Warehouse management for improved
order picking performance:
An application case study from the
wood industry
G.P. Broulias, E.C. Marcoulaki*
G.P. Chondrocoukis and L.G. Laios
Department of Industrial Management & Technology
University of Piraeus, Greece
*emarc@unipi.gr
“Warehouse management for improved order picking performance”, Zakynthos 2005
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Warehouse operation
• Warehouses are important links between the production
sites and the customers
• Need to shorten the throughput times in the supply chain
• Need for faster response to customer demand
–
–
–
–
Fluctuations in customer demand
Increase in the frequency of orders
Decrease in the size of orders
Increase in product proliferation
• Trade-offs between warehouse costs and delivery
performance
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Warehousing performance
• Once a certain order has been placed, the warehousing
performance depends on
– the time required,
– the precision achieved,
– the efficiency achieved in satisfying the customer demand
• High performance provides a competitive advantage, so,
many companies invest on the warehouse operation to
improve their position in the market.
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Order picking
• one of the most significant activities in a warehouse
• Physical procedure of retrieving stock-keeping units
(SKUs) from specified storage locations, to satisfy the
customer demands in the fastest and cheapest way
• Order Picking (OP) activities involve:
–
–
–
–
taking the customer order
searching for the requested SKUs
retrieving the requested SKUs
transporting the requested SKUs
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Order picking
• The most labor intensive all warehouse processes
– typically done manually
• The retrieval cost exceeds by far the storage cost, and
contributes by ~60% in the total warehousing economics
• The most time-consuming procedure in the warehouse.
– Travel time may be up to 50% of the total OP time
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Factors affecting the efficiency of OP
•
•
•
•
•
•
•
product demand
warehouse layout
location of the SKUs
picking methods
routing methods
experience of the employees
extent of automation.
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Review of policies related to OP
• decisions usually concern policies for
– the assignment of the customer orders to the pickers,
– the routing of the pickers in the warehouse, and
– the storage schemes for the products in the warehouse.
• the usual practice is to consider them separately
• current research shifts towards the co-evaluation of all
three policy types
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1. Order assignment policies
• Strict-order picking
– assigns a picker only one order during a picking tour
• Batch picking
– assigns a picker more than one order/tour (order list).
• Zone picking
– assigns a picker to a designated picking zone, where the
picker is responsible for only those SKUs that are in his/her
zone of the warehouse.
• Sequential zone picking
• Batch zone picking
• Wave picking
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2. Routing policies
• Propose route for a picking tour and the picking sequence of
the items on the pick list
• Use of decision-making technologies, e.g.
– mathematical programming tools (may generate confusing
routes, and difficult to implement)
– heuristic routing methods (good but not optimal routes)
• In practice, many warehouses use the traversal policy
– the picker must pass through the entire aisle and in order to
collect the items
• Interaction of warehouse shape and storage policy
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3. Storage policies
• Storage policies remain the least investigated among the
three policy categories
• random storage policy
–
–
–
–
Extensively used and by far the simplest option
Requires less space compared to more sophisticated options
Balanced utilization of the warehouse
Good for few codes – needs WMS
• structured-storage schemes
– Class-based policies
– Volume-based policies (e.g. within-aisle, across aisle)
– Demand-based policies (Pareto principle)
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Demand–based storage
• Today the focus is on faster delivery of small more frequent
orders of inventory at a lower total cost.
– This often precludes the use of full pallet picking in
warehouses, and leads to many broken-lots.
• Pareto principle for world economics
– 80% of the wealth  20% of the population
• For warehouse management the principle is modified to:
– 80% of the demand  20% of the products
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This work
•
Systematic and practical methodology for applying
improvements in a warehouse system.
•
The study is divided into different stages involving:
–
–
–
Data collection
Analysis and implementation of improvement tasks
System simulation and optimization
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Case study
• Case study is conducted in a timber goods production &
trading company warehouse
• The main objective is to reduce the overall OP time that is
quite high due to the lack of proper management and the
nature of the stored products.
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Warehouse description
• The company has 6 warehouses for the finished products
• The panel warehouse has over 6000 codes of stored
products, distributed into 4 individual sections.
– panels are 80% of the total product sales of the company
– panel size is usually 3.66×1.83m, and thickness 6-25cm.
• The present study considers one of these sections, where
– the number of codes is around 1000
– the part has 12 series of piles, 7 meters high and the
products are stored in up to 4 depths of pile levels
“Warehouse management for improved order picking performance”, Zakynthos 2005
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Initial layout of the panel warehouse section
12 series of piles
Main aisle
4 depths of pile levels
“Warehouse management for improved order picking performance”, Zakynthos 2005
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Initial layout of the panel warehouse section
12 series of piles
Main aisle
4 depths of pile levels
“Warehouse management for improved order picking performance”, Zakynthos 2005
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Initial layout of the panel warehouse section
12 series of piles
Main aisle
4 depths of pile levels
“Warehouse management for improved order picking performance”, Zakynthos 2005
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Initial layout of the panel warehouse section
12 series of piles
Main aisle
4 depths of pile levels
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Stages
• 1st series of time measurements
– target the improvement that may be accomplished.
• Suggest, implement and study alternative storage, picking
and routing schemes
– Based on observed situation and past know-how
• 2nd series of time measurements
– investigate the achieved benefits from the transition from a
totally disorderly situation to an organized and controlled
warehouse environment
• Simulate and decide on alternative warehousing policies,
using the time data collected above.
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Stage 1
Initial situation
• The warehouse suffered from many problems that mainly
affected the search and retrieval times
– Order assignment followed the strict-order policy.
– No routing policy - the choice of an efficient route depended
on the experience of the picker.
– Random storage policy. The products were grouped in
section parts according to the type of their surface.
• Tracing a product relied on the experience of the
warehouse managers and the memory of the pickers.
• Warehouse management depended on the experience of
the personnel.
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Time measurements
• The picking procedure is divided into four phases, and the
time measurements concern the:
–
–
–
–
travel time required for the picker to reach the pick point,
search time required for the products to be found,
retrieval time required for the products to be retrieved,
return time required for the picker to transport the products
to the order point.
• Each time measurement considered 15 order plans.
Number of orders ranged from 5 to 17 per plan.
– representative and included a large number of products.
• Times are presented in minutes per cubic meter.
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Sample of the order picking form
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Stage 1
Results of the 1ST measurement series
Phases
Travel time
Search time
Retrieval time
Return time
Travel &
return times
Total
1ST measurement
before modifications
t1 (minutes)
% total
0.51
9.0
2.05
36.0
2.50
43.9
0.63
11.1
1.14
20.0
5.69
100.
Times are presented in minutes per cubic meter.
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Stage 2
Proposed modifications
•
•
•
•
Introduce a WMS
Change order assignment policy from strict to zone picking
Apply optimal routing policies
To reduce the retrieval time
– reduce the storage depths from 4 to 2
– trade off between the time needed to access the products and
the cost of extending the warehouse area
• Relocate fast moving products, to reduce the retrieval time
for small orders
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Stage 2
Implemented modifications
• Installation of a simple WMS and change in the product
locations (ABC analysis).
• Storage mode changed to demand-based, hence the fast
moving products were placed closer to the section
entrance to reduce the travel and return times.
• Two piles were allocated on each side section, to place
broken lots of <20 SKUs
• Reluctance to apply any modification involving the use of
more space, i.e.
– reduction of storage depths levels
– adoption of zone order assignment policy.
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Final layout of the panel warehouse section
12 series of piles
Main aisle
4 depths of pile levels
Piles containing only broken lots
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Stage 3
Results of the 2ND measurement series
1ST measurement
2ST measurement
before modifications
after modifications
Phases t1 (minutes) % total t2 (minutes) % total
Travel
0.51
9.0
0.33
11.5
Search
2.05
36.0
0.37
12.9
Retrieval
2.50
43.9
1.73
60.5
Return
0.63
11.1
0.43
15.0
Travel &
1.14
20.0
0.76
26.6
return
Total
5.69
100.
2.86
100.
Relative time
reduction
(t1 -t2 ) / t1 %
35.3
82.0
30.8
31.7
33.3
49.7
Times are presented in minutes per cubic meter.
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Warehouse simulation
• Simulate the order picking activities to find conditions that
optimize the system performance
– screening of different storage scenarios
– study the trade-offs and suggest optimal alternatives
• Stochastic simulation in the form of a Monte Carlo process
• Performance measure is the total picking time.
– Other objectives e.g. cost or deliverability
considered if relevant data are available.
can also be
• The simulated process is based on available picking data
collected during the normal operation of the warehouse.
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Problem description
• The picking time can be reduced by allocating M of the
front area piles to items of high demand or leftovers
• The simulation results should assist the estimation of
– the optimal number of Broken Lot Piles (BLPs)
– the optimal maximum number of SKUs in the broken
lots moved to the BLP.
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Problem description
N piles
M front piles
for broken lots
….
….
K pile levels
….
….
….
….
….
….
….
….
….
….
Main aisle (clarks)
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System representation – definitions
• given frequency of product code demands, FPC = [FPCP]
• For every product code (P) stored in the warehouse, given are
–
–
–
–
lot size, LP (vector of lot sizes, L = [LP])
thickness, WP (vector of thicknesses, W = [WP])
set of demand quantities, DPQP and quantity frequencies, FPQP.
set of picking times, DPTP, and their picking times frequencies,
FPTP. Time depends on the storage depth J of P, J{1,2,…,K}
• For the BLP’s, given are:
–
–
–
–
the number of piles allocated for broken lots, M, MN,
the maximum pile height allowed in the warehouse, Hmax, and
the maximum allowable broken lot size, Smax.
the set of BLP times, DBT, and the BLP time frequencies FBT.
“Warehouse management for improved order picking performance”, Zakynthos 2005
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System representation – definitions
• given frequency of product code demands, FPC = [FPCP]
• For every product code (P) stored in the warehouse, given are
–
–
–
–
lot size, LP (vector of lot sizes, L = [LP])
thickness, WP (vector of thicknesses, W = [WP])
set of demand quantities, DPQP and quantity frequencies, FPQP.
set of picking times, DPTP, and their picking times frequencies,
FPTP. Time depends on the storage depth J of P, J{1,2,…,K}
• For the BLP’s, given are:
–
–
–
–
the number of piles allocated for broken lots, M, MN,
the maximum pile height allowed in the warehouse, Hmax, and
the maximum allowable broken lot size, Smax.
the set of BLP times, DBT, and the BLP time frequencies FBT.
“Warehouse management for improved order picking performance”, Zakynthos 2005
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Simulation data
• Based on real data collected during the normal operation of the
warehouse, for M=2 and Smax=20 SKUs.
– different picking orders in terms of
• The quantity and product code of an ordered item
• The time required for traveling, finding and retrieving the item.
• The data are used to estimate occurrence probabilities for
different states of the studied OP system,
– adjusted to allow the screening of generic operation schemes
– simulation of different scenarios, other than the normal / original
operation of the system.
“Warehouse management for improved order picking performance”, Zakynthos 2005
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Example of simulation data for P=13
L13 = 50 pieces, W13 = 16 mm, FPC13 = 0.0784
FPQ13 = [0.125, 0.625, 0.000, 0.125, 0.125, 0.000, 0.000, 0.000, 0.000, 0.000]
DPQ13 = [10, 20, 30, 40, 50, 60, 90, 100, 120, 180], in pieces
FPT13 = [0.125, 0.750, 0.125, 0.000]
DPT13 = [1.50, 3.20, 5.15, 10.5], in minutes
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Monte Carlo process
• At any simulation time, T:
– a dynamic vector of P quantities stored in BLP, VBQT = [BQT,P].
• the algorithm selects stochastically:
– a P, according to the FPC frequencies.
– a P quantity PQT,P  DPQP, according to FPQP
– a picking time instance PTT,P, depending on PQT,P
• simulation constraints: satisfy Hmaxand Smax
• New simulation time T = T + PTT,P
• New quantities of P in the BLPs:
– if PQT,P  BQT,P, then BQT,P = BQT,P – PQT,P
– if PQT,P > BQT,P and , then BQT,P = BQT,P + RQT
– if PQT,P > BQT,P and , then BQT,P = BQT,P
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• The simulation terminates once a user-specified number of iterations
(orders) has been completed.
• This number is sufficiently high to ensure that the simulation results
depend on the given distribution, and not the distribution instances (i.e.
the products, and their quantities and picking times) selected
stochastically during the simulation.
Deviations obtained for 20 runs and 0 piles
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System initialization
• The simulation starts with an initial vector of product quantities
stored in the BLPs
• Different initialization options can be
– random initial state
– empty front piles at the beginning of the simulations, i.e. VBQ0= 0
– to place the broken lots on the M piles proportionally based on the
demand frequency and quantity for each product code.
• The last option provides a more rational instance of the system
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Results table [1]
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Results table [2]
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Simulation results
Overal OP time (days)
No piles
10
160
20
30
40
130
50
60
100
70
80
70
0
2
4
6
8
10
12
Number of broken lot piles
“Warehouse management for improved order picking performance”, Zakynthos 2005
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Simulation results
Overal OP time (days)
No piles
10
160
20
30
40
130
50
now
60
100
70
80
70
0
2
4
6
8
10
12
Number of broken lot piles
“Warehouse management for improved order picking performance”, Zakynthos 2005
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Simulation results
Overal OP time (days)
No piles
10
160
20
30
40
130
50
now
60
100
70
80
70
0
proposed
2
4
6
8
10
12
Number of broken lot piles
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Proposed modifications
• The company is currently using 2 BLPs and up to 20 pieces in
the broken lots. This scenario has a time benefit 6% compared
to the zero-piles scheme.
• The optimum is found at 3 BLPs and >80 pieces. This reduces
the overall time by 47% compared to the current situation, and
by almost 50% compared to zero-piles.
• The estimated time reduction is high enough to suggest that that
the company should consider these (very simple) modifications.
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Conclusions
• Methodology to improve the performance of order picking in an
existing company warehouse
– register the situation in the warehouse. The total time is divided into
travel, search, retrieval and return time.
– Adoption of WMS, change of storage and order assignment policies
• The implemented modifications resulted to a mean 50%
reduction in the total picking times, even though the company
avoided expensive modifications.
• Simulation results indicate further benefits from increasing the
BLP from two to three, and moving all the broken lots to the
frontal area, regardless of their size.
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Warehouse simulation tool
• Evaluate the effect of different policies on the picking times,
evaluate their performance, using the time data collected in this
work, and propose optimal scenarios.
• The results provide qualitative incentives and suggest promising
policies for modifications in the current warehouse layout.
“Warehouse management for improved order picking performance”, Zakynthos 2005
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Warehouse management for improved
order picking performance:
An application case study from the
wood industry
G.P. Broulias, E.C. Marcoulaki*
G.P. Chondrocoukis and L.G. Laios
Department of Industrial Management & Technology
University of Piraeus, Greece
*emarc@unipi.gr
“Warehouse management for improved order picking performance”, Zakynthos 2005
.
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Multiobjective optimization & Pareto principle
• The principle is applied on multiobjective optimization
• A solution is Pareto-optimal if the value of any objective
function fi(x) cannot be improved without degrading at
least one of the other objective functions.
• Generate a set of Pareto-optimal solutions, according to
the weight vector (w)
• The final choice relies on the preferences of the decision
maker
“Warehouse management for improved order picking performance”, Zakynthos 2005
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Multiobjective optimization & Pareto principle
f2(x)
z( x 0 )
w2
(f1(x0), f2(x0))
Optimization objective
Maximize fi(x)
or
Maximize z(x) =  wi·fi(x)
e.g. z(x) = w1·f1(x)+ w2·f2(x)
f1(x)
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References (CUT)
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