1. i
ABSTRACT
A supply chain is a system of organizations, people, activities, information and resources
involved in moving a product or service from supplier to customer. It is, therefore,
essential that the supply chain be balanced in all respects to ensure the best possible
service to the customer. The objective of this project is two-fold. First is to balance the
supply chain network for one product at Bosch Limited and then modify this solution to
be dynamically applicable to the other range of products manufactured at Bosch.
Secondly, to emphasize the importance and need for balancing supply chain networks in
modern industries for the purpose of achieving market demands despite fluctuations.

2. ii
ACKNOWLEDGEMENT
We would like to take this opportunity to thank all the people who have helped directly or
indirectly with this project. The satisfaction and euphoria that accompanies the successful
completion of any task would be incomplete without the mention of these people.
In particular, we would like to thank our internal project guide Mr. B M Nagesh,
Assistant Professor, Department of Mechanical Engineering, Sir M. Visvesvaraya
Institute of Technology, Bangalore, for providing us with guidance and extending the
required facilities to carry out this project. Very warm thanks to Dr. D N Drakshayani,
Professor and Head, Department of Mechanical Engineering, Sir M. Visvesvaraya
Institute of Technology, Bangalore and Dr. M S Indira, Principal, Sir M.
Visvesvaraya Institute of Technology, Bangalore, for allowing us to pursue this project
at Bosch Limited, Bangalore.
We would also like to express our sincere gratitude to Mr. Venkatesh Polali, Senior
Manager, AA-AS/PUR-IN, Bosch Limited, Bangalore, for his patience, guidance and
invaluable inputs, without which this project would never have been a success. We are
also grateful to Mr. Shashi Kiran, Mr. Joseph Anthony Raj M, Mr. Mahaveer
Chhajed, Mr. Prashanth Karkera and all others from AA-AS/PUR-IN, Bosch Limited
for their constant support. The entire experience has been thoroughly engaging and highly
intellectual.
We are deeply indebted and grateful to our parents, friends and family for the
encouragement, moral support and motivation that they provided us with during the
course of our project.
ARJUN VYAZ
NAKUL KISHORE DUMBLEKAR
PRATEEK RAJSHEKHAR
NIKHIL VENKAT

3. iii
TABLE OF CONTENTS
1. CHAPTER 1 INTRODUCTION .....................................................................................1
2. CHAPTER 2 LITERATURE REVIEW ..........................................................................4
2.1 Deterministic Analytical Models ...............................................................................4
2.2 Stochastic Analytical Models.....................................................................................6
2.3 Economic Models.......................................................................................................7
2.4 Simulation Models .....................................................................................................7
3. CHAPTER 3 SCOPE OF THE PROJECT......................................................................9
4. CHAPTER 4 PROJECT METHODOLOGY ................................................................11
5. CHAPTER 5 PROJECT DETAILS...............................................................................13
5.1 Product Study...........................................................................................................13
5.2 Bill of Materials .......................................................................................................17
5.3 Inventory Status........................................................................................................19
5.3.1 Stock Check .....................................................................................................20
5.4 Product Demand.......................................................................................................21
5.4.1 Pull Strategy.....................................................................................................22
5.4.2 Push Strategy ...................................................................................................22
5.5 The Bullwhip Effect & The Production Ramp-Up Drive ........................................25
5.5.1 The Bullwhip Effect.........................................................................................25
5.5.2 Consequences of the Bullwhip Effect..............................................................27
5.5.3 Production Ramp-Up Drive.............................................................................28
5.5.4 Supplier Visits..................................................................................................32
5.5.4.1 Supplier Visit to GMT, Hosur .............................................................32
5.5.4.2 Supplier Visit to Patterns India, Bangalore..........................................33
5.5.4.3 Supplier Visit to Genuine Products, Bangalore ...................................33

4. iv
5.6 The Solution Phase...................................................................................................34
5.6.1 Improvement of Forecasts...............................................................................34
5.6.1.1 The Single Exponential Smoothing Technique ..................................35
5.6.1.2 The Double Exponential Smoothing Technique.................................36
5.6.1.3 Winters‟ Approach to Forecasting......................................................37
5.6.2 Revised Production Plan.................................................................................42
6. CHAPTER 6 CONCLUSIONS .....................................................................................45
6.1 Summary .................................................................................................................45
6.2 Future Scope............................................................................................................45
7. REFERENCES ..............................................................................................................47
8. APPENDIX....................................................................................................................49

5. v
LIST OF TABLES
Table 5.1 EPS A Critical Sub-Assemblies List .................................................................17
Table 5.2 Percentage of Local and Foreign Parts ..............................................................19
Table 5.3 Demand Forecast for the EPS A and the EPS B for the year of 2014...............24
Table 5.4 Initial Demand Forecast.....................................................................................29
Table 5.5 Actual Demand Data..........................................................................................30
Table 5.6 List of Bottleneck Parts for EPS B ....................................................................32
Table 5.7 List of Bottleneck Parts for EPS A....................................................................32
Table 5.8 2013 Q4 and 2014 Q1 Demand Data for EPS A ...............................................39
Table 5.9 Ideal Forecasted Demand from Winters‟ Method .............................................41
Table 5.10 Forecasted Demand (With Factors of Level, Trend and Seasonality).............41

6. vi
LIST OF FIGURES
Fig 1.1 Supply Chain Network ...........................................................................................1
Fig 3.1 Balanced Supply Chain Involves Functional Trade-Offs .......................................9
Fig 5.1 The Bosch EPS Injection Pump Test-Bench ........................................................13
Fig 5.2 Production System ................................................................................................14
Fig 5.3 Assembly Process Flow Chart Sheet 1..................................................................15
Fig 5.4 Assembly Process Flow Chart Sheet 2..................................................................16
Fig 5.5 Bill of Materials ....................................................................................................18
Fig 5.6 Inventory Data ......................................................................................................20
Fig 5.7 Stock Check Sheet ................................................................................................21
Fig 5.8 Push and Pull Strategies .......................................................................................22
Fig 5.9 Effect of Lead Time and Demand Uncertainty on Supply Chain Strategy ..........23
Fig 5.10 Graph Plot of Forecasted Demand v/s Time (in Months) ...................................25
Fig 5.11 Increasing Variability of Orders up the Supply Chain .......................................26
Fig 5.12 The Ramp-Up Triangle .......................................................................................29
Fig 5.13 Forecast v/s Actual Demand of EPS A................................................................30
Fig 5.14 Forecast v/s Actual Demand of EPS B ...............................................................31
Fig 5.15 Single Exponential Smoothing Plot for Demand ...............................................35
Fig 5.16 Double Exponential Smoothing Plot for Demand ..............................................36
Fig 5.17 Trend and Seasonality of a Winters‟ Technique Curve ......................................37
Fig 5.18 Graph of Forecasted Demand using Winters‟ Method .......................................42
Fig 5.19 Production Planner – Construction .....................................................................43
Fig 5.20 Production Planner – Working ...........................................................................44

7. 1
CHAPTER 1
INTRODUCTION
A supply chain consists of all parties involved, directly or indirectly, in fulfilling a
customer‟s request. The supply chain not only includes the manufacturer and suppliers,
but also the transporters, warehouses, retailers and customers themselves. Within each
organization the supply chain includes all functions involved in receiving and filling a
customer request. These functions include new product development, marketing,
operations, distribution, finance and customer service.
Fig 1.1 – Supply Chain Network
A typical supply chain may involve a variety of stages, including the following:
 Customers
 Retailers
 Wholesalers/Distributors
 Manufacturers
 Components/Raw Material Suppliers
Each stage in a supply chain is connected through the flow of product, information and
funds. These flows often occur in both directions and may be managed by one of the
stages or an intermediary. The appropriate design of supply chain depends on both the
customer‟s needs and roles played by the stages involved.
For example, Wal-Mart provides the product, pricing and availability information to the
customer. The customer transfers funds to Wal-Mart. Wal-Mart conveys point-of-sales

8. 2
data as well as replenishment orders to the warehouse or distributor, who transfer the
replenishment order via trucks back to the store. Wal-Mart transfers funds to the
distributor after replenishment. The distributor also provides pricing information and
sends delivery schedules to Wal-Mart, who may in turn send back packaging material to
be recycled. Similar information, material and fund flows take place across the entire
supply chain.
Bosch‟s core products are automotive components, industrial products and building
products. One among these products is the EPS injection pump test bench, which is an
economical entry-level device for testing conventional in-line and distributor pumps.
With the EPS, repair shops can reliably test injection pumps. It is equipped with a
measuring system with measuring glass technology for testing pumps with up to 12
cylinders. It features flow rate measurement, an electronically controlled stroke counter
and automatic test oil heating. Target and actual values are shown on a high-resolution
5.7-inch LCD display. Alternatively, the test bench is also available in a PC version with
a 19-inch monitor.
The EPS A consists of more than 250 parts procured from multiple suppliers. Each of
these suppliers may in turn procure materials from their own set of suppliers. Following
this, they process these materials through production operations and dispatch the final
products to Bosch. The process times and lead times of the suppliers all constitute to the
performance and responsiveness of orders. In order to maximize the output of production,
the supply chain network must be designed to be seamless and efficient. The EPS consists
of more than 20 suppliers. Thus, this entails a complex supply chain network with a
number of intermediaries. In order to meet product demand fluctuations, enhance
production performance and order responsiveness, the supply chain should be optimized
at each level.
The project involves studying the EPS A and analysing the supply chain network of its
parts and sub-assemblies, which assist in the production of the same.
1. The first phase of the project would be to identify all the stages of the supply
chain and their interactions. This will involve understanding internal and external
supply chain networks, production and assembly processes, distribution networks
and inventory system.
2. The second phase of the project would involve data collection pertaining to
demand forecasts, bill of materials, inventory status, lead times, process times and

9. 3
logistics. Following this, analysis will be carried out on the data collected to
identify the maximum lead times, critical components and their process times. The
last part of this phase is to devise a solution which will increase the overall
performance of the supply chain.
3. The third phase of the project will be to evaluate different solutions and suggest
the best one for implementation for the EPS A. The solution will be in the form of
a revised forecast and revised production plan which would facilitate the supply
chain to have high responsiveness.

10. 4
CHAPTER 2
LITERATURE REVIEW
For years, researchers and practitioners have primarily investigated the various processes
within manufacturing supply chains individually. Recently, however, there has been
increasing attention placed on the performance, design and analysis of the supply chain as
a whole. From a practical standpoint, the supply chain concept arose from a number of
changes in the manufacturing environment, including the rising costs of manufacturing,
the shrinking resources of manufacturing bases, shortened product life cycles and the
globalization of market economies. Within manufacturing research, the idea of the supply
chain grew largely out of multi-echelon inventory models. A multi-echelon system refers
to a system consisting of various levels, such as supplier, manufacturer, distributor,
retailer and consumer. Over the years, the focus has shifted from single-echelon models
to multi-echelon models and significant progress has been made in the design and
analysis of these multi-echelon models.
Generally, multi-echelon models for supply chain design and analysis can be divided into
four categories by modelling approach. In the cases studied, the modelling approach is
driven by the nature of the inputs and the objective of the study. The four categories are:
 Deterministic analytical models, in which the variables are known and specified
 Stochastic analytical models, where at least one of the variables is unknown and is
assumed to follow a particular probability distribution
 Economic models
 Simulation models
2.1 Deterministic Analytical Models
Williams (1981) [2]
presented seven heuristic algorithms for scheduling production and
distribution operations in an assembly supply chain network. The objective of each
heuristic is to determine a minimum production cost and/or product distribution schedule
that satisfies final product demand. The total cost is a sum of average inventory holding
and fixed (ordering, delivery, or set-up) costs. Finally, the performance of each heuristic
is compared using a wide range of empirical experiments and recommendations are made
based on the type of improvement necessary and the network structure.

11. 5
Williams (1983) [3]
developed a dynamic programming algorithm for simultaneously
determining the production and distribution batch sizes at each node within a supply
chain network. As in Williams‟ paper two years earlier, it is assumed that the production
process is an assembly process. The objective of the heuristic is to minimize the average
cost per period over an infinite horizon, where the average cost is a function of processing
costs and inventory holding costs for each node in the network.
Ishii, et. al (1988) [4]
developed a deterministic model for determining the base stock
levels and lead times associated with the lowest cost solution for an integrated supply
chain on a finite horizon. An integrated supply chain is a supply chain of close
collaborative relationships with integrated data and business processes. These may
include internal integration, customer integration, relationship integration, technology and
planning integration, management integration and supplier integration. The stock levels
and lead times are determined in such a way as to prevent stockout and to minimize the
amount of obsolete (dead) inventory at each stock point. In this case, the model utilizes a
pull-type ordering system which is driven by linear demand processes.
Newhart, et. al. (1993) [5]
designed an optimal supply chain using a two-phase approach.
The first phase is a combination mathematical program and heuristic model, with the
objective of minimizing the number of distinct product types held in inventory throughout
the supply chain. This is accomplished by consolidating substitutable product types into
single stock-keeping units (SKUs). The second phase is a spreadsheet-based inventory
model, which determines the minimum amount of safety stock required to absorb demand
and lead time fluctuations. The authors considered four facility location alternatives for
the placement of the various facilities within the supply chain. The next step is to
calculate the amount of inventory investment under each alternative, given a set of
demand requirements and then select the minimum cost alternative.
Voudouris (1996) [6]
developed a mathematical model designed to improve efficiency and
responsiveness in a supply chain. The model maximizes system flexibility, as measured
by the time-based sum of instantaneous differences between the capacities and
utilizations of two types of resources - inventory resources and activity resources.
Inventory resources are resources directly associated with the amount of inventory held;
activity resources are resources that are required to maintain material flow.

12. 6
The model requires, as input, product-based resource consumption data and bill-of-
material information and generates, as output, (i) a production, shipping and delivery
schedule for each product and (ii) target inventory levels for each product.
2.2 Stochastic Analytical Models
Cohen and Lee (1988) [7]
develop a model for establishing a material requirements policy
for all materials for every stage in the supply chain production system. In this work, the
authors use four different cost-based sub-models (there is one stochastic sub-model for
each production stage considered). Each of these sub-models is listed and described
below:
 Material Control – Establishes material ordering quantities, reorder intervals and
estimated response times for all supply chain facilities, given lead times, fill rates,
bills of material, cost data and production requirements.
 Production Control – Determines production lot sizes and lead times for each
product, given material response times.
 Finished Goods Stockpile (Warehouse) – Determines the economic order size and
quantity for each product, using cost data, fill rate objectives, production lead times
and demand data.
 Distribution – Establishes inventory ordering policies for each distribution facility,
based on transportation time requirements, demand data, cost data, network data
and fill rate objectives.
Each of these sub-models is based on a minimum-cost objective. In the final
computational step, the authors determined approximate optimal ordering policies using a
mathematical program, which minimizes the total sum of the costs for each of the four
sub-models.
Pyke and Cohen (1993) [8]
developed a mathematical programming model for an
integrated supply chain, using stochastic sub-models to calculate the values of the
included random variables included in the mathematical program. The authors considered
a three-level supply chain, consisting of one product, one manufacturing facility, one
warehousing facility and one retailer. The model minimizes total cost, subject to a service
level constraint and holds the set-up times, processing times and replenishment lead times
constant. The model yields the approximate economic (minimum cost) reorder interval,

13. 7
replenishment batch sizes and the order-up-to product levels (for the retailer) for a
particular production network.
Tzafestas and Kapsiotis (1994) [9]
utilize a deterministic mathematical programming
approach to optimize a supply chain, then use simulation techniques to analyse a
numerical example of their optimization model. In this work, the authors perform the
optimization under three different scenarios:
 Manufacturing Facility Optimization – Under this scenario, the objective is to
minimize the total cost incurred by the manufacturing facility only; the costs
experienced by other facilities are ignored.
 Global Supply Chain Optimization – This scenario assumes a cooperative
relationship among all stages of the supply chain and therefore minimizes the total
operational costs of the chain as a whole.
 Decentralized Optimization – This scenario optimizes each of the supply chain
components individually and thus minimizes the cost experienced by each level.
The authors observed that for their chosen example, the differences in total costs among
the three scenarios were very close.
2.3 Economic Models
Christy and Grout (1994) [10]
developed an economic, game-theoretic framework for
modelling the buyer-supplier relationship in a supply chain. The basis of this work is a
2 x 2 supply chain relationship matrix, which may be used to identify conditions under
which each type of relationship is desired. These conditions range from high to low
process specificity and from high to low product specificity. Thus, the relative risks
assumed by the buyer and the supplier are captured within the matrix. For example, if the
process specificity is low, then the buyer assumes the risk; if the product specificity is
low, then the supplier assumes the risk. For each of the four quadrants (and therefore,
each of the four risk categories), the authors assigned appropriate techniques for
modelling the buyer-supplier relationship.
2.4 Simulation Models
Towill, et. Al (1992) [11]
used simulation techniques to evaluate the effects of various
supply chain strategies on demand amplification. The strategies investigated were as
follows:

14. 8
 Eliminating the distribution echelon of the supply chain, by including the
distribution function in the manufacturing echelon.
 Integrating the flow of information throughout the chain.
 Implementing a Just-In-Time (JIT) inventory policy to reduce time delays.
 Improving the movement of intermediate products and materials by modifying the
order quantity procedures.
 Modifying the parameters of the existing order quantity procedures.
The objective of the simulation model is to determine which strategies are the most
effective in smoothing the variations in the demand pattern. The just-in-time strategy and
the echelon removal strategy were observed to be the most effective in smoothing
demand variations.
Wikner, et. al. (1991) [12]
examined five supply chain improvement strategies, then
implemented these strategies on a three-stage reference supply chain model. The five
strategies were:
 Fine-tuning the existing decision rules.
 Reducing time delays at and within each stage of the supply chain.
 Eliminating the distribution stage from the supply chain.
 Improving the decision rules at each stage of the supply chain.
 Integrating the flow of information and separating demands into “real” orders,
which are true market demands and ”cover” orders, which are orders that bolster
safety stocks.
Their reference model includes a single factory (with an on-site warehouse), distribution
facilities and retailers. Thus, it is assumed that every facility in the supply chain holds
some inventory. The implementation of each of the five different strategies is carried out
using simulation, the results of which are then used to determine the effects of the various
strategies on minimizing demand fluctuations. The authors concluded that the most
effective improvement strategy was improving the flow of information at all levels
throughout the chain and separating orders.

15. 9
CHAPTER 3
SCOPE OF THE PROJECT
The objective of managing the supply chains is to synchronise the requirements of the
customer with the flow of material from suppliers in order to effect a balance between
what are often seen as the conflicting goals of high customer service, low inventory
investment and low unit cost. The design and operation of an effective supply chain is of
fundamental importance to every company.
To provide higher service level, without incurring an undue burden of cost, requires that
all the activities along the supply chain are balanced. To achieve the necessary balance
between cost and service involves trade-offs through the chain. For the benefit of such
trade-offs to be achieved it is necessary to think in terms of a single integrated chain
rather than narrow functional areas.
Fig 3.1 – Balanced Supply Chain Involves Functional Trade-Offs

16. 10
Those companies that consider the supply chain during strategic analysis, manage it as a
single entity and ensure the appropriate use of tools and techniques in order to meet the
needs of the market, will obtain the real benefits resulting from increased market share.
The role of most manufacturing industries is to generate revenue through value addition
of its products. Common to all manufacturing companies, however, is the need to control
the flow of material from suppliers, through the value adding processes and distribution
channels, to customers. The scope of the supply chain begins with the source of supply
and ends at the point of consumption. It extends much further than just physical
movement of materials and is just as much concerned with supplier management,
purchasing, materials management, facilities planning, customer service and information
flow as with transport and physical distribution. The core supply chain functions
primarily relate to the demand and supplier management processes directly controlled by
the enterprise and the extended functions relate to the processes at either end of the
supply chain spectrum, enabling relevant collaborative processes.
The project essentially deals with identifying, understanding and analysing the different
stages of the supply chain concerned with specific products of Bosch Limited. The
solution generated through this project aims to balance the supply chain, keeping in mind
the market needs. This solution is to be designed dynamically so that it can be applied to
the other range of products offered at Bosch Limited.

17. 11
CHAPTER 4
PROJECT METHODOLOGY
The methodology employed in the execution of the project include the use of certain
supply chain management tools and statistical tools to collect data, refine the same into
critical information and draw inferences and conclusions using the output obtained. The
step-by-step processes carried out are as follows:
1. Examining and understanding the production process of the EPS A machine.
 This was carried out by studying the machine drawing sheet and understanding
the assembly process. The assembly and working of the machine was observed
and average production process times were recorded.
2. Identification of the critical parts, which form a major part of the process time.
 The Bill of Materials (BOM) was drawn out from the SAP database and the
crucial sub-assemblies and parts were identified.
3. Measurement of inventory status.
 Stock checks were carried out. This was done by selecting a set of random parts
from the BOM and performing manual stock checks at the inventory. This
assisted in checking the quantity of material in hand as well as the amount
required for meeting market demands on a monthly basis.
 The MOQ (Minimum Order Quantity), SPQ (Standard Packing Quantity) and
Lead Times of each part are noted. This is used to shorten the list of critical parts,
on which the major obstacles could be faced in the production of the machine.
4. Forecasting demand and comparing the same with respect to production
capacity.
 The market demands were forecasted on a per-month basis for the year of 2014.
The average demands were calculated for each month. The stock check results
were compared with this forecasted demand and a conclusion was reached
regarding the producible capacity.
5. Supplier visits and sourcing strategies
 The vendor list was drawn out from the SAP database and those supplying the
critical parts were identified. Supplier visitations were planned and carried out to

18. 12
further understand their production processes and the obstacles they encounter in
their processes.
 Alternative solutions and possible remedies to these obstacles are investigated to
decrease overall time spent on each critical sub-assembly or part.
6. Devising multiple solutions and selection of the best one
 Using all acquired data, graphs and calculations, multiple solutions will be
arrived at. Each solution will be theoretically vetted and checked for efficiency in
comparison to existing practice. The best among these will be selected for
implementation.
7. Evaluation phase
 Data will be collected on a regular basis. The avenues of data collection will
include stores checks, inventory levels and ease of logistic handling.
 The collected data will be used to plot graphs, from which conclusions may be
drawn regarding the capacity of the new system to balance the production
capabilities with the market demands.

19. 13
CHAPTER 5
PROJECT DETAILS
5.1 Product Study
The EPS injection pump test-bench is an economical entry-level device for testing
conventional in-line and distributor pumps. With the EPS, repair shops can reliably test
injection pumps from Bosch and other manufacturers according to the engine and vehicle
manufacturer‟s specifications.
Fig 5.1 – The Bosch EPS Injection Pump Test-Bench
It is powered by a low-maintenance 18.5 kW electrical drive and is equipped with a
measuring system with measuring glass technology for testing pumps with up to 12
cylinders. It also features flow rate measurement, an electronically controlled stroke
counter and automatic test oil heating. Target and actual values are shown on a high-
resolution 5.7-inch LCD display. Alternatively, the test bench is also available in a PC
version with a 19-inch TFT monitor.
In addition to these features, an extensive range of supplementary equipment sets are
available for the various diesel components from Bosch and other manufacturers. This
range is supplemented with different tool sets for repairing passenger car and commercial
vehicle common rail injectors/high-pressure pumps and unit injector/unit pump systems.

20. 14
The production system employed at Bosch is called the Bosch Production System
(BPS). For the EPS, the supply chain works on a push process system as detailed in Fig
5.2.
Fig 5.2 – Production System
The product study for the project was carried out by first observing a finished machine
and understanding its features and capabilities. The second step was to get the machine
drawing sheet and identify the critical assembly sections. The third step performed
included getting the assembly process flow chart and understanding the different
processes and the sequence followed in the assembly process. This was followed by
observing the assembly process taking place in the plant. This activity was repeated a
number of times to thoroughly understand the set of operations performed.
The assembly process consists of two major processes, namely, the mechanical
assemblies and the electrical assemblies.
 Mechanical Assemblies – It consists of mounting and assembling all the sub-
assemblies in a specific sequence as shown in the process charts. The total cycle
time for one run-through of only mechanical assemblies is 30.35 hours.
 Electrical Assemblies – It consists of all wiring, grounding and routing
operations. Oil level sensors and temperature sensors are attached to the assembly.
The total cycle time for one run-through of only electrical assemblies is 12.17
hours.
The mechanical and electrical assembly processes occur simultaneously. At the end of all
assemblies, the machine is run through a series of tests to ensure quality of performance.
This is followed by packaging and subsequent shipping. The total time taken to complete
entire assembly of one machine is 42.51 hours. The testing and packaging processes take
12.67 hours resulting in a total cycle time of 55.18 hours for one machine.
Market/Customer Suppliers
Bosch
Production
System
Demand
AssemblyDispatch

21. 15
Fig 5.3 – Assembly Process Flow Chart Sheet 1

22. 16
Fig 5.4 – Assembly Process Flow Chart Sheet 2

23. 17
The critical sub-assemblies identified in the mechanical assemblies are as follows:
Table 5.1 – EPS A Critical Sub-Assemblies List
Part Number Description Qty Unit Sourcing Location
F00281NK00 Coupling Guard 1 Number Local Bangalore
F002DG2903 Frame Assy. 1 Set Local Bangalore
F002DG2908
Measuring Glass Tray
Assy.
1 Number Local Bangalore
F002DG2919
Pressure Control Valve
Assy.
1 Number Local Bangalore
F002DG3937 Cabinet Empty 1 Number Local Bangalore
The electrical assemblies involve the wiring process and setting up of PCBs and assisted
units in the power cabinet. This involves setting up connections to the oil and temperature
sensors, grounding the connections and soldering operations.
At the end of the mechanical and electrical assemblies, the product is tested by mounting
a test fuel injector and checking the values displayed on the LCD. Upon successful test
results, the product is sent for packaging and subsequently to stores for dispatch.
5.2 Bill of Materials
A bill of materials (BOM) is a list of the raw materials, sub-assemblies, intermediate
assemblies, sub-components, parts and the quantities of each needed to manufacture
an end product. A BOM may be used for communication between manufacturing
partners, or confined to a single manufacturing plant.
A BOM can define products as they are designed (engineering bill of materials), as they
are ordered (sales bill of materials), as they are built (manufacturing bill of materials), or
as they are maintained (service bill of materials). The different types of BOMs depend on
the business need and use for which they are intended. Bosch Limited uses a modular
type of BOM based on the engineering bill of materials. Modular BOMs are hierarchical
in nature with the top level representing the finished product, which may be a sub-
assembly or a completed item.
A modular BOM can be displayed in the following formats:
 A single-level BOM that displays the assembly or sub-assembly with only one level
of children. Thus it displays the components directly needed to make the assembly
or sub-assembly.

24. 18
 An indented BOM that displays the highest-level item closest to the left margin and
the components used in that item indented more to the right.
The figure shown is an excerpt of a BOM of one of the critical sub-assemblies of the
EPS A. This BOM is a multi-level modular type of BOM which expresses the
different parts in a hierarchical format, depicting the parent part or sub-assembly at
the top and bifurcation of the same into multiple levels of “children” parts. It is
understood that one unit of the level 1 part or sub-assembly is obtained through the
combination of all level 2, 3 and 4 parts under it. The different fields in the BOM
include hierarchy level, part no., part name, quantity and unit of measurement.
Fig 5.5 – Excerpt of Bill of Materials
Using the BOM and comparing the lead times of all part procurements, the critical sub-
assemblies were identified. The specific BOMs for each of these sub-assemblies were
drawn out from the SAP database and further studied for sourcing strategies and work-
around solutions. Also, the quantity of parts required for the manufacture of one machine
is defined by the BOM. This can be expanded to give the parts requirement for „n‟
number of machines, where „n‟ represents the market demand for the machine.

25. 19
Table 5.2 – Percentage of Local and Foreign Parts
Total Number Of Parts 272
Sourcing: Number of Local Parts 262 (96.32%)
Sourcing: Number of Foreign Parts 10 (3.68%)
The above table describes the distribution of sources for parts of the EPS A. Since more
than 95% of the parts are sourced locally, emphasis is placed on improving delivery
efficiencies from these suppliers.
5.3 Inventory Status
Inventory management is primarily about specifying the size and placement of stocked
goods. Inventory management is required at different locations within a facility or within
multiple locations of a supply network to protect the regular and planned course of
production against the random disturbance of running out of materials or goods.
The scope of inventory management also concerns the fine lines between replenishment
lead time, carrying costs of inventory, asset management, inventory forecasting, inventory
valuation, inventory visibility, future inventory price forecasting, physical inventory,
available physical space for inventory, quality management, replenishment, returns and
defective goods and demand forecasting.
The inventory system used at Bosch Limited is the First-In-First-Out (FIFO) system. The
inventory status was found out for the EPS A machine. This inventory data was used to
get a better understanding about the storage requirements of the parts and sub-assemblies
for the machine. Fig 5.6 shows an excerpt of the inventory data collected. The different
parameters which were considered for data collection include Minimum Order Quantity
(MOQ), Safety Stock, Lead Time and Current Stock.
The inventory data for current stock was collected through the process of stock checks.
These were carried out periodically for a random set of items generated through the SAP
database. The stock checks are conducted to verify the quantities of materials held in
inventory, while simultaneously assisting in confirming the position of the materials in
the inventory.

26. 20
Fig 5.6 – Excerpt of Inventory Data
5.3.1 Stock Check
Stock-checking or inventory checking is the physical verification of the quantities and
condition of items held in an inventory warehouse. This may be done to provide
an audit of existing stock valuation. It is also the source of stock discrepancy information.
Stock-checking may be performed as an intensive annual check or may be done
continuously by means of a cycle count. This is also referred to as Periodic Count.
Periodic counting is usually undertaken for regular, inexpensive items. The term 'Periodic'
generally refers to annual stock count. However, periodic may also refer to half yearly,
quarterly, monthly, bi-monthly or daily.
Efficient stock control allows you to have the right amount of stock in the right place at
the right time. It ensures that capital is not tied up unnecessarily and protects production if
problems arise with the supply chain. One of the main purposes of stock check is the
determination of Cut-off point. Cut-off point determines the stock position of the
company/organization at a specific point of time. Stock check is carried out regularly to
maintain a sufficient amount of stock at the stores, in order to fulfil demands of the next
level in the supply chain.

27. 21
Optimum inventory can be maintained by conducting regular stock checks. Items to be
procured and their numbers are determined through this process and adequate measures
are made to procure the required products, thereby maintaining an adequate inventory
level.
A regular stock check is carried out by first listing out the items in the stores to be
examined. The next step involves the physical stock check itself. Here, each item in the
list is traced back to its location in the stores, based on the component serial number. The
number of components are counted using an inventory counting scale, for large number of
small items (such as screws or nuts), or by manually counting them. Similarly all the
items on the list are checked and the exact number of each item present is noted. This data
is further compared with the stock required for production and the necessary actions are
undertaken to procure the items from the suppliers to replenish the inventory.
The following figure shows an excerpt of the stock check sheet which is used during the
process of counting and confirming the inventory stocks.
5.4 Product Demand
The processes in a supply chain are divided into two categories depending on whether
they are executed in response to a customer order or in anticipation of customer orders.
One set of processes, called „pull processes‟, are initiated by a customer order and another
set of processes, known as „push processes‟, are initiated and performed in anticipation of
customer orders.
Fig 5.7 – Excerpt of Stock Check Sheet

28. 22
Fig 5.8 - Push and Pull Strategies
5.4.1 Pull Strategy
In a pull-based supply chain, procurement, production and distribution are demand-driven
so that all activity is based on actual customer orders. Under these strategies, products
enter the supply chain only when customer demand justifies it. With a pull strategy,
companies avoid the cost of carrying inventory for products that may not sell. The risk is
that they might not have enough inventory to meet demand if they cannot ramp up
production quickly enough. Pull models are used in response to growing uncertainty in
demand and short product lifecycles. Some of the characteristics of this model include:
 Volatile demand
 A high rate of customization
 Minimal inventory holding
 A highly dynamic and effective distribution network
An example of a pull inventory control system is the just-in-time, or JIT system. The goal
is to keep inventory levels to a minimum by only having enough inventory, not more or
less, to meet customer demand. The JIT system eliminates waste by reducing the amount
of storage space needed for inventory and the costs of storing goods.
5.4.2 Push strategy
A push-model supply chain is one where demand forecasts determine what enters the
process. Companies must predict which products customers will purchase along with
determining what quantity of goods will be purchased. The company will in turn produce
enough product to meet the forecast demand and sell, or push, the goods to the consumer.

29. 23
An advantage to the push system is that the company is fairly assured it will have enough
product on hand to complete customer orders, preventing the inability to meet customer
demand for the product. An example of a push system is Materials Requirements
Planning, or MRP. MRP combines the calculations for financial, operations and logistics
planning. It is a computer-based information system which controls scheduling and
ordering. Its purpose is to make sure raw goods and materials needed for production are
available when they are needed.
Fig 5.9 - Effect of Lead Time and Demand Uncertainty on Supply Chain Strategy
A supply chain is almost always a combination of both push and pull, where the interface
between the push-based stages and the pull-based stages is known as the push-pull
boundary, or the decoupling point. A fully-push based system still stops at the retail store
where it has to wait for a customer to "pull" a product off of the shelves. However, a
chain that is designed to be a hybrid alternates between push and pull somewhere in the
middle of the process. For instance, manufacturers might choose to build up inventories
of raw materials, especially those that go up in price, knowing that they will be able to
use them for future production.

30. 24
At Bosch Limited, the different variants of the EPS machine, the EPS A and the EPS B, are produced based on a pull strategy. The demand for
the machines from within the country and from abroad are considered as being steady and forecasts are made for the entire year. The demand
forecast for the year 2014 is shown below.
Product
Family
Channel Product
Jan
‘14
Feb
‘14
Mar
‘14
Apr
‘14
May
‘14
Jun
‘14
Jul
‘14
Aug
‘14
Sep
‘14
Oct
‘14
Nov
‘14
Dec
‘14
Total
EPS
Export EPS A 415 V, HMI 1 2 2 3 3 3 3 3 3 4 3 3 33
Export EPS A 220 V, HMI - - - - - - - - - - - - -
Export EPS A 415 V, PC 2 - - 2 - 1 1 - 2 1 1 1 11
Export EPS A 220 V, PC 1 - - - - - - - - - - - 1
Inland EPS B 415 V, HMI - 5 5 5 - - - - - - - - 15
Inland EPS A 415 V, HMI 4 4 4 3 4 4 4 4 3 3 4 4 45
Total
Export 4 2 2 5 3 4 4 3 5 5 4 4 45
Inland 4 9 9 8 4 4 4 4 3 3 4 4 60
Export
+
Inland
8 11 11 13 7 8 8 7 8 8 8 8 105
Table 5.3 – Demand Forecast for the EPS A and the EPS B for the Year of 2014

31. 25
Fig 5.10 – Graph Plot of Forecasted Demand v/s Time (in Months)
The above graph shows the plots of the forecasted demand for each variant of the EPS A
and EPS B machine against time in months for the year of 2014. This graph shows us the
fluctuation in the forecasted demands. This forecast helps us determine the production
quantities for each month of the year 2014.
5.5 The Bullwhip Effect & the Production Ramp-Up Drive
5.5.1 The Bullwhip Effect
The goal of any supply chain is to get the right selection of goods and services to the
customers in the most efficient way possible. To meet this goal, each link along the
supply chain must not only function as efficiently as possible, but it must also coordinate
and integrate with links both upstream and downstream in the chain. Coordination within
all levels of the supply chain network only strives to mutually benefit all involved. A firm
that employs effective coordination within and beyond its boundaries, will be in the right
position to maximise the potential for converting competitive advantage into profitability.
There is an inherent lack of coordination in any supply chain network which arises due to
unavoidable problems such as conflicting objectives of the supply chain, or because the
information moving between the stages is delayed and distorted.

32. 26
One outcome of the lack of supply chain coordination is the „bullwhip effect‟. It is an
occurrence detected by the supply chain where fluctuations in orders increase as they
move up the supply chain from retailers to wholesalers to manufacturers to suppliers. The
bullwhip effect distorts demand information within the supply chain, with each stage
having a different estimate of what demand looks like. This can be illustrated with the
following figure.
Fig 5.11 – Increasing Variability of Orders up the Supply Chain
The causes for this demand uncertainty are numerous. Because customer demand is rarely
perfectly stable, businesses must forecast demand to properly position inventory and other
resources. Forecasts are based on statistics and they are rarely accurate. This results in the
need for a “safety stock” which helps firms respond to unexpected demand orders. The
fluctuations in order quantities over time can be much greater than those in the demand
data. The result is reduced coordination between all entities involved and in turn, reduced
efficiency of the supply chain network.
The concept is also known as the Forrester Effect, after appearing in Jay Forrester‟s
Industrial Dynamics in 1961. This effect was first noticed by logistics executives at
Procter & Gamble (P&G). While examining the order patterns for a particular product,
they noticed a high degree of variability in the distributors‟ orders. They also noticed that
while the customers consumed the product at a steady rate, the demand order variability
in the supply chain was amplified as they moved up the supply chain. P&G called this the

33. 27
“bullwhip effect” as the oscillating demand magnification upstream is reminiscent of a
cracking whip (hence also called the “whiplash effect”).
5.5.2 Consequences of Bullwhip Effect
One of the main causes of the bullwhip effect is the lack of coordination among the
supply chain entities. A supply chain lacks coordination if each stage optimizes only its
local objectives, without considering the impact on the complete chain. Total supply
chain profits are thus less than what could be achieved through coordination. Such actions
end up hurting the entire performance of the supply chain. The bullwhip effect also
results if information distortion occurs within the supply chain. As a result of the
bullwhip effect, orders received from distributors are much more variable than the actual
demand at retailers.
The impacts of bullwhip effect on various measures of performance in the supply chain
are as follows:
 Manufacturing Costs
o The bullwhip effect increases manufacturing costs in supply chain. As a result
of the effect the firm and its suppliers must satisfy a stream of orders that is
much more variable than customer demands.
o The firm can respond to increased variability by either building increased
capacity or holding excess inventory, both of which increase manufacturing
costs per unit produced.
 Inventory Costs
o The bullwhip effect increases inventory costs in a supply chain. To handle the
increased variability in demand, firms have to carry a higher level of inventory
that would not be required if the supply chain were not coordinated.
o The high levels of inventory also increase the warehousing space required and
thus the warehousing cost incurred.
 Replenishment Lead Time
o The bullwhip effect increases replenishment lead time in the supply chain. The
increased variability as a result of this effect makes scheduling at the firm and
supplier plants much more difficult than when the demand is level.
 Transportation Costs
o The bullwhip effect increases transportation costs in supply chain.
Transportation requirements over time at the firm and the suppliers are

34. 28
correlated with orders being filled. This raises transportation cost because
surplus transportation capacity needs to be maintained to cover high-demand
periods.
 Other Consequences
o Labour costs associated with shipping and receiving in the supply chain
increase due to this effect. Labour requirements for shipping at the firms and
the suppliers fluctuate with orders. A similar fluctuation occurs for receiving at
distributors and retailers. The various stages have the option of carrying excess
labour capacity or varying labour capacity in response to fluctuating orders.
Either option increases total labour costs.
o The bullwhip effect hurts the level of product availability and results in more
stock-outs in the supply chain. The large fluctuations in orders make it harder
for the firm to supply all distributor orders on time. This results in lost sales for
the supply chain. It also has a negative performance on every stage. It leads to a
loss of trust among different stages of the supply chain and makes any potential
coordination efforts more difficult.
Thus by understanding carefully the causes of the effect, managers can find strategies to
mitigate it. The bullwhip effect can be effectively countered through a number of
measures. Some of them are information sharing, channel alignment and operational
efficiency in the supply chain.
5.5.3 The Production Ramp-Up Drive
Ramp-up is a term used in business and economics to describe an increase in firm
production ahead of anticipated rise in product demand. Ramp-up typically occurs when a
company strikes a deal with a distributor, retailer, or producer, which will substantially
increase product demand. It is usually a consequence of the bullwhip effect which distorts
the demand data while moving up the stages of the supply chain. The ramp-up activity is
the most important challenge for the firm and provides a considerable opportunity for
achieving competitive benefits in high-technology organizations. At the end of the ramp-
up stage, the manufacture or production system must have achieved its planned or
anticipated goals together with the targeted levels of quality, cost and volume.
In the ramp-up triangle, the base is quality, since without quality the volume is only a
waste, since end consumers will not accept a product without the essential quality that is
demanded by the customer.

35. 29
Fig 5.12 – The Ramp-Up Triangle
At Bosch Limited, the ramp-up process was initiated to cope with a sudden increase in
customer orders for the EPS A and EPS B. The bullwhip effect was created which led to
the need for a ramp-up in production operation for the machines. The initial demand
forecast up to the month of May was as follows.
Table 5.4 – Initial Demand Forecast
Product
Family
Channel Jan ‘14 Feb ‘14 Mar ‘14 Apr ‘14 May ‘14
EPS
Export
(Initial)
4 2 2 5 3
Inland
(Initial)
4 9 9 8 4
Export
+
Inland
8 11 11 13 7
As seen above, for the months of March, April and May, the initial demand forecast for
both, the EPS A and B, varied from 13 numbers to 7 numbers (Average of 11 numbers)
respectively.

36. 30
The actual demand through increased customer orders was as follows.
Table 5.5 – Actual Demand Data
Product
Family
Channel Jan ‘14 Feb ‘14 Mar ‘14 Apr ‘14 May ‘14
EPS
Export
(Initial)
4 2 2 5 3
Inland
(Initial)
4 9 9 8 4
Excess
ordered
(A & B)
- - 19 17 23
Export
+
Inland
8 11 30 30 30
Fig 5.13 – Forecast v/s Actual Demand of EPS A

37. 31
Fig 5.14 – Forecast v/s Actual Demand of EPS B
As seen above, the demand for the machines went up to 30 numbers for the months of
March, April and May. This meant an average increase of finished products by 20 units
every month. This sudden increase in demand for the machines necessitated the
production ramp-up activity. The ramp-up process involved the following activities.
1. Identifying the crucial steps which have to be carried out in order to satisfy the
increased customer demand.
2. Establishing the responsibilities for each member involved with the ramp-up drive.
3. To identify the critical components for the EPS A and B.
4. Gathering data concerned with the critical components such as supplier information,
inventory, stock in-hand and lead times.
5. Releasing the purchase orders for the components that are not in stock.
6. Following up on supplier activity and ensuring the parts are delivered on-time.
7. Ensuring no delay in the production and assembly activities in-house.
8. Packaging and delivering the required number of products within the given time
horizon.

38. 32
The ramp-up drive resulted in the following data.
Shortage number of parts – 133 numbers
Table 5.6 – List of Bottleneck Parts for EPS B
EPS B
Frame
Bed
Motor
Flywheel
Cabinet
Power pack
Transformer
Packing Material
Table 5.7 – List of Bottleneck Parts for EPS A
EPS A
Flywheel
Motor 13.5
Motor 0.55 kW
Cabinet
Heat Exchange
Castings
5.5.4 Supplier Visits
After identifying the critical components for the EPS A & B machines, based upon the
lead times and inventory & stocks data, suppliers were sent the appropriate purchase
orders for delivery of the required components. Follow-up activities were also conducted
to ensure orders were dispatched in accordance with the delivery commitment and
schedules.
5.5.4.1 Supplier Visit to GMT, Hosur
One of the critical components for the EPS B was the packing material required for
dispatch. The packing material was a thermocol/polystyrene covering to be casted. The
order was placed and follow-up visits to the supplier were conducted.

39. 33
The summary of the data collected is as follows:
 Supplier - GMT Private Limited
 Product - Packing Material
 Ordered quantity - 5 numbers
 Status of order - 2 numbers completed
 Process time - 3 days
 Capacity - 50-60 castings/8hrs
 Delivery time - 2 hours
 Lead time - 9 days
5.5.4.2 Supplier Visit to Patterns India, Bangalore
Another critical component for the EPS B was the wooden pattern required for the
machine bed. This pattern was to be made using Teak wood. The finished pattern would
be sent to the foundry for castings. The supplier visit for this component yielded the
following data:
 Supplier – Patterns India Private Limited
 Product – Wooden Pattern
 Wood Quality – Teak Wood
 Ordered quantity – 1 numbers
 Status of order – In progress
 Process time - 3 weeks/pattern
 Lead Time – 4 weeks
5.5.4.3 Supplier Visit to Genuine Products, Bangalore
Most of the critical parts and sub-assemblies for the EPS A are sourced from Genuine
Products, Bangalore. Each of these critical sub-assemblies has a lead time of 60 days.
Hence, a supplier visit was required to understand the lead time data and the various
operations involved in the production of the sub-assemblies. The data collected is
summarized as follows:
 Supplier – Genuine Products, Bangalore
 Products – Coupling Guard, Frame Assembly, MGT Assembly, Control Valve and
Cabinet Empty (Electrical)
 Ordered Quantity – 10 sets (maximum of 20 sets)

40. 34
 Processes involved:
o Laser Cutting – 15 numbers in one batch; involves bending, punching, welding,
deburring, finishing and powder coating (10 days)
o Stage Inspection
o Full welding and finishing
o Leak Test (24 hours for 1 frame)
 Lead Time – 60 days for 1 set of 10-15 frames
5.6 The Solution Phase
5.6.1 Improvement of Forecasts
A forecast is never completely accurate. Forecasts will almost always deviate from the
actual demand. It is the principal objective of the forecasting function to keep this
deviation to a minimum and make the forecasts as accurate as possible. There are several
measures of accuracy of forecasts. The most commonly used measure of accuracy is the
mean absolute percentage error (MAPE), which measures the size of the error in
percentage terms. Other measures of accuracy include the mean average deviation
(MAD), which is the average deviation of the forecast from the mean of the actual
demand over a period and the mean standard deviation (MSD). The forecast that has the
highest accuracy in terms of the above measures and that most accurately describes the
demand pattern is chosen and production plans are made based on this forecast.
At Bosch, a production ramp-up drive was required to meet the increased demands for the
EPS A and EPS B. This was largely due to the initial forecast not being accurate in
estimating the actual demand, causing the amplification of the bullwhip effect. A more
accurate forecast would greatly reduce the strain on the supply chain across all of its
phases. So the first step in balancing the supply chain for the EPS A was attempting to
improve the demand forecast for the machine.
Three forecasting approaches were considered:
 The Single Exponential Smoothing Technique
 The Double Exponential Smoothing Technique
 The Winters‟ Approach to Forecasting

41. 35
5.6.1.1 The Single Exponential Smoothing Technique
Exponential smoothing is probably the most widely used approach to forecasting, largely
due to its ease of computation and simplicity of understanding. The single exponential
smoothing technique is one that is used to forecast the next period of a time series that has
no trend or seasonality.
Based on the past demand data for the EPS A, the single exponential model is applied in
an attempt to accurately forecast the demand.
Fig 5.15 – Single Exponential Smoothing Plot for Demand
As it can be seen from the above graph, the time series fit based on the single exponential
model does not accurately follow the demand. Quantitatively, it can be seen that the value
of MAPE and MSD are quite high. So this approach is replaced by the double exponential
smoothing approach.

42. 36
5.6.1.2 The Double Exponential Smoothing Technique
The double exponential smoothing technique follows a similar approach to the single
exponential smoothing technique, the only difference being that this approach considers a
trend component in the demand data. This approach was applied to forecast the demand
for the EPS A machine.
Fig 5.16 – Double Exponential Smoothing Plot for Demand
While this approach generates acceptable results of high accuracy, the demand data for
the EPS A also suggests a seasonal component, which the double exponential smoothing
technique does not take into account. So, the Winters‟ method of forecasting is applied.

43. 37
5.6.1.3 Winters’ Approach to Forecasting
Winters‟ method or the triple exponential smoothing method is an exponential smoothing
method of forecasting which uses three smoothing parameters – one for the level (signal),
one for the trend and one for seasonality. To successfully apply the Winters‟ technique,
the demand data must follow a pattern resembling the following figure.
Fig 5.17 – Trend and Seasonality of a Winters’ Technique Curve
The method uses the following mathematical model:
Dt = (µ + Gt) ct + Ɛt ………. ( 5.1 )
Where,
„Dt‟ is the demand at time „t‟.
„µ‟ is the base signal (intercept of demand) at time t=0.
„G‟ is the trend component of demand.
„ct‟ is the seasonal component for the time period under consideration.
„Ɛt‟ is the error term.
The time series component „St‟ is given by
St = (
Dt
ct-n
) + (1- )(St-1+ Gt-1) .......... ( 5.2 )
Where, „ ‟ is the level smoothing constant ( .

44. 38
The trend component „Gt‟ is given by the equation
(St St-1) (1- )( .......... ( 5.3 )
Where, „ ‟ is the trend smoothing constant ( .
The seasonal component „ct‟ is given by the equation
ct = (
Dt
St
) + (1- )( t-n) .......... ( 5.4 )
Where, „ ‟ is the seasonality constant ( .
It is typically assumed that , although these values may change depending on
the actual situation present. Deriving the initial estimates of trend and demand takes at
least two complete cycles of data.
The first step of computing the forecasts is to compute the sample mean of each cycle of
data.
∑
∑
Where, „V1‟ and „V2‟ are the average demand for two cycles ago and one cycle ago,
respectively and „n‟ is the number of periods in each cycle. Also, j = 0 represents the
present cycle.
The slope (trend) estimate is given by
.......... ( 5.5 )
The base signal (first term of the time series) „S0‟ is given by the equation
* + .......... ( 5.6 )
The various seasonal factor estimates are computed using the equation
*( ) +
.......... ( 5.7 )
Where, „j‟ is the particular period in the cycle. Here, i=1 for the first period and i=2 for
the second period.

45. 39
After the seasonal factor estimates are computed, they are then averaged out as follows:
Normalizing the factors,
(
∑
)
The final forecast is computed using the equation
( (
Where, „ ‟ represents the period beyond time „t‟.
The demand for the EPS A for the previous two quarters (Oct ‟13 – Dec ‟13 and Jan ‟14 –
Mar ‟14) are shown in the following table:
Table 5.8 – 2013 Q4 and 2014 Q1 Demand Data for EPS A
Period Demand Period Demand
Oct 2013 6 Jan 2014 8
Nov 2013 5 Feb 2014 6
Dec 2013 4 Mar 2014 20
The sample means of the two cycles are computed as
The slope estimate

46. 40
The base signal
( )
The various seasonal factor estimates are computed as follows:
( )
( )
( )
( )
Similarly, it can be shown that = 0.56 and = 1.49.
Averaging out the seasonal components,
Similarly, and
Normalizing the factors,
Similarly, and

47. 41
The forecast for the next quarter is calculated as
[ ( ](
[ ( ](
[ ( ](
Allowing for a standard 10% error,
The forecast for the months April, May and June in 2014 using an idealized form of
Winters‟ method is tabulated as:
Table 5.9 – Ideal Forecasted Demand from Winters’ Method
Month Forecast
April 2014 25
May 2014 15
June 2014 21
For the EPS A, past data suggests quite a level of seasonality in the months between
March and June and the forecast generated by the idealized form of Winters‟ method does
not take into consideration this seasonality.
Using a levelling factor of 0.9, trend factor of 0.1 and seasonality factor of 0.7, the actual
forecasts for the months April 2014, May 2014 and June 2014 are computed to be:
Table 5.10 – Forecasted Demand (Calibrated with Factors of Level, Trend and Seasonality)
Month Forecast
April 2014 21
May 2014 21
June 2014 17

48. 42
A graph showing the actual demand (to May 2014) and forecasts from January 2013 for
EPS A is plotted.
A graph showing actual demand (black), ideal forecast until May 2014 (red) and forecasts
for 12 months beyond May 2014. Considering the trend that the demand has followed, the
lower blue line is taken as the ideal forecast.
Although the MAPE for this approach is higher than that for the double exponential
smoothing approach, this method is preferred because it is a more accurate representation
of the demand data for the EPS A as it considers both trend and seasonality components.
On comparing the results obtained from all three approaches, it is observed that Winter‟s
approach generates the most accurate forecasts for the subsequent periods.
5.6.2 Revised Production Plan
Using the revised forecasts generated by Winters‟ method, a solution that is meant to aid
the production plans for the EPS A and its operations was developed. The aim of this
solution is to ensure that general production and occasional ramp-up drives can be carried
out without any delays, particularly due to shortage of parts needed for assemble of the
EPS A machine.
Fig 5.18 – Graph of Forecasted Demand using Winters’ Method

49. 43
Fig 5.19 – Production Planner – Construction
1. From the complete BOM for the EPS A, the parts which had the maximum lead
times (60 days or 120 days) were identified.
2. The various suppliers of these critical parts were identified and listed down.
3. The forecast demand for all the months is also listed.
4. The actual demand is to be entered for the particular month in the yellow cell.
Based on the demand, the total numbers required of each part is computed and
displayed.
5. The stock available on hand for each part is to be entered into the column labelled
„STOCK‟. This inventory data is obtained by carrying out thorough stock checks
periodically.
6. Once the stock is entered, the difference between the demand and inventory on
hand is computed. If the demand for a particular part exceeds the inventory, the
difference, which indicates the quantity of that part that is to be ordered, is
displayed in the column labelled „Δ‟. If the on-hand inventory exceeds the
demand, „A‟, indicating the availability of stock, is displayed in a green cell in the
column labelled „Δ‟.

50. 44
Fig 5.20 – Production Planner – Working
The above extract depicts the working of the system. Once the values of stock (assumed)
are entered for the months of May and June along with the actual demand for June, the
quantities of parts to be ordered is automatically computed and displayed.
This solution can help ease stock check activities and ensure that the critical parts are
present in order to maintain smooth production flow. Since these critical parts are those
with maximum lead times, other parts can be subsequently ordered once the critical parts
are adequately planned for and production operations can continue without delays. The
other salient feature is that the steps involved while developing the solution can be
diversely applied to almost every range of products so that the production operations can
be optimized to be without delays.

51. 45
CHAPTER 6
CONCLUSIONS
6.1 Summary
In this project, a complex supply chain for a machine with a large number of assemblies
was understood and a two-step solution has been developed in order to help the supply
chain meet its objectives of optimum efficiency and responsiveness. The first step of the
solution involved developing a forecast using Winters‟ approach for the machine that was
found to be more accurate than the initial forecast. This increased accuracy would help to
reduce the uncertainty in the planning and scheduling functions for the machine.
Subsequently, a revised production plan for the parts with the longest lead times was also
developed, providing the planners with a convenient tool to assist in the procurement of
components for the assembly of the machine.
The Winter‟s approach to forecasting could be applied to any product with significant
trend and seasonality, leading to a forecast that would be more accurate than the ones
presently in use. The approach used to develop the revised production plan may also be
extended to other products that involve a high number of critical components with large
lead times. The application of these solutions would result in reduced strain across the
supply chain and allow for larger planning horizons.
6.2 Future Scope
Common to all manufacturing companies, regardless of size, type of product or process is
the need to control the flow of material from suppliers, through manufacturing and
distribution to the customer. Traditionally, the flow of material has been considered only
at an operational level, at best driven by efficiency improvement and cost reduction. For
many companies the need to react to market changes is paramount. This means that
integration and balancing the supply chain between demand and flow is essential. With
the ever-present Bullwhip Effect in the supply chain, the need to manage variations in
demand and the consequent ramp-up drives to satisfy this demand is of utmost
importance. One of the key methods to manage the Bullwhip Effect is to improve the
existing information systems. An effective information system to manage the flow of
materials from the suppliers, keeping in mind the actual market demand, is beneficial as it

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would lead to lesser lead times, lower inventories, lower costs, and thus, increased supply
chain performance.
In this project, developing new and improved forecasts and better strategies for
procurement of materials from suppliers led to a better balance in the supply chain that
could effectively cope with changing market needs. The solution devised in this project
can be applied to all products that suffer from demand variations. Since the methodology
used is not specific to a particular product, it can be used to develop new strategies that
work towards better material procurement and optimised level of stock availability for a
number of products.
With further detailed study focused on increased information accuracy, value stream
mapping of supplier manufacturing processes and an overall improved supplier
performance in responsiveness and quality, steps can be taken towards effectively
integrating and enhancing the supply chain that will result in maximum possible profits
for the company.

53. 47
REFERENCES
1. Supply Chain Management – Sunil Chopra, Peter Meindl, D.V. Kalra, 5th
Edition.
2. Williams, Jack F., 1981. Heuristic Techniques for Simultaneous Scheduling of
Production and Distribution in Multi-Echelon Structures: Theory and Empirical
Comparisons, Management Science, 27(3): 336-352.
3. Williams, Jack F., 1983. A Hybrid Algorithm for Simultaneous Scheduling of
Production and Distribution in Multi-Echelon Structures, Management Science,
29(1): 77-92.
4. Ishii, K., K. Takahashi and R. Muramatsu, 1988. Integrated Production, Inventory
and Distribution Systems, International Journal of Production Research, 26(3): 473-
482.
5. Newhart, D.D., K.L. Stott, and F.J. Vasko, 1993. Consolidating Product Sizes to
Minimize Inventory Levels for a Multi-Stage Production and Distribution Systems,
Journal of the Operational Research Society, 44(7): 637-644.
6. Voudouris, Vasilios T., 1996. Mathematical Programming Techniques to
Debottleneck the Supply Chain of Fine Chemical Industries, Computers and
Chemical Engineering, 20: S1269-S1274.
7. Cohen, Morris A. and Hau L. Lee, 1989. Resource Deployment Analysis of Global
Manufacturing and Distribution Networks, Journal of Manufacturing and Operations
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8. Pyke, David F. and Morris A. Cohen, 1993. Performance Characteristics of
Stochastic Integrated Production-Distribution Systems, European Journal of
Operational Research, 68(1): 23-48.
9. Tzafestas, Spyros and George Kapsiotis, 1994. Coordinated Control of
Manufacturing/Supply Chains Using Multi-Level Techniques, Computer Integrated
Manufacturing Systems, 7(3): 206-212.
10. Christy, David P. and John R. Grout, 1994. Safeguarding Supply Chain
Relationships, International Journal of Production Economics, 36: 233-242.
11. Towill, D.R., M.M. Naim, and J. Wikner, 1992. Industrial Dynamics Simulation
Models in the Design of Supply Chains, International Journal of Physical
Distribution and Logistics Management, 22(5): 3-13.
12. Wikner, J, D.R. Towill and M. Naim, 1991. Smoothing Supply Chain Dynamics,
International Journal of Production Economics, 22(3): 231-248.

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13. Supply Chain Management: Processes, Partnerships, Performance – Douglas M.
Lambert (editor), Third edition.
14. Introduction to Time Series and Forecasting – Volume 1 – 2008 - Peter J. Brockwell,
Richard A. Davis.
15. Smoothing, Forecasting and Prediction of Discrete Time Series – 2004 – Robert
Goodell Brown.
16. Inventory Management and Production Planning and Scheduling – 1998 – Edward A.
Silver, David F. Pyke, Rein Peterson.

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APPENDIX
Bosch Limited is a member of the Bosch Group, Germany. Founded in 1951, Bosch
Limited pioneered in manufacture of automotive spark plugs and diesel fuel injection
equipment in India. Access to the international technology of Bosch, with a conscious
commitment of quality of its 10,300 employees has made Bosch Limited the largest
manufacturer of diesel fuel injection equipment in the country and one of the world‟s
largest. In addition Bosch Limited manufactures industrial equipment, auto-electrical,
hydraulics gear pumps for tractor applications, electric power tools, packaging machines,
Blaupunkt car multimedia systems and security systems.
Partnering Bosch Limited in its quest for quality are its suppliers, including those in the
small-scale sector. An All-India network of over 4000 authorised representations ensures
widespread availability of both products and after sales services.
All around the world, the Bosch name stands for competence and product diversity in the
following sectors: electrical and electronic automotive technology, power tools and
accessories, thermo-technology, household appliances, security systems, broadband
networks, automation and packaging technology. The “Workshops for Precision
Engineering and Electrical Engineering” that Robert Bosch founded in Stuttgart,
Germany in 1886 have grown in the course of more than one hundred years to become a
global player.
Worldwide, the Bosch Group has approximately 2,50,000 employees today. It has around
253 subsidiaries and associated companies in over 50 countries and has a total of 236
manufacturing sites. There are 20,000 scientists, engineering and technicians engaged in
R&D. As a result, Bosch applies for over 2,000 patents each year. This places the
Company at the top in the entire automotive industry.

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The product range of Bosch Limited includes Diesel Fuel Injection Equipment, Fuel
Injection Pumps, Governors, Injection Timers and Feed Pumps, Nozzle-holder, Delivery
Valves, Spark Plugs and Common Rail Diesel Fuel Injection System.
In Corporate Research Bosch‟s highly specialised employees all over the world work on
technological breakthroughs such as in software development, in robotics or in engine
management. In this way new ideas are constantly taking shape that make existing
products even more efficient, more comfortable, safer and more environment-friendly,
while also opening up entirely new lines of business, both to the organization and to the
rest of the competition.