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Transcript of 2nd Scm Final
1. SAINT GOBAIN
1.1 CONCEPTION & HISTORY
Saint-Gobain is a French multinational corporation, founded in 1665 in Paris and
headquartered on the outskirts of Paris at La Défense. Originally a mirror manufacturer,
it now also produces a variety of construction and highperformance materials. Saint-
Gobain was created as part of the plan devised by Louis XIV and Colbert to restore the
French economy. Entrusted to private entrepreneurs,
the company broke with the factory tradition by organizing glass production on an
industrial basis. Thanks to the invention of glassware casting (1688), Saint-Gobain
established a near-monopoly in 17th-century Europe and ousted Venice, which was
then the leader in this sector. With the first half of the 20th century, came the
diversification of glass applications (glass wool, glass fiber, hollow glass). In 1970, Saint-
Gobain's merger with Pont-à-Mousson, the world leader in cast iron piping, gave birth
to a producer of materials and capital goods geared to the global dimension of its
markets. Since 1997, the group focuses on business sectors in which it holds strong
positions and the assets necessary for growth. The acquisition of Poliet in 1996
completed its expertise in distribution.
Saint-Gobain has also been established for many years in North and South America. In
1831, it opened its first glass sales depot in New York. In 1920, Saint-Gobain invested in
several cast glass companies in order to build up its industrial position in the United
States. After developing the TEL glass wool production process, Saint-Gobain signed its
first agreements with CertainTeed
in 1967. Following the acquisition of Norton in 1990, Saint-Gobain acquired Ball Foster
Glass in 1995.
The Saint-Gobain Group will now have completed a decade of far-reaching changes in
both business sectors and structure. The Group has focused its efforts on core business
lines which are less cyclical or less exposed to the economic fluctuations. It has
strengthened its technology and marketing skills, putting a greater emphasis on
distributing to end customers. And it has also stepped up its international expansion.
1
1.2 STRATEGY
The Group has in recent years implemented the strategy for steady and profitable
growth to:
develop genuine leadership in all if its businesses
enhance its technological and sales capacities
reduce its exposure to cyclical changes and market fluctuations
increase profitability and free cash flow.
The Group intends to focus its strategy on:
prioritizing development of construction and housing related businesses, in
particular through bolt-on acquisitions in Building distribution and Construction
Products sectors
pushing ahead with R&D and innovation initiatives, particularly in High-
performance Materials and Flat Glass sectors
stepping up expansion efforts in emerging countries for all businesses.
1.3 2008 OUTLOOK & TARGETS
In 2008, the Group will have to contend with a more difficult and far more uncertain
macro-economic climate than in 2007, with a possible recession for the US economy
and growth in housing starts across Europe losing momentum due chiefly to stricter
lending criteria. However, Saint-Gobain is well positioned to face these challenging
business conditions:
a strong position on the European building renovation market,
global leadership on markets related to energy efficiency in buildings, which
account for almost 30% of Group sales,
significant contributions from Asia and emerging countries to Group operating
income (around 20%, i.e. double the North American contribution in 2007),
the positive impact on the Group’s results of further acquisitions,
a solid financial structure and high levels of free cash flow.
In view of the above, for 2008 the Group is targeting:
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modest growth in operating income at constant exchange rates (average
exchange rates for 2007) and recurring net income,
a solid financial structure and continuing high levels of free cash flow.
1.4 BUSINESS UNITS
The company is built around five business sectors: Building Distribution, Construction
Products, Flat Glass, Containers / Packaging and High-Performance Materials.
1.4.1 Building Distribution
Since its creation in 1996, the Building Distribution Sector has experienced rapid
expansion through internal growth and acquisitions, first in France with Point P. and
Lapeyre; then in the UK with Jewson and Graham; followed by Germany, the
Netherlands and Eastern Europe with Raab Karcher; and finally in the Nordic Countries
with Dahl, the leading bathroom, kitchen and heating products distributor and
Optimera. With almost 4,000 stores in 24 countries, the Building Distribution Sector is
the leading building materials and kitchen, bathroom, heating and plumbing supplies
distributor in Europe, and the leading ceramic tile distributor in the World.
A Few Facts
2006 Sales: 17.6 billion Euros
Global Workforce: 63,000
Subsidiaries
SGBD UK
Raab Karcher
Point P.
Lapeyre
Dahl
Norandex Distribution
3
1.4.2 Construction Products
The Construction Products business unit provides the following products: acoustic and
thermal insulation, façade coatings, roofing, interior and exterior products and pipes.
1.4.3 Flat Glass
Active in 39 countries, the Flat Glass business unit is targeting so-called"emerging"
countries for expansion, a market that now accounts for more than one third of its sales.
Products include self-cleaning, electrochromic, lowemissivity and sun-shielding glass.Flat
Glass is currently building a plant to produce photovoltaic cells jointly with Shell, and is
developing a pilot factory for the production of electronic glass in Spain.
A Few Facts
2006 Sales: 5.1 billion Euros
Global Workforce: 37,100
Businesses
Production of flat glass
Manufacturing, transformation and distribution of glass for construction,housing
and interior decoration
Manufacturing, transformation and distribution of glass for the automotive –
OEM and after-market – and transportation industries
Specialty glass: household appliances, electronics, photovoltaic applications
1.4.4 High Performance Materials
The High Performance Materials business unit has research centers in Cavaillon
(France), Northboro, MA (United States), and now Shanghai (China). The fuel cell and
the particle filter are two current projects of the research centers. New sources of
growth are appearing in areas like energy, the environment, and medicine. Overall, the
HPM sector's sales are usually made up of at least 30% new products.
A Few Facts
2006 Sales: 4.9 billion Euros
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Global Workforce: 35,800
Businesses
Ceramics
Grains and Powders
Crystals
Plastics
Abrasives
Textile Solutions
Composites
1.4.5 Packaging
With a workforce of 20,000 worldwide, the Packaging business unit focuses on glass
packaging for the food and beverage industry.
A Few Facts
2006 Sales: 4.1 billion Euros
Global Workforce: 20,000
Businesses
Glass bottles and jars
The breakdown of total sales for the group as per 2006 figures is depictedbelow.
BREAKDOWN OF SALES PER SECTOR IN 2006
5
2. SAINT GOBAIN INDIA
Saint-Gobain established its presence in India by acquiring a majority stake in Grindwell
Norton in 1996, and thereafter went on to consolidate and strengthen its presence
within the country. The Group has adopted a systematic focus in launching its individual
businesses in India and currently operates in three business sectors: Flat Glass, High
Performance Materials and Construction Products.
Within these sectors, a variety of products are manufactured by eight different
companies:
2.1 FLAT GLASS
2.1.1 Saint-Gobain Glass India Ltd. (SGGI)
SGGI manufactures and markets float glass and mirrors from its plant near Chennai, and
2.1.2 Saint-Gobain Sekurit India Ltd. (SGSI)
SGSI offers a range of automotive glass products.
2.2 HIGH PERFORMANCE MATERIALS
2.2.1 Grindwell Norton Ltd. (GNO)
GNO manufactures and markets abrasives, silicon carbide, high performance
refractories and performance plastics from its four manufacturing locations.
2.2.2 Saint-Gobain Crystals & Detectors India Ltd. (SGCD)
SGCD manufactures and markets radiation detection and measurement products.
2.2.3 SEPR Refractories India Ltd. (SEPR)
SEPR manufactures and markets electrofused refractories.
2.3 CONSTRUCTION PRODUCTS
2.3.1 Saint-Gobain Weber India Ltd. (SGWI)6
SGWI offers facade and tiling solutions and technical mortars
2.3.2 Saint-Gobain SEVA Engineering India Ltd. (SGSEIL)
SGSEIL manufactures top rolls, tempering furnaces and toolings for the automotive
sector, moulds for containers and some building hardware products.
2.3.3 India Gypsum
India Gypsum manufactures an extensive range of Gypsum boards and plasters systems
and solutions for partitions, wall panels, ceilings and internal wall linings.
In order to further its business growth in the Indian sub-continent, Saint- Gobain also
established the General Delegation for India, Sri Lanka and Bangladesh in 1996. The
Delegation facilitates the establishment of new businesses in India, ensures synergy and
co-ordination between the businesses and companies in India.
7
3. GRINDWELL NORTON LIMITED
Founded in the year 1941, Grindwell Abrasives, as Grindwell Norton was then known,
pioneered the manufacture of grinding wheels in India at its plant located in a small
fishing village near Mumbai. Grindwell Norton Ltd. (GNO) came into being when a
technical collaboration in 1967 between Grindwell and the then world leader in
abrasives – Norton Company, USA, grew into a financial collaboration in 1971. In 1990,
Saint-Gobain acquired Norton Company, USA, worldwide, and six years later, GNO
became the first majority-owned subsidiary of Saint-Gobain in India.
Today, GNO is India’s leading manufacturer of Abrasives (Bonded, Coated, Non-Woven,
Superabrasives and Thin Wheels) and Silicon Carbide. It also manufactures and markets
High Performance Refractories and Performance Plastics products. GNO’s Project
Engineering Group (PEG), with its portfolio of diverse projects, is a proven engineering
resource for Saint-Gobain companies in India and internationally. Headquartered in
Mumbai, GNO has four manufacturing locations (Mumbai, Nagpur, Bangalore and
Tirupati) and 12 sales offices across the country. This broadly sums up the extensive
reach of GNO. In October 2006, GNO had the honour of featuring in Forbes Asia’s “Best
Under a Billion” list. It was one of only 23 Indian companies listed among the top 200
companies, with sales of under a billion dollars, in the Asia-Pacific Region.
GNO remains committed to the pursuit of becoming a world-class company with world-
class products and processes. It strongly emphasises cutting-edge technology, restless
innovation, customer service and operational freedom. With the distinct advantage of
being a part of the Saint-Gobain and Norton family, GNO has access to the best of
products and technology, enabling it to provide tomorrow’s products to customers
today.
3.1 PRODUCTS & SERVICES
3.1.1 Abrasives
In 1941, Grindwell made the first grinding wheel in India. It has since been offering the
best abrasive technology to Indian Industry. In the abrasives segment, Grindwell offers a
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full product range in Bonded Abrasives, Coated Abrasives and Non-woven abrasives
including grinding wheels, abrasive discs and handpads.
3.1.2 Ceramics
Ceramics, the material of the future has a wide range of industrial applications ranging
from high temperature refractories, wear parts, fused cast ceramics to filters for
chemical plants and much more. The Ceramics business of GNO is divided into HPR and
SiC.
The High Performance Refractories (HPR) division of Grindwell Norton offers complete
solutions, through it’s expertise in design engineering and manufacturing refractory
systems for high temperature and wear applications.
Silicon Carbide (SiC) is a synthetic material most commonly produced by the so-called
Acheson process in electrical resistance furnaces. SiC does not occur naturally – except
in some types of pre-solar meteorites, along with diamonds! SiC can be produced either
black or green, depending on the raw materials. The GNO SiC product range includes:
SiC MET: Products for Metallurgical Applications
SiC REF: Products for Refractory Applications
SiC ABR: Products for Abrasives Applications
SiC WS: Products for SlurryWire Sawing
SiC TECH: Products for Technical Ceramics
3.1.3 Performance Plastics
Saint-Gobain’s Performance Plastics division is a recognised authority in advanced
polymer technology. It produces and markets more than 800 standard and custom
polymer products through three businesses: Engineered Components, Fluid Systems and
Composites. Each demonstrates innovation, responsiveness to customer needs and
polymer expertise.
9
Engineered Components uses advanced technology to create narrow tolerance products
such as bearings, seals used in the automotive, aerospace and chemical industries. Its
products include seals, polymer products, and bearings.
Fluid Systems produces silicone and thermoplastic tubes and hoses, connectors, process
vessels etc. for critical fluid handing in demanding applications - pharmaceutical,
medical, food, beverage and laboratories.
Composites business products include specialty films, composite foams and coated
fabrics.
3.1.4 Project Engineering Group
The Project Engineering Group (PEG) is a division of Grindwell Norton Ltd. established in
the early seventies. It was primarily established for setting up Grindwell Norton's plants
and equipment in-house. PEG has come a long way and has several achievements to its
credit. Over the years, it has set up large scale complex projects for Grindwell Norton
and for other Saint-Gobain group companies in India and abroad. PEG has two sub-
divisions – Projects & Building Products & Solutions.
Projects Today, PEG resources are available from concept to commissioning, be it a
green-field venture or design and manufacture of special purpose machines. PEG
provides services in the field of Design & Engineering, Planning, Project Management,
Environmental Management / Consultancy / Total Solutions, Construction and
Supervision, Erection and Commissioning.
Along with design and development, the planning and management of the entire
project is carried out through meticulous detailing and documentary back up, inclusive
of budgets and time schedules. Specialized Project Management Software is utilized for
the timely and economical execution of the Projects. With a wealth of experience and
expertise, PEG ensures the completion of projects to specifications, within budget and
as per schedule.
10
PEG is headquartered at Bangalore, within the Grindwell Norton campus and is
equipped with modern infrastructure for design and engineering and a Design center
with advanced software.
Building Products & Solutions The PEG actively markets Building Hardware in the Indian
sub-continent, through the Building Products and Solutions (BPS) business group. This
sub-division of PEG offers a wide range of products to customers with reliable techno-
economic solutions, thus ensuring an array of repeat customers. The present range of
products is listed in the BPS section, and the major strategy for this business focuses on
expansion of the product range with synergistic products.
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4. THE PROJECT
4.1 PROJECT
Forecasting and Reordering Model for source products for Coated Abrasives Business
4.2 PROJECT INTRODUCTION
Grindwell Norton Limited manufactures and markets abrasives, silicon carbide, high
performance refractories and performance plastics from its four marketing locations.
However many of GNO’s products are sourced from other Grindwell Norton locations
outside India. In sourcing these materials from abroad, GNO incurs many costs such as
Purchasing Cost, Transportation Cost, Storage Cost, Interest on Held Capital, Ordering
Cost.
Since these materials have a growing market in India, the company can’t afford a stock-
out. At the same time, over-ordering and storage of the materials with its associated
costs will make the business unviable.
This project therefore aims at developing a sales forecasting model for the highest value
sourced Stock Keeping Units (SKUs) of the coated abrasives business of GNO, and
thereafter devise a reordering model so as to minimise ordering and storage costs.
4.3 PROJECT OBJECTIVES
Developing a sales forecasting model for each of these SKUs depending on the
sales pattern shown in the past taking care of the trend, business cycle and
seasonal effects.
Projecting the future sales for each identified SKU for next two years.
Recommending the reordering point alongwith the safety stock for each SKU as
per the required service level.
4.4 PROJECT PROGRESS
4.4.1 Development of Sales Forecasting Model
Forecasting is the process of estimation of unknown situations. Forecasting is required
because of the time lag between awareness of an impending event or need and
occurrence of that event. Now since each area of an organisation is related to others, a 12
good or bad forecast can affect the entire organisation. Some areas, in which forecasting
plays an important role are production planning, scheduling and resource acquisition.
In this specific case, GNO sources some of its products from overseas, and therefore the
lead time is quite high. Since overstocking will lead to holding up of capital, and
understocking will cause business opportunity loss, maintaining the correct inventory
for these items is important for GNO. Herein forecasting steps in, and tries to give a fair
estimate of future demand of these items to the manager so that their purchasing can
be planned accordingly.
4.4.1.1 Selecting a Forecasting Method
Forecasting is mainly done relying on the information of past sales data. In this case,
with the time series of sales figures obtained in the previous exercise, analysis will be
done to develop individual forecasting models. For developing such models, various
methods are used. Some of the questions that must be considered before deciding on
the most appropriate forecasting technique for a particular problem are the following:
What are the characteristics of the available data?
What time period is to be forecast?
What are the minimum data requirements?
How much accuracy is desired?
A general idea can be had from the following table about the capabilities of different
forecasting techniques, and their applicability.
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Data Pattern: ST, stationary; T, trended; S, seasonal; C, cyclicalTime Horizon: S, short term; I, intermediate; L, long termType of Model: TS, time series; C, casualSeasonal: L, length of seasonality
T a b l e 1 : C ha r a c t e r i s t i cs o f v a r i ou s F o r e c a s t i n g M e t hods
As can be observed from the figure, the sales pattern is trended upwards with strong
seasonal variations. For example, there is a definite upward spike in the month of
November, and sales are particularly down in the month of January.
Similar analysis was done for all the 17 selected items, and they all tended to show a
trend, and seasonal variations. Taking this into account, the Winter’s Exponential
Smoothing Method was used for projecting future sales. Here, even Box-Jenkins
Method could have been used but Winter’s Method was chosen above it because of
the following reasons:
Box-Jenkins Method is suitable for forecasting for short smaller time horizons,
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MethodData
PatternTime
HorizonType ofModel
Minimum DataRequirements
Non- seasonal
Season al
Naïve ST, T, S S TS 1Simple Averages ST S TS 30Moving Averages ST S TS 4-20ExponentialSmoothing
ST S TS 2
Linear ExponentialSmoothing
T S TS 3
Quadratic Exponential Smoothing
T S TS 4
Winter’s ExponentialSmoothing
T, S S, I TS 2 x L
Simple Regression T I C 10Multiple Regression C, S I C 10ClassicalDecomposition
S S TS 5 x L
Box Jenkins ST, T, C, S S TS 24 3 x L
while Winter’s Method can be used for medium term forecasts also, which was the
requirement.
Box-Jenkins Method is considered to be more accurate than Winter’s Method
of forecasting, but it is not adaptive, and a new model has to be developed after each
time period.
The time consumed in developing a model by Box-Jenkins Method is very time
consuming, and since in its case, the model is required to be built every time-period,
the overall investment is quite high.
Winter’s Method can be easily programmed into a computer program for
automatic model building, while it is not possible to do it in Box- Jenkins Method
since it requires human judgement.
The accuracy required for the job can be easily achieved by Winter’s Method,
which makes it imprudent to make substantial investment in developing models by Box-
Jenkins Method.
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4 . 4 . 1 .2 W i n t e r ’s M e t hod
Winter’s Forecasting Method is actually an improvement over the Exponential
Smoothening Method of Forecasting so that it takes care of the Seasonal & Trend
components. One of the main advantages of the method is that it is adaptive, implying
that the model developed tends to improve itself with each data point added into
the data base. So, it with time the estimated components of level, trend, and
seasonality change, the model detects that and modifies itself accordingly.
The Winter’s Model assumes the following pattern:
S yst e m a t i c C o m ponen t o f D e m an d = ( Le v e l + Tr e n d ) x S ea s on a l F a c t o r
To begin, the model needs estimates of level (L0), trend (T0) and seasonal factors
(S1, S2, …..Sp), where p is the periodicity of demand. These initial estimates are
obtained using regression techniques on the initial data-figures of sales. Then, further
data-figures are employed to refine these initial estimates.
In period t, given estimates of level Lt, trend Tt, and seasonal factors St,
…..St+p-1, the forecast for future periods is given by:
Ft+1 = (Lt + Tt) St+1 and Ft+l = (Lt + l.Tt)
St+l
On observing the demand for period t+1 (Dt+1)we revise the estimates for
level, trend and seasonal factors as follows:
Lt+1 = α (Dt+1/St+1) + (1- α) (Lt +
Tt) Tt+1 = β (Lt+1 – Lt) + (1 – β) Tt
St+p-1 = γ (Dt+1/Lt+1) + (1 – γ)
St+116
Where α is the smoothing constant for the level, 0 < α < 1; β is the smoothing
constant for the trend, 0 < β < 1; and γ is the smoothing constant for seasonal factor, 0
< γ < 1. These smoothing constants determine the rate at which the estimates of level,
trend and seasonality are updated by the model. For determining suitable values of
these constants, hit-and-trial method is used, wherein several combinations of
smoothing constant values are tried on the past sales data to observe the forecasting
error. Thereafter the combination of values of these constants, which minimises the
forecasting error is used for actual estimation of future sales.
4 .4 .1 .3 Mode l De vel op ment
Using the Winter’s Method explained above, a model was developed for each of the
selected items using their past sales data. The individual models for each of these
items are shown hereunder in tables 3 to 19.
Calculated Figures
Level 12594.44Trend Factor 158.93
Seasonal Factors
Month 1 1.58Month 2 1.13Month 3 2.33Month 4 1.57Month 5 1.35Month 6 1.94Month 7 2.25Month 8 1.11Month 9 1.31Month 10 0.76Month 11 1.28Month 12 1.99
Adjustment Factor Alpha 0.0000Adjustment Factor Beta 0.0000Adjustment Factor Gamma 0.3000
T a bl e 2 : F o re ca sti ng Mod el fo r ANS57
17
Calculated Figures
Level 2296.73Trend Factor 41.45
Seasonal Factors
Month 1 0.58Month 2 0.86Month 3 1.03Month 4 0.75Month 5 1.32Month 6 2.05Month 7 0.64Month 8 2.47Month 9 1.77Month 10 0.64Month 11 3.07Month 12 0.82
Adjustment Factor Alpha 0.0000Adjustment Factor Beta 0.0000Adjustment Factor Gamma 0.1000
T a b l e 3 : F o r e c a s t i ng M od e l f o r A N S 60
Calculated Figures
Level 28282.76Trend Factor 558.72
Month 1 0.53Month 2 0.09Month 3 0.37Month 4 2.06Month 5 1.09Month 6 0.51Month 7 1.43Month 8 0.56
18
Seasonal Factors
Month 9 0.08Month 10 0.49Month 11 0.83Month 12 0.97
Adjustment Factor Alpha 0.0125Adjustment Factor Beta 0.0125Adjustment Factor Gamma 0.0125
T a bl e 4 : F o re ca sti ng Mod el fo r AOP23
19
Calculated Figures
Level 12207.79Trend Factor 348.79
Seasonal Factors
Month 1 0.50Month 2 3.53Month 3 1.82Month 4 2.51Month 5 3.47Month 6 1.29Month 7 0.90Month 8 1.15Month 9 0.87Month 10 1.19Month 11 1.91Month 12 0.79
Adjustment Factor Alpha 0.0000Adjustment Factor Beta 0.0000Adjustment Factor Gamma 0.1000
T a bl e 5 : F o re ca sti ng Mod el fo r AOP25
Calculated Figures
Level 35973.80Trend Factor 402.44
Seasonal Factors
Month 1 0.91Month 2 0.85Month 3 0.83Month 4 1.25Month 5 2.42Month 6 1.13Month 7 0.89Month 8 1.21Month 9 1.59Month 10 1.25Month 11 1.66Month 12 0.83
Adjustment Factor Alpha 0.0250Adjustment Factor Beta 0.0000Adjustment Factor Gamma 0.4000
T a b l e 6 : F o r e c a s t i ng M od e l f o r A O P 26 20
Calculated Figures
Level 208385.49Trend Factor 7499.95
Seasonal Factors
Month 1 0.65Month 2 0.57Month 3 0.49Month 4 0.88Month 5 2.05Month 6 1.09Month 7 0.51Month 8 0.39Month 9 0.53Month 10 0.50Month 11 0.75Month 12 0.57
Adjustment Factor Alpha 0.0250Adjustment Factor Beta 0.2000Adjustment Factor Gamma 0.3000
T a bl e 7 : F o re ca sti ng Mod el fo r AOP27
T a b l e 8 : F o r e c a s t i ng M od e l f o r A O P 28
21
Calculated Figures
Level 247815.16Trend Factor 10196.08
Seasonal Factors
Month 1 1.88Month 2 0.69Month 3 0.80Month 4 0.46Month 5 1.27Month 6 1.38Month 7 0.34Month 8 0.52Month 9 0.90Month 10 0.74Month 11 0.63Month 12 0.74
Adjustment Factor Alpha 0.0250Adjustment Factor Beta 0.1500Adjustment Factor Gamma 0.5000
Calculated Figures
Level 79587.21Trend Factor 1046.75
Seasonal Factors
Month 1 0.07Month 2 0.25Month 3 0.27Month 4 0.29Month 5 0.07Month 6 0.06Month 7 0.74Month 8 0.32Month 9 0.59Month 10 0.32Month 11 1.34Month 12 0.15
Adjustment Factor Alpha 0.0125Adjustment Factor Beta 0.0125Adjustment Factor Gamma 0.0125
T a bl e 1 0: F or e ca sti n g Mode l fo r AOP29
Calculated Figures
Level 68723.41Trend Factor 2000.25
Seasonal Factors
Month 1 0.10Month 2 0.00Month 3 0.59Month 4 0.56Month 5 0.30Month 6 0.14Month 7 0.55Month 8 0.03Month 9 1.03Month 10 0.83Month 11 0.33Month 12 0.00
Adjustment Factor Alpha 0.0250Adjustment Factor Beta 0.0500Adjustment Factor Gamma 0.0000
T a b l e 1 1 : F o r e c a s t i n g M ode l f o r A O P 30
22
Calculated Figures
Level 8918.13Trend Factor 232.09
Seasonal Factors
Month 1 0.72Month 2 1.55Month 3 0.62Month 4 3.21Month 5 1.05Month 6 2.10Month 7 1.31Month 8 1.23Month 9 1.46Month 10 0.72Month 11 1.64Month 12 1.23
Adjustment Factor Alpha 0.0000Adjustment Factor Beta 0.0000Adjustment Factor Gamma 0.1000
T a bl e 1 2: F or e ca sti n g Mode l fo r AOP54
Calculated Figures
Level 13788.28Trend Factor 209.68
Seasonal Factors
Month 1 0.81Month 2 1.03Month 3 0.69Month 4 1.23Month 5 1.40Month 6 3.16Month 7 0.53Month 8 0.99Month 9 2.46Month 10 1.03Month 11 2.10Month 12 1.32
Adjustment Factor Alpha 0.0000Adjustment Factor Beta 0.0000Adjustment Factor Gamma 0.3000
T a b l e 1 3 : F o r e c a s t i n g M ode l f o r A O P 55
23
Calculated Figures
Level 52515.42Trend Factor 781.24
Seasonal Factors
Month 1 0.30Month 2 1.13Month 3 0.68Month 4 0.99Month 5 0.66Month 6 2.63Month 7 0.61Month 8 0.93Month 9 1.19Month 10 1.29Month 11 2.04Month 12 0.44
Adjustment Factor Alpha 0.0000Adjustment Factor Beta 0.0000Adjustment Factor Gamma 0.2000
T a bl e 1 4: F or e ca sti n g Mode l fo r AOP56
24
Calculated Figures
Level 97339.69Trend Factor 1383.01
Seasonal Factors
Month 1 0.22Month 2 0.76Month 3 0.72Month 4 1.12Month 5 0.84Month 6 3.70Month 7 0.46Month 8 1.19Month 9 1.17Month 10 1.09Month 11 2.13Month 12 0.48
Adjustment Factor Alpha 0.0000Adjustment Factor Beta 0.0000Adjustment Factor Gamma 0.2000
T a b l e 1 5 : F o r e c a s t i n g M ode l f o r A O P 57
25
Calculated Figures
Level 186658.32Trend Factor 2877.56
Seasonal Factors
Month 1 0.00Month 2 1.04Month 3 0.53Month 4 0.81Month 5 1.05Month 6 2.33Month 7 0.46Month 8 0.46Month 9 0.61Month 10 1.34Month 11 2.59Month 12 0.00
Adjustment Factor Alpha 0.0250Adjustment Factor Beta 0.0000Adjustment Factor Gamma 0.0000
T a bl e 1 6: F or e ca sti n g Mode l fo r AOP58
Calculated Figures
Level 24512.17Trend Factor 500.54
Seasonal Factors
Month 1 0.22Month 2 1.23Month 3 0.26Month 4 0.71Month 5 0.55Month 6 1.16Month 7 1.12Month 8 0.61Month 9 1.00Month 10 1.15Month 11 2.62Month 12 0.00
Adjustment Factor Alpha 0.0250Adjustment Factor Beta 0.0500Adjustment Factor Gamma 0.0000
T a b l e 1 7 : F o r e c a s t i n g M ode l f o r A O P 59
26
Calculated Figures
Level 17305.91Trend Factor 253.70
Seasonal Factors
Month 1 0.43Month 2 1.98Month 3 1.20Month 4 1.63Month 5 1.50Month 6 2.19Month 7 1.77Month 8 0.51Month 9 2.14Month 10 1.71Month 11 1.65Month 12 1.48
Adjustment Factor Alpha 0.0000Adjustment Factor Beta 0.0000Adjustment Factor Gamma 0.2000
T a bl e 1 8: F or e ca sti n g Mode l fo r AOP60
4.4.2 P ro j e c t i n g F ut u re S a l es
Using the sales forecasting models developed for each SKU, their respective future sales for
next two years were projected. These figures feature in the “Forecasted Figures” part of the
“Input & Output Sheet” for each item as shown in Appendix 1. Also, a table showing
consolidated projected sales figures is given in the appendix.
4.4. 3 R ecomm en di ng R e or der P oi n t & O rd er Qua nti ty
4 . 4 . 3 .1 R eo r de r P o i nt
The reorder point for replenishment of stock occurs when the level of inventory drops
down to zero. In view of instantaneous replenishment of stock the level of inventory jumps to
the original level from zero level. But in real life situations one never encounters a zero lead-
time. There is always a time lag from the date of placing an order for material and the date on
which materials are received. As a result the reorder level is always at a level higher than
zero, and if the firm places the order when the inventory reaches the reorder point, the new
goods will arrive before the firm runs out of goods to sell. The decision on how much stock to
hold is generally referred to as the order point problem, that is, how low should the inventory 27
be depleted before it is reordered.
The two factors that determine the appropriate order point are the procurement or
delivery time stock which is the Inventory needed during the lead time (i.e., the difference
between the order date and the receipt of the inventory ordered) and the safety stock
which is the minimum level of inventory that is held as a protection against shortages.
T he r e f o r e R eo r d e r P o i n t = N o r m a l c o n s u m p t i o n d u r i n g l e ad - t i m e + S a f e ty S t o ck .
Several factors determine how much delivery time stock and safety stock should be held. In
summary, the efficiency of a replenishment system affects how much delivery time is needed.
Since the delivery time stock is the expected inventory usage between ordering and receiving
inventory, efficient replenishment of inventory would reduce the need for delivery time stock.
And the determination of level of safety stock involves a basic trade-off between the risk of
stock-out, resulting in possible customer dissatisfaction and lost sales, and the increased costs
associated with carrying additional inventory.
Normal consumption during lead time is calculated as
D0 x L0
where D0 is average demand per period during lead time, and
L0 is average lead time
And Safety Stock is calculated is
2 2 2 ½
F.(L0.σD + D0 .σL )
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where F is a factor pertaining to the service level required
σD is standard deviation of demand per period, and
σL is standard deviation of lead time
For the project, the service level considered was 95%, therefore the value of F used is 1.96.
4.4.3.2 Order Quantity
Order Quantity is an important variable in Inventory Management. This is because a lot of
cost drivers are dependent on the order quantity.
o the more the order quantity, the more the capital held up in inventory
o the more the order quantity, the more storage space required
o the more the order quantity, the more chances of pilferage and obsolescence
o the more the order quantity, the more discounts available
o the more the order quantity, the less follow-up required
o the more the order quantity, the less the over-all transportation cost, and so
on.
In general, with increase in the order quantity, total holding cost for inventory increases and
total ordering cost for inventory decreases. Therefore, one needs to strike a balance
between the holding cost and the ordering cost by changing the order quantity so as to
minimise total cost.
Generally, in cases where the lead time is not very high, firms calculate the Economic Order
Quantity (EOQ) for the items and order them accordingly. EOQ is that level of inventory that
minimizes the total of inventory holding cost and ordering cost, and is calculated as:
EOQ = (2.R.CO/CH) ½
WhereR is the total item demand for the year
CO is the ordering cost (fixed cost associated with processing each order)
CH is the holding cost per piece of item per year.
In other cases, where the lead time is quite high, sometimes the EOQ is not even able to
take care of the product demand in the lead time for the next order. In these cases, the
order quantity is taken to be same as the product demand in the lead time.
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4.4.3.2.1 Ordering Cost
The ordering cost includes all incremental costs associated with placing or receiving an extra
order that are incurred regardless of the size of the order. For the project, the Ordering Cost
was calculated taking care of the following components.
Buyer Time
Transportation Costs
Receiving Costs
Fee, tax and other applicable charges
The ordering cost was finally calculated as INR 1550 for sea transported consignment and
INR 550 for air transported consignment.
4.4.3.2.2 Holding Cost
Holding Cost is estimated as a percentage of the cost of a particular product and is the sum
of the following components.
Cost of capital
Obsolescence Cost
Handling Cost
Occupancy Cost
The handling cost of the items in the project was calculated as 13% of item cost.
4.4 . 3. 3 C a l c u l at i o n o f R e or d e r P o i n t ( R O P ) a n d O r d e r Q u a nt i ty
For ready calculation of ROP and order quantity, an excel model was developed, which
calculated both the figures given some required inputs. The features of the Excel Model
are:
The model integrated with the sales forecasting model, making things easier.
The model provides updated results every time the sales history is updated
with recent data.
The model not only suggests the ROP and order quantity, but also
recommends the mode of order transport that would be economical taking
care of the capital costs and product demand.
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Since the foreign exchange rates and even prices of the items keep varying frequently,
no fixed values for the ROP and order quantity were provided. It was recommended
that inventory level of the items be regularly kept track of, and ROP and order
quantity be calculated each time an order is to be placed.
The model however, is depicted in Appendix 2 (with hypothetical figures).
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5. INPUTS & SOURCES OF INFORMATION
For progress of the project, data and information was obtained from various parts of the
organisation. The sources of all required information are given hereunder.
5.1 SALES FIGURES
The month-wise sales figures for each SKU was sought from the marketing department. The
department uses a central software database that keeps tracks of the sales figures of all
SKUs.
5.2 LEAD TIMES
For the purpose of calculating safety stock and reordering point, the lead times for each
were required. Information regarding sources of individual items, alternative modes of
transportation, and standard deviation in lead times of each item was required. All this data
was provided by the Customer Service Department which is responsible for tracking the
inventory levels and ordering items as and when required.
Complete history of past orders placed, along with order date and date of arrival was
provided by the department, which was then used to calculate the required figures. Other
modes of information gathering, like interviews were also made use of for getting a clearer
picture.
5.3 HOLDING COST
The holding cost for items contains many different components as discussed earlier.
o the main component of holding cost was discovered to be the Capital Cost,
standing at 12.9%. The data regarding the WACC for the company was
collected from the Finance Department.
o since the products have no practical expiry, that part required no care.
o In as much as GNO has a large storage space with no incremental cost for
individual items, no addition in holding cost is made.
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o handling costs per item were calculated, and they contributed little to the
overall holding cost.
Finally the value of CH was set to 13% of item cost.
5.4 ORDERING COST
Ordering Cost again is an aggregate of various costs. These individual cost data was
collected from various sources.
the main component of the ordering was discovered to be the per order fee
paid to the Consignment Clearing Agents employed. The agents charge a
fixed sum of INR 5000 for clearing of sea transport consignments and INR
1000 for clearing of air transport consignments.
other major component of the ordering cost is a fixed fee paid to the
customs department for warehousing of consignments
finally the contribution of buyer’s time was added in the ordering cost to get
the final figure
All this time, information was sought from various departments including Accounts
Department, Stores Department etc.
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6. CONCLUSION
Inventory Management is an important aspect of any business involving manufacturing &
marketing because of the sheer magnitude of its impact, may it be positive or negative.
Recently, some businesses have based their business strategy itself on better management
of inventory.
Sales forecasting is an important of inventory management. It gives the management an
overview of the things to come in the future, and therefore readies them for eventualities –
including inventory management.
In the project, improvement in inventory management system of the company has been
endeavoured through future sales projections, and then calculating the Reorder Point and
Order Quantity for many items.
From the project, finally the following outcomes have been found:
A generic Excel® program for forecasting of sales (for any item) based on past sales
data
Calculated values of sales forecasts for selected items in a consolidated table
A generic Excel® program for finding the ROP and order quantity (for any item)
based on sales forecasts generated by the program described in (ii).
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7. REFERENCES
Box, E.P. and Jenkins, G.M. (1976), Time Series Analysis: forecasting & control,
Holden-Day Inc., San Francisco
Hanke, J.E. and Reitch, A.G. (1981), Business Forecasting, Allyn & Bacon, London
Spyros M.; Wheelright S.C. and Hyndman R.J. (1998), Forecasting: methods and
applications, JohnWiley & Sons Inc., Singapore
Chopra, Sunil and Meindl, Peter (2003), Supply Chain Management: strategy,
planning and operation, Pearson Education, New Delhi
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