ADVANCED APPAREL
MANUFACTURING
MANAGEMENT
{FINAL SUBMISSION}
SUBMITTED TO :MS. NILIMA R.TOPNO
SUBMITTED BY : ARYAN RATHORE (BFT/18/519)
KOMAL PRIYA (BFT/18/563)
АСKNОWLEDGMENT
The соmрletiоn оf this аssignment gives us muсh Рleаsure. We wоuld like tо shоw оur
grаtitude tо our Аsst. Рrоfessоr Ms. Nilima Regina Topno, NIFT fоr giving us а gооd
guideline fоr аssignment аnd соnsultаtiоns. He intrоduсed us tо the Methоdоlоgy оf wоrk,
аnd whоse раssiоn fоr the “underlying struсtures” hаd lаsting effeсt. We wоuld аlsо like
tо exраnd our deeрest grаtitude tо аll thоse whо hаve direсtly аnd indireсtly guided us
in writing this аssignment.
We аlsо thаnk the University оf NIFT fоr соnsent tо inсlude this tорiс аs а раrt оf our
рарer.
Mаny рeорle, esрeсiаlly our сlаssmаtes, hаve mаde vаluаble соmment suggestiоns оn this
рrороsаl whiсh gаve аn insрirаtiоn tо imрrоve our аssignment. We thаnk аll the рeорle
fоr their helр direсtly аnd indireсtly tо соmрlete our аssignment.
APPLICATION OF OPERATION RESEARCH IN GARMENT
INDUSTRY
OR reflects an analytical method of problem solving and decision-making that is useful in the
management of organizations of fashion industry. OR improves the effectiveness and the
efficiency of an institution, hence some of the benefits offered by OR include:
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Decrease Cost or Investment
Increase Revenue or Return on Investment
Increase Market Share  Manage and Reduce Risk
Improve Quality
Increase Throughput while Decreasing Delays
Achieve Improved Utilization form Limited Resources
Demonstrate Feasibility and Workability
OR Functions and Methods: OR supports the key decision making process, allows to solve
urgent problems, can be utilized to design improved multistep operations (processes), setup
policies, supports the planning and forecasting steps, and measures actual results.
Application of OR in Fibre and Fabric Industry OR applications include :
Scheduling, routing, workflow improvements, elimination of bottlenecks, inventory control,
business process re-engineering, site selection, or facility and general operational planning.
Revenue and supply chain management reflect two growing applications that are distinguished
by their use of several OR methods to cover several functions. Revenue management entails
first to accurately forecasting the demand, and secondly to adjust the price structure over time
to more profitably allocate fixed capacity. Supply chain decisions describe the who, what,
when, and where abstractions from purchasing and transporting raw materials and parts,
through manufacturing actual products and goods, and finally distributing and delivering the
items to the customers.
Application of OR is useful in various department of garment manufacturing
organizations
In cutting department cut scheduling problem concerns finding a feasible cutting schedule
having the minimum number of lays. The availability of multiple solutions allows greater
flexibility and permits decision makers to apply additional criteria in selecting an appropriate
cutting schedule.
A hybrid flowshop (HFS) problem on the pre-sewing operations and a master production
scheduling (MPS) problem of apparel manufacture are solved by a proposed twotier scheduling
model.
OR helps to plan a MPS for the factory so that the costs are minimized when the production
orders are completed before and after the delivery dates required by the customers and it also
helps to minimize the completion time of the pre-sewing operations in the cutting department
while the production quantities required by the sewing department at several predetermined
times can be fulfilled by the cutting department.
OR projects focus on the industrial deployment of computer-based methods for assembly line
balancing, business process reengineering, capacity planning, pull scheduling, and setup
reduction, primarily through the integration of the philosophies of the Theory of Constraints
and Lean Manufacturing.
The main benefits are :
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Defects and wastage - Reduce defects and unnecessary physical wastage, including excess
use of raw material (inputs), preventable defects, costs associated with reprocessing
defective items, and unnecessary product characteristics which are not required by
customers
Cycle Times - Reduce manufacturing lead times and production cycle times by reducing
waiting times between processing stages, as well as process preparation times and
product/model conversion times
Inventory levels - Minimize inventory levels at all stages of production, particularly
works-in-progress between production stages. Lower inventories also imply lower working
capital requirements
Labor productivity - Improve labor productivity, both by reducing the idle time of
workers and ensuring that when workers are working, they are using their effort as
productively as possible
Utilization of equipment and space - Use equipment and manufacturing space more
efficiently by eliminating bottlenecks and maximizing the rate of production though
existing equipment, while minimizing machine downtime
Flexibility - Have the ability to produce a more flexible range of products with minimum
changeover costs and changeover time
Output – In regards to reduced cycle times, increased labor productivity and elimination
of bottlenecks and machine downtime can be achieved.
Application of OR in Retail Industry In the retail industry application of OR is as
follows:
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ROI Maximization: The retailers try to know “the number of units to build in a particular
DMA to maximize return on total investments within that DMA. In such situation the
optimization model would need to consider the variables like warehousing, distribution,
and supply chain costs, overhead, operating costs, media advertising costs, positioning,
marketing strategy, the breadth and depth of merchandise , real estate and construction
costs, employee training and sharing efficiencies among the stores etc.
Optimal Distribution System: Transportation models, inventory models, and advertising
response models is used to get optimal distribution system.
INTRODUCTION TO LINEAR PROGRAMMING
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Linear Programming (LP) is a mathematical modelling technique useful for allocation of
limited resources such as material, machines etc. to several competing activities such as
projects, services etc.
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A typical linear programming problem consists of a linear objective function which is to
be maximized or minimized subject to a finite number of linear constraints.
It may be defined as the problem of maximizing or minimizing a linear function that is
subjected to linear constraints. The constraints may be equalities or inequalities. The
optimization problems involve the calculation of profit and loss. Linear programming problems
are an important class of optimization problems that helps to find the feasible region and
optimize the solution in order to have the highest or lowest value of the function.
In other words, linear programming is considered as an optimization method to maximize or
minimize the objective function of the given mathematical model with the set of some
requirements which are represented in the linear relationship. The main aim of the linear
programming problem is to find the optimal solution.
Linear programming is the method of considering different inequalities relevant to a situation
and calculating the best value that is required to be obtained in those conditions. Some of the
assumptions taken while working with linear programming are:
 The number of constraints should be expressed in the quantitative terms
 The relationship between the constraints and the objective function should be linear
 The linear function (i.e., objective function) is to be optimized
CHARACTERISTICS OF LINEAR PROGRAMMING
The following are the five characteristics of the linear programming problem:
1. Constraints – The limitations should be expressed in the mathematical form, regarding the
resource.
2. Objective Function – In a problem, the objective function should be specified in a
quantitative way.
3. Linearity – The relationship between two or more variables in the function must be linear.
It means that the degree of the variable is one.
4. Finiteness – There should be finite and infinite input and output numbers. In case, if the
function has infinite factors, the optimal solution is not feasible.
5. Non-negativity – The variable value should be positive or zero. It should not be a negative
value.
6. Decision Variables – The decision variable will decide the output. It gives the ultimate
solution of the problem. For any problem, the first step is to identify the decision variables.
REQUIREMENTS OF A LINEAR PROGRAMMING PROBLEM
1. There must be a well-defined objective function (profit, cost or quantities produced) which
is to be either maximized or minimized and which can be expressed as a linear function of
decision variables.
2. There must be constraints on the amount or extent of attainment of the objective and these
constraints must be capable of being expressed as linear equations or inequalities in terms of
variables.
3. There must be alternative courses of action. For example, a given product may be processed
by two different machines and problem may be as to how much of the product to allocate to
which machine.
4. Another necessary requirement is that decision variables should be interrelated and
nonnegative. The non-negativity condition shows that linear programming deals real life
situations for which negative quantities are generally illogical.
5. As stated earlier, the resources must be in limited supply. For example, if a firm starts
producing greater number of a particular product, it must make smaller number of other
products as the total production capacity is limited.
LINEAR PROGRAMMING APPLICATIONS
A real-time example would be considering the limitations of labors and materials and finding
the best production levels for maximum profit in particular circumstances. It is part of a vital
area of mathematics known as optimization techniques. The applications of LP in some other
fields are
 Engineering – It solves design and manufacturing problems as it is helpful for doing shape
optimization
 Efficient Manufacturing – To maximize profit, companies use linear expressions
 Energy Industry – It provides methods to optimize the electric power system.
 Transportation Optimization – For cost and time efficiency.
IMPORTANCE OF LINEAR PROGRAMMING
Linear programming is broadly applied in the field of optimization for many reasons. Many
functional problems in operations analysis can be represented as linear programming problems.
Some special problems of linear programming are such as network flow queries and
multicommodity flow queries are deemed to be important to have produced much research on
functional algorithms for their solution.
INTRODUCTION TO TRANSPORT PROBLEM
Objective
The Objective of transportation problem is to determine the amount to be transported from each
origin to each destinations such that the total transportation cost is minimized.
Limitation
Total Supply from each store should not exceed the capacity of that store and total actual
demand of each warehouse should be met.The transportation problem is one of the subclasses
of LLPs (Linear Programming Problems) in which goods are transported from a set of sources
to a set of destinations subject to the supply and demand of the sources and destination,
respectively, such that the total cost of transportation is minimized.
There are two types of Transportation Problem:
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Balanced (Total Demand=Total Supply)
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Unbalanced (use of dummy stores for calculations)
Warehouses play a vital role in mitigating variations in supply and demand, and providing
value-added services in a supply chain. However, our observation of supply chain practice
reveals that warehousing decisions are not included when developing a distribution plan for
the supply chain. This lack of integration has resulted in substantial variation in workload
(42%-220%) at our industry partner’s warehouse costing them millions of dollars. We address
this real-world challenge by investigating the interdependencies between warehouse,
inventory, and transportation decisions, integrate them in a mathematical programming model,
and develop managerial insights based on solutions of industry-sized problem instances. Our
three contributions to research in supply chain are as follows.
First, we introduce the warehouse-inventory-transportation problem (WITP), which determines
the optimal distribution strategy from vendors to customers via one or more warehouses in
order to minimize total distribution costs. We model WITP as a nonlinear integer programming
model considering multiple vendors, stores, products, and time-periods, and one warehouse.
The model also considers worker congestion at the iv warehouse that could affect worker
productivity. Our experiments indicate that the distribution plans obtained via the WITP, as
compared to a sequential approach of solving an integrated inventory-transportation problem
first and then solving the warehousing problem, result in a substantial reduction in workload
variance at the warehouse, while considerably reducing the total distribution cost. These plans,
however, are sensitive to the aisle configuration and technology at the warehouse, and the level
and productivity of temporary workers. It searches for a better solution in two alternating
phases, a local search phase and a perturbation phase.
Second, to solve industry-sized problems, we developed a heuristic framework. This
framework incorporates key features from the well-established Iterated Local Search (ILS)
meta-heuristic. The heuristic implements three sets of neighborhood moves intended to
improve warehousing, inventory, and transportation costs. It searches for a better solution in
two alternating phases, a local search phase and a perturbation phase. We found that the
solutions from the heuristic were close to optimal on small problem instances. Additionally,
the heuristic was able to solve efficiently industry-sized problems with up to 500 stores and
1,000 products.
Third, we extend the WITP to model distribution decisions for supply chains that manage the
flow of products with varying life cycles. The varying demand patterns of such products (e.g.,
basic and fashion) require different sets of decisions with different objectives; cost-efficiency
for basic products and time-effectiveness for fashion products.