“Warehouse management for improved order picking performance”, Zakynthos 2005 0 Warehouse...

“Warehouse management for improved order picking performance”, Zakynthos 2005 1

Warehouse management for improved Warehouse management for improved order picking performance: order picking performance:

An application case study from the An application case study from the wood industrywood industry

G.P. Broulias, E.C. Marcoulaki*

G.P. Chondrocoukis and L.G. Laios

Department of Industrial Management & Technology

University of Piraeus, Greece

*[email protected] .


Warehouse operationWarehouse 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


Warehousing performanceWarehousing 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.


Order pickingOrder 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


Order pickingOrder 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


Factors affecting the efficiency of OPFactors affecting the efficiency of OP

• product demand

• warehouse layout

• location of the SKUs

• picking methods

• routing methods

• experience of the employees

• extent of automation.


Review of policies related to OPReview 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


1. Order assignment policies1. 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


2. Routing policies2. 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


3. Storage policies3. 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)


Demand–based storageDemand–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


This workThis 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


Case studyCase 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.


Warehouse descriptionWarehouse 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


Initial layout of the panel warehouse sectionInitial layout of the panel warehouse section

12 series of piles

4 depths of pile levels

Main aisle



12 series of piles


Main aisle


StagesStages

• 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.


Stage 1Stage 1Initial situationInitial 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.


Time measurementsTime 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.


Sample of the order picking formSample of the order picking form


Stage 1Stage 1Results of the 1Results of the 1STST measurement series measurement series

Phases t1 (minutes) % total

Travel time 0.51 9.0Search time 2.05 36.0

Retrieval time 2.50 43.9Return time 0.63 11.1Travel &

return times1.14 20.0

Total 5.69 100.

1ST measurementbefore modifications

Times are presented in minutes per cubic meter.


Stage 2Stage 2Proposed modificationsProposed 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


Stage 2Stage 2Implemented modificationsImplemented 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.


Final layout of the panel warehouse sectionFinal layout of the panel warehouse section

Piles containing only broken lots

12 series of piles


Main aisle


Stage 3Stage 3Results of the 2Results of the 2NDND measurement series measurement series

Phases t1 (minutes) % total t2 (minutes) % total (t1-t2) / t1 %

Travel 0.51 9.0 0.33 11.5 35.3Search 2.05 36.0 0.37 12.9 82.0

Retrieval 2.50 43.9 1.73 60.5 30.8Return 0.63 11.1 0.43 15.0 31.7

Travel & return

1.14 20.0 0.76 26.6 33.3

Total 5.69 100. 2.86 100. 49.7

1ST measurement 2ST measurement Relative time reductionbefore modifications after modifications

Times are presented in minutes per cubic meter.


Warehouse simulationWarehouse 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 can also be considered if relevant data are available.

• The simulated process is based on available picking data collected during the normal operation of the warehouse.


Problem descriptionProblem 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.


Problem descriptionProblem description

Main aisle (clarks)

….

….….

….

….

….

….

….

….….

….

….

N piles

K p

ile

leve

ls

M front piles for broken lots


System representation – definitionsSystem 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, MN,– 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.


Simulation dataSimulation 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.


Example of simulation data for P=13Example of simulation data for P=13

[0.125, 0.625, 0.000, 0.125, 0.125, 0.000, 0.000, 0.000, 0.000, 0.000]

[10, 20, 30, 40, 50, 60, 90, 100, 120, 180], in pieces

[0.125, 0.750, 0.125, 0.000]

[1.50, 3.20, 5.15, 10.5], in minutes

50 pieces, W13 = 16 mm, FPC13 = 0.0784L13 =

FPQ13 =

DPQ13 =

FPT13 =

DPT13 =


Monte Carlo processMonte 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


Deviations obtained for 20 runs and 0 piles

• 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.


System initializationSystem 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


Results table [1]Results table [1]


Results table [2]Results table [2]


70

100

130

160

0 2 4 6 8 10 12

Number of broken lot piles

Ove

ral O

P tim

e (d

ays)

10

20

30

40

50

60

70

80

Simulation resultsSimulation results

No piles


70

100

130

160

0 2 4 6 8 10 12


Ove

ral O

P tim

e (d

ays)

10

20

30

40

50

60

70

80


No piles

now


70

100

130

160

0 2 4 6 8 10 12


Ove

ral O

P tim

e (d

ays)

10

20

30

40

50

60

70

80


No piles

now

proposed


Proposed modificationsProposed 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.


ConclusionsConclusions

• 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.


Warehouse simulation toolWarehouse 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 Warehouse management for improved order picking performance: order picking performance:

An application case study from the An application case study from the wood industrywood industry

G.P. Broulias, E.C. Marcoulaki*

G.P. Chondrocoukis and L.G. Laios

Department of Industrial Management & Technology

University of Piraeus, Greece

*[email protected] .


Multiobjective optimization & Pareto principleMultiobjective 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


f1(x)

f2(x) Optimization objective Maximize fi(x)

orMaximize z(x) = wi·fi(x)

e.g. z(x) = w1·f1(x)+ w2·f2(x)

2w

)(z 0x(f1(x0), f2(x0))

Multiobjective optimization & Pareto principleMultiobjective optimization & Pareto principle


References (CUT)References (CUT)

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