ISSN: 2277-3754
ISO 9001:2008 Certified
International Journal of Engineering and Innovative Technology (IJEIT)
Volume 2, Issue 7, January 2013
Inventory Management of Spare Parts by
Combined FSN and VED (CFSNVED) Analysis
Vaisakh P. S., Dileeplal J., V. Narayanan Unni

Abstract— Inventory management is the process of efficiently
overseeing the constant flow of units into and out of an existing
inventory. Inventory management of spare parts plays an
important role in achieving the desired plant availability at an
optimum cost. This paper presents a spare part classification
method based on item movement in store department and
criticality. FSN analysis is used to find out the fast moving, slow
moving, and non-moving items in a store department and VED
analysis is applied to non-moving items. Combined FSN and VED
analysis is carried out to find the non-moving items which are less
critical.
Index Terms—Cause and Effect Diagram, FSN Analysis,
Pareto Chart, VED Analysis.
I. INTRODUCTION
Inventories constitute the most significant part of current
assets of a large majority of companies. A considerable
amount of funds is required to maintain the large size of
inventories. It is therefore absolutely necessary to manage
inventories efficiently in order to avoid unnecessary
investment and the companies have to reduce the level of its
inventories to a considerable level without any adverse effects
on production and sales, by using simple inventory planning
and control techniques. The reduction in “excessive”
inventories carries a favourable impact on a company‟s
profitability. Identifying the right kind of production and
inventory management (P&IM) system for a manufacturing
firm can be a difficult and complex task. Since the investment
in a P&IM system is large and remains fixed over a
considerable length of time, the correct system choice is
critical to both a firm‟s short and long-term profitability. The
process industries, consisting of firms that add value by
mixing, separating, forming and/or chemical reactions by
either batch or continuous mode, continue to have difficulty in
realizing the benefits of many of the management system
developments in the discrete industries [1]. The spare parts
management plays an increasingly important role in factories.
It not only directly influences the equipment‟s operation and
yields rate, but also influences the slack risk and inventory
level. Demand for spare parts is often difficult to forecast
using historical data only. It is well known that spare parts
management is difficult because the parts can be expensive,
their demand is highly erratic and intermittent, yet their
shortage costs can be very large [2]. Managing production
and inventory for large-scale continuous processing plants is a
key to success in the chemical process industry [3]. For many
asset-intensive industrial plants, classification of the total
spare parts assortment into relevant categories is a crucial task
in order to control the wide and highly varied assortment.
Spare parts differ strongly in terms of stock-out effects, item
value and demand pattern. Herman Baets and Liliane Pintelon
developed a multi-criteria classification method based on
spare parts criticality. The different parameters influencing
spare parts criticality are equipment criticality, probability of
failure of the item, replenishment time, number of potential
suppliers, availability of technical specifications and
maintenance type. Based on these characteristics, spare parts
are classified into three classes representing different levels of
criticality (high, medium, low). The multi-criteria
classification method is based on the AHP and the logic of
decision diagrams. By combining these two techniques,
numerous potential attributes influencing spares parts
criticality are taken into account in an easy and rational
manner [4].
The critical spare parts (CSP) are considerably expensive,
and the price can be thousand dollars. The CSP also have the
characteristics of huge demand variation, long purchasing
lead time, and necessary for machine operations. When the
machine works, the production efficiency would be reduced
due to the decay and abrasion of CSP. If the number of CSP is
insufficient to supply the production machine, it would be has
low yield or break down, therefore the factories may face the
extra costs and risks [5]. In this paper a combined FSN and
VED analysis is carried out in the store department of a
continuous chemical process industry for managing spare
parts and reducing the higher inventory cost.
The remainder of this paper is organized as follows:
Section 2 presents problem description. The methodology is
explained in section 3. The results and discussions are
included in section 4 and the final conclusions are presented
in section 5.
II.
PROBLEM DESCRIPTION
In a continuous chemical process industry there are nearly
40,000 inventory items including raw materials, spare parts
and work in process inventory. These inventories need a
proper inventory control system for minimizing the cost. On
analysis of these, it is found that there is scope of
improvement in the inventory management followed by the
company. Table I shows the inventory cost of the company.
The total annual cost of the company during the year 2011-12
is 75997.56 lakhs and the inventory cost turns on to be
39.42% of the total annual cost. According to industry wide
standard, the inventory cost of a continuous chemical process
industry should be in between 25 – 30% [6]. It is clear from
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Volume 2, Issue 7, January 2013
Table II Spare Parts Holding Cost Of the Company
the calculation that, the inventory cost of the company
(39.42%) is higher than the industry wide standard (25-30%).
Components
Cost (Lakhs)
Its shows the inventory management of the company is not
Obsolescence and storage facility cost
2334.50
proper, and the cost of inventory is on a higher side compared
Deterioration or its prevention
1518.36
to other similar process industries where industry wide
standard is justified. A cause and effect diagram is drawn for
Handling and distribution
671.16
higher inventory cost in which possible causes are displayed.
Transportation
198.02
This is shown in figure 1.
Table I Inventory Cost Of the Company
Inventory cost components
Cost (Lakhs)
Raw material ordering and purchase cost
8928.00
Raw material holding cost
8437.44
Spare parts ordering and purchase cost
7067.40
Spare parts holding cost
5525.40
Total inventory cost
Taxes
Insurance
387.16
416.20
29958.24
The raw materials (chemicals) in the company have a
continuous consumption and it follows EOQ method. There is
less scope of further cost optimization in raw materials.
Optimization of spare parts cost will help to reduce inventory
cost. The inventory spare parts holding cost of the company is
5525.4 lakhs. The various components of spare parts holding
cost is given in table II and the percentage contributions of
components are shown in figure 2.
Pareto chart based on the data of spare parts holding cost
components reveals that obsolescence and storage facility
cost are the major causes of higher inventory holding cost of
spare parts [7]. It is shown in figure 3. A combined
FSN –VED (CFSNVED) analysis is performed for reducing
the obsolescence and storage facility holding cost.
Fig 2 Pie chart for spare parts holding cost
Fig 3 Pareto chart for inventory holding cost
III. METHODOLOGY
Fig 1 Cause and Effect Diagram for Higher Inventory Cost
Several methodologies have been adopted for inventory
management of spare parts. A combined FSN and VED
(CFSNVED) analysis for spare parts classification is shown
in figure 4. FSN analysis is used to find out the non-moving
items in store department and VED analysis is performed to
classify non-moving items based on their criticality. Higher
the stay of item in the inventory, the slower would be the
movement of the material. Accordingly spare parts are
classified in to three classes, via fast moving (F), slow moving
(S) and non-moving (N) items based on their consumption
and average stay in the inventory for a specified period using
FSN analysis.
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Volume 2, Issue 7, January 2013
FSN and VED analysis
1. FSN based on average stay
2. FSN based on consumption
3. Combined FSN
FSN analysis
Identifying non-moving items
Classifying items to non-moving vital,
non-moving essential, non-moving
desirable
Applying VED analysis to non-moving items
Drive conclusion based on Combined FSN –
VED analysis
Result of FSN analysis and VED applied to
non-moving items of FSN analysis
Conclusion based on combined FSN- VED
analysis
Fig 4 Combined FSN-VED analysis
Steps followed in the FSN analysis are
 Calculation of average stay and the consumption rate
of the material in warehouse
 FSN Classification of materials based on average stay
in the inventory
 FSN Classification of the material based on
consumption rate
 Finally classifying based on above FSN analysis.
Several factors contribute to the criticality of a spare part. If
a spare is for a machine on which many other processes
depend, it could be of very vital importance. Also if a spare is,
an imported component for which procurement lead time
could be very high its non-availability may mean a heavy loss.
In general, criticality of a spare part can be determined from
the production downtime loss, due to spare being not
available when required. Based on criticality, spare parts are
conventionally classified into three classes, viz. vital,
essential and desirable
Vital (V): Vital category items are those items without which
the production activities or any other activity of the company,
would come to a halt, or at least be drastically affected. In a
process industry, most spare parts for the bottleneck machine
or process will be of vital nature.
Essential (E): A spare part will be considered essential if, due
to its non-availability, moderate loss is incurred.
Desirable (D): A spare part will be desirable if the production
loss is not very significant due to its non-availability. Most of
the parts will fall under this category
The VED analysis helps in focusing the attention of the
management on vital items and ensuring their availability by
frequent review and reporting. Thus, the downtime losses
could be minimized to a considerable extent. Combined FSN
and VED (CFSNVED) analysis classifies the non-moving
items in to non-moving vital (NV), non- moving essential
(NE) and non-moving desirable classes (ND).
IV. RESULT AND DISCUSSIONS
The list of materials for CFSNVED analysis is shown in
Table III. FSN classification based on material average
consumption rate and material average stay in inventory is
shown in Table IV. FSN analysis reveals that 6 items were of
„F‟ class, 44 items of „S‟ class and the remaining 44 items of
„N‟ class. It shows that there are lot of spare parts which
comes under non-moving category resulting in higher
inventory cost. Higher inventory cost is mainly due to higher
storage cost and inventory holding cost. Non-moving items
need to be controlled in order to reduce inventory cost. A
VED analysis is performed on the non-moving items, which
are identified by the FSN analysis (Table IV). The
non-moving items are classified in to vital (NV), essential
(NE) and desirable (ND) items based on the criticality rate.
Result of VED analysis on non-moving items is shown in
Table V.
Combined FSN and VED analysis shows that out of the 44
non-moving items, 26 items are of desirable category. ND
items are less critical and their unavailability will not affect
the production process. These ND items cause higher
inventory holding cost at stores department and reducing the
space availability as they are non-moving. So avoiding or
eliminating the ND class items from store department will
increase the space availability and reduce inventory holding
cost, which in turn helps to attain industry wide standard.
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ISSN: 2277-3754
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Volume 2, Issue 7, January 2013
Table III List of Materials for CFSNVED Analysis
Material no
Material
Material no
Material
1
Pump rotor
48
Castable medium drive
2
Plate inconel
49
Pipe rubber lined FD 100x3000 mm
3
Screw machine
50
Seal leat SS 150x450x1.2 mm
4
Clamps hose
51
Ring Rider 12 Y-7609007
5
Cap bear inner drive
52
Stud P coated 5/16 BSW 65
6
Socket soldering
53
Valve plug nickel FD 150
7
Bag product 550*420mm
54
Resin cotton exchanger
8
Tube assy
55
Sheet FRP BISP CORR 3 m
9
Bag product 585*500mm
56
Pipe CS SMLS ASTM A 106 S8080mm
10
Gear box for digester
57
Pipe PTFE lined FD 50x3000mm
11
Recycle gas cooler
58
Tube copper PVC JACKETED 20SWG
12
Inlet stand pipe
59
Gas SF in cylinder
13
Packing box for pump
60
Product classifier MOD C40
14
Hose PTEF
61
Pipe CS ER WIS 3589 x8mm
15
Motor gear
62
Bar round inconel 10 mm diameter
16
Tickle condenser
63
Bearing 7226 BYG
17
Peripheral wall assembly
64
Motor 0.37 KW RF 37DT
18
Roof panel
65
Chain roller 4 P with K2 attachement
19
Kit oil separator
66
Indion 1593
20
Distributor plate
67
Anchor SS310 flat type
21
Reactor cooling tube
68
Side couple for cable tray
22
Linear AR steel
69
Steel inconel 7/8x4 L
23
Bellow inconnel NB 18
70
Box ALU 150x150mm
24
Nozzle for grinding
71
Pipe FRP BISP FW 200mm
25
Gas frexon
72
Brick circle 108-117-3 chord 85/16
26
Connectors cell
73
Lathe HD all geared
27
Seal kit for pinch valve
74
Carrier bag P/N 167194
28
Cable sensor
75
Disc valve 2 stage P/N 165106
29
Fan pedestal
76
Valve ball Gemco mannel T type 8
30
Cable control copper
77
Transformer furnace 6.6Kv/200V
31
tickle storage tank
78
Tickle fee vaporizer cup900 NM/HR
32
pully tail array L2512
79
Bag product 590x535 mm
33
Pump shaft drive DRG
80
Tank F 201 Tickle feed tank
34
Tube bundle 2 stage LKE
81
Recycle gas cooler E 324/325
35
Chain roller
82
Carbon analyzer
36
Casing for 6x4-14A 20
83
Nozzle blade
37
Cable control copper
84
Pump Lawrence 8 DV G203/204
38
Plate heat exchanger E403
85
Rotor assembly
39
Economizer coil
86
Cement Durax 1700
40
Valve bleeder automatic 50 mm
87
Brick ark 9x4 1/2
41
Pipe glass lined jack 150x1010 mm
88
Ring rider 8 1/2 Y-79609005
42
Transmitter DP ELEO-10000 mm WC
89
Pallet wooden export quality
43
Pipe SS316 ERW ASTW A 358S
90
Podering gland CHA 1960 size
44
Air jet P/N
91
Saddle ceramics intalo x 50mm
45
Bag product 530x 470mm for RC804
92
Level switch for G
46
Ring pistion 20 1/2 Y7609024
93
Flat MS 25 x 3 mm
47
Channel FRP 100x50x8 mm
94
Motor 200 kW FT MTD 355 M 6 HI 100 rpm
306
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Volume 2, Issue 7, January 2013
Table IV FSN Combined Classification
Material no
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
FSN - Cons.
N
N
N
N
F
F
N
N
N
N
N
N
N
N
N
S
S
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
F
N
N
N
S
N
N
N
N
N
N
Material no
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
307
FSN - Stay
N
S
S
F
F
F
N
N
S
N
F
F
N
S
S
F
F
N
N
N
S
N
N
F
F
N
N
F
F
F
S
F
N
S
S
N
F
F
N
S
S
N
F
S
F
F
F
S
F
N
F
N
F
F
F
S
F
F
S
FSN
N
N
N
S
F
F
N
N
N
N
S
S
N
N
N
S
S
N
N
N
N
N
N
S
S
N
N
S
S
S
N
S
N
N
N
N
S
S
N
N
N
N
S
N
S
S
S
N
F
N
S
N
S
S
S
N
S
S
N
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Volume 2, Issue 7, January 2013
Table IV (Continued)
Material no
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
FSN - Cons.
N
N
N
N
N
N
N
N
N
N
N
N
F
N
N
N
N
N
N
F
N
N
N
N
S
N
N
S
N
N
N
N
F
N
N
Material no
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
FSN - Stay
N
S
F
F
S
N
N
F
N
F
F
F
F
F
F
S
F
F
N
F
F
F
F
F
F
S
F
F
F
F
F
N
F
S
F
FSN
N
N
S
S
N
N
N
S
N
S
S
S
F
S
S
N
S
S
N
F
S
S
S
S
S
N
S
S
S
S
S
N
F
N
S
Table V Result of VED applied to non-moving items
NV
Pump rotor
Packing box for pump
Gear box for digester
Motor gear
Transmitter DP ELE-10000
mm WC
Indion 1593
Castable medium drive
NE
Screw machine
Motor 0.37 KW RF 37DT
Roof panel
Kit oil separator
Pipe CS SML ASTM A 106
S8080mm
Rotor assembly
Tube bundle 2 stage LKE
Economizer coil
Air jet P/N
Bellow Inconnel NB 18
Hose PTEF
Casing for 6x4-14A 20
Bag product 550*420mm
Tube assy
Plate Inconel
Pipe glass lined jack
150x1010 mm
Bag product 585*500mm
Pipe CS ER WIS 3589 x8mm
Linear AR steel
Seal kit for pinch valve
Saddle ceramics intalo x
50mm
Product classifier MOD
Pump shaft drive DRG
Chain roller
Casing for 6x4-14A 20
308
ND
Flat MS 25 x 3 mm
Stud P coated 5/16 BSW 65
Gas SF in cylinder
Distributor plate
Seal leat SS 150x450x1.2 mm
Flat MS 25 x 3 mm
Tickle fee vaporizer cup900
NM/HR
Reactor cooling tube
Side couple for cable tray
Chain roller 4 P with K2
attachment
Disc valve 2 stage P/N
Tickle storage tank
Connectors cell
Valve bleeder automatic
ISSN: 2277-3754
ISO 9001:2008 Certified
International Journal of Engineering and Innovative Technology (IJEIT)
Volume 2, Issue 7, January 2013
[4] Molenaers, Herman Baets, Liliane Pintelon & Geert
V. CONCLUSION
Combined FSN-VED analysis revels that there are lot of
items under „non-moving – desirable‟ (ND) category. The 26
items under ND category should be discarded from the store
in order to reduce spare parts holding cost. This will not
affect the production process, since it is less critical. This will
also increase the space availability in store department. Based
on the analysis and results, suggestions are given in order to
improve the inventory management.
Better co-ordination among purchase, production,
marketing and finance department will help in achieving
greater efficiency in inventory management of spare parts.
The company can avoid dumping of unnecessary spare parts
in the store. Implementation of KANBAN system is
appreciable for proper stock maintenance. Company can also
implement „automatic storage and retrieval system‟ (AS/RS)
and AGVs in stores department for better inventory
management. Company can improve the efficiency of the
present inventory management systems by replacing the old
method of inventory control with the modern methods like
just in time (JIT).
REFERENCES
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industry production and inventory management systems,
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[2] Rommert Dekker & Cerag Pince, on the use of installed base
information for spare parts logistics, Int. J. Production
Economics, (2012) doi:10.1016/j.ijpe.2011.11.025.
[3] David L. Cooke, Thomas R. Rohleder, Inventory evaluation
and product slate management in large-scale continuous
process industries, Journal of Operations Management, 24
(2006) 235–249.
Waeyenbergh, Criticality classification of spare parts: A case
study, Int. J. Production Economics, 140 (2012) 570–578.
[5] Fei-Long Chen, Yun-Chin Chen, Applying Moving
back-propagation neural network and Moving fuzzy-neuron
network to predict the requirement of critical spare parts,
Expert Systems with Applications, 37 (2010) 6695–6704.
[6] Ken D. Duft, Business management, Cooperative extension,
Washington state university. PDF.
[7] Dale H. Besterfield, Total Quality Management, Revised Third
Edition, Pearson Publications (2008).
AUTHOR BIOGRAPHY
Vaisakh P. S., PG Scholar, Department of Mechanical
Engineering, Mar Athanasius College of Engineering, Kothamangalam,
Kerala.
Dileeplal J., Associate Professor, Department of Mechanical
Engineering, Mar Athanasius College of Engineering, Kothamangalam,
Kerala.
V. Narayanan Unni, Professor, Department of Mechanical
Engineering, Mar Athanasius College of Engineering, Kothamangalam,
Kerala.
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