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Mistakes to Avoid in
Distribution Center Planning
From Our Free "Best
Practices" Booklet
Operations
Associates has executed nearly one-thousand projects. We
have documented key mistakes that are typically made by
less experienced project teams. Our goal is to share
this critical information with you so you can avoid
these mistakes. We hope this helps your project team
successfully plan and design your DC improvement
project.
We would
like to share "Mistake #5" with you. Please contact us
for a free pamphlet to view all 10 of the lessons. In
subsequent newsletters, we will focus on each of the 10
Mistakes to Avoid in Distribution Center Planning.
Mistake
#5
Planning Without Data
Just like
form follows function in architecture, design follows
data in Distribution Center planning. Too often, teams
assume or just don’t know critical information prior to
planning. The facility then becomes a larger version of
the current operation. This is fine if you already are
best in class. Most companies squander a huge
improvement opportunity. Proper data analyses uncover
hidden opportunities to make operational changes that
will impact financial performance for many years.
To begin
planning, the team should collect data in electronic
format, analyze the data, and document the results. The
team should seek consensus on the design criteria
developed from the data analysis. The data includes:
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What’s
moving? The inventory profile for the items stocked
in the facility. It is not sufficient to say design
for 10,000 SKUs. The design team needs the
characteristics of families of SKUs that are handled
alike. This includes size, weight, handling (e. g.
fragile, temperature controlled, etc.), and
throughput. The number of SKUs drive selectivity,
slotting profiles, and value- added operational
processes. The higher the number and diversity of
SKUs, the greater the overall complexity of the
facility.
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How
fast does it move? The average and peak daily
throughput of the facility by major SKU family. An
SKU family can be organized by physical size (for
example tote size, pallet size, and non-
conveyable), distribution mode (for example, secured
item, vendor redistributed, normal pick) or
commodity type (e. g. chocolate, cigarettes, other
ambient, cold, frozen). The data should include at
least 12 months of daily demand data at the order
line level to ensure the team understands
seasonality and worst- case throughput requirements.
The throughput of the facility is the key driver to
selection of materials handling equipment and degree
of automation.
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How
fast will it grow? The growth of both the SKU
profile and the throughput of the facility. Sales
and marketing projections, along with historical
data, should be reviewed prior to design. It is
often helpful to have multiple growth scenarios to
consider a range of solutions. Companies typically
design facilities to accommodate five years growth,
and some design for 10 years. Growth assumptions
over this period and longer (for future
expandability) can have a huge impact. For example,
a modest 5% growth rate requires 27.6% more space,
pick slots, and/ or pick sorting capacity in five
years.
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Who
does it go to and how often? The facility demand
profile. The variety and the specific handling
requirements of the customer base are a critical
planning component. If a product must be custom
packaged or custom palletized or identified a
certain way for each customer, then the amount of
space and handling after picking is dramatically
increased. In addition, more frequent shipments will
drive down inventory levels but increase sorting and
dock space. The mix of full truck, LTL, and
parcel handling is also a major planning
consideration that drives wave planning and order
batching. For companies with same day shipping
requirements, the order cut- off time and the travel
distance to the customer will drive wave release
strategies and pick sequence.
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How
are we doing? The current performance metrics.
Baseline measurements of productivity, service,
accuracy, etc. are useful to compare performance
against industry best practices. This information
allows the planning team to review alternative
designs against the company’s own metrics for
evaluation. These metrics may also reveal the
quality of management and supervision, and the
ability to adapt and change as the business grows.
Highly adaptable and flexible companies are better
suited to adopt leading- edge technology.
For
example, one of our clients told us at the start of the
project that 99% of picking was case pick. In fact,
after looking at the numbers, a significant percentage
was pallet pick. This totally changed the materials
handling system concept.
Your data
is based on both facts and assumptions. It will be
exactly wrong. However, if we can be assured through
analysis that it is approximately right and get group
consensus, we can make progress into planning with
minimal risk of delay and rework.
Launch Planning only with solid data to uncover
improvement
opportunities.
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booklet:
Ten Mistakes To
Avoid In
Distribution Center Planning
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Management Seminar
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Mike
has over 30 years of experience in physical distribution
operations and engineering design with the primary focus
in warehouse/light duty manufacturing, supported with
statistical analysis, computer controlled processes,
writing equipment specifications, project management and
commissioning.
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Inventory
Rationalization Identifies Significant Inventory
Reduction Potential
by: David C Edwards
CPIM
Inventory
rationalization is a powerful process used to focus
inventory management activity on the segments of
inventory with the highest potential for reduction. Ever
increasing pressure from customers to provide higher
levels of service can influence distributors to lose
focus on managing their capital investment in inventory.
When dealing with thousands of SKUs from hundreds of
vendors serving thousands of customers, day to day
inventory management processes become overwhelmed by the
detail of the trees and quickly lose sight of the
forest. Inventory rationalization is the most effective
process available to bring focus back to managing
inventory investment.
The
devil is in the details. The inventory held on each
individual SKU is driven by a multitude of factors.
Customer demand variability, service level requirements,
delivery lead times, minimum purchase quantities, vendor
capacity constraints, shipping restrictions (full
container, etc.), vendor delivery performance
variability, and contractual obligations all influence
the decision making process of when to purchase
inventory. Traditional MRP systems analyze these details
and calculate the required purchase replenishment
quantity and delivery date. What they don't do is
identify which detailed factors can have the most
significant impact on reducing inventory investment
while maintaining required service levels.
Inventory
rationalization starts with a detailed analysis to
calculate the optimum average inventory investment for
each individual SKU based on the existing detailed
factors that drive the purchasing decision. This forms a
base-line for each individual SKU that can then be
compared to existing inventory to identify SKUs that are
out of control based on current management policy
details.
Methods
that assume every SKU should be 'turning' at some
arbitrary target based on a traditional ABC
classification, do not recognize the unique factors that
drive each SKUs turn rate, and can lead to irrational
inventory management expectations and decisions. Once an
SKUs true base-line turn rate is determined,
stratification or segmentation of the existing inventory
can be used to target specific product classes, product
lines, customers, or vendors where inventory reduction
potential is greatest.
Stratification allows for the rapid elimination of
inventory segments that cannot be reduced by improving
an SKUs replenishment parameters. Obsolete inventory can
be segmented out for extraordinary actions such as
disposal or promotion. Inventory that is driven by
contractual obligations can be isolated with a complete
understanding of the additional inventory investment
driven by the contract commitments.
Active
inventory that significantly exceeds optimal inventory
balances can be identified for deeper root cause
analysis and corrective action. Then all active
inventory can be segmented by class, type, customer
and/or vendor to identify groups of SKUs with the
largest potential inventory reduction by improving the
detailed parameters that drive purchasing replenishment
(Variability, Lead Time, Minimum Buys, etc.)
Case
Example
A recent
engagement with a distributor of outsourced products
serves as a clear example of the power of rationalizing
inventory. Over the past few years, the company had
moved away from domestic manufacturing of it's own
product to offshore outsourcing of most of it's SKUs.
What had normally been a 10 Million inventory investment
had ballooned to over 14 Million on the same sales
dollars.
Optimal
inventory levels were calculated for each SKU based on
their current replenishment purchasing parameters and
were compared to their existing inventory balances. The
inventory was then segmented into 5 major categories of
obsolete, inactive, committed (contractual obligations),
slow moving, and active.
This
segmentation placed clear focus on managing the slow
moving and active inventory segments. Root cause
analysis of these excess inventories revealed a
fundamental flaw in the daily management of inventory
levels. Customer demands to provide high service levels
had shifted the focus of the materials management and
purchasing group solely to ensuring on-time delivery of
products.
As
individual SKU forecast accuracy varied greatly from
month to month, the purchasing group focused on
expediting delivery of product for SKUs that were over
performing against forecast, but did little to slow the
inflow of product for SKUs that were underperforming
forecast. This admirable focus on on-time delivery to
their customers had not been balanced by an active
process of managing the delivery of underperforming
SKUs.
Simple
tools were developed to calculate how much product was
on order in excess of requirements. While the MRP system
calculated this excess and produced purchase order
change requirements, the value of these excesses were
not extended into meaningful totals that would drive the
recognition to take action.
After
determining that orders in process were 3.1M dollars
more than required during lead time, action plans were
put in place to slow the inflow of existing orders to
match needs. The timeline to achieve
a 4.1M reduction was calculated to be 9 months
without changing current purchase minimums.
In
addition to the inventory reduction potential from
actively monitoring and slowing the inflow of
underperforming product, analysis of changes to current
purchase minimums on high volume items indicate an
additional reduction potential of 1.0M.
The
results have been encouraging,
over the course of 3 months, active and excess
inventories have been reduced
by more than 1.8M dollars. The company is now on track
to achieve these results with
a clear focus on the big picture as provided by a
detailed inventory rationalization process.
"Our client has been pleased with the results so far and
believes the inventory reduction targets can be achieved
with little impact on service levels." - Charley
McNealy, Partner - BDO Seidman, LLP
For more information
contact:
Alan Nager, Principal
AlanNager@oallp.com
Mike Rigg, Principal
MikeRigg@oallp.com
Christi Suchyna, Marketing Manager
ChristiSuchyna@oallp.com
800-860-4902 |
