final project anish
TRANSCRIPT
A PROJECT REPORT ON
A study on the inventory management of tools in BHEL trichy
Submitted to
BHEL.
By
ANISH K JOSEPH
Register No. 322
RAJAGIRI SCHOOL OF MANAGEMENT
(Rajagiri School of Social Sciences, Rajagiri valley, Kochi-39 )
B.SANJEEVI
E-Mail : [email protected]
DT:
29th May ‘08
certificate
This is to certify that Mr. ANISH.K.JOSEPH (Regn No. 322) Final
year student of M.B.A., (RAJAGIRI SCHOOL OF MANAGEMENT, KOCHI - 682
039) did his project titled,
"A study on inventory management of tools”
Under my guidance during the period from April 2008 to May 2008 and found
successful in his project work.
During this period he was sincere and involved himself in the project. He has
also showed keen interest and enthusiasm during the course of the project.
I wish him all success in his future assignments.
B.SANJEEVI
MANAGER
Materials Management Systems
BHEL Trichy - 620014
AN ISO 9001 COMPANY
BHARAT HEAVY ELECTRICALS LIMITED
(A Govt. of India Undertaking)
High Pressure Boiler Plant, Tiruchirapalli - 620 014
Materials Management-FB/SYSTEMS
Manager/MM systems Phone: 91-(0)431 – 2575049
2
ACKNOWLEDGEMENT
The satisfaction and euphoria that accompany the successful completion of
any task would be incomplete without the mention of the people who made it
possible, whose constant guidance and encouragement crowned the effort with
success.
If words are considered as the symbols of approval, and tokens of
acknowledgement, then let the following words play the heralding role of
expressing my gratitude.
I wish to acknowledge my sincere gratitude to my faculty guide,
Mrs.Neetha.J. Eappen, Faculty Member, Rajagiri School Of Management, Kochi,
for all the support and kind co-operation during the academic year and also for the
valuable guidance and suggestions to execute the project as per organization
requirement.
Also reserved on priority are my special wishes and acknowledgement for
BHEL for according me the permission to complete my project in their
organization.
I wish to put on record my sincere thanks to Mr. B.SANJEEVI (Manager,
materials planning/FB) for his valuable guidance given to me. I extend my gratitude
to Mr.Gopalakrishnan (Manager, materials planning/FB) and Mr.Velachamy
(Manger, Tool engineering department) for their valuable advices and
encouragement.
I am thankful to all the respondents, for their kind co-operation to do this
work.
3
ABOVE ALL IT IS GOD’S GRACE THAT HELPED ME TO ACHIEVE THIS
GOAL.
4
DECLARATION
I, ANISH K JOSEPH, Student of Rajagiri School of Management, Kochi, here by declare
that this project report entitled “A study on inventory management of tools in BHEL.” is a
record of original work done by me. I further declare that any part this project itself has not
been submitted elsewhere for award of any degree.
PLACE: TRICHY
DATE:
ANISH K JOSEPH
Rajagiri School of Management
5
EXECUTIVE SUMMARY
6
Consumption of tools in a factory or in an industry is by there highly probabilistic demand.
Sometimes a tool records very fast rate of consumption and at other times very slow rate of
consumption. The rate of consumption depends upon many factors like the type of work
being handled, life of tool etc... Hence the inventory management of tools considerable
amount of attention is to be shown on forecasting demand.
Present study is concerned with designing of an inventory management system of tools
high pressure boiler manufacturing plant (bharat heavy electrical limited tiruchirapalli).
The objectives of the studies are: to avoid existing state of frequent stock outs, to decrease
the average inventory carried per year, to suggest some general improvements over the
present system of inventory management followed by the concern.
From preliminary analysis it is found that the reasons for the frequent stock outs are many;
namely,
1) Random fluctuations in demand,
2) Error in forecasting the demand,
3) Inconsistent and very long lead times, and
4) Wrong timing of raising stock recoupment memos (purchase requisitions).
Each of the above reason is analyzed in detail. In trying to avoid these difficulties it is
found that equally rigorous treatment in any respect of analysis is not necessary for all
items. Hence all the tools are classified in two different ways, one depending upon the
average usage value (ABC analysis), and other depending upon the rate of consumption or
movement (fast, regular, slow, non-moving items).
Statistical methods of forecasting, namely, exponential smoothing, Monte Carlo simulation
and averages are used to forecast the demand. Lead times are very long, (varying from 4
months to one year) as the concern is of public sector. All the available lead time details are
analyzed ‘category of the tool wise’. for each category a lead time equal to the average plus
one standard deviation is fixed as representative lead time .depending upon the lead times ,
7
a time table is prepared to raise stock recoupment memos once ion an year for annual
requirement.
Due to long lead times and very high ordering cost it is found that indenting of annual
requirement at a time is economical. But to avoid high inventory pile ups, the suppliers are
to be asked to supply in staggered deliveries. Three costs are associated with this method of
procurement, namely ordering cost, cost of staggered deliveries and inventory carrying
cost. To decide the optimum number of staggered deliveries an equation is developed
which minimizes the total inventory costs. Safety stocks are determined considering the
standard deviation of the lead time, demand of items and the value of item, unlike the
existing system where in only rupee value of the item is considered.
8
Contents page no
Chapter 1 Introduction 9
Chapter 2 Inventory management 11
2.1 Basic inventory model 12
2.2 Buffer stock 14
2.3 The re order level policy 16
2.4 The re order level with periodic counts 16
2.5 The reorder cycle policy 16
2.6 The (s,S) policy 16
2.7 Demand forecasting 17
2.8 Simulation of inventory situations 21
Chapter 3 Identification of problem and objective 25
3.1 Introduction 26
3.2 Existing method of inventory management 27
Chapter 4 Company Profile 32
Chapter 5 Research Methodology 39
Chapter 6 Data collection and analysis 43
6.1 Demand forecasting 46
6.2 ABC analysis 54
6.3 Staggered deliveries 57
6.4 Lead time analysis 59
6.5 Buffer stock determination 59
6.6 Re order level 62
Chapter 7 recommendations and conclusion 65
Bibliography
9
Chapter1
INTRODUCTION
10
INTRODUCTION
1.1 Inventory management is the science –based art of controlling the amount of stock held, in
various forms, with in the business to meet economically the demand placed upon the
business.
Stocks held by a business can occur in many forms. One usually thinks in terms of finished
products stocks or raw material stock held in stores which are used to make finished
products.
Tools and other consumables and even machinery used in the business could be regarded as
stocks of production capacity.
1.2 REASONS FOR HOLDING STOCK
In an ideal situation ,where demand upon the business is known exactly and well in
advance and where suppliers keep there due dates, there would be little need to hold any
form of inventory other than a limited amount of in-process stocks.
In practice demand is not known well in advance and suppliers will be often be late or even
early in delivering.
In this imperfect but practical situation, stocks can act as a buffer between the vagaries of
supply and demand. The principal reason for holding stocks is:
(a) To act as an insurance against higher than average demand
(b) To act as an insurance against longer than average suppliers or delivery times, this usually
being termed in inventory control and lead times.
(c) To take advantage of seasonal and other price fluctuations.
(d) To minimize delay in production caused by lack of materials or parts.
11
1.3 DISADVANTAGES OF LOW STOCK LEVELS
(a) Customer demands are often not be satisfied. This can lead to an immediate loss of
business, also to a loss of future business through customer dissatisfaction.
(b) Because of (a), costly emergency procedures such as special production runs and upset
schedules are often resorted to in an attempt to maintain goodwill.
(c) To maintain a reasonable service it will be necessary (on average) to place replenishment
orders more frequently than in the situation where high stock levels are kept. Thus higher
replenishment costs are incurred.
1.4 DISADVANTAGES OF HIGH COST LEVELS
(a) Storage costs incurred are very high. These costs not only cover building, labor etc.. But
also allow for deterioration and spoilage.
(b) The increased loss on capital invested in stocks can become prohibitive. Interest lost on
money invested in can boost the holding cost of an item up to a value of about 25%.
(c) Where the storage product become obsolete, a large stock holding of items could, in worst
situation, represent a large capital investment in an unsalable or unusable product whose
cash value is only that of scrap.
(d) A high capital investment in stocks necessarily means there is less money available within
the business for other requirement.
(e) When high stock level of raw material is held, a sudden drop in the going market price of
the material represent a cash loss to the business for having bought at the higher price
previously existing.
The aim of an inventory management system is to maintain the stock held by the business
at a level which optimizes some management criteria such as minimizing the cost incurred
by the whole business enterprise as a result of holding stocks and maximizing the business
profit or providing a stated minimum customer service.
12
CHAPTER 2
INVENTORY MANGEMENT
13
2.1 A BASIC INVENTORY MODEL
The logical starting point for a discussion of scientific inventory management is a basic
inventory model, which though quite simple, has proved widely useful. The model is some
what of a classic, having appeared in the literature more than seventy five years ago, and it
illustrates effectively the typical assumptions and simplification involved in model of a
business operation. This basic model is the fixed order quantity system. In this system, the
inventory reorder quantity is fixed, a reorder is placed whenever the inventory on hand
drops to a particular level, referred to as reorder level.
2.1.1 THE ECONOMIC ORDER QUANTITY (EOQ): (How much to reorder?)
The fixed order quantity system is based on selecting that order quantity which will
minimize the total variable cost of managing inventory. In determining this economic
order quantity, the model assumes that the cost of managing inventory is solely made up of
two parts: ordering cost and carrying cost.
Ordering cost is additional cost of placing order, a cost which is considered to be
independent of size of order. In manufacturing, this might include setup costs: in procuring
from outside it would be limited to costs incurred in processing the purchase order. If K is
the cost of placing an order and q is the order quantity, the unit cost of placing an order is
K/q, and this decreases with increase in order quantity. The annual cost of ordering can be
determined by annual sales (S) in units. As illustrated in fig (2.1.1) this annual ordering
cost (KS/q) decrease with increase in order quantity.
Carrying cost is the cost of physical storage of inventory plus the opportunity cost of the
money tied up in the form of inventory. The cost of carrying an item in inventory is usually
expressed as the percentage of unit purchase cost of an item and in relation to a certain
period of time, such as 20% per year.
If P is the unit purchase cost and’ i` is the carrying cost expressed as an annual percentage
of this unit cost, then Pi is the annual carrying cost per unit of inventory. But this has not
yet been related to order quantity. Assuming that inventory decreases at a constant rate
14
from the order quantity to zero and is then replenished by another order quantity, the
specification of a particular order quantity results in a corresponding average inventory
equal to one half of the order quantity, carrying costs are based on this average, and the
annual cost of carrying inventory is there fore, Piq/2. As shown in figure (2.1.2), this cost
increases linearly with increasing order quantity.
As shown in figure (2.1.3), which combines the two preceding graphs the total cost of,
managing an item in inventory decreases as the order quantity increases because of the
rapid reduction in the annual ordering cost? As the reduction in ordering cost become
progressively smaller and is eventually equaled by the linear increase in the carrying cost,
the total annual cost start to increase again. The annual cost curve is expressed
mathematically by the equation.
Total annual cost (Tc) = KS/q + Piq/2
Where;
S = annual sales in units
K = cost of placing an order
P = unit cost of an item
I = cost of carrying inventory in percent per year
2.1.2 REORDER POINTS: When to order?
15
How much to order has been determined, but this is intern bound up with when to reorder.
The calculations of carrying cost used in determining the economic order quantity is based
on a particular model of inventory behavior as illustrated in fig 2.1.4
The average inventory under such behavior is Q/2, a direct measure of the effect of the
order quantity on carrying cost. Since this kind of idealized behavior does not occur in
practice, however, the model of inventory behavior is made some what sophisticated, as
indicated in fig 2.1.5. First it is necessary to recognize the time lag between placing a
reorder and receiving it.
The need for buffer stock is clearly seen from the fig (2.1.6) where constant sale rates have
been replaced by more realistic examples of varying sale rate. The variation in the sales
during lead time results in inventory level falling above and below, the buffer level at the
time a reorder is received. If there were no buffer stocks, those sales which dips into it
could not be made.
2.2 BUFFER STOCK:
It is provided to meet the fluctuations in demand and fluctuations in lead time.
A measure of these fluctuations is the lead time demand standard deviation.
Lead time standard deviation can be calculated as follows:
σL = /n
Where = each of the individual leadtime demand values
=average lead time demand
n=number of individual lead times
Buffer stock (B) = K.σL
16
K is standard normal deviate
σL is lead time standard deviation
It is the value of K which determines what the service level or probability of a stock out
occurring will be. Values of k and the corresponding probability of stock out or service
level can be found in normal probability table, and the following values will give some
indication of what level of service is provided for k = 1,2 and 3
K= 1, a stock out will occur on average 15.9% of the time
K=2, a stock out will occur on average 2.3% of the time
K=3, a stock out will occur on average 0.1% of the time
2.2.1 BUFFER STOCK DETERMINATION WITH VARIABLE LEAD TIMES
If both lead time and actual sales during lead time display significant variation about the
average, the determination of buffer stock become more complicated. The net effect of
interaction between fluctuating average sales and fluctuating lead times can be simulated,
however by a technique called Monte Carlo simulation.
To carryout this technique, one sets up tables which contain individual sale rates and lead
time values in proportion to relative occurrence. By randomly selecting both sale rate and a
lead time, one expected value of sales during lead time can be computed. If this value is
less than or equal to the average sales during the lead time, no buffer stock is needed. If this
value is greater than the average sales, the difference indicates the level of buffer stock
required to avoid stock out.
2.3 THE RE ORDER LEVEL POLICY
17
In this inventory level policy, an order of replenishment is placed when the stock on hand
equals or falls below a fixed value P known as reorder level. In this policy amount of
inventory held must be reviewed continuously.
When a replenishment order is placed with a reorder level policy, it is for a fixed quantity.
This is shown in fig 2.3.1
2.4 THE REORDER LEVEL POLICY WITH PERIODIC COUNT
As mentioned earlier, for a reorder level policy to operate successfully the amount of stock
on hand must be checked continuously. The rules governing this policy are that at beach
review, only if stocks on hand lies below or at the reorder level an order for replenishment
is placed. When replenishment order is placed it is for fixed quantity as in true reorder level
policy.
2.5 THE REORDER CYCLE POLICY
The stock on hand is reviewed periodically and a replenishment order is placed at every
review. However unlike other policies described earlier, when a replenishment order is
placed in this policy, its size is variable. This variable level of quantity is calculated as the
amount of stock which, if there were no lead time, bring the stock on hand up to some fixed
level M. Thus size of the replenishment order is M minus stock on hand, and can be
different at every review. This can be quite clearly seen in figure 2.5.1 which shows typical
stock situation when operating a reorder cycle policy.
2.6 THE (s,S) POLICY
The (s,S) policy is again a policy which reviews stock on hand periodically. The rules
governing the operation of this policy are that if at review stock on hand is below s a
replenishment order is placed; if stock on hand is above s no replenishment order is placed.
The criteria of when to place order is same as that of reorder level policy with periodic
reviews, with s now replacing P. however the size of replenishment is calculated on the
same basis as that of reorder cycle policy. The name (s,S) is given as S represents the fixed
18
inventory level from which the replenishment order size is calculated, and s the level to
which stock on hand must have fallen to for further replenishment order to be placed. The
figure 2.6.1 shows the typical inventory balance situation for this policy.
The (s,S) policy and reorder level policy with periodic review are exactly similar in there
method of assessing when a replenishment order is placed; but it is different methods by
which the size of the replenishment quantity is calculated that distinguishes the two
2.7 DEMAND FORECASTING
Before one can attempt to implement an effective inventory control system, one must
analyze the customer demand to which the business‘s inventory is subjected. All the
inventory policies are dependent on time, either in the form of review periods or lead time
distribution. The unit of time issued may perhaps a year for a slow moving item like such
as spare part for capital equipment to a day for a fast moving stock such as perishables.
When analyzing the customer demand per unit time, three main factors should be known.
The first is the average demand per unit time. An estimate of average demand per unit time
will give an indication of what demand will be expected in typical time period. It must be
realized that such a average data can only be calculated from past data
The second parameter that is required is the standard deviation which gives an indication of
how the actual demand per unit time fluctuates about the mean value already described.
With a measure of standard deviation available, one can begin to estimate the demand per
unit time will exceed a specified value during a certain time period.
Although the mean and standard deviation give an indication of the central tendency of the
value of demand per unit time and the spread of values about the central figures
respectively, for any statistical analysis of demand data to be complete it is necessary to
know from what type of probabilistic distribution the data on demand orders may be drawn.
Rarely will the pattern of demand will have an exact mathematical probability distribution,
but, for practical purposes the pattern of demand will come very close to a particular
distribution.
19
2.7.1 MOVING AVERAGE
This is the simplest form of an estimate for the mean value of a stationary demand process.
The moving average is calculated very simply by dividing the sum of demand in the last n
number of periods (say six months) by n (i.e. 6). Although simple to calculate, the moving
average has two main disadvantages, namely;
(a)It is necessary to store data for the past n-1 time period to calculate a fresh forecast.
(b)When beginning the calculation of moving average from demand data, because for
previous n-1 periods must be available, no true forecast can be made u until at least n
periods have passed. this can be overcome to a certain extent by using initialized moving
average
An initialized moving average is calculated by dividing the sum of the data so far available
by the number of periods from which that data is drawn until n-1 periods have passed; then
from the n period onwards the true moving average can be calculated.
At the beginning of the forecasting process it is always necessary to make an initial guess
at what the average might be. Without such guess the assumption is that the mean value is
zero, which will intern lead to large forecasting errors for the first few periods.
A measure is often used to indicate whether one method of forecasting is better than
another based on the sum of squares of forecasting errors which is one way of ensuring that
all errors make a contribution to the comparison. It can be generally stated that any type of
moving average. With an initial estimate will produce less than one without.
20
2.7.2 EXPONENTIAL SMOOTHING
Moving average has many of the characteristics of a practical method for smoothing out
the fluctuations in demand history to get a stable estimate of an expected rate of demand.
They have stable response to changes, and the rate of response can be controlled by the
selection of number of months included in the average.
The most serious draw back is to keep track of the past demand, so that we can adjust to the
moving totals, adding new information and dropping old.
Exponential smoothing is a special kind of moving average that doesn’t require keeping
long historical data and thus cuts down the data processing time required. It has stable
response to change, but the rate of response can be adjusted readily. The method can be
extended to the calculation of trends, and distribution of forecast errors.
To get new estimate of the average demand, add to the previous estimate a fraction of the
amount by which demand this month exceed that estimate. Demand below estimate can be
got by adding a fraction of negative quantity to the previous demand. The fraction is called
SMOOTHING CONSTANT, and is conventionally represented by α, the Greek letter
alpha; the rule can be abbreviated in the form of an equation.
New estimate =old estimate +α (new demand –old estimate)
=α (new demand) + (1-α) (old estimate)
Substituting a similar equation for old estimate
New estimate = α (new demand) + (1-α) [α (previous demand) + (1-α) (previous old
estimate)]
21
2.7.2.1 RESPONSES TO CHANGE
If there is a sudden temporary spurt in the demand, the exponential smoothing estimate will
increase to α times the magnitude of the spurt and then decrease steadily along a geometric
curve.
2.7.2.2 CORRECTION FOR TREND
Since we know that the average computed by the exponential smoothing will lag behind the
demand with a systematic trend, if we could estimate the magnitude of the trend, we could
make necessary correction to estimate the lag. We know that increase in estimate average
in successive months will equal the increase in actual demand. Therefore we could take the
estimate of current trend, the difference.
Current trend= new average – old average
Random fluctuations in the demand will cause minor fluctuations in the estimated average
demand and hence in the current trend.
The average trend could be computed by
New trend = α(current trend)-(1-α)(old trend)
And
Expected demand = new average+(1-α)/α( new trend)
In this form, it is necessary only to store the previously calculated values for the average
and for the trend, so that the data processing is simple.
22
2.7.2.3 SELECTING THE SMOOTHING CONSTANT
The value chosen for the smoothing constant determines how much the past demand has
any significant effect on the estimated average. As in the case of moving average , the more
past data added in the average, the smaller the error in the estimate, provide the basic
pattern of demand doesn’t change in the interval. If only fewer past months are included in
the averaging process, the response will be faster to the changes that do occur.
If a small value, say α=0.1 , is chosen as the smoothing constant , the response will be slow
and gradual, since it is based on the average of many past months. A high value, α=0.5, will
cause the estimate respond quickly, not only to real changes, but also to random
fluctuations.
2.7.3 MONTE CARLO SIMULATION
This method of forecasting is discussed below.
2.8 SIMULATION OF INVENTORY SITUVATION
Given an inventory situation in which either or both the demand and lead time distributions
cannot be assumed to approximate any specified mathematical distribution, the only
method of analysis can be using the method of simulation.
As the name suggest, the technique of simulation is used to reproduce a typical series of
situation which could have well occurred in practice. If the situations are simulated and
there mean value taken, it is assumed that this mean value represents what would most
likely happened in practice
2.8.1 GENERATION OF PSUEDO RANDOM NUMBERS
Ton simulate a series of typical cases existing in industry inventory situation , it is first
necessary to generate a series of what is hoped to be typical demand and lead time values.
These values and there corresponding probability of occurrence will presumably already by
23
past analysis as a probability distribution, and the method by which sample values are
extracted from such distribution to represent a typical situation is now detailed.
To generate data suitable for simulation purpose from information held in probability
distribution form, a source off random numbers must be available. These are drawn from a
uniform distribution and thus all numbers in the series will have, theoretically an equal
probability of occurring. Such a series of random numbers is termed pseudo because, as the
numbers are generated artificially, statistically speaking the series is bound to repeat itself
sometimes. In practice it is simple to ensure that the number is not repeating, therefore the
numbers can be considered truly as random.
(a) For a simple manual simulation, sets of pseudo random numbers can be obtained from
mathematical tables.
(b) When using computer simulation, many computers have programs that automatically
generate pseudo random numbers.
(c) A simple method of generating random numbers with equal probability of occurrence
between 00 and 99 is to take a two figure number, square it and take the two central digits
as the next random number and then repeat.
Take 76, square is 5776, next random number is 77
77, 5929, 92
92, 8464, 46
46, 2116, 11
11, 0121, 12
The above method is known as mid square method
Having obtained a series of random numbers it is then quite simple to extract typical
demand and lead time values from there respective distributions. Consider the demand and
24
lead time distributions indicted in tables 2.9.1 and 2.9.2. For the demand distribution it can
be seen that the probability of a weekly demand value between 0 and 9 is 10 percent.
It is apparent that if for this example a series of random numbers between 1 and 1000 is
generated will, then those numbers between 1 and 100 be allocated to the 0 to 9 weekly
demand class, the probability of the class of demand values will also be 10 percent.
The number allocate to all other classes can be similarly arranged and the midpoint value of
the class is usually taken to represent the whole class.
Thus series of random numbers 48,560,876,849 and 251 would generate the weekly
demand values 4.5 , 24.5, 34.5, 34.5 and 14.5 units respectively or could be used to
generate the following lead time values ; 1,4,5,4,and 3 weeks.
Table (2.8.1) DEMAND DISTRIBUTION
Demand /week
In units
Class mid - point Probability of
occurrence, %
Allocated random
number range
0- 9 4.5 10 0-100
10- 19 14.5 30 101-400
20- 29 24.5 30 401- 700
30- 39 34.5 20 701- 900
40- 49 44.5 5 901- 950
50 and above 54.5 5 950- 1000
25
Table (2.8.3) LEAD TIME DISTRIBUTION
LEADTIME
DURATION , WEEKS
PROBABILITY OF
OCCURRENCE,%
ALLOCATED RANDOM
NUMBER RANGE
1 5 1-50
2 5 51-100
3 30 101-400
4 45 401- 850
5 10 851-950
6 , and longer 5 951-1000
26
CHAPTER 3
IDENTIFICATION OF THE
PROBLEM AND OBJECTIVE
27
3.1 INTRODUCTION
BHEL Tiruchirappalli plant was established in the year 1963 for the manufacture of high pressure boilers with technical assistance from M/S Skoda Exports of Czechoslovakia under Indo – Czech economic co-operation programme with an initial investment of Rs.24.5 Crores. It started its production in 1965 and reached its rated capacity of 750 Mw in record time. In 1979, the Trichy complex took a significant step towards backward integration by starting production of steel pipes and tubes for its Boilers at the SSTP within the Trichy complex. This project was initiated with an investment of Rs.58 Crores. Now this unit has more than 10,000 employees and its operations expanded over a township of its own which comes to nearly 2000 acres.
THE PROBLEM
There are about 1206 different varieties of tools including turning, drilling, reaming,
tapping and milling tools in the stock list of tools whose annual usage value is about 5
crores.
The inventory control department is unable to supply tools regularly to shop floor due to
frequent stock outs. Management is also feeling that large amount of capital is locked up in
the form of inventory of tools.
OBJECTIVES
The objective in broader terms can be stated as:
(a) To avoid frequent stock outs
(b) To decrease the average inventory carried per year
(c) To suggest general improvement to the system
3.2 EXISTING METHOD OF INVENTORY MANAGEMENT
There are six tool cribs in different sections of the factory which furnish tool to the shop
floor. Stores issue tools to tool cribs whenever tool cribs request. Issue is marked by raising
28
of tool issue voucher in five copies which details, along with particulars of tool issued, the
quantity demanded, quantity issued, value, bin card balance and number of tool crib
(indenter).
A copy of store issue voucher goes to inventory control department where a record is
maintained, which records the details of date of issue, name of indenter, voucher number,
quantity issued and balance remaining. This is the point from where control is exercised.
Inventory control department advices stores regarding the quantity to be issued to different
tool cribs, keeping in mind the inventory position of the tool, probable future demands and
pending purchase orders. But there is not much restriction for major consumers regarding
the quantity to be drawn each time. They are allowed to draw in bulk. So, if a tool crib
draws a certain quantity of a particular tool, normally it will not come for the same tool for
a few months, ranging from 1 or 2 months to one year at times. But records in inventory
control department shows once drawn quantity as a consumed quantity.
ABC CLASSIFICATION
In the existing ABC classification, the range of rupee value of annual usage value is as
follows:
Rs. Classification % of items % of annual
usage
Up to 9999 C 45.0 4.0
10000 to 199999 B 51.0 69.0
200000 and above A 4.0 27.0
ECONOMIC ORDER QUANTITY (EOQ)
29
It is the optimal order size to minimize the inventory costs
Assumptions
(a) Demand is known with certainty over a period of time
(b) No shortages are allowed
(c) Lead time for receipt of order is constant
(d) The order quantity is received all at once
The formula for calculating EOQ is;
Annual ordering cost=
Annualcarryingcost=
Total cost= +
Where; cost per order
Carrying cost
Demand
Order size
The ordering cost and carrying cost taken into account are Rs.15 and 25%respectively.
Practically EOQ will be adjusted to AOQ (actual order quantity), which is a near whole
number or other figure around EOQ that will fetch some discount; and an order is placed.
30
SAFETY STOCK
The present policy of safety stock is as follows:
For A items 1 month’s consumption is kept as safety stock
For B items 3 months consumption is kept as safety stock
For C items 6 months consumption is kept as safety stock
Irrespective of whether it is fast moving, slow moving or non moving tool.
LEAD TIME
The present policy is to consider 4 months as lead time for carbide tips, inserts and spares
for t-max tool holders and 6 months for remaining tools.
REORDER LEVEL POLICY
Reorder level is got by adding safety stock to lead time consumption of the particular item.
A stock recoupment memo is raised by the inventory control department whenever the
inventory level falls to reorder level.
ORDERING PROCEDURE
A systematic procedure is followed for procuring each and every item.
Inventory control department receives copy of the following documents.
1. Stock recoupment memo
31
2. Purchase enquiry
3. Purchase order
4. Forwarding note for way bill
5. Inspection note
6. Stores receipt voucher /rejection advice
Due to this , inventory control department can at any time refer to and tell at what
procurement stage a particular item is, for which SRM is raised.
The latest average ordering cost is rs.156/-per order ,as computed by the material planning
department as against rs.15/- now under use for calculating the EOQ .This average cost includes
the cost incurred by the purchase department on some open and global tenders also. But most of the
tenders for tools are limited tenders so an optimistic estimate of rs.120/- per order is considered
suitable for tools and the same is used in calculations.
Whenever the level of inventory of tools falls to reorder level the SRM is raised and sends
to purchase department for procurement action. If another tool of same type of tools, but of
different specification /size falls below ROL after few days another SRM is raised. So
within few days another purchase enquiry is floated by the purchase department to the
same supplier who can supply both the sizes of same tool. There are in some categories of
tools 100 sizes existing .so this process of raising SRM and floating purchase enquiries
become frequent.
Due to this, inventory control department has started raising SRMs for all tolls once in a year. So
the SRM is raised for annual requirement and supplier is asked to supply in staggered deliveries. In
deciding the number of staggered deliveries the following heuristic approach is followed.
For A items annual requirement is to be supplied in 3 or 4 equally spaced, staggered deliveries
For B items 2 equally spaced staggered deliveries
32
For C items only one delivery
CHAPTER 4
COMPANY PROFILE
33
PROFILE OF THE ORGANIZATION
BHEL – AN OVERVIEW
Bharat Heavy Electricals Ltd. is the largest engineering and manufacturing enterprise
in India in the energy related/infrastructure sector. BHEL was established more than 40
years ago, ushering in the indigenous Heavy Electrical Equipment industry in India, a
dream which has been more than realized with a well-recognized track record of
performance. It has been earning profits continuously since 1971-72 and achieved a sales
turnover of Rs. 3,736 Crore in 2006-07.
BHEL caters to core sectors of the Indian economy viz., Power generation and
Transmission, Industry, Transportation, Renewable energy, Defence etc. The network of
BHEL is very wide with 14 manufacturing divisions, 4 power sector regional centers, over
100 project sites, 8 service centers and 15 regional offices .This enables the company to be
closer to its customers and provide them with suitable products, systems and services
efficiently and at competitive prices.
BHEL has attained ISO 9001 certification for quality management and all
manufacturing units/divisions of BHEL have been upgraded to the latest ISO-9001:2000
version. All the major units/divisions of BHEL have been awarded ISO – 14001
certification for Environment Management Systems and OHSAS-18001 certification for
Occupational Health and Safety Management Systems. BHEL is now on its journey
towards Total Quality Management .It is the only PSU among the 12 Indian companies to
figure in ‘Forbes Asia Fabulous 50’ list .BHEL has its head quarters at Delhi and its
corporate research and development division at Hyderabad .The company’s inherent
potential coupled with its strong performance over the years has resulted in it being chosen
as one of the’ NAVARATNA’ Public Sector enterprises.
BHEL – AS A MANUFACTURER
Backed by technical tie-up with reputed international organizations BHEL offers
total services to customers in conventional and non-conventional
energy ,industry ,transmission , oil ,transportation and telecommunication .BHEL
34
manufactures and supplies major capital equipment and systems like Captive power plants,
Centrifugal compressors , Drive Turbines , Industrial boilers and auxiliaries , Waste heat
recovery boilers ,Gas turbines ,Pumps ,Heat exchangers, Electrical machines,
Valves ,Heavy castings and Forgings ,Electrostatic precipitators, ID/FD fans ,Seamless
pipes etc to a number of industries other than power utilities like metallurgical , mining,
cement, paper, fertilizers, refineries and petro-chemicals etc .BHEL has emerged as a major
supplier of controls and instrumentation systems especially distributed digital control for
various power plants and industries
Most of the trains in Indian Railways are equipped with BHEL’s traction propulsion
systems and controls. The systems supplied are both with conventional DC drives and state
of the art AC drives. India’s first underground metro at Kolkata runs on drives and controls
supplied by BHEL. It has been manufacturing and supplying a range of Renewable Energy
systems and products. It includes Solar Energy systems like PV modules ,PV power
plants ,Street Lighting ,Solar pumps and Solar water heating systems .BHEL is supplying
onshore Drilling rig equipment like Draw works, Rotary –table ,travelling
block ,Swivel ,Mast and Sub structure ,Mud systems and Rig electrics to ONGC and Oil
India Ltd. It has also the capacity to supply complete onshore Drilling rigs , Super-deep
drilling rigs ,Desert rigs ,Mobile rigs ,Work over rigs and sub -sea well heads .BHEL
supplies a wide range of products and systems for transmission and distribution
applications. The products manufactured by BHEL include Power transformers ,Instrument
transformers ,Dry type transformers ,Shunt reactors ,Capacitors ,Vacuum and SF6
switchgear ,Gas insulated switchgears ,Ceramic insulators etc. To remain competitive and
meet customer’s expectations, BHEL lays great emphasis on the continuous up-gradation
of products and related technologies and development of new products. The company has
upgraded its products to contemporary levels through continuous in-house efforts as well as
through acquisition of new technologies from leading engineering organizations of the
world .BHEL’s investment in R&D is among the highest in the corporate sector in India.
Products developed in-house during the last 5 years contributed 14.5% to the revenues in
2006-07.
35
BHEL – AS AN INTERNATIONAL PLAYER
BHEL has established its references in 70 countries across the world. These
references encompass almost the entire range of BHEL products and services ,covering
Thermal ,Hydro and Gas-based turnkey power projects ,Substation projects ,Rehabilitation
projects ,Transformers ,Compressors ,Valves and oil field equipment ,Electrostatic
precipitators ,Photovoltaic equipment ,Insulators ,Heat Exchangers ,Switchgears ,Castings
and Forgings etc.
Some of the major successes achieved by BHEL have been in power projects in
Oman ,Libya ,Malaysia ,Saudi Arabia ,Iraq ,Bangladesh ,Sri
Lanka ,China ,Kazakhstan ,Cyprus ,Malta ,Egypt ,Thailand ,Indonesia ,Sudan ,New
Zealand ,Azerbaijan ,Bhutan ,Nepal ,Taiwan ,Tajikistan ,Afghanistan and substation
projects and equipments in various countries. Execution of these overseas projects has also
provided BHEL the experience of working with world renowned Consulting Organizations
and Inspection Agencies .The company has been successful in meeting demands and
requirements of international markets in terms of complexity of the works as well as
technological, quality and other requirements .BHEL has entered into collaboration with
TOA-VALVE company of Japan and National Supply Company of USA for oil field
equipment and also with Dresser Industries Inc ,USA.
VISION:
A world-class, innovative, competitive and profitable engineering enterprise, providing total business solutions.
MISSION:
To be an Indian Multi-national Engineering enterprise, providing total business solutions through quality products, systems and services in the field of energy, transportation, industry, infrastructure and other potential areas.
36
VALUES:
Zeal to excel and zest change
Integrity and fairness in all matters Respect for dignity and potential of individuals strict adherence to commitments Ensure speed of response Foster learning ,creativity and team work Loyalty and pride in the company
CORPORATE FINANCIAL HIGHLIGHTS 2006-2007
FINANCIAL HIGHLIGHTS(Rs .in Crores)
2006-2007 2007-2008
Turnover (up 15%) 18,739 21,608
Profit before tax (up 18%) 3,736 4,395
Net profit (up 17%) 2,415 2,815
Orders booked (up 41%) 35,643 50,256
Outstanding order book (up 56%) 55,000 85,500
BHEL TIRUCHIRAPPALLI
BHEL Tiruchirappalli plant was established in the year 1963 for the manufacture of high pressure boilers with technical assistance from M/S Skoda Exports of Czechoslovakia under Indo – Czech economic co-operation programme with an initial investment of Rs.24.5 Crores. It started its production in 1965 and reached its rated capacity of 750 Mw in record time. In 1979, the Trichy complex took a significant step towards backward integration by starting production of steel pipes and tubes for its Boilers at the SSTP within the Trichy complex. This project was initiated with an investment of Rs.58 Crores. Now
37
this unit has more than 10,000 employees and its operations expanded over a township of its own which comes to nearly 2000 acres.
MANUFACTURING DIVISIONS UNDER BHEL TIRUCHIRAPPALLI :
High pressure boiler plant Seamless steel tube plant Piping centre Industrial valves plant
PRODUCT PROFILE
Fossil boilers Steam generators Pressure vessels Heat exchangers Studded tubes Piping systems and pipe fittings Soot blowers Chemical recovery boilers Gravimetric feeders Valves Nuclear stream generator and reactor headers Sub sea well heads Armoured vehicles for defence Thermo pressed components Seamless steel tube Spiral finned tubes Rifled tubes Studded tubes Scraped –fin tubes
FINANCIAL HIGHLIGHTS
38
Record turnover of Rs.1000 cr. jump to reach Rs. 4,575 Profit more than double, touches all time high of Rs.804 cr. 63% rise in value added per employee
FINANCIAL HIGHLIGHTS 2006-2007 2007-2008
Turnover (up 22%) 4,606 cr. 5,606 cr.
Profit before tax (up 72%) 872 cr. 1,496 cr.
Value added / employee (up 40%) 19.7(lakh) 27.6 (lakh)
DIVISIONAL PLAN OF BHEL TIRUCHIRAPPALLI COMPLEX
The Divisional Plan of 2007-2012 of BHEL Trichy aims at top line growth from the present budgeted level during 2007-2008 Rs. 5,606 crore to about Rs.10,005 crore in the next 5 years . This translates into a compounded annual growth rate of around 18.7%.
MALOR PARAMETER
Growth Technology Investment Competitive scenario Manpower
AWARDS WON BY BHEL 2007-08
Employees’ Suggestion scheme award- INSSAN CII – Exim for significant achievement in TQM Gold Medal for QC from International Quality Circle meet at Beijing Prime Minister’s Shram Awards Vishwakarma Rashtriya Puraskars Tamil Nadu Government’s highest awards for workmen
39
National /regional work skills competition prizes Prize for 3-D Plant Model from USA
CHAPTER 5
RESEARCH METHODOLOGY
40
TITLE OF THE PROJECT
A study on the inventory management of tools in BHEL trichy.
OBJECTIVES OF THE RESEARCH
The objective in broader terms can be stated as:
(a)To avoid frequent stock outs
(b)To decrease the average inventory carried per year
(c)To suggest general improvement to the system
RESEARCH PROBLEM
Inventory control department is unable to supply tools regularly to shop floor due to
frequent stock outs.
SCOPE OF THE STUDY
POPULATION
There are about 1206 different varieties of tools including turning, drilling, reaming,
tapping and milling tools in the stock list of tools.
41
TIME FRAME
Present
PLACE OF STUDY
Trichy
LIMITATIONS OF THE RESEARCH
Unavailability of demand details for many tools.
Analysis was done for the entire population of tools in BHEL using the best available
statistical tools. Not sure whether some other statistical tools were available for more
accurate analysis.
It is assumed that the information provided by respondents is true.
RESEARCH DESIGN
TYPE OF RESEARCH: Analytical
SAMPLING DESIGN
Sampling technique used: Census
For all 1206 varieties of tools past available consumption details were collected either
month wise or year wise and lead time details have been collected tool wise.
42
Sample size:
1206 different varieties of tools including turning, drilling, reaming, tapping and milling
tools in the stock list of tools.
INSTRUMENT USED FOR DATA COLLECTION
Interview schedules
Interview schedules are used to collect information regarding the process of the tool
engineering department and the procedure for procurement of tools. These interviews
helped to understand the activities of tool cribs. It is also used to find out some of the
intricate details about the problems in the present system and their recommendations to
improve the present system.
Observation
Activities of the tool cribs were directly observed.
43
CHAPTER 6
DATA COLLECTION
AND
PRELIMINARY ANALYSIS
44
CONSUMPTION DETAILS
Month wise or year wise past available consumption details of all tools have been collected
from the tool engineering department. For A items and some of the fast moving B and C
items month wise consumption details are collected and for the remaining only year wise
details are collected as there is no regular movement and as a few2 bulk withdrawals mark
the years consumption.
LEAD TIME ANALYSIS
Lead time is the time elapsed between raising SRM by the indenter and raising of SRV
(stores receipt voucher) by the stores. All the available past lead time details have been
collected tool wise.
ANALYSIS
The tools available in the stock list, after a minor classification depending upon their use,
are shown in column (1), (2) and (3) of appendix -1
A cursory check revealed that the reasons for frequent stock outs in tools are multifold,
namely due to:
(1) Random fluctuations in demand
(2) Errors in forecasting demand
(3) Inconsistent lead times
(4) Wrong time of raising SRMs.
45
RANDOM FLUCTUATIONS IN DEMAND MAY BE DUE TO FOLLOWING
REASONS:
(1) The fact that they are tools and the life of the tool varies from make to make and
situation to situation.
(2) The concern itself is a work order type of concern. so the type of work changes
frequently and hence the need for different types of tools at different time arises.
These difficulties can be overcome to some extent, by providing proper amount of safety
stock. This is discussed in detail in later sections
ERRORS IN FORECASTING DEMAND
Before discussing the reasons for this it will be worthwhile knowing the present practice of
forecasting demand.
PRESENT PRACTICE
From the past one or two year’s consumption pattern, probable demand for the coming year
is estimated. Demand at times is taken as equal to the annual requirement suggested some
years back in the report on rationalization of tools.
The possible reason for the se estimates to go wrong at times can be:
1) When there is a bulk withdrawal of an item by a tool crib, it is followed by a slow moving
or non- moving period. This slow/non-moving period is at times as long as one year, which
is wrongly interpreted as decrease in demand and a forecast, is made on this basis. But
actually the items drawn in bulk are being used during the following period. This shows
that another sub inventory is maintained in the tool cribs, which the record of inventory
control fail to show, immediately after items of bulk withdrawal are consumed , concerned
46
tool cribs come for another bulk withdrawal , which results in the stock out. This sudden
increase in the demand was not anticipated.
2) Annual requirement suggested by the report on rationalization of tools is going wrong
frequently as the work content of the concern has changed considerably from the time those
recommendations were made.
Even though there are 1206 items in the stock list of tools, all are not moving with same
speed from the stock. Some are consumed very fast, some are moving regularly, some
slowly and some are not at all moving. So it is recommended equal treatment in any
respect of analysis is not needed and hence the tools are classified four categories , namely
, fast moving(F), regularly moving(R) ,slow moving (S), and non-moving (N) tools
depending upon the their movement and some could not be classified which are new items
and have not yet established any movement pattern.
DEMAND FORECASTING HAS BEEN DONE USING THE FOLLOWING
STATISTICAL METHODS:
1) Monte Carlo simulation
2) Exponential smoothing
3) Simple average, depending up on the suitability.
The details of which are discussed in later section ….
ABC analysis is done and details are discussed in section
INCONSISTENT LEAD TIMES:
The present policy is to consider 4 months as lead time for carbide tips, inserts, and spares
for t max tool holders and 6 months for the remaining. But practically lead time is varying
from 3 to 12 months and even more at times.
WRONG TIMING OF RAISING SRMs
After following the procedure for raisin the SRMs yearly once, the concepts like reorder
level and lead time consumption are ignored. SRMs are normally raised only when the
stock falls to safety stock level or just above that. Safety stock assures only 1,3 , or 6
47
months consumption for A,B and C items respectively, hence invariably a stock out occurs
immediately after 1,3,and 6 months for A,B and C items respectively, after the SRM is
raised and replenishment doesn’t occur. Theoretically speaking, we don’t need ROLs if we
order once in a year the annual requirement, but due to various reasons the stock may be
exhausted even earlier. Another reason is that for some items annual procurement is not
done when the inventory control department feels that the stock in hand will be sufficient
for the year following. But this also becomes untrue due to various reasons stated earlier.
Because of these reasons, it is felt that procurement action should be taken whenever an
items stock falls to reorder level.
6.1 DEMAND FORECASTING
As mentioned earlier the tools have been classified into four classes depending on their movement
(consumption rate) as follows:
Fast moving items (F) items which records withdrawals in 8 or more months /year
Regular moving ,, (R) items which records withdrawals in 4 to 7 months /year
Slow moving ,, (S) items which records withdrawals in 1 to 3 months /year
Non moving ,, (N) items which records no withdrawals in one or more years.
Non classified ,, (NC) items which are mew and have not yet recorded sufficient
number of Withdrawals.
The reasons for demand forecasting and the techniques used for it is discussed in earlier sections.
The techniques are discussed in detail in section 2.8 and 2.9.
Monte Carlo simulation is used whenever the demand recorded is highly random and not following
any statistical distribution or trend.
Exponential smoothing is used whenever the demand has shown any trend in consumption pattern.
For many items, these techniques of forecasting could not be applied due to non availability of
sufficient demand details. In such case average demand per year from the past 2 to 3 years
consumption is calculated and multiplied with a factor which will take into account the time trend.
48
And the factor is being decided heuristically, keeping in mind the value classification (A,B and C)
and movement classifications (F,R,S, or N). The factors used in different situations are shown in
table below.
TIME TREND MULTIPLYING FACTOR USED FOR FORECASTING
MOVEMENT
CLASSIFICATION
TIME TREND
MULTIPLYING
FACTOR
REFERENCE NUMER
USED IN COLUMN OF
APPENDIX 3
AF 1.20 2
AR 1.20 2
AS 1.10 1
BF 1.30 4
BR 1.25 3
BS 1.10 1
CF 1.50 5
CR 1.30 4
CS 1.20 2
Reference numbers 6 and 7 represent Monte Carlo simulation and exponential smoothing
respectively.
49
EXAMPLES:
MONTE CARLO SIMULATION:
Monte Carlo simulation for 5 years has been done (for 300 months) average consumption per year
is calculated, which is considered as future demand. The following example shows simulation done
on past monthly consumption details of hack saw blade (code no 28/580/002).
Table below shows demand distribution and corresponding allocated random number ranges.
DEMAND DISTRIBUTION
DEMAND PER
MONTH
CLASS MID
POINT
PROBABILITY
OCCURANCE ,
%
ALLOCATED
RANDOM
NUMNER
RANGE
0- 250 125 35 0-350
251- 500 375 28 351-630
501- 750 625 22 631-850
751- 1000 875 2 851-870
1001-1250 1125 5 871-920
1251-1500 1325 8 921-1000
50
SIMULATION RUN FOR 5 YEARS
Hack- saw blade – 12” × ” ×23G × 24 TPI
Code No.28/580/002.
Total demand for 60 months =31700 UNITS
Average demand per year = ×12
=6340=6400(approximately)
Earlier forecast was 5500 units per year
51
Hack saw blade (28/580/002) Demand simulation run
DEMAND
RANDOM
NUMDER
CONSEQUENT
MONTHLY
DEMAND
DEMAND
RANDOM
NUMDER
CONSEQUENT
MONTHLY
DEMAND
371 375 842 875
950 1325 513 375
781 625 718 625
687 625 506 625
471 375 814 875
682 675 878 875
869 875 710 625
093 125 656 625
577 375 225 125
189 125 884 875
220 125 844 875
362 375 116 125
785 625 921 875
163 125 664 625
069 125 896 875
204 125 321 375
545 375 624 625
016 125 696 625
155 125 781 875
614 625 569 625
52
783 875 301 375
194 125 936 875
414 375 225 125
168 125 084 125
679 625 616 625
070 125 316 375
874 1125 961 875
710 625
263 125
664 625
940 1325
490 375
612 625
53
EXPONENTIAL SMOOTHING:
This method can be more useful and representative if the record of the inventory control
shows the regular consumption details. Departmental records shows only bulk withdrawals
but will not reveal the pattern of consumption. For example if there is a withdrawal of 250
numbers of a particular tool in a particular month and no withdrawals in following months,
it cannot be taken as demand in the month when the withdrawal was made was very high
and no demand in the following months. Hence it gives a wrong picture if we take monthly
or bi monthly demand for the forecast. So for convenience and to make preliminary attempt
of forecasting by statistical methods, 6 months withdrawals are taken as one reading and a
forecast is made.
The table below shows a method of applying exponential smoothing technique. The tool
considered is insert (28/843/004) with an old forecast of 960 no per year.
Reference for the table below
Column (1): Period - period during which demand occurred
Column (2): Demand- demand during the current period
Column (3): Average – initial estimate of average is to be put. Then to get the average for
current period, average in the previous month is multiplied by (1-α) and times the
demand in the current period is added.
Column (4): Change – average computed fore the current month minus average computed
in the previous month.
54
Column (5): Trend – initial trend can be taken as zero, there after new trend is got by
multiplying the previous trend by (1-α) and adding α times the current trend.
Column (6): Expected demand – is got by multiplying the trend in (column 5) by
,calculated below, and adding average.
Column (7): Forecast – forecast for one year is got by multiplying the expected demand
(column 6)by 2 and adding L(L+1)/2 times trend to extrapolate trend , where in L is the
number of periods in forecast. Here it is 2 (because 2 six months period in an year)
EXPONENTIAL SMOOTHING
Name of the tool : INSERT
Code no. : 28/843/004
Smoothing constant α : 0.5; = 1
PERIOD
(1)
DEMAND
(2)
AVERAGE
(3)
CHANGE
(4)
TREND
(5)
EXPECTED
DEMAND
(6)
FORECAST
(YEARLY)
(7)
INITIAL 300
JAN– JUN 05 325 312.5 12.5 6.25 319 657
JULY-DEC
05
400 356.0 43.5 28.0 384 852
JAN- JUN 06 670 513.0 157.0 92.5 605 1488
55
JULY–DEC
06
800 656.0 143.5 118.0 775 1904
JAN-JUN
07
900 778.0 118.75 122.0 900 2166
JULY–DEC
07
1065 921.0 143.4 132.7 1054 2506
A high value of α=0.5, chosen as it causes the estimate to respond quickly, not only to real
changes , but also to random fluctuations.
6.2 ABC ANALYSIS
EXISTING classification has been given in previous section. It can be observed that the
percentage of B items (51.0%) more than C items (45.0%) and A items occupy
comparatively less percentage (27.0%) of annual usage value compared to B items(69.0%).
A new ABC classification is developed taking into account the new forecasts. According to
new classification A items are those whose AUV is greater than 5000 Rs. , B items are
those whose AUV is less than 5000 and greater than 1000, and C items are those whose
AUV is less than 1000 Rs.
The ABC break up is as follows:
RS. CLASS % of items % of annual
usage value
100000 and above A 8.5 49.5
20000 to 49999 B 35.5 39.0
Up to 19999 C 56.0 11.5
56
57
RUPEE VALUE ANALYSIS
AUV range Noof
items
%of items Cumulative
%
AUV %of
AUV
Cumulative
% of AUV
From To
200000 & above 36 3.0 3.0 14,210,520 29.8 29.8
180000 199999 8 0.6 3.6 1,477,600 3.1 32.9
160000 179999 12 1.0 4.6 2,032,800 4.3 37.2
140000 159999 13 1.1 5.7 1,937,640 4.0 41.2
120000 139999 16 1.3 7.0 2,036,280 4.3 45.5
100000 119999 17 1.4 8.4 1,870,860 3.9 49.4
80000 99999 40 3.3 11.7 3,617,380 7.6 57.0
60000 79999 52 4.3 16.0 3,422,400 7.2 64.2
40000 59999 98 8.2 24.2 4,917,180 10.3 74.5
20000 39999 238 19.8 44.0 6,703,700 14.1 88.6
0 19999 665 56.0 100 5,416,840 11.4 100.0
Total 1195 100 47643200 100
6.3 STAGGERED DELIVERIES
At present only one order is placed per year per item, for complete annual requirement the
supplier is asked to supply in staggered deliveries. The numbers of equally placed
staggered deliveries are: 3 or 4 for A items, 2 for B items and C items are procured in one
installment. So the conventional economic order quantity is not made use of, but an attempt
is made to avoid very heavy inventories by asking the supplier to supply them in staggered
deliveries.
In this part, an equation is found suitable for deciding up on the number of equally spaced
staggered deliveries so that the cost incurred is minimum.
58
The following costs are associated with a staggered delivery:
1) The cost of reminding the supplier whenever the time of delivery is nearing.
2) The cost of preparing inspection note.
3) The cost of inspection.
4) The cost of preparing the stores receipts voucher/rejection advice.
The cost of staggered delivery is taken as one third of ordering cost (Rs.40/-per delivery). It
may be noted that the cost of first delivery after purchase order is placed is included in the
ordering cost. The cost of staggered deliveries is taken into account only in the following
deliveries.
ECONOMIC NUMBER OF STAGGERED DELIVERIES
N(optimum) =
Where;
A= annual requirement in rupees
I = inventory carrying cost in % in year
K'= cost of staggered delivery
59
Example:
Name of the tool: Tool bit (square)
Code no: 28/501/004
Annual requirement forecasted: 250 Nos.
Unit cost: 33.20
Hence the annual requirement in rupees (A) =250×33.20
= 8300/-
Inventory carrying cost (I) = 25% = 0.25
Therefore, N (optimum) =
=5.09
i.e. N = 5
For our calculations I and are constants ,hence the formula can be rewritten
as
N =
As the number of staggered deliveries cannot be a fraction number, the N obtained is
rounded off to the nearest whole number.
60
The method of getting material in staggered deliveries is recommended only for A and B
items.
6.4 LEAD TIME ANALYSIS
One the reason for the stock outs is inconsistent lead times in the present system. The
present policy is to consider 4 months as lead time for carbide tips, inserts and spares for t-
max tool holders and 6 months for remaining tools. But in reality the assumptions are going
wrong very frequently. The percentage of orders that is getting default with present policy
of lead time for different categories of tools is analyzed. So it is felt that just two
classifications of lead times are not sufficient and a more meaningful classification is
necessary.
6.5 BUFFER STOCK DETERMINATION
Buffer stock is provided to meet the fluctuations in demand and fluctuations in lead time.
A measure of these fluctuations is the lead time demand standard deviation.
Lead time standard deviation can be calculated as follows:
σL = /n
Where = each of the individual leadtime demand values
=average lead time demand
n=number of individual lead times
61
Buffer stock (B) = K.σL
K is standard normal deviate
σL is lead time standard deviation
It is the value of K which determines what the service level or probability of a stock out
occurring will be. Values of k and the corresponding probability of stock out or service
level can be found in normal probability table, and the following values will give some
indication of what level of service is provided for k = 1,2 and 3
K= 1, a stock out will occur on average 15.9% of the time
K=2, a stock out will occur on average 2.3% of the time
K=3, a stock out will occur on average 0.1% of the time
TREATMENT FOR A AND B ITEMS:
The lead times are very long from 4 to 10 months. The availability of past consumption
details are very less (3 to 4 years consumption details.). Hence the readings available are
less even for the fast moving items.
Buffer provisions:
1) Buffer sock is provided at 2 times lead time standard deviation for:
(a) Items which have 5 or more staggered deliveries per year
(b) All fast moving and regular moving A items
(c) All fast moving B items
2) Buffer stock is provided 1.5 times lead time standard deviation for all regular moving B
items
62
3) Buffer stock is provided at 1 lead time standard deviation consumption for all slow moving
items.
C ITEMS:
The lead time assumed for C items is one month. No rigorous treatment is recommended as
the money value carried by them is comparatively less.
The movement classification and recommended safety stock in month’s consumption are shown
in the following table.
Movement
classification
No of items Recommended safety
stock in months
consumption
F 1 3
R 95 2
S 226 1
N 220 0
NC 121 2
It can be seen from the table that the maximum safety stock is of 3 months consumption
unlike 6 months consumption for all C items in the present policy. It will reduce the
average inventory carried by C items.
63
6.6 REORDER LEVEL
The quantity to be intended annually is not the annual requirement forecasted but it is
= (annual requirement forecasted +lead time consumption + buffer stock) - stock in hand.
Stock on hand not only includes the current stock actually held but also any outstanding
replenishment orders.
It can be observed that if the if stock on hand is more than lead time consumption plus
buffer stock, the annual indented quantity will be less than the annual requirement
forecasted. Due to this, inventory is not piled up if the item is not moving.
It can be observed that if stock on hand is less than the lead time consumption plus buffer
stock, the annual indented quantity will be more than the annual requirement forecasted,
which means the increased demand has been taken into account.
Replenishment of buffer stock is done, if it get consumed.
Practically there is no need for separate reorder levels as the annual requirement is ordered
at once and this is done yearly as per the time table, depending up on the lead time.
Any how the complete annual requirement is not received at a time. It is received in
staggered deliveries. A mail is sent to supplier one month in advance of a staggered
delivery, reminding him of promised date of supply and quantity due in the delivery.
The conventional formula for reorder level is ,
Re order level = buffer stock +lead time consumption
64
This will not hold good due to following reason
The sum of buffer stock and lead time consumption will be very high , on average it is 11
(2+9) months consumption. The quantity require for 11 months cannot be predicted
accurately.
So following method of procurement is recommended if the annual requirement is
exceeded by the demand.
Local procurement action should be taken. The replenishment quantity should be sufficient
to meet the demand till the subsequent staggered delivery of the annual indent arrives. For
the local procurement, the lead time is taken as one month. Therefore for thus case,
Reorder level = buffer stock + one months consumption
65
CHAPTER 7
RECOMMENDATIONS
AND
CONCLUSION
66
As pointed out in the earlier section the objective of the studies are to avoid stock outs and
to decrease the average inventory carried. At the outset it looks as if the objectives are
contradicting to each other. But the present work shows that it is possible to avoid stock
outs and also to decrease the average inventory carried if some scientific methods of
controlling inventory are followed.
The main reason for the present state of frequent stock outs are , namely ,errors in
forecasting, non recognition of very high lead time prevailing and wrong timing of raising
stock recoupment memos. In trying to avoid these difficulties it is found that equally
rigorous treatment is not needed for all items. Hence all the tools are classified into two
categories , one depending upon the annual usage value (ABC classification) and the other
depending upon the rate of consumption or movement ( fast ,regular, slow and non-moving
items).
The following recommendations are made after analyzing in detail each of the situation:
1) Statistical methods of forecasting, namely, exponential smoothing, Monte Carlo simulation
and averages are to be used for more representative forecast. It is recommended to closely
follow up and record the consumption details of all tools in all the tool cribs.
2) All the C items are recommended for local purchase to avoid high ordering cost. It is
recommended that equally rigorous treatment is not needed for C items. More attention is
to be given for A and B items whose annual usage values are higher.
67
3) The stock recoupment memos are raised annually for A and B items according to a time
table for a quantity = (annual requirement forecasted +lead time consumption + buffer
stock) - stock on hand. Due to this 90% of the orders mature before the lead time taken into
account elapses as against 30% to 80% at present. The time table is drawn so that the
requirement will be available for a calendar year. Time table can also be formed to fit
financial year. A time table is recommended to avoid confusion and to distribute the work
evenly.
4) Buffer stock for each tool, depending on the lead time standard deviation of demand can be
calculated. This calculated buffer stock differs considerably from the existing method of
fixing, where in, the pattern of consumption which is the major factor to find out the buffer
stock is not taken into account but only annual usage value is considered. It may be pointed
out that in the new method both the rupee value and consumption rate are considered.
5) Another objective is to minimize the average inventory carried. the conventional economic
order quantity formula is not wholly applicable here as the quantity ordered per order is
annual requirement and it is not equal to economic order quantity. This is a constraint.
Hence a new formula to find economic number of staggered deliveries (N) to minimize the
total cost is developed. This formula considers only three cost : namely , ordering cost, cost
of staggered delivery and the inventory carrying cost.
N(optimum) =
Where;
A= annual requirement in rupees
I = inventory carrying cost in % in year
K'= cost of staggered delivery
68
For our calculations I and are constants, hence the formula can be rewritten as
N =
Using this equation N (optimum) is found and rounded off to a whole number. The quantity
of order should be divided by N to get the quantity per staggered delivery.
6) There is always the probability of forecast going wrong and demand exceeding the
forecast. When demand exceeds the forecast up to some extent buffer stock meets the
requirement. If even the buffer stock is not sufficient to meet the demand an action should
be taken for procurement to avoid stock out. It is recommended to procure that extra
quantity needed for meeting demand to be procured locally.
69
CONCLUSION
Objectives of the studies are to avoid stock outs and to decrease the average inventory
carried. At the outset it looks as if the objectives are contradicting to each other. But the
present work shows that it is possible to avoid stock outs and also to decrease the average
inventory carried if some scientific methods of controlling inventory are followed.
The main reason for the present state of frequent stock outs are , namely ,errors in
forecasting, non recognition of very high lead time prevailing and wrong timing of raising
stock recoupment memos. In trying to avoid these difficulties it is found that equally
rigorous treatment is not needed for all items. Hence all the tools are classified into two
categories , one depending upon the annual usage value (ABC classification) and the other
depending upon the rate of consumption or movement ( fast ,regular, slow and non-moving
items).
It is hoped that the project will serve as another starting point for adopting better inventory
management using scientific approaches.
70
BIBLIOGRAPHY
(1) Tony wild, best practices in inventory management: Butterworth Heinemann.,2002
(2) J. R. Tony Arnold, Stephen N Chapman. Introduction to material management, forth
edition ,Pearson Education Asia, 2000
(3) Seetarama N Narasimha, Dennis W Mcleavey, Peter J Billington. Production planning and
inventory control. Prentice hall of India pvt ltd ,2003
(4) Russel & Taylor , Operations Management, Forth edition , Prentice hall of India pvt ltd,
2003
(5) Meyer H.A. Symposium on Monte Carlo methods, John Wiley and Sons. Inc.,1976
(6) Brown, R.G. Exponential Smoothing for Predicting Demand. Operations research society
of America. November 16 , 1986
71