How to Escape the Variant Jungle Schuh & Company Software Support for Successful

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How to Escape
the Variant Jungle
Software Support for Successful
Variant Management
Schuh & Company
Complexity Management
Complexity Manager –
The Ultimate Software Tool
for Managing Variants
Hands-on planning and control of variant
diversity
A large number of cases consistently show how cumbersome it is to handle multiple product variants. A
high and increasing amount of variants affects many
processes, leads to ever increasing costs and incredible variety and complexity of data. Creating an urgently needed transparency over the existing product
variety appears to be impossible. Where to start? Is
there a leitmotif which may provide orientation and
may lead to a solution?
Re-assessing and streamlining the existing product variety constitutes a major challenge, which
often intimidates employees. This is the situation where doubters and preservers argue for
the “status quo” as there is no time available to
conduct the complicated task of reducing the
existing product variety. Moreover, these people
advise against the reduction of product variety
due to fear of lost sales stemming from customer
disappointment.
These issues are highly dangerous in the medium
and long run. Companies, that are (still) performing
well, need to be proactive and look ahead. Action,
not reaction is the name of the game. Profit needs to
be prioritized over revenue. The ‘leave it as it is’ attitude does not only postpone the problem, it worsens
it. Instead of decreasing the problem, the problem
grows in size.
Finding the starting point is the most difficult part.
This is exactly where appropriate tools and methods help to alleviate the anxiety for streamlining the
product variant diversity and to find the leitmotif
leading to a reasonable level of product variety.
The two-perspectives approach to variant
management
In at least 100 projects conducted throughout more
than 20 years, two perspectives have been proven to
be helpful in analyzing product variety: external and
internal complexity.
Figure 1: External and internal perspective on complexity due to product variety
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Pictures taken from an advertising
folder of Mann+Hummel
Figure 2: Product example “Oil Filter”
The market perspective (external complexity)
Describing the product based on customer-relevant
options and features provides an overview of the
number of variants that need to be taken into consideration for the product. Here, the central questions are:
ƒƒ What product configuration does
the market require?
ƒƒ What does the customer want?
ƒƒ What is unnecessary?
This perspective ensures that the product range is
not restricted as far as the current market requirements are concerned. Instead, extremely low running
variants which do not contribute to operating profit
are simply removed. The basic principle is “As few
variants as possible and as many as necessary.”
The external complexity can be quickly made
transparent by using the Complexity Manager
Module F (Feature Tree).
The definition of variant-related features and their
options, as well as rules on combination restrictions
due to technical or market restrictions, are quickly
incorporated. These features can be automatically
generated by importing data directly into the complexity manager. The restrictions can be either imported or they can be defined by “point and click”
in the software.
Figure 2 shows the parameter values of an oil filter
that are important from the point of view of the customer, meaning, these parameter values are recognized by the customer.
Without combination restrictions of individual feature characteristics, this small table alone would lead
to 324 possible oil filter variants. However, due to
technical reasons and the realization that certain
combinations are not required by the market, specific rules can be defined regarding combination
restrictions or mandatory combinations. Rules are
defined using logical AND (▪) or OR (+) chains.
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Figure 3: Overview of combination restrictions and mandatory combinations
When analyzing existing product families often
times data is already existing which can be directly imported or which needs little adaptation to be
transformed into an importable format.
A popular way of displaying data is the matrix form.
Here combination restrictions are labeled by an “x”
(see red fields in figure 4).
This matrix display can be easily imported by the
Complexity Manager and the shown combination
restrictions are automatically created.
Based on these restrictions, which can be either
manually created or imported via the matrix form,
the number of variants in this example is reduced to
26 oil filter variants. The required data for visualization is entered when the rules are defined.
Figure 4: Overview of combination restrictions shown as matrix form
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Figure 5: Visualization of the external complexity (market) in the feature tree
On the basis of this data, the Complexity Manager
provides numerous possibilities of simulation, for
instance:
ƒƒ Changing the number of variants by partial replacement, addition or removal of features
ƒƒ Creation of different scenarios (“What if…”),
simulation and their evaluation
ƒƒ Calculating the probability of occurrence of
certain variants due to sales forecasts (assists in
the early recognition of possible low- or nonrunning variants)
ƒƒ ABC analysis based on actual sales figures (separates the ‘wheat from the chaff ’)
The Company View (internal complexity)
After evaluating the market view on the product
including questioning every variant, a range of products has been created which needs to be actively
offered to the market. Taking the internal perspective is supported by the Complexity Manager
Module V (Variant Tree). The main question which
has to be asked about the internal view is: How to
get the demanded product variety flowing through
the factory as efficiently as possible? The guideline
here is “As few parts as possible and as many as
necessary.”
As the following figure shows, required parts variants can be assigned in the system on a step-by-step
basis and in accordance with the assembly sequence
on the production line.
ƒƒ Comparisons of cost and price
Figure 6: Variant tree visualization using the assignment of parts to product variants
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Of course, corresponding data can also be imported via the interface in Module V. The overview in
the completed variant tree (see example in figure below) provides information about parts that are the
biggest variant drivers. It highlights the part of the
production line were the actual variety occurs and
shows which assemblies and parts are suited to be
pre-assembled and which should really be removed
from stock (for variant optimization purposes). Furthermore, in case new products are planned, early
warning is given of ‘imminent’ parts variants and
quantities, which proved to be particularly useful.
This also supports material requirements planning.
To go one step further, it is also possible to derive
information in order to plan needed manufacturing
resources, for example, the necessary investment in
tooling can be determined. A clear and early oversight over the amount of parts variants leads to
the necessary amount of tool variants which have
to be procured by the firm or by a supplier. Parts
Figure 7: Variant tree of an oil filter
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variants, which differ significantly with regard to
their design, will probably demand different tools
resulting in high investment. Variant planning,
enabled and conducted by the Complexity Manager, supports companies in evaluating the need of
tool variants already during product planning and,
if necessary, to search for alternatives to avoid high
investment costs.
Results of a separately conducted complexity cost
analysis can be considered in the Module V. According to the cost-by-cause principle, higher costs are
allocated to rare and exotic variants while fewer
costs are allocated to standard variants, since the
latter easily pass through the value chain. Creating
this cost transparancy largely supports the decision
wether to introduse a new variant or not.
The following issues, among others, may be simulated:
ƒƒ Implications of substituting variants with standard products along the assembly sequence.
ƒƒ Potential restructuring of the assembly sequence
in order to optimally place the variant creation
point.
ƒƒ Decision support to what degree a product shall
be assembled in one production site when several alternative production sites exist.
ƒƒ Implications due to the elimination of single variants and options from the feature tree on part
and product variety in the variant tree.
ƒƒ Prediction which additional part variant demands
a new tool variant and which part variant can be
produced with an existing tool.
Summary
During the numerous applications, when Schuh
& Company applied the Complexity Manager, it
showed that transparency over the variance of an
existing product family as well as the variety of a
planned product program can be achieved with
greatly reduced resource input.
structure has already been made, these expenditures
cannot be regained by an ex post elimination of existing “unnecessary” variants. However, there is cost
savings in every product manufactured in the future.
It has to be made very clear that the implementation of a consistent variant management is greatly
beneficial without any exception. However, as is
well known, taking the first step is the most difficult
part. On the one hand, the existing “variant jungle”
appears too complex, on the other hand, there is
hesitation to reduce the product offering and in turn
fear to lose market share.
Since the problem of variant diversity will not be
solved without effort, quick action is urgently
needed. By applying appropriate methods and tools
like the Complexity Manager Module F/V, one will
easily come to realize that first partial successes will
show quickly and that the light at the end of the
“variant jungle” will emerge. In the end, variant management is even fun.
These are our experiences gathered together with
our customers in more than 20 years of successfully
applying the Complexity Manager.
Moreover, it showed that avoiding variety in the
first place is far more important than reducing existing variety, since the cost reduction potential is far
higher in case of the former. When integrated early
in the planning phase, variant management helps
firms to prevent unnecessary investments in infrastructure, tooling, and machinery.
By integration of sales forecasts, low-selling and
non-selling variants can be identified, avoiding
unneeded development costs, investments in tools,
machinery and infrastructure. In this way higher
effects on cost savings can be achieved.
The effects of minimizing an existing product program are far smaller compared to avoiding “unnecessary” variants in the first place. Nevertheless, the
effects are not insignificant either. However, since
the investment in tooling, machinery and and infra-
Figure 8: Effects of avoidance of variants
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Module PM: The Meaningful
Complement to Analyze Data
From the Modules F and V
Module PM provides further opportunities to analyze data, which is created and processed in the
Modules F and V. The direct connection to MS
Excel enables an automated creation of diagrams
for illustration purposes of the results.
An essential component of Module PM is the integration and consideration of planned life spans / life
cycles of single variants. Which variant is valid when?
Which variants are going to be created next year?
Which variant will be taken out of the portfolio soon?
These are essential questions which can be answered
by a precise and chronological variant planning.
Module PM offers, for example, the possibility to
display the variant diversity that will be valid over
the next two years. The feature tree graphic shows
life cycles of the single variants and displays them
clearly arranged in a GANTT chart (see figure 9).
Figure 9: Feature Tree and overview of variants’ life cycle
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Figure 10: Validity of options during life cycle
In case of already existing data regarding the validity of single variants, the data can be displayed
and clearly arranged. The validity of single variants
consequently affects the validity of single product
features and product options. Which option has to
be provided when? Which option will be phased out
when? Module PM derives this information from
life spans of single variants and answers these
types of questions in an extended option statistic
(see figure 10):
What is true for life spans of variants is also valid for their bills of material. In the list of parts
and components, Module PM calculates the
life spans for needed parts and components
based on life spans of single variants. This is shown
in figure 11:
Figure 11: Validity of parts during life cycle
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Figure 12: Example of line charts for illustrating sales volumes (past and forecast)
Moreover, this figure clearly shows that information regarding the quantity of necessary parts can
be automatically generated from the sales figures of
the variants. Dependant on which figure is entered
(annual sales figures, or sales figures per life cycle),
Module PM calculates the respective demands for
parts and components.
The variants which have been sold up to now provide concrete sales figures in the respective periods. However, how can future sales or sales forecasts be considered? How will the sales figures of
a single variant develop in the future based on its
previous sales figures? Which framework conditions can be expected? Module PM provides
an answer by offering the possibility to include forecasts in the extended option statistic. For
variants where no previous values exist, Module
F/V provides the possibility to enter probabilities. In the case that previous sales figures exist
for a variant, estimation formulas can be entered
in Module PM that calculate future forecasts. By
directly exporting this data into Excel, the results
can be displayed in a chart. This is illustrated with
the following example of the length and the surface
of the filter (in this example forecasts values are
used from 2012, see figure 12):
Moreover, Module PM provides the opportunity to
filter variants, for example based on sales figures/
quantities or life spans/validities. After entering the
respective filter criteria, all changes adapted accordingly.
Figure 13: Defining the period to check for valid variants
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Figure 14: Extract of Feature Tree: Valid variants in defined period
The variants can be filtered according to date, for example, April 2015 until September 2017. The Module PM directly displays the respective feature tree
for the valid variants in this period, which in turn,
affect part variants.
Summary
Module F/V contributes significantly to differentiating
necessary from less necessary variant diversity. Furthermore, it answers the following questions: Which
variants are really demanded by the market? Which
part variety is necessary to provide the demanded
variants? How high are the expected necessary
investments in tooling and equipment?
Once the necessary level of variant diversity is
defined, Module PM offers the feature which displays at what point single variants have to be scheduled. By providing the timeline and life spans
of single variants it is now possible to plan specific periods from a variant perspective and analyze sales quantity during that period. Firms are
supported to focus on the important key issues
especially with regard to ramping-up and singling out
products. Receiving information when specific variants with the respective components, sales quantities, and tools are needed, significantly releases the
burden of product complexity and helps to control it. Product-specific variant planning based on
Module F/V is now complemented with a time specific variant based on Module PM.
Contact
Product Variety Management
Visit our website for more
information:
www.schuh-group.com
Joerg Starkmann
CEO, Schuh Complexity Management Inc.
Phone: +1 770 614 9384
[email protected]
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Company
Schuh & Company focuses on providing solutions and methods
for managing the ever increasing complexity of today‘s
enterprises, products and processes. With this approach,
the company was established as an implementationoriented problem solver in the industry. Today the company
consists of about 40 experts committed to ensure your
company’s success through their work as strategy and
organizational consultants, as well as management coaches.
Schuh & Company is headquartered in Aachen, Germany,
with subsidiaries in St. Gallen, Switzerland (since 1991), and
Atlanta, GA, USA (since 1997).
Offices
Schuh Complexity Management, Inc.
3625 Greenside Court
Dacula, GA 30019, USA
Phone: +1 770 614 9384
Fax:
+1 678 730 2728
E-Mail: [email protected]
Schuh & Co. GmbH
Campus-Boulevard 57
52074 Aachen, Germany
Phone: +49 241 51031 0
Fax:
+49 241 51031 100
E-Mail: [email protected]
Schuh & Co. Komplexitätsmanagement AG
Langgasse 13
9008 St. Gallen, Switzerland
Phone: +41 71 243 60 00
Fax:
+41 71 243 60 01
E-Mail: [email protected]
www.schuh-group.com