and proven four-step approach that generates
improvements in cost as well as throughput. It is
based on a solid data baseline that optimizes the
allocation of production orders within the network. As
shown in Exhibit 2, the results of this approach could
be significant. Based on Booz Allen’s experience,
the application of this methodology can generate
cost advantages of US$20–$40 per ton for up to
20 percent of the combined production volume.
Moreover, the methodology can achieve an increased
throughput between 15 and 20 percent, and this
offers a consolidated steel network the flexibility
to react to changing supply and demand scenarios.
Details of Booz Allen’s four-step approach to network
optimization are illustrated in Exhibit 3, page 4.
Step 1: Scoping the Combined Production Volume
The composition of an optimized production network
configuration should derive from well-grounded
operation status; therefore, describing and scoping
the combined production program is a necessary
first step for understanding site-related constraints
and customer requirements that prevent certain
steel qualities from being produced elsewhere in
the production network. As an example, site-related
constraints can occur in the primary and secondary
metallurgy because of limited capabilities in attaining
different sulfur or silicon contents. These limitations
can reduce the scope of the production volume
Exhibit 2
Benefits of Cost and Output Optimization
Cost Optimization
�
Output Optimization
10%–20% of the production volume in scope can be
optimized (cost difference of US$20-$40/ton)
�
$520
Upper limit
$60
Base case
$50
Lower limit
$40
$30
Cost Difference
(US$/ton)
$20
Cost Advantage (US$/ton)
Cost Advantage (US$/ton)
$70
15%–20% more output can be generated (cost benefit of
US$80-$120/ton)
Upper limit
$270
Base case
$220
Lower limit
$170
Cost Difference
(US$/ton)
$120
20
30
40
Volume in Scope (MTPA)
1.2
1.4
US$25–$60 Million
Source: Booz Allen Hamilton
80
100
120
Throughput Increase (MTPA)
1.6
1.85
2.15
2.45
US$150–$300 Million
4
Exhibit 3
Four Steps for Optimization of Steel Production Networks
2a
Cost Optimization
�
1
�
Description of Production
Volumes
�
�
�
Description of combined
production program
Identification of site-related
constraints
Customer requirements
�
3
�
Output Optimization
�
�
4
Production Volumes
Network Optimization
2b
�
Optimization Scope
Identification of cost drivers
Evaluation of
process-related cost
differences
Identification of steel
grades to be exchanged
Identification of boundary
conditions:
– Quality
– Logistics
Description of product/
asset allocation principles
Evaluation
�
�
�
Financial benefits
Implementation steps
Investments
Identification of production
bottlenecks
Optimization of steel-graderelated slab throughput
Optimization Levers
Optimization Concept
Optimization Benefits
Source: Booz Allen Hamilton
for a network optimization by 30 to 50 percent. In
addition to understanding customer and technical
requirements, it is necessary to generate transparent
and comparable data for chemical analysis; key
quality data; production routing; and casting width,
length, or speed. A common, singular technical
language from the beginning is the prerequisite for
any network optimization.
Step 2: Evaluating the Optimization Levers
Before the most important optimization levers can
be identified, it is essential to verify two things:
the units (from desulfurization to casting) that are
causing bottlenecks and the related root causes
(casting width, casting speed, vacuum capacity, or
any other technical constraints). One way of gaining
transparency into the production process is by
compiling the operating asset effectiveness (OAE) of
the main bottleneck units. The OAE places the most
common and important sources of productivity losses
into three categories: utilization rate, throughput rate,
and quality rate. Exhibit 4 illustrates the OAE
for a continuous casting unit. The OAE analysis
clearly indicates the main levers for a higher and
more cost-efficient steel output. For continuous
casting units in particular, most output losses occur
as a result of a very low throughput rate. Based
on Booz Allen’s experience, poorly planned order
configuration within the network is the key root
cause for such losses. Optimizing slab width by
combining production orders is one of the levers
with a very high impact. Of the overall throughput
improvements, 70 to 80 percent can be generated by
a better aligned forecasting and planning process for
production orders.
Another important lever for an increased throughput
rate is the casting speed. Analysis of steel
production networks indicates there are significant
differences in casting speed among different casting
units for the same steel quality. Optimizing the
5
Exhibit 4
Illustration of the Operating Asset Effectiveness for a Continuous Casting Unit
Operating Asset Effectiveness (OAE)
Utilization
(percent)
8,760
hours
X
Throughput
(percent)
X
8,760
hours–x
hours
x hours
Acceptance
(percent)
=
OAE
(percent)
OAE Analysis: Levers for OAE Losses
tons
Utilization Losses:
Changeover (low sequence rates),
breakdowns, planned and
unplanned maintenance
�
tons
Throughput Losses:
Lower casting speed, no optimized
slab width, poor adaptation of steel
grades to process requirements
�
Quality Losses:
Quality defects and rework,
startup losses
�
Annual
Production
Time
Utilization
Losses
Utilization
Rate
Output at
Maximum
Throughput
Throughput
Losses
Net
Output
Devaluation
and Scrap
Achieved
Output
Source: Booz Allen Hamilton
production order configuration to the most suitable
production plant for the particular steel quality
results in additional benefits. Allocating steel grades
to just one or two specific steel plants creates less
complexity and a more standardized production
program. As a consequence, longer production cycles
can be run; because there are fewer changeover
activities, the utilization rate increases. Booz Allen
has identified four main levers, shown in Exhibit
5, that generate capacity increase within a steel
production network.
In addition to output optimization, cost benchmarking
among plants for specific steel grades offers further
benefits from an aligned production network. A more
standardized and focused production program leads
to a reduced material flow and improved internal
logistics, which lower the overall transportation
and distribution cost. By taking into account the
specific technological strengths and weaknesses
in the primary and secondary metallurgy, still other
cost improvements are realized. Exhibit 6, page 6,
summarizes some of these cost drivers. For example,
while one plant has improved its substitution of scrap
for some steel grades, other plants have advantages
because of their reduced use of additives for other
grades. Exhibit 7, page 6, illustrates Booz Allen’s
cost benchmarking approach. Based on the main
cost driver, steel grades A and B have clear cost
Exhibit 5
Levers to Increase Capacity within Steel Production Networks
1. Optimization of slab
width by combining
production orders
Identification
of production
bottlenecks
Identification
of throughput
improvement
levers
2. Increase of
casting speed
3. Combination of
production orders
to increase
sequence rates
4. Adapting steel grades
to desulfurization,
converter and casting
process requirements
Source: Booz Allen Hamilton
Throughput
improvements
from primary
metallurgy to
slab casting
6
Exhibit 6
Influencing Site-Specific Production Requirement on Cost Drivers
Cost drivers...
...are influenced by production process requirements
�
1. Raw iron supply
�
2. Alloys, scrap,
and additives
Specific
“consumption”
(kilograms/ton)
�
�
3. Auxiliary and
supporting
systems
�
Cost reduction due to captive raw material supply
Optimization of blast furnace marginal cost
Scrap quality adaptation
Reduced use of additives (e.g., for desulphurization or oxygen reduction)
Reduced use of refractory due to longer sequence rates and bundling of
production orders
multiplied by
�
material prices
(US$/kilogram)
4. Quality
�
5. Asset utilization
�
6. Transport and
distribution
�
Reduced devaluation and scrap volumes due to the optimal allocation of
steel grades to production equipment
Improved throughput (tons per hour) due to the optimal allocation of
steel grades to production equipment
Reduced idle time due to standard production programs with fewer
planning requirements
Reduced transportation needs and external distribution costs due to
reduced material flow and improved internal logistics
Source: Booz Allen Hamilton
Exhibit 7
Cost Differences by Shifting Steel Grades
Sum of Cost Benchmarking for All Drivers in US$/ton
Cost Advantage
Steel Plant A
Shift to Plant A
Booz Allen Hamilton Experience
A
Steel
Grade
High-alloyed
steel
�
B
C
Micro-alloyed
steel
Peritectic
steel
D
IF
steel
E
Source: Booz Allen Hamilton
�
�
F
Cost Advantage
Steel Plant B
Mild
steel
Shift to Plant B
G
Average of US$20–$40/ton cost
difference in specific steel grades
(assumption)
Steel grade specific difference
depends on site-specific production
requirements
Cost difference highly affected by
specific scrap and raw iron rates
and their prices (level of backward
integration)
7
advantages in Plant A, while the steel grades E, F,
and G can be produced more cost-efficiently in Plant
B. Assuming that the exchanged production volume
between plants is equal so that production capacity
is not lost, the overall cost benefit is substantial
(see Exhibit 2, page 3) even if some steel grades
(such as grade D) have to be produced at the
nonpreferred plant.
Step 3: Fine-Tuning the New Production Network
As a result of the throughput and cost analysis, a
first set of planning principles can be developed
using the identified bottlenecks most efficiently. Booz
Allen has identified three main restrictions that need
to be analyzed up front, before executing the new
planning principles. First, customer requirements
such as delivery dates or preferred hot or cold strip
mills need to be considered. Second, it is necessary
to understand the impact of an increased OAE for
bottleneck units, including converter or casting lines
on downstream aggregates such as vacuum units or
finishing lines. Finally, transportation and logistics
have to be reviewed to find bottlenecks within the
logistics supply chain from slab production to the
final strip mills.
Step 4: Executing the Concept
To sustain the benefit and continuously improve the
internal production network, certain conditions must
be met (see Exhibit 8). The formulation of planning
principles for each steel grade is essential for
setting up distinct standard operating procedures
for production scheduling. It is important that the
supply chain organization works within this process
and is not captured in a functional ivory tower.
This is key to having a transparent and consistent
forecasting process that determines the effect of
planning and production principles. The continuous
coverage of the OAE for all bottleneck units
within the network is also a necessary task for
operations. Order planning and capacity utilization
have to be aligned and follow the same rules. It
is well known that only what is measured can be
improved; therefore, a consistent key performance
indicator (KPI) system both for planning and
production efficiency and customer satisfaction
Exhibit 8
Prerequisites for Successfully Optimizing a Steel Production Network
Objective
Step
�
�
1. Scoping
�
�
�
2. Evaluating
and
Fine-Tuning
�
�
Data transparency
Comparable operational definitions
Standardization
Identification of interchangeable
production volume
Evaluate main levers for output
optimization
Evaluate main cost driver
Prepare planning principles for an
optimized production network
Prerequisites
�
�
�
�
�
�
�
�
�
3. Executing
Generate output and cost benefits
Sustain and continuously improve
the internal production network
Be responsive to changing market
conditions or customer
requirements
�
�
�
�
�
�
Source: Booz Allen Hamilton
Development of standard operational definitions for production and quality
parameters
Clear understanding of technical capabilities of individual plants with a special
focus on primary and secondary metallurgy
Development of planning principles for every steel grade based on the optimized
production network
Development production principles (standard slab production versus flexible slab
production; pull versus push production)
Development of a supporting stock concept (make to order versus make to stock)
Continuous OAE coverage for all bottleneck units within the production network
Supply chain organized by process, not function, following common supply chain
principles
Development of an IT-based planning system following the introduced planning
principles
Transparent and consistent forecasting process
Cross-functional training classes for all involved employees
Consistent KPI system for planning, production efficiency, and customer
satisfaction
Cross-functional approach of an incentive bonus system
8
is essential for driving continuous improvement
activities within the production network. All of
these things need to be implemented within the
entire network, bringing together cross-functional
and cross-regional employees. Thus, a consistent
company-wide approach to training and coaching
key personnel is necessary to tap the full potential.
Operational network optimization meets cultural
network improvements—this makes any production
network optimization a cornerstone of successful
postmerger integration for newly established
steel conglomerates.
Conclusion
Optimization of combined steel production networks
is strategic by nature because it differentiates a
company from its competition, delivers revenues as
well as cost benefits, and addresses supply chain
challenges. Optimization guarantees a high return on
investment: The initial analysis is fairly quick—four
to six months—and payback time is often less than
one year. The benefits of a consistent and robust
network optimization are long lasting, and because
the initiative has to be cross-functional, the entire
organization is included. For recent mergers in
particular, the proven Booz Allen approach ensures
a global rather than local optimization that uncovers
hidden synergies and eliminates duplications.
A comprehensive network optimization is still cuttingedge thinking for most steel companies, and to
date there is limited evidence for fully integrated
and aligned steel production networks. Booz Allen’s
innovative and comprehensive approach provides
clear guidance on how to establish an optimized
network, which could lead to a sustained competitive
advantage in the current steel industry environment.
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