ANALYZING THE PROCESS CAPABILITY FOR AN AUTO MANUAL TRANSMISSION BASE PLATE M...
final year report of (11BIE024,037,056)
1. 1 | P a g e
Supply Chain Analysis at Nirmax Distribution Centre
Report submitted in partial fulfillment of the requirements for the
B. Tech. degree in Industrial Engineering
By
1. Dhruv N Patel 11BIE024
2. Hardik V Mehta 11BIE037
3. Malav N Bhatt 11BIE056
Under the supervision
of
Dr. Poonam Savsani
Assistant Professor
SCHOOL OF TECHNOLOGY
PANDIT DEENDAYAL PETROLEUM UNIVERSITY
GANDHINAGAR, GUJARAT, INDIA
2015
2. 2 | P a g e
Supply Chain Analysis at Nirmax Distribution Centre
Report submitted in partial fulfillment of the requirements for the
B. Tech. degree in Industrial Engineering
By
1. Dhruv N Patel 11BIE024
2. Hardik V Mehta 11BIE037
3. Malav N Bhatt 11BIE056
Under the supervision
of
Dr. Poonam Savsani
Assistant Professor
SCHOOL OF TECHNOLOGY
PANDIT DEENDAYAL PETROLEUM UNIVERSITY
GANDHINAGAR, GUJARAT, INDIA
2015
3. 3 | P a g e
CERTIFICATE
This is to certify that this report on “Supply Chain Analysis at Nirmax Distribution Centre”
submitted by the following students, as a requirement for the degree in Bachelor of Technology
(B. Tech) in Industrial Engineering, Session 2014-2015. This work was carried out under my
guidance and supervision.
Name of the student Roll No. Signature
1. Dhruv N Patel 11BIE024
2. Hardik V Mehta 11BIE037
3. Malav N Bhatt 11BIE056
Date: 05-05-2015 Signature of HOD Signature of Supervisor
Place: PDPU,Gandhinagar Dr. Aneesh Chinubhai Dr. Poonam Savsani
4. 4 | P a g e
CONTENTS
Acknowledgements………………………………………………………………………...…5
Abstract…………………………………………………………………………………………6
List of Tables…………………………………………………………………… ..................... 7
List of Figures……………………………………………………………………... ................ 8
Abbreviations Used……………………………………………………………………………9
1. INTRODUCTION……………………………………………………………………10
1.1 Details and Working of Distribution Centre……………………....…11
1.2 Problem Description……………………………………………... .............12
2. LITERATURE REVIEW………………………………………………………….14
2.1 Literature Review…………………………………………………….……14
2.1.1 VRP Software…………………………………….............................14
2.1.2 Minimal Spanning Tree…………………………………….………15
2.1.3 Moving Average …………………………………………………….16
2.1.4 Arena Simulation Software………………………………………17
3. SOLVING METHODS………………………………………………….………….19
3.1 VRP Solver Software…………………………………………………….…19
3.2 Minimal Spanning Tree Method………………………………….…..…23
3.3 Forecasting using Moving average……………………………………..28
3.4 Arena Simulation Model…………………………………………………..31
4. CONCLUSIONS AND RECOMMENDATIONS…………………...….33
4.1Conclusion……………………………………………………………………….33
4.2Recommendations……………………………………………………………..33
5. REFERENCES………………………………………………………………………..34
5. 5 | P a g e
Acknowledgement
I would like to sincerely thank my supervisor Dr. Poonam Savsani for her excellent direction,
invaluable feedback, her constructive suggestions, detailed corrections, support and
encouragement that resulted in this successful project. I would also like to thank all the lecturing
staff of the Industrial Engineering department for past four years of guidance.
I would especially like to thank Mr. Vipinbhai Mehta, the managing partner ofNirmax
distribution center, Adalaj, Gandhinagar for providing all the data required for the project.
Name of the Student Signature of the Student
1. Dhruv N Patel
2. Hardik V Mehta
3. Malav N Bhatt
6. 6 | P a g e
Abstract
Nirmax distribution Centre (NDC) is a firm in its introduction phase. The objective of this study
is to plot a network routes for given demand pointsforecast using Vehicle Routing Problem
(VRP) software, forecast demand using appropriate forecasting methods and other minor
analysis of the Supply Chain.
We have prepared a simulation model in arena software to understand the working of NDC.It
bridges the analytical real world complexities to statistical data for their inclusion in the process
of planning. Through this simulation model any changes in constraints will be easily adjusted in
the system. It will also unmask any hidden deformities which were not considered because of
their complexities.
In this Vehicle Routing Problem we study the available methods for VRP solution and apply the
best suitable method to solve the problem. This software for the plotting the network routes to
the scale for better understanding. The observation was that if NDC implement the proposed
method then they can reduce total distance travelled and which ultimately results in reduction of
total transportation cost and increase in profit.
7. 7 | P a g e
List of Table
1. Sample Input to VRP Solver……………………………………………………………..20
2. Demand for 6 nodes……………………………………………...………………………26
3. Comparison between distances…………………………………..………………………26
4. Present cost (without Minimal Spanning Tree)………………………………………….26
5. Cost with Minimal Spanning Tree…………………………..….………………………..26
6. Saving for January month………………………………………..………………………27
7. Saving for February month………………………………………………………………27
8. Saving for March month…………………………………………………………………27
9. Three Months Demands for Clients……………………………………………………..28
10. Moving averages for all Clients ………………………………………………………...29
8. 8 | P a g e
List of Figures
1. Arcs and Vertices ……………………………………………………………………….10
2. Working Model of NDC………………………………………………………………...12
3. Plotted coordinates of demand points…………………………………………………...19
4. Data Loading in VRP Solver…………………………………………………………....20
5. Output in VRP Solver…………………………………………………………………...21
6. Initial Network of Minimal Spanning Tree……………………………………………..23
7. Second step of Minimal Spanning Tree………………………………………………...24
8. Third step of Minimal Spanning Tree…………………………………………………..24
9. Forth step of Minimal Spanning Tree…………………………………………………..24
10. Fifth step of Minimal Spanning Tree…………………………………………………...25
11. Sixth step of Minimal Spanning Tree…………………………………………………..25
12. Flow Chart of Working Process of NDC…………………………………………….....31
13. Blocks of Arena Simulation Model…………………………………………………….32
9. 9 | P a g e
INTRODUCTION
The distribution of goods concerns the service, in a given time period, of a set ofcustomers by a
set of vehicles, which are located in one or more depots, are operated by asset of crews (drivers),
and perform their movements by using an appropriate road network. In particular, the solution of
a VRP calls for the determination of a set of routes, each performed by a single vehicle that starts
and ends at its own depot, such that all the requirements of the customers are fulfilled, all the
operational constraints are satisfied, and the global transportation cost is minimized.[7]
The road network, used for the transportation of goods, is generally described through graph,
whose arcs represent the road sections and whose vertices correspond to the road junctions and
to the depot and customer locations. The arcs (and consequently the corresponding graphs) can
be directed or undirected, depending on whether they can be traversed in only one direction (for
instance, because of the presence of one-way streets, typical of urban or motorway networks) or
in both directions, respectively. Each arc is associated with acost, which generally represents its
length, and a travel time, which is possibly dependent on the vehicle type or on the period during
which the arc is traversed.
Fig. 1 Arcs and Vertices
10. 10 | P a g e
Typical characteristics of customers are:
Vertex of the road graph in which the customer is located;
Amount of goods (demand), possibly of different types, which must be delivered or
collected at the customer;
Periods of the day (time windows) during which the customer can be served (for
instance, because of specific periods during which the customer is open or the location
can be reached, due to traffic limitations);
Times required to deliver or collect the goods at the customer location (unloading or
loading times, respectively.[10]
Typical characteristics of the vehicles are
Home depot of the vehicle, and the possibility to end service at a depot other than the
home one.
Capacity of the vehicle, expressed as the maximum weight, or volume, or number of
pallets, the vehicle can load.
Possible subdivision of the vehicle into compartments, each characterized by its capacity
and by the types of goods that can be carried;
Devices available for the loading and unloading operations;
1.1 Details and Working of Distribution Centre
The distribution warehouse is locate Adalaj Industrial Area, Adalaj, Gandhinagar with total
working staff of 28. The annual revenue of distribution center is expected around 15crore rupees.
The annual demand is expected to be around1.12 million bags.There are two types of Cement
bags, one is Ordinary Portland Cement (OPC) and other is Pozzolona Portland Cement
(PPC).There is online data software available at distribution center which is used to collect the
demand and supply is provided within 24 hours of delivery time.
11. 11 | P a g e
The below figure shows working of the NDC:
1.2 Problem Description
First of all, for better representation and understanding of delivery system we have used
VRP software to plot network diagram of clients to the scale. This network diagram will
be very useful planning and identifying all necessary tasks to be completed and also in
adjusting the increasing number of clients.
The distribution Centre receive goods from the manufacturing company, its job is to
unload the goods in warehouse, then load the goods in appropriate vehicle and then
unload it to the pre-decided destinations. The firm considers each clients as an individual
Fig. 2 Working of NDC
12. 12 | P a g e
unit and sends them goods uniquely. We analyzed the supply chain and using Minimal
Spanning tree method we find the consequences of sending the goods in larger vehicle
and tried to complete demands of multiple number of clients on a single route.
We also have used Moving Averageforecasting method to predict demand of each and
every client based on their demand of previous three months. This forecasting will help
to identify their interest in product and prioritize important customers. The company is
expanding, new clients are added every month, so if we already have the predicted
demand, then the new client’s demand will not affect the delivery of old clients. This
forecast will also help to plan cash flow and order necessary stocks for the upcoming
year.
13. 13 | P a g e
LITERRATURE REVIEW
The transportation problem (TP) is an important Linear Programming (LP) model that arises in
several contexts and has deservedly received much attention in literature. The transportation
problem is probably the most important special linear programming problem in terms of relative
frequency with which it appears in the applications and also in the simplicity of the procedure
developed for its solution. The following features of the transportation problem are considered to
be most important. The TP were the earliest class of linear programs discovered to have totally
uni-modular matrices and integral extreme points resulting in considerable simplification of the
simplex method. The study of the TP‟s laid the foundation for further theoretical and algorithmic
development of the minimal cost network flow problems.[11]
2.1 Literature Review
2.1.1 VRP Software:
The vehicle routing problem (VRP) is a combinatorial optimization and integer
programming problem seeking to service a number of customers with a fleet of vehicles.
Proposed by Dantzig and Ramser in 1959[1]
VRP is an important problem in the fields of
transportation, distribution, and logistics. Often, the context is that of delivering goods located at
a central depot to customers who have placed orders for such goods. The objective of the VRP is
to minimize the total route cost.[12]
To solve our problem using Clarke-Wright saving algorithm we used “VRPsolver” software
developed by Lawrence V. Snyder, Lehigh University,Bethlehem, PA, USA.
Purpose of VRP Solver
VRP Solver implements an adaptation of the Clarke-Wright savings algorithm for vehicle
routing problems. It takes input from a text file listing each customer's location (latitude and
longitude) and demand. It builds vehicle routes that visit every city exactly once and that obey
14. 14 | P a g e
user-specified vehicle volume and distance limits. Results are displayed in graphical (map) form
and in text form.[3]
Algorithm Implemented
The Clarke-Wright savings algorithm is a well-known algorithm in vehicle routing and is
described in various papers and texts. The algorithm implemented by VRP Solver expands the
Clarke-Wright algorithm in the following ways:
Randomization: Instead of choosing the best pairing of routes at each step, choose one of the k
best pairings, chosen randomly. Repeat several times and choose the best overall solution.[6]
Improvement Heuristics: After an initial solution is built, various improvement heuristics are
performed. These include the well-known 2-opt and Or-opt operations (the Or-opt uses group
sizes of 1, 2, and 3), as well as a swap operation in which two customers on different routes may
be removed from their routes and inserted into the opposite route.
2.1.2 Minimal Spanning Tree
Given a connected, undirected graph, a spanning tree of that graph is a subgraph that is a tree and
connects all the verticestogether. A single graph can have many different spanning trees. We can
also assign a weight to each edge, which is a number representing how unfavourable it is, and
use this to assign a weight to a spanning tree by computing the sum of the weights of the edges in
that spanning tree. A minimum spanning tree (MST) or minimum weight spanning tree is then a
spanning tree with weight less than or equal to the weight of every other spanning tree. More
generally, any undirected graph (not necessarily connected) has a minimum spanning forest,
which is a union of minimum spanning trees for its connected components.[5]
One example would be a telecommunications company laying cable to a new neighbourhood. If
it is constrained to bury the cable only along certain paths (e.g. along roads), then there would be
a graph representing which points are connected by those paths. Some of those paths might be
more expensive, because they are longer, or require the cable to be buried deeper; these paths
would be represented by edges with larger weights. Currency is an acceptable unit for edge
weight — there is no requirement for edge lengths to obey normal rules of geometry such as
15. 15 | P a g e
the triangle inequality. A spanning tree for that graph would be a subset of those paths that has
no cycles but still connects to every house; there might be several spanning trees possible.
A minimum spanning tree would be one with the lowest total cost, thus would represent the least
expensive path for laying the cable.
2.1.3 Moving Average
In statistics, a moving average (rolling average or running average) is a calculation to analyse
data points by creating a series ofaverages of different subsets of the full data set. It is also called
a moving mean (MM)[2]
or rolling mean and is a type of finite impulse response filter. Variations
include: simple, and cumulative, or weighted forms (described below).
Given a series of numbers and a fixed subset size, the first element of the moving average is
obtained by taking the average of the initial fixed subset of the number series. Then the subset is
modified by "shifting forward"; that is, excluding the first number of the series and including the
next number following the original subset in the series. This creates a new subset of numbers,
which is averaged. This process is repeated over the entire data series. The plot line connecting
all the (fixed) averages is the moving average. A moving average is a set of numbers, each of
which is the average of the corresponding subset of a larger set of datum points. A moving
average may also use unequal weights for each datum value in the subset to emphasize particular
values in the subset[4]
A moving average is commonly used with time series data to smooth out short-term fluctuations
and highlight longer-term trends or cycles. The threshold between short-term and long-term
depends on the application, and the parameters of the moving average will be set accordingly.
For example, it is often used in technical analysis of financial data, like stock prices, returns or
trading volumes. It is also used in economics to examine gross domestic product, employment or
other macroeconomic time series. Mathematically, a moving average is a type
of convolution and so it can be viewed as an example of a low-pass filter used in signal
processing. When used with non-time series data, a moving average filters higher frequency
components without any specific connection to time, although typically some kind of ordering is
implied. Viewed simplistically it can be regarded as smoothing the data.
16. 16 | P a g e
2.1.4 Arena Simulation Software
Senior managers wanted to redesign the company’s supply chain so they could share resources
and channels in a forward and reverse logistics supply chain. Their goal was to use supply chain
simulation software to determine the best strategy and to design the most effective supply chain
design that would maximize customer service and minimize cost. Forward logistics is the
movement of new product from a manufacturer to an end customer. Reverse logistics is the
return of repaired product to a customer and the movement of failed product from a customer to
the OEM for repair. The supply chain in a combined forward and reverse logistics system is
extremely complex due to new, repaired, and failed products flowing through shared channels
and using shared resources. To design the most effective supply chain configuration, the
manufacturer’s senior managers brought in consultants from Rockwell Automation, who teamed
with a leading business consulting firm.[9]
The “as-is” simulation showed the performance of the current service and repair operations for
new, repaired, and failed products. The supply chain optimization model covered over 150,000
Repair Material Authorizations (RMAs), 20 third party logistics providers (3PLs), 15 OEM
locations, three logistics centres, 100 depots or remote stocking locations (RSLs), and four call
centres. The supply chain optimization model also measured the impact of the design on order
lead time, inventory levels, work-in-process (WIP), repair costs, scrap value, and call centre
utilization.. After verifying the as-is model by using actual data from the previous 12 months,
five “to-be” supply chain network design alternatives were created using Arena’s supply chain
optimization software. The to-be alternatives reflected changes to the company’s logistics
structure, inventory management strategy, call centre management, inventory replenishment
strategy, and repair strategy.
The supply chain optimization model showed that only two of the five alternative designs met all
of the company’s goals, but only one of the two had the lowest cost with the highest customer
service. This final design projected a 39% reduction in repaired and failed product inventory
value and carrying cost. The model illustrated how repair costs could be reduced if the
manufacturer repaired failed parts only when needed. Additionally, it showed how engineering
change management (ECO) costs could be lowered and where excess and obsolete inventory
17. 17 | P a g e
could be eradicated. The design also projected a 16% reduction in transportation costs by using
direct ship as a transport option and instituting advanced ship notices (ASNs). The supply chain
optimization model illustrated how to save costs by consolidating inbound and outbound
shipments, consolidating 3PLs, and sharing resources across Europe. The model determined that
a forward and reverse logistics supply chain would maximize service and minimize costs for
handling new, repaired, and failed products. Arena helped discover the ideal logistics strategy
and network design for sharing resources and channels in the manufacturer’s supply chain. The
savings extrapolated as a result of using Arena’s supply chain simulation software was well over
$50 million[8]
18. 18 | P a g e
SOLVING METHODS
We will present four solving methods:
3.1VRP Solver.
3.2Minimal Spanning Tree Method
3.3Forecasting using Moving Average.
3.4Arena Simulation Software.
3.1 Vehicle Routing Problem (VRP) Solver
In VRP Software we are supposed to arrange the depot (on the top) and delivery points
(following the depot) in a tabular format. That’s why, we have kept Adalaj (our warehouse) at
the top. The rest of clients are following the depot. The demand of the warehouse is zero. For
understanding we have used VRP Solver for eight clients. We followed four steps for getting the
desired output.
1) List out the location of clients and find their longitudes and longitudes with the help
of Google Maps. Following figure shows plotted location coordinates :
Fig.3 Plotted coordinates
of demand points
19. 19 | P a g e
2) The demand shown below is converted into ratio through average of three months, to
prioritize the demand points of the system.
3) Above shown table is loaded into software via a text file, the below shown is the
output in VRP Software after loading into the system. After that we give truck
capacity and truck distance limit as an input.
map
node city
x
coordinate
y
coordinate demand
1 Adalaj 4.9 8.9 0
2 Thaltej 1.7 1.2 1.5
3 bapunagar 6.8 3.2 1
4 naroda 8.5 5.5 2.2
5 ghatlodiya 3.1 6.1 2
6 sabarmati 5.1 6.3 1
7 chandkheda 4.6 6.3 2.15
8 gandhinagar 7.1 10.9 2
9 randheja 7.2 13.5 2
Table 1: Sample Input to VRP Solver
Fig.4 Data Loading in VRP Solver
20. 20 | P a g e
4) This is the output after running the model, here the truck capacity was 17 tons and
truck distance limit was within 15 kilo meters.
Interpretation of Output:
This output shows that we need to send 4 trucks, each on different routes to complete order of
each demand point.
1st
Truck: From NDC to Ghatlodiya
2nd
Truck: From NDC to Thaltej
3rd
Truck: From NDC to Gandhinagar and Randheja
4th
Truck: From NDC to Chandkheda, Sabarmati, Bapunagar and Naroda.
Fig.5 Output in VRP Solver
21. 21 | P a g e
Comparison between Present route and VRP Solver’s route:
o Present route transportation cost:
Currently, NDC each and every delivery unique and independent. In this case, we have 8 clients
so we will deliver to them individually.
Booking Cost of Truck = 250 Rs. (Each Truck has 5 ton capacity)
Travelling Costs = 140 Rs. per Km. (Cost is constant for 20 Kilo meters)
Total Distance Travelled = 73.9 Km.
Therefore, Total Cost = Order Cost (OD) + Travelling Cost (TC)
= 8(250) + 73.9(140)
=12350.2 Rs.
o Proposed route transportation cost:
Here we are delivering in 4 routes, so booking cost is required only for 4 trucks,
Total Distance Travelled = 48.30 Km.
Ordering Cost of Truck = 300 Rs. (Each truck has 17 ton capacity)
Travelling Cost = 160 Rs. per Km.
Therefore, Total Cost = OC + TC
= 4(300) + 48.3(160)
= 8928 Rs.
Cost saved after applying proposed method:
= 12350 – 8928
= 1422 Rs.
22. 22 | P a g e
3.3 Minimal Spanning Tree Method
With help of Minimal Spanning Tree we will try to prioritize the demand points and set a route
for replenishing that demand. We will now show how to apply minimal spanning tree. To
illustrate this method, we have considered six demand points:
1. Chandkheda
2. Chandlodiya
3. Charodi
4. Decabin
5. Gandhingar
6. Gota
Problem Definition:
We are supposed to deliver goods to above mention all 6 demand points at minimum possible
costs. We will use below illustrated minimal spanning tree method.
Methodology:
First, we take node 1as our initial demand point.
Initial Point
Fig.6 Initial Network of
Minimal Spanning Tree
23. 23 | P a g e
Second, we choose the nearest point from 1, in this case 4th
node is the nearest so we
choose 4.
Third, from 4th
we choose the next nearest and likewise after that,
Fig.6.1 step 2 in
MST
Fig.6.2 Step 3
Fig.6.3 Step 4
24. 24 | P a g e
So now we have covered all six demand points, now we will compare the cost of delivery
using current method and cost of delivery after applying minimal spanning tree method.
Fig.6.4 Step 5
Fig.6.5 Step 6
25. 25 | P a g e
Comparison of Costs:
Below shown is the table of demand for six nodes for January, February and March.
Calculation of Costs:
Present cost :
Cost if MSP used:
NAME JAN FEB MARCH
CHANDKHEDA 3 72 67
CHANDLODIYA 56 52 27
CHARODI 0 141 163
D-CABIN 5 3 13
GANDHINAGAR 95 125 130
GOTA 40 86 28
PER MONTH 199 479 428
PER WEEK 49.75 119.75 107
ROUND
ABOUT 50 120 107
Distance Travelled
(Present)
Distance Travelled
(After MSP)
62.3 27.85
Delivery using current methods
(5 tons truck used)
TOTAL COST = O.C + T.C
= 6(250) + 200(50)
= 1500 + 10000
= 11500/-
Delivery using MSP
(17 tons truck used)
TOTAL COST = O.C + T.C
= 3(300) + 180(50)
= 900 + 9000
= 9900/-
Table 2 demand for 6
nodes
Table 3 comparison
between distances
Table 4 present cost (without Minimal Spanning Tree)
Table 5 Cost with Minimal Spanning Tree
26. 26 | P a g e
Conclusion:
If Minimal Spanning Tree Method is used total savings of 1600 (11500-9900) Rs. Per
month can be done.
January:
We ran similar methodology for February and March month and there also we observed
similar savings.
February:
TOTAL SAVINGS FOR FEB. MONTH
TOTAL SAVINGS = OLD COST - NEW COST
= 28800 - 26400
= 2400/-
March:
TOTAL SAVINGS FOR MAR. MONTH
TOTAL SAVINGS = OLD COST - NEW COST
= 27680 - 23400
= 4280/-
So clearly, applicable minimal spanning method could be very profitable.
TOTAL SAVING FOR JAN. MONTH
TOTAL SAVING = OLD COST - NEW COST
= 11500 - 9900
= 1600/-
Table 6 savings for January
Table 7 savings for February
month
Table 8 savings for March
27. 27 | P a g e
3.4 Forecasting using Moving Average
Below is the demand of all the clients for three months (January, February and March).
Using data of demand for three months we are forecasting for next months.
Here we will use MA(3),
MA(3) = ( jan + feb + mar )/3
Example :If we want to calculate Moving average for Gandhinagar city:
Demand: January = 95
February = 125
March = 13
MA(3)= (95+125+130)/3 = 116.6
CITY JAN FEB MAR
AMRAIWADI 0 15 30
BAKROL 60 0 0
BAPUNAGAR 5 6 7
BOPAL 0 10 15
CHANDKHEDA 3 72 67
CHARODI 0 141 163
DAHEGAM 20 44 50
GANDHINAGAR 95 125 130
GATHLODIYA 56 52 27
GOTA 40 86 28
KADI 0 0 50
KALOL 66 23 63
KATHWADA 120 30 170
LAPKAMAN 3 15 20
MOTERA 3 5 12
OGANJ 15 46 5
RACHARDA 70 33 35
RAKHIYAL 10 0 225
RANIP 0 8 14
SABARMATI 5 3 13
SANTEJ 52 45 25
THALTEG 0 19 17
ZUNDAL 5 6 13
Table 9 Three Months Demands for Clients
29. 29 | P a g e
Result:
Looking at what has happened in the past can help companies predict what will happen in the
future. Thus making the company stronger and most likely more profitable. It helps us to predict
the future demand of our product. By forecasting on a regular basis, it forces companies to
continually think about their future and where their company is headed. This will allow them to
foresee changing market trends and keep up with the competition. In order to keep customers
satisfied you need to provide them with the product they want when they want it. This advantage
of forecasting in business will help predict product demand so that enough product is available to
fulfil customer orders. Forecasting does not provide you with a crystal ball to see exactly what
will happen to the market and your company over the coming years, but it will help give you a
general idea. This will provide you with a sense of direction which will allow your company to
get the most out of the marketplace.
30. 30 | P a g e
3.4 Arena Simulation Model
For better understanding we are preparing an Arena Simulation Model. Below present flow chart
represents the basic algorithm which we used to prepare our Arena Simulation Model.
Fig. 1 Flow chart of working process of NDC
31. 31 | P a g e
Below shown figure shows the blocks of Arena Simulation Model.
Fig. 2 Blocks of Arena
Simulation model
Fig. 2 (contd.) Blocks
of Arena Simulation
model
32. 32 | P a g e
CONCLUSIONS AND RECOMMENDATIONS
4.1 Conclusion
The transportation cost is an important element of the total cost structure for any business. The
problem here was vehicle routing problem and building a network diagram for better
understanding and bolstering links of supply chain with certain assumptions.The methods
available to solve the problem are VRP Solver, Arena Simulation Software, Minimal Spanning
Tree method and Moving Average Forecasting. The company is in its introduction phase so
network diagram and simulation model will help cope the adjustments of new clients. Minimal
Spanning Tree method and VRP Solver shows savings of 1600 Rs. and 1440 Rs. per week.
4.2 Recommendations
Based on the results and findings of this study, we recommend to the management of Nirmax
distribution center, Adalaj to use VRP solver and minimal spanning tree for every day tour
planning instead of fixed route of every truck. And also forecast data for upcoming month based
on previous data as it helps in avoiding delays and identifying trends in the market.
33. 33 | P a g e
REFERENCES
1. Management Science, Quantitative Approach to DecisioOliveira, H.C.B.de; Vasconcelos,
G.C. (2008). "A hybrid search method for the vehicle routing problem with time
windows". Annals of Operations Research. Retrieved2009-01-29.
2. Statistical Analysis, Ya-lun Chou, Holt International, 1975, ISBN 0-03-089422-0, section
17.9.
3. Eykhoff, (1974): System Identification: Parameter and State Estimation, Wiley & Sons.
4. Kumar T, and Schilling S.M (2010). : Comparison of optimization Technique in large scale
Transportation Problem.
5. Sweeney J. D, Anderson R, Williams T. A Camm D.J (2010), Quantitative Methods for
Business. Pages219-226.
6. Erlander S.B (2010) Cost-Minimizing Choice Behavior in Transportation Planning.
Theoretical. Page 8-10.
7. Aguaviva. E (1997).: Quantitative Techniques in Decision Making 97 Ed. Pages 57-91.
8. Doustdargholi.S, Als D. A, and Abasgholipour.V (2009). A Sensitivity Analysis of Right-
hand-side Parameter in Transportation Problem, Applied Mathematical Science, Vol.3,no.
30,1501-1511.
9. Int.J. Contemp.Math.Sciences, Vol.5, 2010, no.19, 931-942.
10. International Journal of Management Science and Engineering vol.1 (2006) No.1, pp.47-52.
11. http://www.scribd.com/doc/7079581/Quantitative-Techniques-for-Management.
12. http://extension.oregonstate.edu/catalog/pdf/em/em8779-e.pdf.
