This is the research presentation of Simulation based storage policy in a unit load warehouse, to identify optimum storage policy for different inventory scenarios

1. Simulation based study on storage
policies in a unit load warehouse
S.Jayakanthan
091416u

2. Aisle
Unit load/ Pallet
LITERATURE FINDINGSMETHODOLOGYINTRODUCTION CONCLUSION

3. LITERATURE FINDINGSMETHODOLOGYINTRODUCTION
Storage Policies
• Main Issue: Decide how to allocate the various storage
locations of a uniform storage medium to a number of
SKU’s.
I/O
CONCLUSION

4. Which storage policy is best to apply in a unit load warehouses
under certain situations such as: Different layout configurations,
warehouse dimensions, types of inventory and with different SKU
activity profiles.
Research Problem
LITERATURE FINDINGSMETHODOLOGYINTRODUCTION CONCLUSION

5. • Analyze total travel distance traveled by the material handling
equipment, by implementing different storage policies.
• Analyze changes in picking time by implementing different
storage policies.
• Identify the best storage policy to implement in a unit load
warehouse of concern.
Aims and objectives of the research
LITERATURE FINDINGSMETHODOLOGYINTRODUCTION CONCLUSION

6. • In Goetschalckx and Ratliff (1990) find that a storage
policy is optimal for a certain situation if it minimizes the
average time that is needed to store and retrieve an item
while satisfying the constraints placed upon the system.
Storage operations
Picking operations
• Within the order picking activity, Bartholdi & Hackman
(2010) have found that travelling of the order picker is
the most time consuming activity –usually estimated at
about 50%. As a result it is the most obvious candidate
for improvement.
LITERATURE FINDINGSMETHODOLOGYINTRODUCTION CONCLUSION

7. Types of Storage Policies
• Dedicated storage: Every SKU i gets a number of storage
locations, Ni, exclusively allocated to it. The number of
storage locations allocated to it, Ni, reflects its maximum
storage needs and it must be determined through
inventory activity profiling.
• Randomized storage: Each unit from any SKU can be
stored in any available location
• Class-based storage: SKU’s are grouped into classes. Each
class is assigned a dedicated storage area, but SKU’s within
a class are stored according to randomized storage logic.
LITERATURE FINDINGSMETHODOLOGYINTRODUCTION CONCLUSION

8. Storage policies vs Operational efficiency
• Van den Berg &Zijm, (1999), States travel time will be
reduces when move towards dedicated/Full-turnover
based storage
LITERATURE FINDINGSMETHODOLOGYINTRODUCTION CONCLUSION

9. METHODOLOGY
• Here simulation is selected as a method because this problem
is affected by more variables and from the literature it shows
that most of the researchers suggest simulation as a best tool
to analyze storage policies eg: Kristian, (2009).
• Here I have chosen Microsoft Excel and Visual basic for
applications (VBA) as a programming tool to create the
simulator
• The overall objective of the simulation will be measuring the
travel time by the material handling equipment, under
different conditions.
Simulation
FINDINGSINTRODUCTION CONCLUSIONLITERATURE

10. METHODOLOGY
• The warehouse size is fixed and has the capacity to store the
entire stock of the selected SKU database
• Quantities are measured in terms of pallets of in equal size
• Dispatch and receiving both occurred in full pallet loads
• Dispatch quantity and order quantity are fixed amounts and
there is no inventory shortage during the simulation period
• These SKU’s following Pereto’s 80/20 rule
Simulation Assumptions
FINDINGSINTRODUCTION CONCLUSIONLITERATURE

11. METHODOLOGY
Data collection
FINDINGSINTRODUCTION CONCLUSIONLITERATURE
Databases
Distance Matrix Inventory activity profile
• Layout from Finlays Cold
Storage
• With changes in
input/output locations to
and from the racking area
• Hypothetical database to
get generalized solution
• Following Pareto’s rule

12. METHODOLOGY
Distance Matrix (Layout)
FINDINGSINTRODUCTION CONCLUSIONLITERATURE
Number of racks 10
Locations per rack 210
Total locations 2100
Aisle width 3 meters
Number of columns 35
Number of rows 6
Rack deep single
Cell width 1.5 meters
Cell height 1.4 meters
Cell deep 1.5 meters
Maximum carrying weight 750 kgs

13. METHODOLOGY
Layouts studied
FINDINGSINTRODUCTION CONCLUSIONLITERATURE

14. METHODOLOGY
Inventory database
FINDINGSINTRODUCTION CONCLUSIONLITERATURE
SKU’s percentage Dispatch frequency
10% of the SKU’s 1 (Each day dispatch)
Next 30 % of the SKU’s Greater than 1 and less than 7 (Within a week)
Rest of the 60% of the SKU’s Greater than 7 (Takes more than a week)
SKU id
range Demand
%of
demand
Cumulative
% of
demand
% in the
Categor
y
Categor
y
1 to 10 18250 50% 50% 50% A
11 to 20 6648 18% 18% B
21 to 30 4536 13% 13% 31% B
31 to 40 3972 11% 11% C
41 to 50 429 1% 1% C
51 to 60 499 1% 1% C
61 to 70 572 2% 2% C
71 to 80 434 1% 1% C
81 to 90 390 1% 1% C
91 to 100 534 1% 1% 19% C
36263 100% 100%
0%
50%
100%
150%
10 30 50 70 90
Series1
ABC classification of the inventory SKU popularity distribution

15. Steps in Simulation
Create distance matrix for that particular layout
Run Inventory movement simulation
Call storage policy
Calculate cost of travel
METHODOLOGY FINDINGSINTRODUCTION CONCLUSIONLITERATURE

16. METHODOLOGY
Inventory movement simulation
FINDINGSINTRODUCTION CONCLUSIONLITERATURE
• i = the id of the simulation number.
This value will be increased by one at the end of each simulation
period. Here the value of (i) has been considered as one day.
Therefore within a loop of (i) the actions have been considered as
inventory activities within a day.
• Sku = the id of the sku
Each sku’s will be considered separately to find out whether it
reached a re-order level and scheduled dispatches.
• AvaiQty = Available quantity of that particular sku on that particular day
• DisQty = Dispatching quantity of that particular sku
• OrdQty = ordering quantity of that particular sku
• Next order date = scheduled next (i) where that particular sku need to be
dispatched
• Dispatch frequency = The time duration in between two successful
dispatch operations based on this value next order date will be
calculated
• No.of Sku’s = This is the total number of sku’s available in the scenario
for our simulation No.of Sku’s has been taken as 100 skus
• Max.no of simulation = This indicates the number of days we want to run
this simulation, for this study it has been taken 1 year (365days) as
maximum number of simulation

17. METHODOLOGY
Random storage policy algorithm
FINDINGSINTRODUCTION CONCLUSIONLITERATURE
• Random storage does exactly what it says. For each incoming
product, a random location is assigned to it.
• The only prerequisite is that the chosen location is still available.
The storage location then needs to be recorded for future retrieval
jobs.
• Some authors (e.g. de Koster et al., 2007) claim that it can only be
implemented correctly in a computer-controlled environment. If
operators are to choose freely, they will most likely opt for the first
open location, which would result into closest-open location
storage instead.
• This particular policy has been extensively used in other research
as a basis to look at the performance of picking policies. The
reason why it has been used so much is quite straightforward; it is
very simple to apply, no additional data is needed and it often
requires less space than other storage policies (Petersen II et al.,
1999).
• Random storage can also be described as an extreme case of class-
based storage, where there is only one class. Class-based storage
will be discussed later in this section. According to Chan et al.
(2011), random storage is often used in bulk storage areas that
utilize a computerized inventory system.

18. METHODOLOGY
Class based storage policy algorithm
FINDINGSINTRODUCTION CONCLUSIONLITERATURE

19. METHODOLOGY
Simulation Scenarios
FINDINGSINTRODUCTION CONCLUSIONLITERATURE
Layout scenario Storage policy
Simulation ID
Case 1 Random storage
WS001
Class based storage
WS002
Case 2 Random storage
WS003
Class based storage
WS004
Case 3 Random storage
WS005
Class based storage
WS006
Case 4 Random storage
WS007
Class based storage
WS008
Case 5 Random storage
WS009
Class based storage
WS010

20. Total travel distance & time during 365 days of simulation
FINDINGSINTRODUCTION CONCLUSIONLITERATURE METHODOLOGY
Simulation ID Layout Type Storage policy
Total Travel Distance
(Km)
Total Travel Time (hrs)
WS001
Case1
Random Storage 7445.50 930.69
WS002 Class based storage 4485.06 560.63
WS003
Case2
Random Storage 6687.31 835.91
WS004 Class based storage 3881.09 485.14
WS005
Case3
Random Storage 6714.44 839.30
WS006 Class based storage 5387.08 673.39
WS007
Case4
Random Storage 5148.83 643.60
WS008 Class based storage 2645.45 330.68
WS009
Case5
Random Storage 5151.56 643.94
WS010 Class based storage 4627.86 578.48

21. Total travel distance & time during 365 days of simulation
FINDINGSINTRODUCTION CONCLUSIONLITERATURE METHODOLOGY
0.00
1000.00
2000.00
3000.00
4000.00
5000.00
6000.00
7000.00
8000.00
Case 1 Case 2 Case 3 Case 4 Case 5
Random Storage
Class based storage

22. Total travel savings when applying class based storage policy
FINDINGSINTRODUCTION CONCLUSIONLITERATURE METHODOLOGY
Layout
type
Random
Storage
Class based
storage
Savings
% of cost
saving
Case 1 7445.50 4485.06 2960.44 39.76
Case 2 6687.31 3881.09 2806.22 41.96
Case 3 6714.44 5387.08 1327.36 19.77
Case 4 5148.83 2645.45 2503.38 48.62
Case 5 5151.56 4627.86 523.69 10.17
0.00
1000.00
2000.00
3000.00
4000.00
5000.00
6000.00
7000.00
8000.00
Case 1 Case 2 Case 3 Case 4 Case 5
Random Storage Class based storage Savings
• If this warehouse practices random storage policy then there will be no much
impact on using across aisle layout or U type layout.
• If this warehouse practices class based storage policy then U type layouts are much
more effective than across aisle type layouts.

23. Layouts studied
FINDINGSINTRODUCTION CONCLUSIONLITERATURE METHODOLOGY

24. Total travel savings when applying class based storage policy
FINDINGSINTRODUCTION CONCLUSIONLITERATURE METHODOLOGY
Layout
type
Random
Storage
Class based
storage
Savings
% of cost
saving
Case 1 7445.50 4485.06 2960.44 39.76
Case 2 6687.31 3881.09 2806.22 41.96
Case 3 6714.44 5387.08 1327.36 19.77
Case 4 5148.83 2645.45 2503.38 48.62
Case 5 5151.56 4627.86 523.69 10.17
0.00
1000.00
2000.00
3000.00
4000.00
5000.00
6000.00
7000.00
8000.00
Case 1 Case 2 Case 3 Case 4 Case 5
Random Storage Class based storage Savings
• If this warehouse practices random storage policy then there will be no much
impact on using across aisle layout or U type layout.
• If this warehouse practices class based storage policy then U type layouts are much
more effective than across aisle type layouts.

25. • The best performance in terms of total travel distance and time savings
come from class based storage.
• The layout type with multiple input output locations at one side of the
racking area achieve 48.62% of total travel distance saving when applying
class based storage, this is the best possible combination with highest
cost saving.
• And two across aisle I type layouts has been analyzed in case 3 and case 5
both are having travel distance savings respectively 19.77% and 10.17%.
These are two lowest values in this study.
• Class based policies are less affective to across aisle layouts with input
and output points at opposite sides, compare to layouts which have input
and output points at same side.
INTRODUCTION CONCLUSIONLITERATURE METHODOLOGY FINDINGS
Conclusions and Recommendations

26. • It has been assumed dispatch and receiving both occurred in full pallet loads.
But in practical situation lots will be ordered in cases also, which may lead to
order batching.
• The recommendation found here is significant only if the inventory follows
Pereto’s 80/20 rule. Otherwise applying class based storage policy is
questionable.
• The congestion factor of the research has not been studied, that also has to
be taken into consideration. If the sku’s has high correlation in dispatch or
receiving times then the congestion will be have high impact that need to be
studied.
INTRODUCTION CONCLUSIONLITERATURE METHODOLOGY FINDINGS
Limitations of the research

27. • Probabilistic inventory movement can be used to study this simulation
again.
• Simulation program can be re-developed in a way to simulate case picking
warehouse, here different pickling policies also can be studied.
• Congestion factor can be taken into account when simulating and
analyzing the results then time delay due to congestion also taken into
consideration.
• Finally there is lack of research in warehouse domain studying storage
policies qualitatively and quantitatively but at once that may lead to a
better decision support tool if studied.
INTRODUCTION CONCLUSIONLITERATURE METHODOLOGY FINDINGS
Suggestions and recommendations for future research

28. Thank you

29. INTRODUCTION METHODOLOGYLITERATURE PROBLEM
Create distance matrix (Notations)
Nr : No of Racks
Nc : No of Columns in rack
Nr : No of Rows in rack
Dr : Deep of the rack
Wa : Aisle Width
Lc : Cell Length
Hc : Cell Height
Dc : Cell Depth
Ia : Input Aisle
Oa : Output Aisle
n : Rack
i : Column
j : Row
k : Deep
Lnijk : Location
SDnijk : Distance from input point to location Lnijk (Storage Distance)
PDnijk : Distance from output point to location Lnijk (Picking Distance)
TDnijk : Total Distance

30. INTRODUCTION METHODOLOGYLITERATURE PROBLEM
Create distance matrix (Notations)
If Ia and Oa are in same side then
SDnijk = i.Lc + j.Hc + |(Ia - (n + k – 1)) * (Dr.Dc + Wa)|
PDnijk = i.Lc + j.Hc + |(Oa – (n + k-1)) * (Dr.Dc + Wa)|
If Ia and Oa are in opposite side then
SDnijk = i.Lc + j.Hc + |(Ia - (n + k – 1)) * (Dr.Dc + Wa)|
PDnijk = (Nc-i + 1).Lc + j.Hc + |(Oa – (n + k-1)) * (Dr.Dc + Wa)|
TDnijk = SDnijk + PDnijk

31. INTRODUCTION METHODOLOGYLITERATURE PROBLEM
Screen shots of the simulation program