Winters Project Report

Winters Project Report
On

Establishing Dynamic Inventory Norms for High
Gross Margin Products
At

Submitted in partial fulfilment of
Post Graduate Diploma in Industrial Engineering

Under the able guidance of: Submitted by:
Mr. V.G.S. Mani Manoj Sharma
Director Logistics, Whirlpool Roll no.- 36
& PGDIE-35

Prof. Narayanan N.
Professor, NITIE, Mumbai

National Institute of Industrial Engineering [NITIE], Vihar Lake Mumbai
0

CERTIFICATE

This is to certify that the Project Work titled “Establishing Dynamic Inventory Norms for High Gross

Margin Products” has been successfully completed at Whirlpool of India Ltd. (Faridabad Plant) by Manoj

Sharma under my guidance, in partial fulfillment of the Post Graduate Diploma in Industrial Engineering at

National Institute of Industrial Engineering (NITIE), Mumbai.

Prof. Narayanan N.,
NITIE, Mumbai.

1

ACKNOWLEDGEMENTS

“The only constant thing in this world is change. The world hates it, but yet it is the only thing that leads to
progress”.

Hence, I take this opportunity to extend my sincere thanks to NITIE, Mumbai and Whirlpool of India Ltd.
for offering a unique platform to earn exposure and garner knowledge in the field of Logistics
management, inventories assessment and demand planning.

I am thankful to my project guide, Mr. V.G.S. Mani, for his kind support, guidance and encouragement he
has extended to me throughout the project. I would like to thank Mr. Deepak Bhatnagar for his vital inputs
and able guidance provided during the project. I also thank the entire logistics team at Faridabad for there
unconditional support and co-operation throughout the project, in spite of their hectic activities and work
schedules. I am also thankful to the people in the plant for their direct and indirect inputs towards this
project.

I am also thankful to my college guide Prof. Narayanan N., who has been guiding me throughout my
project.

Working through this project has been indeed a very enriching experience.

MANOJ SHARMA
PGDIE 35th batch, NITIE,
MUMBAI

2

TABLE OF CONTENTS

CERTIFICATE ................................................................................………………………………….2
ACKNOWLEDGEMENTS ..............................................................................................................2
EXECUTIVE SUMMARY ...............................................................................................................4
COMPANY PROFILE ....................................................................................................................5
WHIRLPOOL CORPORATION ........................................................................................................5
WHIRLPOOL OF INDIA LIMITED ....................................................................................................6
INTRODUCTION…………………………………………………………………………………………...7
NEED OF THE PROJECT……………………………………………………………………………….10
OBJECTIVE ……………………………………………………………………………………………….11
PROBLEM STATEMENT………………………………………………………………………………...12
CURRENT PRACTICE…………………………………………………………………………………...13
INVENTORY POLICY DECISION .............................................................................................14
CONTINUOUS REVIEW POLICY……………………………………………………………………….15
PERIODIC REVIEW POLICY……………………………………………………………………………16
HYBRID APPROACH POLICY……………………………………………………………………….....17
REPLENISHMENT PLANNING…………………………………………………………………………18
BASIS OF MODEL FORMULATION……………………………………………………………………18
ASSUMPTIONS AND CONSTRAINTS………………………………………………………………...22
REPLENISHMENT MODEL FRAMEWORK…………………………………………………………...23
REPLENISHMENT MODEL……………………………………………………………………………..23
MODEL BEHAVIOR………………………………………………………………………………………25
MODEL FUNCTIONING………………………………………………………………………………….26
TERMINOLOGIES USED IN MODEL…………………………………………………………………..27
CALCULATION............................................................................................................................28
EXCEL FORMULAS………………………………………………………………………….30
HOW TO USE THE MODEL ........................................................................................................31
SIMULATION TEST ....................................................................................................................39
CONCLUSION…………………………………………………………………………………………….43
FUTURE SCOPE…………………………………………………………………………......................44
BIBLIOGRAPHY…………………………………………………………………………………………..45

3

Executive Summary

We all know the Supply Chain buzzword of all time quot;right product at right place at right time,quot; but how
much we are living up to it. Today, goods are made anticipating the customers' demands. quot;Anticipation,quot;
are always anticipations, they may be right, may be wrong. If they are less than actual value, we will lead
into loss of sales, and if it is more, it will pile up the inventory, which is the root cause of all the evils in
factories. Increase in inventory level might lead to obsolesce of the product due to aging and blocking of
the working capital and thus reducing cash flow and bottom line. Inventory might serve as a cover to
increase service level, but the cost ultimately will increase the price of the product.
At present one of the challenges the company is facing is the cut throat competition. To survive in this
competitive market the company needs to improve the availability of products at the bottom of the supply
chain to 100%. Among all other products high gross margin products are in top priority for the think tanks
of the company. High Gross Margin (HGM) products are such products which have gross profit margin
between15% to 25%. The project is aimed at establishing the stock norms and defining appropriate safety
stocks, reorder point, maximum stock level of products at branch and regional level that will ensure the
service level for HGM products to be 95% or above.

Defining the stock norms for a product is essential to ensure its availability at the storage locations.
Generally the planning tools used for this purpose define it at an aggregate level which results in
erroneous allocation of SKUs. In this project we have tried to consider the fact that demand history of
each product at a particular location might not be correctly reflected at aggregate level and should be
analyzed separately for each SKU and storage location. Here we have designed a replenishment model
to generate dynamic stock norms on a runtime basis. The replenishment model is capable of prompting
triggers to the user as to when the order should be placed and how much quantity must be ordered. The
replenishment model works on the basic principal of hybrid approach inventory policy where the inventory
position is monitored continuously and an order of (S-IP) is generated when IP reaches the ROP level.
The inventory policy used in the model exploits the advantage of both continuous and periodic review
approach. The model has been tailored to fit the company’s requirement, hence three ROP levels has
been maintained at branch levels to fulfill the skewed demand of the month while regional stock are
maintained using only one ROP for the month. The model has the ability to run on the real time in
synchronization with the calendar date and update the stock norms automatically both during the month
and across the months. Before going for the pilot run of the model it has been tested using simulation
through MS Excel. If the model is implemented and stock norms dictated by it are followed properly this
can result in to huge savings by reducing IBT and improving customer service levels.

4

Company Profile

Vision and Mission
Our pervasive vision, “Every Home, everywhere, with pride, passion and performance”, rests on the
pillars of innovation, operational excellence, customer-centric approach and diversified talent. These are
embedded within our business goals, strategy, processes and work culture. Be it our products that are the
result of innovation and operational excellence to meet every need of our consumers or the people
behind these products that come from a wide spectrum of backgrounds, everything we do features a
distinct Whirlpool way

Introduction to Whirlpool Corporation

Whirlpool Corporation is a global manufacturer and marketer of major home appliances, with annual sales
of more than $13 billion (for year 2005), 68,000 employees and nearly 50 manufacturing and technology
centers around the globe. The company manufactures in 13 countries and markets products in
approximately 170 countries under 11 major brand names such as Whirlpool, KitchenAid, Roper, Estate,
Bauknecht, Laden and Ignis.

Whirlpool originated from a company called Upton Machine Company in 1911 near Lake Michigan in St.
Joseph, Michigan, which was setup by the Upton brothers. Their first ever product was the electric, motor-
driven wringer washer. The Upton Machine Company was the exclusive supplier to a then retail giant
Sears, Roebuck and Co. for the washer’s line which used to be sold under the name “Allen”. Today,
Whirlpool Corporation is the largest North American supplier of major appliances to Sears under the
Kenmore brand. In 1929, the Upton Machine Company merged with the Nineteen Hundred Washer
Company of Binghamton, New York. Shortly after the Second World War, Nineteen Hundred made the
first ever top loading washing machine, which was followed by a highly successful Whirlpool brand
automatic washer launched in 1948.

Following the success of its automatic washer, the company was rechristened as Whirlpool Corporation.
This was the beginning of the Whirlpool of today. Post 1956, Whirlpool Corporation has grown both in
terms of technology and markets. The company has expanded its facilities worldwide. In today’s era of
globalization, the company has also realized the need to shift manufacturing to low cost and high quality
countries like India and China. These countries are looked upon not just as potential markets but also as
low cost centers, which helps reduce the purchase value for Whirlpool Global.

5

Introduction to Whirlpool of India Limited

Whirlpool Corporation is a global manufacturer and marketer of major home appliances, with annual sales
of more than $19 billion (for year 2005), 80,000 employees and nearly 60 manufacturing and technology
centers around the globe. Whirlpool of India’s net sales for the period April 2005 -March 2006 stood at
Rs. 1274 crores with an operating profit of Rs. 14.57 crores. Company witnessed a growth of 25%
(Approx.) in net sales over the same period last year. The company manufactures in 13 countries and
markets products in approximately 170 countries under 11 major brand names such as Whirlpool, Maytag
KitchenAid, Roper, Estate, Bauknecht, Laden and Ignis. The parent company is headquartered at Benton
Harbor, Michigan, USA with a global presence in over 170 countries and manufacturing operation in 13
countries with 11 major brand names such as Whirlpool, KitchenAid, Roper, Estate, Bauknecht, Laden
and Ignis. The company boasts of resources and capabilities beyond achievable feat of any other in the
industry.
Whirlpool initiated its international expansion in 1958 by entering Brazil. However, it emerged as truly
global leader in the1980's. This encouraging trend brought the company to India in the late 1980s. It
forayed into the market under a joint venture with TVS group and established the first Whirlpool
manufacturing facility in Pondicherry. Soon Whirlpool acquired Kelvinator India Limited in 1995 and
marked an entry into Indian refrigerator market as well. The same year also saw acquisition of major
share in TVS joint venture and later in 1996, Kelvinator and TVS acquisitions were merged to create
Indian home appliance leader of the future, Whirlpool India. This expanded the company's portfolio in the
Indian subcontinent to washing machines, refrigerator, microwave ovens and air conditioners.

Today, Whirlpool is the most recognized brand in home appliances in India and holds a market share of
over 25%. The company owns three state-of-the-art manufacturing facilities at Faridabad, Pondicherry
and Pune. Each of these manufacturing set-ups features an infrastructure that is witness of Whirlpool's
commitment to consumer interests and advanced technology. In the year ending in March 2006, the
annual turnover of the company for its Indian enterprise was Rs.1, 375 Crores. According to IMRB
surveys Whirlpool enjoys the status of the single largest refrigerator and second largest washing machine
brand in India.
The company's brand and image speaks of its commitment to the homemaker from every aspect of its
functioning. It has derived its functioning principles out of an undaunted partnership with the homemakers
and thus a slogan of “You and whirlpool, the world's best homemaker” dots its promotional campaigns.
The products are engineered to suit the requirements of ‘smart, confident and in-control' homemaker who
knows what she wants. The product range is designed in a way that it employs unique technology and
offers consumer relevant solutions.

6

Introduction
Many real-world supply chains have complex network structures, which consist of multiple layers of
production and distribution facilities. To cope with uncertainties in demand and supply, these supply
chains often have many millions of Rupees of capital tied up in inventories. One important question, of
course, is how to best manage inventories in complex multistage supply chains, so as to meet customer
expectations with minimum system wide inventory holding cost. One way to answer this question is to
characterize the optimal inventory policies.
In practice, many companies employ simple heuristic policies, such as the installation base-stock policies,
to control inventory at each facility. In an installation policy, each facility only needs the inputs from
immediate upstream and downstream facilities, and makes ordering decisions based on its local order
and inventory status. An important and challenging question for the company is how to optimally
coordinate the installation policies at all facilities, so as to minimize system wide inventory holding cost
while meeting the end customers’ service requirements. In other words, given that each facility is
managed autonomously by an inventory policy, how can a central planner determine the policy
parameters for all facilities in the best possible way? In this project, we attempt to address this question
for a class of HGM products. The project is focused on establishing optimum safety stocks at all stock
points, for which a replenishment model has been designed to continuously monitor the inventory position
and trigger the ROP as soon as the inventory goes below a determined level. The replenishment model
assume practical demand pattern and has been tested by using simulation with the help of MS Excel.
Before going in to details let us first understand what are HGM products and their importance to the
company.

High gross margin products

Products having gross profit margin of greater than 15% are declared as the high grass margin products.
The percentage contribution of these products from the total product sales is given below:

Category Total % of Total
Direct Cool 790929 58.00%
Frost Free 214334 15.72%
Washing Machines 288102 21.13%
Air Conditioners 24043 1.76%
Micro Wave Ovens 39339 2.88%
Front Loading Terminals 2023 0.15%
SUMO 4809 0.35%
Grand Total 1363579 100.00%

Table 1. Annual Sales 2006

7

Annual Sales 2006

DC NF DC
58% 16% NF
WSR
AC
M WO
FLT
SUMO
WSR
SUM O
21%
0.35%
FLT AC
MWO
0.15% 1.76%
2.88%

Figure 1. Categorywise Product Sale year 2006

Category Total % of Total HGM HGM %
DC 790929 58.00% 278788 35.25%
NF 214334 15.72% 64304 30.00%
WSR 288102 21.13% 104370 36.23%
Total 1293365 94.9% 447462 34.60%
Table 2. HGM Products Vs Total

790929
800000

700000

600000

500000

288102 400000

214334 300000
278788
200000
104370
100000
64304
0
Total WSR DC
NF
HGM DC
NF
WSR

Figure 2. HGM Products Vs Total

8

The management of inventory systems is a crucial part of supply chain management. In order to reduce
costs and to improve efficiency an integrated approach is needed. For instance, decreasing inventory
levels and thereby lowering inventory costs induces more frequent shipments of smaller sizes, which in
general increases transportation unit costs. We believe that careful coordination of shipment and
replenishment policies can lead to substantial cost savings. In this project we propose a model to allocate
safety stocks in a multi item multi location distribution network. We consider a stock-based shipment
policy which means that orders for a destination are shipped when inventory position of the product drops
below the reorder point level. The required amount to be shipped depends on the inventory position, the
maximum stock level and the replenishment lead-time of the supplied stock points or branch. We model
this interaction considering the replenishment lead-time as constant. We consider a divergent network of
warehouses keeping stock of different items. Inventories are controlled using an (s, S) installation stock
policy, which means a replenishment order is placed at the moment where the local inventory position,
which is defined as the physical stock plus the stock on order or the material in transit minus backorders,
is equal or smaller than the reorder level ROP. Customer demand is stochastic, only observed at the
lowest echelon, and modeled as a compound renewal process which means that inter-arrival time of
orders as Well as demand sizes are modeled as deterministic. Demand which cannot be satisfied is
backordered. We assume the batch sizes Q to be calculated by the standard EOQ formula. Measures of
product availability like Target service levels, the fill rate and ESC i.e. expected shortage per cycle are
used to avoid stock out. Hybrid inventory review policy has been modified taking in to consideration the
skewed billing pattern of the company. The monthly demand is thus divided in to three phases of 10 days
each and average percentage of monthly sale is assigned to each phase. The percentage of each phase
can be altered as per the expected billing pattern which is more or less constant for every month. Thus
depending on which phase of the month we are running in the ROP level would be decided while the
safety stock will remain at a constant level throughout the month. This indicates that for each month we
will have three ROP levels and one safety stock. While at the regional level we will be having only one
ROP and SS for the month.

Billing Pattern Series1
70%
70%
60%
50%
% Billed

40%
20% 30%
10% 20%
10%
0%
1-10 10-20 20-31
Days and Phases

Figure 3. Monthly billing pattern

9

Need of the project
Profit margin on goods and services are shrinking day by day due to the inevitable competitive business
environment. The scenario at whirlpool of India limited is not much different. In such a situation products
which are still yielding high profit margins are of greater importance to the company. In each of the three
major product categories (DC, FF, WM) High Gross margin products contributes around 35% to the
annual sales of the total category. The company simply cannot afford to lose even a fractional percentage
of sales in these products. Hence company wanted to be very particular in terms of availability of these
products and maintaining specified customer service level at each branch and regional level.
The project requirement is evident from the following reasons

There was no minimum or maximum stock bound defined for the stocks at any branch.
ROP was not maintained at any location which caused frequent stock-outs.
There were no measures or KPIs to check the availability of products (like CSL or Fill rate etc).
No standards were defined as to when to place order and how much to order.
No replenishment model was in place to monitor the inventory position of HGM products.
Increased number of IBT (inter branch transfers) due to inappropriate allocation.

Apart from the basic requirements of solving the above mentioned problems the company is planning to
establish regional warehouse to increase the product availability and refrain from the opportunity cost lost
due to stock-out. The company is interested in knowing the minimum and maximum amount of
decoupling inventory that should be placed at the regional warehouse.

To reduce the above listed inefficiencies and eradicate these problems following factors are essential:
• The inventory policy to be followed.
• Calculate Optimum safety stocks at branch and regional levels.
• Define the maximum stock levels, ROP levels at branch level.
• Following the stock norms at all stock points.
• Design a replenishment model through which the inventory position at all stock points can be
tracked and necessary reordering steps could be taken.
• Maintain the customer service level required to meet the desired ESC constraint.

Only defining these terms would not be sufficient until we follow the service level requirement and control
the lead time variation.

10

Objective

The objective of the project is:

To optimize inventory levels while maintaining or increasing service levels.
To increase product availability.
To determine the optimum safety stock, ROP and maximum stock limit for HGM product at
branches.
To design a replenishment model that account for the demand skew ness during the month.
To come up with regional inventory levels of high gross margin products.
To limit ESC to the minimum possible level.
To increase customer service level to 95% and above if possible.

11

Problem statement

Since forecasted demand in general differs from actual demand safety stock is hold to be able to satisfy
customer demand. One major flaw in the system was that the safety stock SS and ROP for the products
at the stock-point level was not worked out. The company was loosing some sale due to unavailability of
the right amount of SKU at the right place. One of the major aims of inventory management is to balance
service requirements with the costs related to inventory availability. Therefore, it is important to know how
to allocate optimum safety stocks among different stock-points, respectively which reorder levels ROP the
stock-points should use. In this project we provide a model to compute these reorder levels in a multi-
echelon multi-item distribution network under a continuous review stock based inventory policy such that
prescribed service requirements can be met.

Some Seasonal cycles like summers are common to all the regions i.e. global seasonality while some are
specific to the region only like regional festivals and new year surge in demand are local seasonality. As
we all know that India is a country of huge geographical and religious diversity and so does their buying
pattern. Even in the lean season some regions show sudden surge in demand due to some regional or
local festivals. It has normally happened that company encountered stock-out conditions due to such
sudden increase in demand.

Another problem that is encountered is the inappropriate allocation of stocks at certain storage locations
due to aggregate forecast. The aggregate forecast of the product is drilled down to SKU level many a
times it is different from the actual demand in a particular location.
HGM products are categorized as separate class of product due to their huge contribution to the profit
margin of the company but still there is no separate planning for managing inventory of these SKUs and
assuring their availability to the end customer.

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Current practices

The points given below will provide brief overview of the current practices at whirlpool:
Yearly forecast is released at the start of each year which is then revised on a quarterly basis to account
for the changes in the demand pattern or in anticipation of demand due to new product introduction or any
promotional activity. Based on these quarterly forecast numbers the central logistics department arrives at
monthly sales plan which is then drilled down to branch level and conveyed to the branches till 25th of
every month for any corrections required.
After making necessary changes at the model/SKU level the branches should revert back to the
central logistics department till 28th of each month, where the aggregation of the plans is done and final
requirement for the month is conveyed to production department and to the respective branches. The
inventory requirement is taken care of by the central logistics department with an objective to meet the
target closing inventory levels for each month at branches. This target closing inventory is basically the
buffer the company uses to counter any uncertainties in demand and fluctuation in lead times. The
closing inventory for the current month is calculated by using the formula

Target Closing inventory = Actual Closing inventory (current Month) + % of next month sale plan

The percentage of next month’s sales plan included varies from 10% to 50% depending on the proximity
to the peak season. This formula is really helpful in protecting the stock-out at an aggregate level but
when it comes to the inventory of a particular model/SKU at a specific branch, the formula doesn’t work
effectively. For some models we stock at let say at location A in excess, while the same model is out of
stock at location B at the same time. This is evident from the fact that company is incurring high cost in
making IBT s (inter branch transfers) from one branch to other. Over Rs.36000000 Annually is invested in
IBTs. This wrong allocation also results in delayed replenishment due to extended lead times. Thus it is
very important to ensure that right material reaches the right location at the right time.

13

Inventory Policy decision

For a single inventory location that serves a number of downstream nodes in the supply chain, several
stochastic inventory policies can be applied. The size of the safety directly depends on the type of the
inventory policy that is in effect. Here we are following continuous review inventory policy. The underlying
conception for a single-stage inventory policy is as follows. An inventory node is supplied from a quot;sourcequot;
which fulfills orders for the considered product after a certain replenishment lead time. If the source is a
production segment or rather production stage of the same company, then the replenishment lead time is
a function of the flow time of a production order and depends on numerous factors, the utilization of the
production stage being one of them. If the source is another inventory node of the company, then the
order is a demand observed by this inventory node and the replenishment lead time depends on the
inventory available on hand as well as on the time required for material handling and transportation
processes. If the source is an external supplier, then the replenishment lead time is equal to the customer
order waiting time provided by the supplier, plus an additional time required for material handling and
transportation. In all mentioned cases it is clear that the replenishment lead time may be subject to
random variations.

Inventory policies differ in two aspects, namely the mechanism used to trigger replenishment orders and
the decision rule that specifies the determination of the order size. The specific inventory policies are
defined through the combination of the decision variables s (reorder point), r (review interval, order cycle),
q (order quantity) and S (order level) as follows:

Continuous review policy
Periodic review policy
Hybrid approach policy

A replenishment policy consist of decisions regarding when to reorder and how much to reorder. These
decisions determine the cycle and safety inventories along with fill rate and the CSL. There are several
forms of replenishment policies. We restrict attention to three instances listed above. There are pros and
cons of each type of policy, by this exercise we will be able to know the best fit policy for whirlpool of India
limited. Further we will do certain customization if required.

14

Continuous review policy (s, Q)

Figure 4. Schematic diagram of continuous Review Policy

Under the (s, Q) policy, the point in time at which replenishment orders are triggered, depends on the size

of the reorder point s, whereas the order quantity q is constant over time. In the ideal (textbook) form of

the (s, Q) policy, the inventory position is continuously monitored. The inventory position is the sum of the

inventory on hand plus the inventory on order minus the outstanding backorders (backlog). The inventory

management system (or the inventory manager) acts according to the following decision rule: If at a

review instant the inventory position has reached the reorder point s (from above), and then launches a

replenishment order of size q.

15

Periodic review (r, S) policy

Figure 5. Schematic diagram of Periodic Review Policy

If an (r, S) inventory policy is in effect, the points in time at which replenishment orders are released are

determined through the review interval r. The inventory management system proceeds according to the

following decision rule: In constant intervals of r periods launch a replenishment order that raises the

inventory position to the target order level S. Obviously, the ( r, S ) policy is an inventory policy with

periodic review. The order size at a time of a review depends on the demands and the development of

the inventory observed in the preceding periods. If r=1, then this policy is called base-stock policy.

16

Hybrid Approach (s, S) policy

Figure 6. Schematic diagram of Hybrid Approach Policy

Under an (s, S) inventory policy, the points in time when an order is triggered are determined in the same

way as with the (s, Q) policy, i.e. through the reorder point s. However, the order quantity is now, similar

to the (r,S) policy, a function of the inventory development over time. In the literature this policy is

sometimes characterized with the help of a third parameter which specifies the length of the review

interval r. In this notation the policy is called (r, s, S) policy.22 In the case of r = 0, continuous review is in

effect. If demands arrive unit-sized, then the (r = 0, s, S) policy is identical to the (s, Q) policy with

continuous review.

17

Replenishment planning

The most important part of inventory optimization process is to decide the when to order and how much to
reorder. These in turn depends upon the replenishment policy followed by the company. Effective
replenishment planning can result in increased sales, reduced labor costs, and reduced order cycle time
by keeping stores and distribution centers appropriately and efficiently stocked. Replenishment Planner
can automate the task of examining millions of SKU-location combinations to determine the right
replenishments to maintain high service levels with efficient, profitable inventory investments.
Replenishment orders take into consideration forecasts, safety stock, available inventory, and ordering
policies. Several ordering policies can be chosen, ranging from simple min-max order point comparisons
to dynamic order point/order up to level and time-supply calculations, depending on the nature of the item
(whether it is slow or fast moving, or a basic or seasonal item).

Basis of model formulation

Optimizing Safety Stock levels by calculating the magical balance of minimal inventory while meeting
variable customer demand is sometimes described as the Holy Grail of inventory management, many
companies look at their own demand fluctuations and assume that there is not enough consistency to
predict future variability. They then fall back on the trial and error best guess weeks supply method or the
over simplified 1/2 lead time usage method to manage their safety stock. Unfortunately, these methods
prove to be less than effective in determining optimal inventory levels for many operations. If our goal is
to reduce inventory levels while maintaining or increasing service levels we will need to investigate more
complex calculations.
One of the most widely accepted methods of calculating safety stock uses the statistical model of
Standard Deviations of a Normal Distribution of numbers to determine probability. This statistical tool has
proven to be very effective in determining optimal safety stock levels in a variety of environments. The
basis for this calculation is standardized; however, its successful implementation generally requires
customization of the formula and inputs to meet the specific characteristics of our operation.
Understanding the statistical theory behind the formula is necessary in correctly adapting it to meet our
needs.
Normal distribution: Term used in statistical analysis to describe a distribution of numbers in
which the probability of an occurrence, if graphed, would follow the form of a bell shaped curve.
This is the most popular distribution model for determining probability and has been found to work
well in predicting demand variability based upon historical data.

18

Standard deviation: Used to describe the spread of the distribution of numbers. Standard
deviation is calculated by the following steps:
• Determine the mean (average) of a set of numbers.
• Determine the difference of each number and the mean
• Square each difference
• Calculate the average of the squares
• Calculate the square root of the average.

We can also use Excel function STDEVPA to calculate standard deviation. In safety stock
calculations, the forecast quantity is often used instead of the mean in determining standard
deviation.

Lead time: Highly accurate lead times are essential in the safety stock/reorder point calculation.
Lead time is the amount of time from the point at which we determine the need to order to the
point at which the inventory is on hand and available for use. It should include supplier or
manufacturing lead time, time to initiate the purchase order or work order including approval
steps, time to notify the supplier, and the time to process through receiving and any inspection
operations.

Lead-time demand: Forecasted demand during the lead-time period. For example, if our
forecasted demand is 3 units per day and our lead time is 12 days our lead time demand would
be 36 units.

Forecast: Consistent forecasts are also an essential part of the safety stock calculation. If we
don't use a formal forecast, we can use average demand instead. The forecast is usually revised
every month. The model is also capable of updating its SS ROP and Max. Inventory levels every
month in synchronization with the calendar date.

Forecast period: The period of time over which a forecast is based. The forecast period used in
the safety stock calculation may differ from our formal forecast periods. For example, we may
have a formal forecast period of four weeks while the forecast period we use for the safety stock
calculation may be one week. In our case we have considered the forecast period of one year as
per the company standards. And the standard deviation for each SKU at a particular location is
specifically calculated by the model.

19

Demand history: A history of demand broken down into forecast periods. The amount of history
needed depends on the nature of our business. Businesses with a lot of slower moving items will
need to use more demand history to get an accurate model of the demand. Generally the more
history the better as long as sales pattern remains the same. The demand history should include
at least one complete seasonal cycle thus last years data is considered for analysis.

Reorder point. Inventory level which initiates an order.
• Reorder Point = Demand During Lead Time(DDLT) + Safety Stock(SS)

Service level: Desired service level expressed as a percentage. Our target is to maintain it equal
to or above 95%.

Service factor: Factor used as a multiplier with the Standard Deviation to calculate a specific
quantity to meet the specified service level. I have included a service factor table below or we can
use Excel function NORMSINV to convert service level percentage to service factor.

Service Level Service Factor Service Level Service Factor

50.00% 0.00 90.00% 1.28
55.00% 0.13 91.00% 1.34
60.00% 0.25 92.00% 1.41
65.00% 0.39 93.00% 1.48
70.00% 0.52 94.00% 1.55
75.00% 0.67 95.00% 1.64
80.00% 0.84 96.00% 1.75
81.00% 0.88 97.00% 1.88
82.00% 0.92 98.00% 2.05
83.00% 0.95 99.00% 2.33
84.00% 0.99 99.50% 2.58
85.00% 1.04 99.60% 2.65
86.00% 1.08 99.70% 2.75
87.00% 1.13 99.80% 2.88
88.00% 1.17 99.90% 3.09
89.00% 1.23 99.99% 3.72

Table 3. Service Level and Corresponding Service factor

20

Role of safety inventory in the supply chain

Safety inventory is the inventory carried for the purpose of satisfying demand that exceeds the amount
forecasted for a given period. Safety inventory is carried because demand forecasted are uncertain and a
product shortage may result if actual demand exceeds the forecast demand. Demand forecast are
unlikely to be completely accurate. Given forecast errors, actual demand over a period may be higher or
lower than the forecasted. To protect the loss of sale due to uncertainty in demand one needs to have
buffer in stock, this buffer is known as safety inventory.

Trade-off between availability and holding cost

A trade-off that must be considered when planning safety inventory, on one hand raising inventory
increases product availability and thus the margin captured from customer purchase. On the other hand
raising level of safety inventory increases inventory holding costs. This issue is particularly significant in
industries where demand is very volatile. Carrying excessive inventory can help counter demand volatility
but can really hurt if new products come up on the market and demand for the product in inventory dries
up.

Importance of product availability

The level of product availability is measured using the cycle service level or the fill rate, which are metrics
for the amount of customer demand satisfied from the available inventory. The level of product availability
is an important component of any supply chain’s responsiveness. A supply chain can use high level of
product availability to improve its responsiveness and attract customers. This increases revenues of the
company by increasing the sales through high product availability. However a high level of product
availability requires large inventories and large inventories tend to raise cost for the organization.
Therefore the project is focused at achieving a balance between the level of availability and the cost of
inventory. This optimal level of product availability is one that maximizes supply chain profitability.
Whether the optimal level of availability is high or low depends on where the company believes they can
maximize profits.

21

Assumptions and Constraints

Constraints:
• The Expected shortage per cycle ESC should not exceed 2 units.
• The Cycle Service level should be above 95%.
• The regional warehouse will run at single stock norm at least for a month.

Assumptions:
• Demand is normally distributed over the planning horizons.
• Monthly billing pattern is assumed skewed in nature.
• Demand during each of the period is independent and normally distributed.
• Production process is flexible enough to accommodate and fulfill the demand of branches and
• Internal Suppliers are able to replenish the quantity ordered under the specific replenishment lead
times.
• Database of all the HGM products are maintained in the required format.

22

Replenishment Model framework:

As we are following hybrid approach to the inventory management we need to continuously monitor the
inventory position. At the same time we should be informed enough of the reorder point, safety stocks
and maximum stock levels at each of the stocking location. Since these variables will vary depending
upon the product and the storage location we are looking. These requirements encouraged us to build a
replenishment model that can help us in tracking inventory positions and abiding the stock norms at
storage locations.

Replenishment Model

Figure 7. Pictorial view of Replenishment Model

Note: The screenshot of the model shown above depicts only Regional stocks. The branch stocks corresponding to
the regions can be viewed just by unhiding the columns in the model sheet.

23

As it is clear from the above screenshot there are three major fields in the model namely

1. Adjustable field: This field consists of six variables but the cells those are in white color only are
the adjustable ones. First component is average daily demand D during the first 10 days of the
month. It means we can adjust the percentage sales expected in the first 10 days of the month in
the picture above this has been set to 10% for central region. This adjustment we can do at
regional level. The second element is average demand during next 10 days of the month (day 10
to day 20). The cell in white color corresponding to this row can also adjust in the same way. The
percentage sale in the last 10 days of the month (day 20 to 30) will be automatically entered.
User need not bother about. Another component in this field is SL (service level) that can be
adjusted to reduce the expected shortage per cycle ESC. As soon as the ESC goes above 2 units
the user will be prompted by changing the specific ESC cell to pink. We need to increase the
service level to reduce the ESC. It is worth noting that we can adjust the monthly sale percentage
only at regional level.

2. Inputs field: Under this field we have only three variables and all the three component rows are
mend to be changed by the user as per the inventory status of the respective branch or region.
As we know that this is the only input field in the model which has to be monitored and updated
on a continuous basis.

3. Outputs field: this is the most important field and thus its variables like SS, ROP, IP, Max. Stock,
ESC. This field would be the triggering field of the model, which will notify the user as to what
step need to be taken to maintain optimum stocks at storage locations to minimize ESC and
increase the availability of product at these locations.

24

Replenishment Model capabilities

Replenishment Model is designed to reduce overstock and out-of-stock conditions, leading to increased
sales and lower inventory levels by:

• Enabling dynamic model stocks based on forecasted demand rather than static models that
cannot reflect demand variability over time
• Providing a constraint-based approach to modeling hard supply chain constraints (such as item
availability, shipping, receiving and location calendars, ordering frequency, item affectivity dates,
lot sizes, etc.) as well as soft constraints (such as handling and storage capacity at distribution
centers and stores, and vendor minimums).
• The system can generate plans that can be achieved because they are in sync with the retailer’s
real supply chain capabilities and capacity
• Providing forward looking, time-phased demand and replenishment plans so the organization
understands its inventory commitment at any point in time and can communicate with vendors
• Providing advanced techniques for replenishing high gross margin SKUs.
• Allocating appropriate inventory while increasing customer service levels

Model Behavior

The model has been further customized to balance the skewed billing pattern observed by the company.
The model is capable of generating the optimum regional inventory levels where the billing pattern during
is assumed as linear. The table given billow will show the variables and their frequency of assuming
different values in a specified period of time as per the storage location.

Variable Period Branches Regions
ROP Month 3 1
SS Month 1 1
Max. Stock Month 1 1
Table 4. Frequency of assuming values

The model has been formulated to help managers predict optimal safety stock and ROP levels of different
SKUs at specific branch and regional levels along with maintaining desired service level and ESC. The
model is easy to use and provides essential outputs for effective inventory management. The model is a
customized version of continuous review policy. It differs from the basic policy in a way that it exploits the
concept of dynamic ROP during the month to negate the skewed billing pattern at branch level while at

25

regional level there is a single value of ROP for the month. Since demand for the products are highly
seasonal we can’t maintain a single ROP throughout the year. Hence the regional warehouse smoothen
the weekly demand fluctuation by supplying the additional amount from its buffer stock. The regional
warehouse automatically changes ROP, SS and Max Stock levels each month.

The replenishment model take last one year’s demand data as the input to the model and works out the
corresponding month’s safety stock, ROP, maximum stock at branch and regional level that needs to be
maintained. Model is capable of changing the ROP level automatically in synchronization with calendar
day of the month. As the ROP and SS depend on the monthly demand and its deviation from mean
demand for the year, these limits would change depending on the input to the model. The user only
needs to fill either the past one year’s data or the annual forecast data of the corresponding SKU/model
and the current inventory position data like (OH,GIT,PQ) the model will automatically calculate the
inventory position IP at branch and regional level.

Model Functioning

It is necessary to understand the functioning of the model. Basically there are three main indicators that
will help the user in determining whether he is running at the correct inventory level or not. The three
indicators and the color prompt assigned to each of them are given below:

Indicator Row Color Indication
ROP ORANGE ROP reached Replenishment order should be placed
OQ BLUE Number of units to be ordered
SS RED Immediate attention is required (Stock-out condition)
Max. Stock YELLOW Overstock condition
Table 5. Color indications used in the replenishment model

If the IP is greater than the maximum stock limit the maximum stock cell for corresponding branch will
show YELLOW color. Indicating that the branch is running over stock and need not be supplied anymore
till the ROP level is reached. In the same way as the inventory position at the branch or regional level
decrease below its ROP level it prompts the user by showing ROP of respective location in RED color. As
the inventory policy followed in the model is (SS, T) each time the ROP is reached the order quantity will
show the quantity (T-IP) to be ordered.

26

Terminologies Used in the Model

The following is a list of the variables and the terminology used in model:

Dm Demand Per Month
D Avg. Demand Per Day
σ Stand. Dev of Demand
Q Order Quantity
LT Transit Lead Time in Days
SL Desired Service level
DDLT Demand During Lead Time
OH On hand Inventory
GIT Goods in transit
σL Stand Dev. Of Demand during Lead time
Nm No. of Trips/orders per month
SS Safety Stock
ROP Reorder Point
CSL Cycle Service Level
ESC Expected Shortage Per Cycle
Fr Fill Rate
PQ Pending quantity
IP Inventory position
OQ Order quantity
S Max. Stock level
Table 6. Symbols used in the replenishment model

27

Calculation
Inventory management is about two things: not running out, and not having too much. The organization’s
aim to not run out, along with uncertainties in demand and supplier lead times are why we have inventory
in the first place. Essentially, inventory is a reserve system to prevent a stock out. However, as important
as it is to prevent such a stock out, we also don’t want to hold onto too much inventory because of holding
costs.
So how do we balance the two? And
What is the right amount that should be ordered in each replenishment?
More importantly, when should we re-order in order to prevent a stock out?
The answer to this can be determined by obtaining and applying the following information about the
inventory we wish to manage.

Safety inventory is carried for the purpose of satisfying demand that exceeds the amount forecasted for
the period.

Safety Stock = Z*SQRT (LT*σD^2 + D^2*σL^2)

Where
LT = Average Lead Time

σD = Standard Deviation of Demand
D = Avg. Demand

σL = Standard Deviation of Lead Time

Standard deviation of lead time: It is very important to track how long shipments take from suppliers.
Assuming we have tracked the data, excel can very easily help we determine our standard deviation. In
excel, go to the toolbar and click on Insert, then click on Function, and choose STDEV and click ok. Then,
enter in as much lead time data we have and presto, we have our standard deviation. Here in our model
we have assumed that the lead time and their variation are correlated and can be assumed as constants
after consulting with the subject matter experts. Thus the deviation of lead time used in the model is given
below.
Lead time range Standard deviation of lead time
1-4 days 1
5-7 days 2
8 and above 3
Table 7. Lead time Deviation limits

28

Re-order point=LT*D + Z*SQRT (LT*σD^2 + D^2*σL^2)

Where
In this formula, the first term
LT*D= Demand during lead time

The second term {Z*SQRT (LT*σD^2 + D^2*σL^2)} is the term that allows for the safety stock. In other
words, the second term is the optimal safety stock level. It is not simple to gather all the data that is
needed for the calculations. For a product with multiple parts, each part needs to have its own re-order
point calculations and its own safety stock calculation. This can all become very confusing if proper
computer modeling is not employed.

Although as mentioned excel earlier, excel is probably not sufficient for our company’s software needs. If
we have not already done so, it is very important to look into an integrated software package for these
calculations and many others. But the model designed during the project is a customized one which can
serve whirlpool’s purpose. There is a scope of further refinement in the model after few pilot runs.

IP = (On Hand) + (On-Order) – (Back Orders)

Policy: When Inventory Position is less than or equal to the Reorder Point, R, order (S-IP) units.
Where S is the Maximum inventory level

Fill rate: It measures the proportion of customer demand that is satisfied from available inventory.

Fr = 1-ESC/Q
Where
Fr is the Fill rate
ESC is the Expected Shortage per replenishment cycle and
Q is the Economic order quantity

ESC: is the average units of demand that are not satisfied from inventory in stock per replenishment cycle

ESC = (-SS) {1-NORMSDIST(SS/σL) } + σL { NORMSDIST(SS/ σL) }

29

Excel Formula Sheet
Copied to
Description Cell No. Formula Copied To Row Remarks
Column
Standard Deviation C17 STDEVPA(C5:C16) C17 to AF17
Calender Day of where I33 is the
G33 DAY(I33)
Month date

It is automatically
Date I33 TODAY()
updated daily

IF(MONTH($B5)=MONTH($B$46),C
May-07 C34 C34 to AF34 C34 to C45
5,0)
May-07 C46 SUM(C34:C45) C46 to AF46
Average Daily
C20 C5/30 C20 to AF20 C20 to C31
Demand
S.D. of Daily
Standard Deviation C32 STDEVPA(C20:C31) C32 to AF32
Demand

It is close to the
IF($G$33<=10,C48,IF($G$33<=20,
Applied Demand C47 C47 to AF47 practical Demand
C49,C50))
Observed

C48 to G48, I48 to L48,
Demand During 1st 10
C48 C46*$H$48/10 N48 to R48,T48 to X48,Z48
Days
to AD48,AF48
C49 to G49, I49 to L49,
Demand During 2nd
C49 C46*$H$49/10 N49 to R49,T49 to X49,Z49
10 Days
to AD49,AF49
C50 to G50, I50 to L50,
Demand During 3rd 10
C50 C46*$H$50/10 N50 to R50,T50 to X50,Z50
Days
to AD50,AF50
Standard Deviation C51 C32 C51 to AF51
Lead Time C52 Constant C52 to AF52
Standard Deviation of
C53 IF(C52<=4,1,IF(C52<=7,2,3)) C53 to AF53 Assumed
LT
Service Level C54 Adjustable Variables C54 to AF54
Max. Stock C60 ROUND(C68+C62+C50*C52,0) C60 to AF60
Inventory Position C61 C56+C57-C58 C61 to AF61
Economic Order SQRT(2*SUM(C5:C16)*200/(4241*0
C62 C62 to AF62
Quantity .045))
Order Quantity C63 IF(C61<=C69,C60-C61,0) C63 to AF63
IF($G$33+C52<10,C48*C52,IF($G$ C65 to F65, I65 to K65,
Demand during LT C65 33+C52<20,C49*C52,IF($G$33+C5 N65 to Q65,T65 to
2<=31,C50*C52,C48*C52))) W65,Z65 to AC65
G65,L65,R65,X65,AD65,AF For Regional
Demand during LT G65 G50*G52
65 Stocks
S.D. of Demand during
C66 SQRT(C52*C51^2+C49^2*C53^2) C66 to AF66
LT

Number of trips/month C67 C46/C62 C67 to AF67

Safety Stock C68 NORMSINV(C54)*C66 C68 to AF68
Reorder Point C69 C65+C68 C69 to AF69
Cycle Service level C70 NORMDIST(C65+C68,C65,C66,1) C70 to AF70
(-C68)*(1-
Expected Shortage
C71 NORMDIST(C68/C66,0,1,1))+C66*( C71 to AF71
Per Cycle
NORMDIST(C68/C66,0,1,0))
Fill Rate C72 (C62-C71)/C62 C72 to AF72
Table 8. Excel Formula Sheet

30

How to use the model

The model is designed on the basis of continuous review inventory policy. According to this policy the
inventory position should be reviewed continuously and an order should be generated as soon as the
ROP is reached. The quantity to be ordered is defined by the OQ (order quantity) and it is equal to the

OQ=T (Target inventory)-IP (Inventory position at ROP).
The model is easy to use and provides valuable outputs for the company. The step by step procedure of
using the model is explained below:
First of all the user should be clear enough of his requirement. Use of the model is demonstrated here
with the help of an example:
Step 1: Select the excel file named “Replenishment Model” from the PROJECT folder

Figure 8. Replenishment Model Step1

31

Step 2: Select the sheet named after the product category for which you want to know the stock norms
(like Direct Cool, Frost Free or Washing Machine).These sheets are Tab colored in green color to
differentiate them from the database sheets.

Figure 9. Replenishment Model Step 2

32

Step 3: Right click on the “Material Group” cell (A4) and choose edit query, one select workbook window
will open click ok.


33

Step 4: A Microsoft query wizard will open go to file menu and select “open” a window with title open
query will open.


34

Step 5: From open Query window Select the category whose status you want to know about (in our case
suppose it’s DC)


• As soon as we select the category and click “open” in the same window all the models/SKUs
corresponding to that category will open.

35

Step 6: Suppose we want to select “Dc Total” and click “open” one years data for that SKU will be shown
in the Microsoft query wizard itself.


36

Step 7: Again go to the file menu and select “Return data to Microsoft office excel” and we will be
automatically redirected to the excel file where we have raised the query.


37

Step 8: Here we will see all the branch and regions in the columns and rows will show three fields namely
Adjustable, Input and Output field.


• We need to fill the current inventory data like OH(on Hand), GIT (goods in transit) and,
PQ(pending quantity) only in the input field
• As soon as this above step gets completed the output of the model is ready to inform us that
whether stocks at each of the regions/branches are adequate enough or there is a need to place

38

an order or the present stock is exceeding the maximum stock limit.
• Under the Adjustable field we can adjust/change only those cells that are represented by white
color.
• The output field is not made for user manipulation.
• If the stock position at any branch or region inventory position falls below the ROP level
corresponding to that location the ROP field will change to “red color” representing that ROP has
reached and order of size OQ(order quantity) whose color is changed to blue has to be ordered to
the immediate supplier.
• Similarly if the inventory position at any location exceeds the maximum stock limit defined for that
SKU/model the “Max. Stock” cell corresponding to that location will change its color to yellow
showing that the maximum stock limit has been exceeded
For each new HGM stock keeping unit the user has to repeat the same exercise.

Simulation Test

Given that demand may not be perfectly normal and may be seasonal, it’s a good idea to test and adjust
inventory policies using a computer simulation before they are actually implemented. The simulation
should use a demand pattern that truly reflects actual demand including any lumpiness as well as
seasonality. The inventory policies obtained using the replenishment model made during the project can
then be tested and adjusted if needed to obtain the desired service level. Surprisingly powerful
simulations can be built using MS Excel. Identifying problems in a simulation can save a lot of time and
money compared to facing these problems once the replenishment model is implemented and inventory
policy is in place
The simulation test in our case has been conducted for the “Premier Steel M3” SKU. The simulation run is
conducted assuming the worst possible demand pattern during the month; this will ensure that if the
model works successfully under this kind of demand behavior it is expected to work smoothly with other
combinations of billing pattern too. Demand for two months was taken and 10%, 20%, 70% billing in the
three phases of the month has been considered. The order placed by all the branches has been assumed
as demand at the regional warehouse on the same dates. The results of the simulation run is shown in
the next page

39

Test Results

Premier Steel M3 Delhi demand
600

500
S t o c k le v e l

400

300 SS
200 ROP
IP
100
Max Stock
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Days

Figure 16. Simulation Result for Delhi Branch

Premier Steel M3 Lucknow
400
350
300
S to c k le v e l

250
200 SS
150 ROP
100 IP
50
Max Stock
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Days

Figure 17. Simulation Result for Lucknow Branch

40

Premier Steel M3 Ghaziabad

350
300
250
S t o c k le v e l

200 SS
150 ROP
100 IP
50 Max Stock
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Days

Figure 18. Simulation Result for Ghaziabad Branch

Premier Steel M3 Indore
250

200
S to c k le v e l

150
SS
100
ROP
50 IP
Max Stock
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Days

Figure 19. Simulation Result for Indore Branch

41

Premier SteeM3 Central Region
1400

1200 SS
1000 ROP
S t o c k le v e l

800
IP
600
Max Stock
400

200

0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Days

Figure 20. Simulation Result for Central Region

The result shows that Replenishment model has cleared the simulation test and the regional warehouse
is successfully able to cater the demand of the corresponding branches. It is assumed that this model will
behave in the same manner for all other regions and products. We can now implement this model to
replenish the storage location. This test provided us the confidence that the stock norms calculated by the
model are realistic and will work successfully in the practical environment.

42

Conclusion

It is believed that this Replenishment model sets out a viable reference for stock management for
all classes of High gross margin products in the companies.
The model is applicable to other products categories along with HGM products with slight
modification.
A key benefit of the methodology is that it allows the management to understand the drivers of
inventory levels. This alone will improve stock management.
As the model is designed in MS Excel it is easy to edit and can be refined further to incorporate
any changes, if considered essential by the management.
The model provides a robust base for appropriate availability of stocks at all storage locations.
Project will help the management in reviewing the inventory position at all storage locations for a
product at a time.

Fortunately, initial implementation of the model is less time consuming and easy due to the amount of
data which must be collected and analyzed is not huge. The appropriate stock level depends on a
number of factors and this will change from period to period. However, the model can be used to
calculate a SS, ROP, S, CSL and fill rate for a particular period. The model is capable of providing
realistic stock norms under practical situations. Successful simulation test suggests that the
replenishment model is ready to be implemented.

Some other factors are also worth considering:

It is quite clear that variability in demand will be a key determinant of inventory levels. Thus, by
reducing variability, it will be possible to reduce inventory.
Variability can be reduced by improved forecasting and through consolidation of stock holding
since then the peaks and valleys of demand will tend to cancel each other out. This must be
considered in future inventory management strategies and can be a key determinant of future
space planning.

43

Future Scope

The replenishment model is working fine right now but there is always a difference between theory and
the practical situation. The model can be refined and repaired as per the demand of the situation after the
implementation. The replenishment model can be easily extended to other products too just by providing
access to the respective database in the required format.

The model is not sufficient for all the software needs of the company related to the replenishment.
Nonetheless it provides an initial framework through which the appropriate stock levels can be
maintained at the branch and regional level to reduce the ESC to as low as two units per cycle by
improving the service levels.
The forecasting accuracy can be improved further to reduce the uncertainty in demand which in
turn will reduce the inventory levels.
Lead time variability should be controlled to further lower the stock levels.
The model needs some more computer programming that could link the database to the model
and manual interventions can be reduced further.

44

Bibliography

Books References

1. Arnold J. R. T. & Chapman S., INTRODUCTION TO MATERIALS MANAGEMENT, 5th edition,

Pearson Education.

2. Sunil Chopra & Peter Meindl, SUPPLY CHAIN MANAGEMENT, 2nd edition, Pearson Education

3. Ballou, Ronald H., Business Logistics/Supply Chain Management, 5th edition, Prentice Hall

Internet References

1. http://ideas.repec.org/s/iim/iimawp.html

2. http://www.advanced-planning.eu/advancedplanninge-366.htm

3. http://www.inventorymanagementreview.org/

45

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