designing an optimal supply chain for a fast growing specialty retail chain

8/16/2019 Designing an Optimal Supply Chain for a Fast Growing Specialty Retail Chain

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DESIGNING AN OPTIMAL SUPPLYCHAIN FOR A FAST GROWING

SPECIALTY RETAIL CHAIN

A thesis submitted to the

Division of Research and Advanced Studiesof the University of Cincinnati

in partial fulfillment of the requirementsfor the degree of

Master of Science

In the department of IndustrialEngineering of the College of

Engineering

by

Krishna Srinivasan

B.E. (Hons) Birla Institute of Technology & Science, 1996

2001

Approved by Dr. Ernest L. HallChairperson of Supervisory Committee


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University of Cincinnati

Abstract

DESIGNING AN OPTIMALSUPPLY CHAIN FOR A FAST

GROWING SPECIALTY RETAILCHAIN

by Krishna Srinivasan

Chairperson of the Supervisory Committee: Professor Ernest L. HallDepartment of Industrial Engineering

There have been many significant advances in the operations research field in the

past years. A lot of work has also been done in supply chain management and

logistics modeling. The effect of mixing of these two fields has been a

tremendous boon to many an industry. Manufacturing industries are focusing on

lean supply chains and the increases competition has benefited the customer

immensely. However, in spite of these developments there has been no

significant work in logistics and supply chain modeling for the specialty retail

industry. Consumer retail on the other hand flourishes on this advantage as the

margins in consumer retail are very small and effective management of the supply

chain could make the difference between being in business and going out.

This work aims at bringing forward through example a thorough application of

sophisticated operations research techniques to the specialty retail supply chain.

Large margins in this industry provide for reaping of very large benefits by the

application of this technology here. Since this would be the very first industry to

experience economic turns in the market an urgent need of the applications of

these techniques is necessary in this area.


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A fast growing specialty retail chain has been selected and its data analyzed and

modeled. A mathematical optimization algorithm was used to effectively reduce

costs in all areas of the supply chain including sourcing, transportation and

warehousing. Several different scenarios and what-if cases were looked into with

the aim of analyzing the effect of different high-level strategic decisions on the

supply chain. The results of the optimization exercise prove that there is a large

scope for operations research work in this area and with sudden turns in the

economy of the country the future of these industries may very well depend on

the management techniques and strategies implemented today.


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TABLE OF CONTENTS

Designing an optimal Supply chain for a fast growing specialty retail chain ....... i

Table of Contents...........................................................................................................1List of figures...................................................................................................................2 Acknowledgments..........................................................................................................3Introduction.....................................................................................................................4

The Logistics Supply Chain............................................................................4Logistics Decisions .......................................................................................... 5Logistics and Retail Industries .......................................................................7Literature Review and Background Work ...................................................8

Retail Supply Chains And Logistics Modeling ........................................................11Retail Supply Chain Questions ....................................................................11Case study of a retail supply chain ..............................................................12

Problem Structure & Analysis....................................................................................16

Data Analysis...................................................................................................16Network Analysis & Modeling Methodology .........................................................24

Network Analysis ...........................................................................................24Modeling Methodology.................................................................................25Mathematical Model Formulation ..............................................................26Base Case .........................................................................................................27Generation and Evaluation of Alternatives ..............................................28

Modeling Results ..........................................................................................................36Recommendations .........................................................................................41

Future Work Requirements and recommendations ..............................................43Bibliography...................................................................................................................44

Appendices ....................................................................................................................45Output Graphics From the Modeling Exercise......................................................46 A Sample of Linear Programming Formulation.....................................................53


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LIST OF FIGURES

Number PageFigure 1: A typical logistics supply chain......................................................................... 4 Figure 2: Illustration of logistics questions ..................................................................... 6 Figure 3: Logistics Composite Modeling ........................................................................ 9 Figure 4: Schematic of the retail supply chain..............................................................13 Figure 5: Geographic representation of our retail supply chain ............................... 14 Figure 6: Receipt Pattern at Distribution Center ......................................................... 17 Figure 7: Example of Product Aggregation..................................................................21 Figure 8: Pie chart Illustration of modeled cost components in the base case...... 28 Figure 9: Schematic of Central Distribution Alternatives.......................................... 27

Figure 10: Schematic of Regional Distribution Alternatives .....................................29 Figure 11: Schematic of Flow Control Center Alternatives ......................................31 Figure 12: Schematic of Strategic Sourcing Case.........................................................32


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ACKNOWLEDGMENTS

The author wishes to thank the following people for their support.

Dr. Ernie Hall, Professor of Industrial Engineering, University of Cincinnati.

Dr. Richard Shell, Professor of Industrial Engineering, University of Cincinnati.

Dr. Amitabh Raturi, Professor of Operations Management, University of

Cincinnati.


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C h a p t e r 1

INTRODUCTION

The Logistics Supply Chain

What is logistics?

Logistics is the collection of activities associated with acquiring, moving, storing

and delivering supply chain commodities (i.e., products in all stages ofmanufacture, services and information) [1]. This leads us to then define a supply

chain as the collection of all components and functions associated with the

creation and ultimate delivery of a product or service.

Components

Finished

Products

Complex Finished

Products

Transportation

Networks

Material handling networks

Figure 1: A typical logistics supply chain


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Logistics supply chains are found all around us in various industry segments and

government functions. The most common and most managed are manufacturing

firms, retailing firms, food producers and distributors and the military. Also other

industries like transportation carriers, service companies and the postal service

make significant use of the science of logistics.

Significance of Logistics

Logistics is a key business function mainly due to the high operating costs of a

supply chain system. Estimated logistics costs incurred by US businesses in 1993

was 670 billion USD[2], or roughly 11% of the GDP. This cost is higher than the

annual US government expenditure in social security, health services and defense.

Costs are not the only reason for logistics to assume an ever-growing role in the

industry today. Deregulation, global sourcing capabilities, customer service

requirements, environmental issues and improvement in technology are forcing

all major corporations to take a closer look at their logistics cost. The marketplace

is becoming increasingly competitive and globalization has helped in a large

fashion. Costs of services vary widely among different countries and hence the

decisions are becoming more complex too.

Logistics Decisions

Types of Logistics questions

There are many important questions requiring serious answers in a logistics case.

These can lead to a variety of difficult issues in design and implementation of

systems. Most decisions embody five important characteristics.


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1. Multiple business functions are impacted.

2. There are tradeoffs among conflicting objectives.

3. Impacts of the decisions are complex and hence difficult to precisely

evaluate.

4. There are businesses unique to each logistics system.

5. Quantitative analysis is essential for intelligent decisions.

W he r e to a c q u i r e

ma t e r i a l s an d c ompon e n t s

W he r e to p r odu c e an d

as s embl e th e goods ?

H o w m u c h t o p ro d u c e?

W he n t o p r odu c e ?

W he r e t o s to r e f i n i sh ed goods ?

W he r e t o st o r e par t s o r s par e s ?

H ow m u ch t o st o r e?

H ow to r e t r ie v e f r om s t o rage ?

H o w m u c h t o sh i p ?

W he n t o s h i p ?

W ha t m od e s o f t r an s p o r ta t i o n ? W hat f l e e t s i z e ?

W hat v eh i c l e r ou t e s ?

W ha t s h i pm e n t r ou t es ?

Figure 2: Illustration of logistics questions


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All the logistics questions illustrated above are at a higher level; an organization

must select appropriate logistics policies or strategies to support its financial

service and goals. These strategies provide the large framework for handling

specific types and scopes of logistics questions. It has been known that often

choosing the right strategy is more important than trying to optimize the lower

level decisions. It is always necessary to move from the strategy to the answer to a

specific logistics question, as the answers will be different if different strategies

are employed. For example, the question of choosing a fleet size will arise only if

the organization has decided to own or lease a private fleet.

Developing a logistics model

In most cases to analyze and evaluate the impact of strategies and other

significant logistics decisions, it is necessary to develop a logistics model. Such a

model, considering the large-scale o the systems involved in today’s marketplace

has to be computer based. Graphics are needed to better understand the supply

chain structure and tradeoffs of possible alternatives and to interactively specify

alternatives. The model has to be simplified and an accurate representation of the

present system. Hence, an evaluation of the model against the system is necessaryto ensure accuracy of results. A simplified model helps better in understanding

the significant elements and costs involved and helps to better understand the

various alternatives and increases its speed and efficiency. Depending upon the

model and the system involved, a large amount of information will be needed.

This can be collected from the historic records of the organization from various

departments. Once initial requirements have been satisfied, various alternatives

can be generated and evaluated in the model before any real implementation

takes place.


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Logistics and Retail Industries

Specialty retail is a special part of the segment of industries that have yet to attach

a large importance to their logistics functions. Consumer retail however, does

employ sophisticated operations research techniques to their supply chain and

hence derives a great benefit from the subject. The main reason for consumer

retail having embraced operations research techniques and specialty retail not

having done so is the margin involved in the sale of a good in either of the

businesses. Specialty retail operates on very large margins while consumer retail

operates on small or insignificant margins relying on large volume of sales.

Delivery of right merchandise at the right time and location plays a large role in

the performance of a retail chain. The components in a retail chain are slightly

different from that of a typical manufacturing supply chain. Most logistics models

and work done so far have been centered on improving and modeling

manufacturing supply chains. The types of questions and issues with retail are

very similar to the manufacturing ones but have some slight differences.

The application of the techniques of operations research will have a significant

impact on specialty retail because of the scope of the improvement opportunities

involved. In the example of this thesis every aspect of the retail supply chain has

been analyzed and an effort has been made to improve the operational efficiency

of the area.

Literature Review and Background Work

There has been a lot of work in the conceptualizing of logistics models in general

around manufacturing based supply chains. Among the foremost modeling

oriented works, was done by Ratliff and Nulty [ 3] in their white paper on logistics

composite modeling. Most importantly, their work brought into perspective all

the questions from different parts of the organization into a single platform for


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evaluation. The structured approach presented by Ratliff and Nulty 2 brings

together a variety of tools and the logistics decision maker in to a composite

model for optimizing the process of decision making. The following figure

illustrates the process of composite modeling.

Figure 3: Logistics Composite Modeling

Anupindi and Tayur [4] in 1995 published a work on managing stochastic multi-

product systems. This work primarily deals with a single stage producing multiple

products. The key focus throughout the work is on inventory and demand

planning techniques to make the supply chain faster and efficient. Swaminathan

and Tayur [ 5] and have published a work in 1996 on managing multiple or broader


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product lines through delayed differentiation techniques. Though the aim of this

work was not discuss key techniques of improving retail performance they have

touched upon inventory storage techniques and transportation issues which can

be applied to our retail chain. The need for speed in the retail industry is driving

the many postponement techniques to delay decision making. Some techniques

applied in the retail industry cause product flow in such a fashion that all product

has to flow through a central distribution center where it gets allocated to the

store. This avoidance of cost at the center means making a decision a few weeks

earlier in case of international sourced product. In the book “Quantitative models

for supply chain management.” Tayur, Ganesan and Magazine [ 6 ] discuss several

facets of the supply chain. Of key interest is the chapter by Jeremy Shapiro,

“Bottom up vs. Top down approach to supply chain modeling.” The key features

of these approaches are that one approaches the modeling and planning stages

from the customer end and the other from the manufacturers end. Each of them

has their own benefits and disadvantages. In the retail industry where product is

sold in a store the approach from the customer end is impossible. However, we

have emulated a strategy with demand starting from an echelon based on future

forecast of demand of products based on fashion trends. In their book

“Economic simulations in Swarm : agent-based modeling and object oriented

programming.” Luna and Stefansson [ 7] have in detail discussed many service sector

and manufacturing industries. The simulation techniques and methodologies used

here are very insightful to approaching our problem.


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C h a p t e r 2

RETAIL SUPPLY CHAINS AND LOGISTICS MODELING

Retail Supply Chain Questions

The main differences between retail and manufacturing supply chains lie in the

merchandising part and the fact that retail industry operators have little visibility

to the manufacturing process of the products they sell. Most specialty retailchains sell the products on which they make the highest margins and hence, there

is a high turnover of products in the stores throughout the year. Typical retail

chains ask questions like where to source the best products and how to manage

the store selling space more effectively. Missing a sale due to lack of inventory at

the store is more distressing in the retail industry than having a high inventory of

an average selling item. Moreover, specialty retail focuses on a high selling item to

push and has many other products with an intention to generate “store traffic”.

These accessory products end up making a large volume of the store and hence it

is necessary to manage them effectively.

For ease of analysis and understanding it is necessary to break down the supply

chain into it’s component objects. The various components that make up our

network and supply chain are

1. Product – The main component essential and central to our work. The

various analysis of product will be demand patterns, supply necessities,

manufacturing requirements and other factors that directly affect the

product and it’s existence in the network.


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2. Network Nodes or Sites – These include all sites and facilities where the

product stops or passes through in the process of reaching its final

destination – the customer. The sites will be

a. Manufacturing facilities – Component vendors and plants and

fillers – all sources of product origin.

b. Flow through facilities – Distribution centers, cross-docks and

other facilities. Storage facilities are a part of this group.

c. Demand Facilities – The customer comes here to get the product.

It is not possible to evaluate a single customer, but the store

would be represented here.

3. Product Flow paths – All routes through which the product flows are

necessary to studied for their inherent competencies and capabilities and

costs associated with them and there on.

Case study of a retail supply chain

It would be very easy to interpret and understand the applications of any science

in a real world situation if it were proved through an example. In order to better

illustrate the application of operations research to improve supply chain efficiency

in a specialty retail case let us take a look at a retail supply chain. The case is

question belongs to a fast growing specialty retailer* dealing in a multitude of

products. The company gets its products predominantly from the continental

USA with a few off-shore suppliers. The market of the retailer is the continental

USA. Such a case makes the analysis and study of the technique easier to

understand.


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Our company is a large retailer with about 1500 stores throughout the continental

United States and about 120 suppliers of which over 80% are domestic. A

number of their international suppliers are located in either South America or

Europe. The domestic suppliers are in concentrated clusters in the Northeastern

part of the country and the in southern California. There are some complex

product assemblers in the Chicago area. The retailer turns around about 10,000

different products through the year with about 6,000 of them present at any given

time.

Figure 4: Schematic of the retail supply chain

* The retailer has specifically requested the author not to divulge any identifying details about the firm.


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The above figure is a very accurate representation of the logistics supply chain of

our company. The assembly center is an optional echelon in the supply chain as

only some products pass through these centers. The retailer sells its products

directly as well as in combination or packages. These assembly center s

manufacture only a small part of the retailer’s products, but they are in great

demand in the holiday period when specialty retailers flourish. For a more

detailed view of the supply chain it is necessary to analyze the components that

make it up. In any such case it is necessary to have a very critical eye on the data

gathering process. The data in our case was gathered from the company sourcing

records for one year. The transportation information comes from the logistics

division of the retailer which provided with one years worth of inbound records

and receipt data and outbound shipment records. The retailer in question shares

its outbound transportation with some other specialty retailers. So these records

had to be filtered to provide only relevant required data.

Figure 5: Geographic representation of our retailsupply chain


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The above figure illustrates the retail supply chain across the United States. In the

above figure the component vendors and stores have not been shown to avoid

clutter in an already crowded picture. In the figure one may observe that a

predominantly large part of the supply base is in New Jersey and southern

California. The distribution center is situated in Columbus as this provides a good

proximity to a large population base of the country. The circles that are delivery

agents are the agents or cross-docks through which delivery is made to the stores.

The retailer as a part of this exercise had three key questions in mind to be

answered.

• What is the operating cost of the present retail chain?

• How can domestic sourcing be altered to achieve better efficiencies and

cost reduction?

• What is the best strategic network to move to in the next three years to

maintain the current growth pace and still be capable of delivering or

improving the present service?


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C h a p t e r 3

PROBLEM STRUCTURE & ANALYSIS

The three key questions need a good deal of analysis of the present retail supply

chain to be answered. This task will be accomplished by systematic analysis of

various objects in the supply chain. The analysis process will aid in developing a

suitable approach for mathematical modeling and optimization later.

Data Analysis

Preliminary demand pattern analysis and store sale records indicated that 15% of

the products accounted for more than 85% of the demand and sales volume.

This is a typical expectation in the retail industry. Hence of the 10,000 products

that are present through the year less than 1,500 were the key products to be

analyzed.

As the graph indicates our retailer had a highly seasonal demand pattern which

peaks during the last three months of the year – also referred to as the holiday

season. The following graph is based on actual receipts at distribution center

during Fall 1999 and spring 2000. It shows a sharp increase in volume during the

“holiday” months (October, November, and December) and a steady lower flow

during the other part of the year.

From the seasonality of the demand patterns as can be observed here, it is clearlynecessary to separate the two patterns and perform our analysis separately for the

peak and off-peak periods. The key inferences from the demand pattern analysis

of the brand reveal that the product velocity increases about 200% during the


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holiday season. This leads to better inbound transportation efficiencies from the

suppliers to the distribution center. There is also disproportionate number of

complex products or packages during the “holiday” season. This would tend to

change the shipping mix of the outbound truck to the stores.

Demand Seasonality

Cartons (in Millions) received by month in 1999

0

0.5

1

1.5

2

2.5

3

1 2 3 4 5 6 7 8 9 10 11 12

Month

V o l u m e r e c e i v e d i n M i l l i o n s o f C a r t o n s

Figure 6: Receipt Pattern at Distribution Center

The products can also be classified based on the logistics requirements and flow

paths they use. In this fashion the retail chain has two types of products – pre-

allocated and replenishment type. Though a large part of the product shipped outis replenishment type, the pre-allocated is a very interesting category. Here the

retail chain pre-allocates the amount of the product going to each of the stores

well in advance, while the product is still being manufactured. Then the shipping

Off Peak

Jan - Se

Peak

Oct - Dec


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labels and allocation amounts are sent to the manufacturer, who in turn makes

store deliverable packs of the product. In essence this product does not need

pick-pack operation in a distribution center. It may by-pass the distribution center

totally and get to the store using alternate flow-paths. In the current network the

pre-allocated product by-passes the distribution center in Columbus, but passes

through the outbound cross-dock center. This strategy is still in its infancy and

has not been tested thoroughly enough yet. The model however will be allowed

to fully make use of this property of the product while making decisions. This is

because the model will be working to build a stronger future network and by then

this would have been tested and fully mature.

The various types of sites or facilities that participate in our system are

component or raw material suppliers, manufacturers, assemblers for packaged

products, distribution centers and cross-docks, delivery agents to the stores and

the stores themselves. The component suppliers or raw material vendors deal in

supplying the manufacturers with the necessary materials and other accessories

involved in the making of the various products of the brand. The vendors for the

commodity items like packaging etc which are required to make the product alsoare included here. The manufacturers are main suppliers of the product to our

distribution center. Our retail chain deals with approximately 120 suppliers with

slight variation in numbers through the year. They supply about 10000 different

products through the year. There are also 6 complex product assemblers in the

network. These assemblers take finished goods and some accessories and

assemble packaged products which have a high demand in the peak retail season.

The central distribution center is located in Columbus and has been designed with excellent capabilities for handling large volumes of product flowing through

it. The distribution center is attached to a sophisticated outbound cross-dock

center with auto-induction capabilities to send out product. The product is


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inducted to outbound trucks directly from the outbound cross-dock center.

These trucks then carry the outbound products to delivery agents who in turn

deliver them to the many stores in the network.

The delivery agents are high speed de-consolidation cross-docks. These facilities

handle products of many specialty retailers and are the serving hub for brining in

product into all of the malls across America.

Software Selection for Modeling

It is definitely beyond doubt that the above problem cannot be solved without

the aid of sophisticated software modeling tools. There are many software

modeling options available to us in the market.

Some of the options considered for the above problem are discussed here. A

complete customized model could be built using base programming capabilities

and interfacing them with native CPLEX algorithms. This option would provide

the maximum flexibility and capability in terms of modeling because it would be

programmed to specific needs of our situation. However, the drawbacks of the

option of the high skill level of programming required and the time and resources

it would consume. It would not have been possible to complete the project in the

allotted time had this option been chosen. Since it was not possible to build a

complete customized solution for our situation it was necessary to turn to market

provided generic software packages with the ability to be customized. After

careful consideration of many logistics packages available, the team chose CAPS

logistics supply chain suite. The software provides the capability to model a

supply chain precisely in an object oriented fashion and has capabilities of geo-

coding various sites on maps for better communication purposes. It has native

CPLEX algorithms built into it for optimization of the problem model. Some of

the drawbacks of this software are its inherent reliance on Windows operating


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systems. So speed is a concern. Traditional optimization models were built on

large computers with better computing capabilities. The software has difficulty in

operating with more than 500 product segments and the optimization engine will

crash with anything more than 50-100 products. However, the other capabilities

for communication and transportation modeling clearly marked this software

ahead of its competition. It must be noted that for handling anything more than

200 products in an optimization model the computing power required increases

dramatically and there are no off the shelf products available in the market with

such capabilities.

Data Manipulation & Modeling

The software selection plays a key part in the analysis procedure and modeling of

data. The questions put forth earlier also are important to keep in mind at this

stage. While manipulating data it is necessary to keep all elements pertaining to

the questions clear.

Time Period for Modeling

It is necessary to set a time-period in the model. It could be a single mode for the

entire year or it may be modeled month-wise. In our case we decided to split the

year into two periods – peak and off-peak. The peak period comprises of the last

three months of the year. This corresponds to the “holiday season” for retail

businesses. The off-peak period is the first nine months of the year. Such a time

division was instituted based on the seasonality observed in the demand patter. If

the whole year were averaged into one period a lot of the detail in modeling

capacities and constraints would be lost.

Product Aggregation Strategies

The model clearly cannot model our 10,000 products individually. Hence it is

necessary to group the products based on certain characteristics into a smaller


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number. The first level grouping was done using the product marketing

characteristics into families. The products were divided by manufacturer and

demand velocity and they were grouped based on both manufacturing properties

and demand properties. This reduced our products from 10,000 to about 400 in

number. In the second stage where the optimization algorithms will be applied to

the model, the same 400 groups were further aggregated based on pure

manufacturing properties into 30 high level product groups.

Figure 7: Example of Product Aggregation

The above table illustrates product aggregation from the dissections to the second

grouping.

Cost Modeling

The most important aspect of the exercise is to cut costs. The costs incurred at

every stage in the supply chain have to be accounted for and included in the

model if there is any scope of modifying it. The costs with no scope for

modification need not be included. The costs incurred in our model were:

• Component Cost – This is the cost of the components incurred at the

site of the raw material vendor.

Dissection New ProductID Name ManufacturerID SiteName UnitWt UnitCost

D102002 Pr0005 Alc-Gel SP0122 TriTech 0.25 $0.68

D102003 Pr0008 Alc-Spray SP0018 Bocchi 0.35 $0.54

D102004 Pr0019 LL-Specialty SP0117 Thibiant 0.25 $0.90D102006 Pr0018 LL-HighVol SP0122 TriTech 0.570833333 $1.09

D102008 Pr0029 Soap SP0033 CERTIFIED PACKAGING CORP. 0.065 $1.30

D102010 Pr0024 Misc-Paper SP0060 H & P Triad 0.35 $1.05

D102012 Pr0018 LL-HighVol SP0122 TriTech 0.6 $1.61

D102014 Pr0019 LL-Specialty SP0117 Thibiant 0.2 $0.94

D102016 Pr0008 Alc-Spray SP0018 Bocchi 0.05 $0.63




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• Component transportation cost – Cost of transporting components to

the manufacturing site or to assembler site.

• Manufacturing Costs – These costs are labor and maintenance and other

costs associated with the manufacturing sites.

• Manufacturer to assembler cost – This cost is incurred in transporting

finished goods from the manufacturer to the assembler sites.

• Manufacturer to DC cost – This transportation cost is incurred in

transportation of goods from the manufacturer to our distribution center.

• Distribution Center handling cost – The cost of picking product at the

DC and the cost of storage handling and induction to the cross-docking

center are included here.

• Cross-dock center handling costs – This is the cost at the cross-docking

center to handle the product and send it out to the stores.

• Outbound Transportation cost – Cost of delivering product from the

cross-dock center to the delivery agent.

• Delivery agent handling costs – The cost of handling products and

deconsolidating them at the delivery agent and distribution to the stores.

The cost of delivery to the stores is included here. The delivery agents are

contract agents and hence it is not possible to include the cost of

transportation to the stores as part of the model.


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• Store handling and display costs – These costs are the costs incurred by

the retailer in displaying product at the stores and the back-room charges

applicable etc.

Of the above described costs it is not possible to control all the costs. For

modeling purposes it was decided to only include certain echelons and costs. Our

cost structure in the model includes the component cost their transportation cost

to the manufacturer at the manufacturing site. The packaged products assemblers

are treated as manufacturers and all the previous echelons to them are summed

up there as a cost at the site of the assembler. These modifications were made to

make the cost calculation exercise easier.


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C h a p t e r 4

NETWORK ANALYSIS & MODELING METHODOLOGY

Network Analysis

In the network it was important to know the cost components over which

control could be exercised. The component vendors are commodity item vendors

for the most part. Hence, it was safely and correctly assumed that there was noroom for improvement in this area immediately. Commodity items can be found

all over the country and so the cost component of transportation from the

component vendors to the manufacturer may be rolled on to the production cost

of the manufacturer without loss of accuracy. Similarly the store costs are rolled

on to the delivery agent without loss of accuracy. The cost components defined

in the model were:

• Production Cost – This cost includes all costs associated with theproduction of the product at the manufacturer and all component and

incurred costs up until there. This may be broadly divided into two

categories for ease of use – costs that can be optimized and those that

may not be. The components and material acquisition costs were not to

be optimized by the model. The variable cost (as costs that the model

may manipulate and optimize) here is the labor or production cost at the

facility of the manufacturer.

• Inbound transportation cost – This cost is under direct control of the

retail logistics division and hence may be manipulated and optimized by

the model.


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• Distribution Center Handling cost – As described earlier this is the

handling and picking cost at the distribution facility. There is very little

scope of improving per unit cost here. However, the flow of the model

may afflict some improvements here.

• Cross-dock center – The cross-dock center for the retailer is a highly

optimized and busy operation and hence there is no scope of improving

per unit cost here. As stated earlier the model flow may affect some

overall costs through the center.

• Outbound transportation – This is scaleable cost and there is benefit to

be derived here.

• Delivery Agent and Store – There is no element of improvement in these

costs. However, for accurate cost representation this has been included in

the model.

Modeling Methodology The network of logistics objects here is facilities linked together by transportation

or product flow paths. Product flows on them to make a supply chain from the

vendor to the store. Cost is calculated at every node and every link on the

network. The objective of the model is minimizing the overall cost with best

possible way of servicing the demand.

The model was initially mathematically formulated and then alternatives

generated and run to compare against a baseline scenario. The baseline is the case

developed to accurately represent the present network costs in the model. The

model uses products weight in lbs as a measure throughout to normalize

products of different shapes and sizes.


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Mathematical Model Formulation

Variables

Let us define products as Pi for i = 1 to max products.

Let V j be vendors for j = 1 to max number of vendors.

Let Sij be the units of product i produced at vendor j.

Let AQij be the acquisition cost of one unit of product i at vendor j.

Let Lij be the labor cost of one unit of product i at vendor j.

Let DCk be a distribution center for k = I to max DCs.

Let us define lanes as links between vendors and distribution centers for inbound

lanes. Let the cost of transporting 1 unit of product i from vendor j to DC k be

defined as ITijk . Let the units of product Pi from supplier V j to distribution center

DCk be Sijk.

Let the cost of handling one unit of product I at DC k be Hik .

Let DAm be defined as a delivery agent for m = 1 to max DAs.

Let the demand for product Pi at delivery agent DAm be defined as Dim.

Let cost of transporting one unit of product Pi from distribution center DCk to

delivery agent DAm be defined as OTikm. Let the number of units of product P i

from distribution center DCk to delivery agent DAm be denoted by Dikm.

Let the cost of handling one unit of product Pi at delivery agent DAm be defined

as Aim.


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We are now read to formulate the model. The objective function to minimize is

the total cost.

Min:( )

++

+++

imimim

ikmikmikm

ijk ijk ik

ijk ijk ij

ijijij

ijijij

D A DOT S H

S * IT S * LS * AQ

***

subject to

Sum(Sij ) over i


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Figure 8: Pie chart Illustration of modeled costcomponents in the base case. The costs are inmillions of US Dollars.

Our base case performed against the retailer’s current cost structure with a 91%

match. This suggests the validity of our data and modeling paradigm. Details of

the base case are available in appendix. The key idea to be taken form the above

illustration is the disproportionate product acquisition cost in comparison to all

other logistics costs. All the later alternatives were designed with this key cost

consideration.

Generation and Evaluation of Alternatives

The model formulates and optimizes the structure of the supply chain as has

been presented to it. However, the purpose of the design is to evaluate

alternatives and choose the best potion in terms of cost and service. Hence it

becomes necessary to generate some alternatives and optimize all the alternativesto choose the best option. In essence this exercise is a combination of simulation

and formulation optimization modeling.

$546.0

$20.6

$28.6

$37.4

$50.0

Product Acquisi tion Costs

Inbound

DC Operations

Outbound

Store Operations Logistics


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The aim of work is to design an optimal supply chain for the retailer for future

growth. Hence it is necessary to have an idea of how and where the retailer

intends to grow. To model the growth the growth plan and sales forecast for year

2003 was obtained and another base case was performed to benchmark and set

an initial point of reference. This would indicate the performance improvement

in the supply chain as each of the alternatives is evaluated.

The above table illustrates the volume of product produced and consumed by

each region in the USA. It may be noted that roughly 12% of the demand is

produced and consumed in the North East region. However, in the present

network this demand has to pass through Columbus to reach its destination. The

southwest region is another significant market for the retailer. However in this

case too, a large amount of production does travel to Columbus and back. This

increases the supply chain costs and the time to market dramatically. The present

growth of the retailer has been quite unplanned as the growth has been primarilyaround ideas and demand segments. The company now wants to take a critical

look at the performance of the supply chain and the strategic growth areas for the

future. The alternatives were designed with this in mind.

Location of Product Demand(Destination)

Location of Product Supply(Source) NE SE NC SC NW SW

Northeast 11.9%

(3.6%)

9.4%

(2.9%)

9.7%

(3.0%)

4.4%

(1.3%)

0.8%

(0.2%)

5.7%

(1.7%)Southeast 4.8%(1.3%)

3.9%(1.1%)

4.0%(1.1%)

1.8%(0.6%)

0.3%(0.1%)

2.4%(0.6%)

North Central 2.8%(0.8%)

2.3%(0.6%)

2.3%(0.7%)

1.0%(0.3%)

0.2%(0.1%)

1.4%(0.4%)

South Central 0.6%(0.0%)

0.5%(0.0%)

0.5%(0.0%)

0.2%(0.0%)

0.0%(0.0%)

0.3%(0.0%)

Northwest 0.0%(0.0%)

0.0%(0.0%)

0.0%(0.0%)

0.0%(0.0%)

0.0%(0.0%)

0.0%(0.0%)

Southwest 3.6%(0.4%)

2.9%(0.3%)

2.9%(0.3%)

1.3%(0.1%)

0.3%(0.0%)

1.7%(0.2%)

International 4.5% 3.6% 3.7% 1.7% 0.3% 2.2%


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The key feature of the alternatives designed is realignment of the manufacturing

to suit the growth of the retailer best. A list of key suppliers was obtained from

the retailer. The capabilities of the key suppliers were analyzed and product

movement was centered around them. Many of the contract manufacturers, it

was found had the capabilities to produce a large portion of the retailers products.

Since production costs are the largest bucket of the retailers cost a large focus was

devoted to it throughout the exercise. Also each alternative is incrementally

improved from the previous one to evaluate the specific benefits of each variable

in the equation.

Alternative 1: Optimal growth in the current network.

In this alternative the network structure was unaltered and the growing demand

was distributed optimally among the current suppliers. The current suppliers were

allowed to retain their present day production levels and the new demand change

from now till 2003 would be distributed among the best and most cost effective


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suppliers. The focus on the demand distribution is due to the disproportionately

high product acquisition and labor costs at the manufacturing facilities.

Alternative 2: Optimal Supplier location in the Centralized network

This alternative studies the best way to reconfigure suppliers and contract

manufacturing to improve the operational efficiency of the supply chain without

changing any other distribution or delivery facilities. In this alternative the entire

supply chain would be reconfigured at the manufacturing level to optimally place

contract manufacturing to derive the best service and cost benefits.

Alternative 3: Current Production Assignment in a Regional Network

This alternative keeps the manufacturing facilities intact with their production

assignments exactly as they are in the base case, but changes the distribution

network. Two new regional distribution centers are added in New Jersey and Los

Angeles. These regional facilities have all the capabilities as are available in the

central distribution facility at Columbus. It is assumed that the cost at the regional

centers will mirror the cost structure of the centralized distribution center. This

assumption was made to make the model a long term strategic model. It is fair to

assume that in the long term the costs at each of the regional distribution centers

will be similar. In this network each of the contract manufacturers will ship only

to the closest regional distribution center. Delivery agents will be similarly served

from the distribution center that is closest to them. In the case of a product that

is not available in a region and has to be procured from a manufacturer in

another region, such a product will be shipped from the distribution center of the

contract manufacturers region to the demanding distribution center. Thus the

distribution centers will act as consolidation points for both inbound andoutbound shipments. This design will result in best shipment efficiencies thought

the drawback of increased time is recognized immediately.


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Alternative 4: Optimal Production Assignment in Regional Network

The production capabilities were available for new all products in all regions.

Hence, the product mix of the contract manufacturers was allowed to change in

this alternative. This alternative builds on the previous scenario from where one

of the fixed production constraints has been relaxed. The production capability

matrix plays a role here because of the fact that our products have been grouped

from 10,000 to 30 in number. So a number of the products when rolled into one

gain the capability of being produced in different facilities. This was an intended

idea during the product aggregation exercise. This scenario however does

constrain the model to keep the production at the supplier to the projected level

at 2003 if no changes would have been made. Hence only the product mix at asupplier may change with this alternative and not the production quantity.


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Alternative 5: Optimal Supplier Growth in a Regional Network

This is a corresponding alternative to alternative 1 in the central distribution

network scenario. As in the central network, the manufacturers are allowed to

maintain their current 1999 production capability and quantity intact, but the

growth of the retailers demand would be positioned optimally the best suppliers

suited for growth. This alternative gives an idea of what are the best suppliers for

the retailer to grow with. This alternative, although not an end by itself is

necessary to chalk out the transition path of allocation if the retailer moved to

regional network distribution.

Alternative 6: Optimal Production in a Regional Distribution Network

This scenario corresponds to alternative 2 of the central distribution network.

Here as in scenario 2, suppliers may be closed to position the production

optimally to achieve best cost benefit and service response to the market. It may

be expected that this scenario would make three clear regions of the country and

make all suppliers as close as possible to the distribution center.

Alternative 7: Centralized distribution through Columbus and regional Flow control centers

In this alternative there will be a central distribution center in Columbus and two

regional center in New Jersey and Los Angeles, with reduced functionality. These

regional “flow control centers” are high speed cross-docks through which only

pre-allocated product may flow. However, in this alternative the manufacturers

are required to institute production in their current day pattern. This will isolate

the effect of the regional flow centers alone. Moreover, in this scenario the flow

paths are restricted in such a fashion that each supplier may ship pre-allocated

product of the region only to the nearest flow center. All other product(unallocated and pre-allocated product of other regions) has to flow through

Columbus.


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Alternative 8: Regional Flow Centers with production variation and transportation variation

In this alternative the suppliers may ship pre-allocated product to all regional flow

centers based on the demand n the region. They only ship unallocated product to

Columbus. All other elements mimic the previous alternative.

Alternative 9: Addition of a new flow center in Dallas

If all the elements of the previous alternative were unaltered and an additional

flow center provided to the model in Dallas, how would the service to store time

improve and what would the cost implications be? This alternative was

specifically designed to answer this question.


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Alternative 10: Regional Network with regional coursing capability

This alternative incorporates a multi-sourcing regional network. This is a

derivative of the earlier modeled regional network alternatives. The outputs of the

earlier runs were analyzed and product movement transferred from surplus

producing regions to deficient regions. In essence supplier capability was added in

the southern California region for some of the product that would immensely

benefit the proximity to a whole new market. Supplier to distribution center flow

paths were further relaxed, allowing any supplier to ship to any distribution center

without constraints. The delivery agents however could only be served by the

closest distribution centers. This network alternative gives the best case scenario

of how the network could be. A few variations of this alternative named as 12

and 13 were run later to test and see implications. In alternative 12 no supplier


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closures were allowed and strategic sourcing was not allowed, but suppliers could

ship to any distribution center. Alternative 13 was the same as alternative 10

without the strategic sourcing suppliers added. These two alternatives help us

understand alternative 10 better.


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C h a p t e r 5

MODELING RESULTS

The model generated some interesting results and insights. It is necessary to

know the significance of these results, before making any choices as to which

alternative was better. Cost is the most important decision maker in most cases,

however it is necessary to keep in mind the service impact on any results

observed. The product acquisition cost forms the most significant part of the cost

of the supply chain. The model does not aim to alter the component costs. The

concern is to optimize the labor and variable labor costs. The labor cost has been

divided into two categories. The variable rate labor is applied at suppliers who are

willing to give us a discount if we increased volume of production with them.

Hence this is a break rate falling with an increase in production. On the other

hand we do have suppliers at present who are quite high on their capacity or are

small operations that cannot offer the volume based variable production rate per

unit. Their cost per unit of manufacture is fixed. In such cases the fixed rate labor

cost is applied to them The cost has been divided into two categories because of

the way it is handled in the modeling software.

In the section below the results have been documented by alternative. The entire

model was run twice for each alternative with different data during each run. The

two periods in the model were peak and off-peak. The results here are a

compilation and sum of the annual costs. It presents an easy to choose picture.


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CCCDDDCCC SSSCCCEEENNNAAARRRIIIOOOSSS ––– 2222222222000000000000000000003333333333 VVVVVVVVVVVVOOOOOOOOOOOOLLLLLLLLLLLLUUUUUUUUUUUUMMMMMMMMMMMMEEEEEEEEEEEE PPPPPPPPPPPPRRRRRRRRRRRROOOOOOOOOOOOJJJJJJJJJJJJEEEEEEEEEEEECCCCCCCCCCCCTTTTTTTTTTTTIIIIIIIIIIIIOOOOOOOOOOOONNNNNNNNNNNNSSSSSSSSSSSS Cost ComponentCost ComponentCost ComponentCost Component Alternative 0Alternative 0Alternative 0Alternative 0 Alternative 1Alternative 1Alternative 1Alternative 1 Alternative 2Alternative 2Alternative 2Alternative 2

Product AcquisitionProduct AcquisitionProduct AcquisitionProduct Acquisition Total Cost($ millions)

TotalCost

($millions)

Changefrom

Baseline

Total Cost($ millions)

Changefrom

Baseline

Component $792.9 $792.9 $792.9

Package ProductAcquisition

$260.6 $260.6 $260.6

Variable Rate Labor $49.1$49.1$49.1$49.1 $50.7$50.7$50.7$50.7 $1.6$1.6$1.6$1.6 $52.2$52.2$52.2$52.2 $3.0$3.0$3.0$3.0

Constant Rate Labor $22.3$22.3$22.3$22.3 $16.2$16.2$16.2$16.2 ($6.1)($6.1)($6.1)($6.1) $11.6$11.6$11.6$11.6 ($10.6)($10.6)($10.6)($10.6)

Inbound TransportationInbound TransportationInbound TransportationInbound Transportation

Supplier to DC $6.1$6.1$6.1$6.1 $5.5$5.5$5.5$5.5 ($0.6)($0.6)($0.6)($0.6) $4.3$4.3$4.3$4.3 ($1.8)($1.8)($1.8)($1.8)

Assembler to DC $1.1$1.1$1.1$1.1 $1.3$1.3$1.3$1.3 $0.1$0.1$0.1$0.1 $1.1$1.1$1.1$1.1 ($0.0)($0.0)($0.0)($0.0)

Distribution HandlingDistribution HandlingDistribution HandlingDistribution Handling

DC & Cross Dock Center $21.0$21.0$21.0$21.0 $21.0$21.0$21.0$21.0 $21.0$21.0$21.0$21.0

OutboundOutboundOutboundOutboundTranspTranspTranspTransportationortationortationortation

DC to DA $14.0$14.0$14.0$14.0 $14.0$14.0$14.0$14.0 $14.0$14.0$14.0$14.0

DADADADA

Handling andDelivery

$43.4 $43.4 $43.4

Total CostTotal CostTotal CostTotal Cost $1,210.6 $1,205.7 $1,201.1

Optimized ValueOptimized ValueOptimized ValueOptimized Value $113.7$113.7$113.7$113.7 $108.7$108.7$108.7$108.7 ($5.0)($5.0)($5.0)($5.0) $104.2$104.2$104.2$104.2 ($9.5)($9.5)($9.5)($9.5)

% Difference% Difference% Difference% Difference BaselineBaselineBaselineBaseline ----4.4%4.4%4.4%4.4% ----8.4%8.4%8.4%8.4%

TransportationTransportationTransportationTransportationAveragesAveragesAveragesAverages

Inbound Miles/Days Inbound Miles/Days Inbound Miles/Days Inbound Miles/Days 668.1 / 1.3 561.1 / 1.1 493.2 / 1.0493.2 / 1.0493.2 / 1.0493.2 / 1.0

Outbound Miles/Days Outbound Miles/Days Outbound Miles/Days Outbound Miles/Days 772.3 / 1.5 772.3 / 1.5 772.3 / 1.5772.3 / 1.5772.3 / 1.5772.3 / 1.5

Total Miles 1440.4 1333.4 1265.51265.51265.51265.5

Open SuppliersOpen SuppliersOpen SuppliersOpen Suppliers 107 107 55

Key Scenario FeaturesKey Scenario FeaturesKey Scenario FeaturesKey Scenario Features

• Current Suppliers

• No Closures • Closures Al Closures Al Closures Al Closures Allowed lowed lowed lowed

Supplier

• ProportionalVolume Increase

• Volume Increase Limited to 3xVolume Increase Limited to 3xVolume Increase Limited to 3xVolume Increase Limited to 3xHistorical Production Historical Production Historical Production Historical Production

IB Transport • Loads Adjusted for Projected Growth• New Rates Estimated for Adjusted Loads

DC • DC/SB in Columbus• Unlimited Capacity• Historic Handling Cost

OB Transport • Historic Rates from Columbus• Historic Brand Mix


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FFFFFFFFFFFFCCCCCCCCCCCCCCCCCCCCCCCC SSSSSSSSSSSScccccccccccceeeeeeeeeeeennnnnnnnnnnnaaaaaaaaaaaarrrrrrrrrrrriiiiiiiiiiiioooooooooooossssssssssss ––– 22000033 VVVOOOLLLUUUMMMEEE PPPRRROOOJJJEEECCCTTTIIIOOONNNSSS

Cost ComponentCost ComponentCost ComponentCost Component Alternative 7Alternative 7Alternative 7Alternative 7 Alternative 8Alternative 8Alternative 8Alternative 8 Alternative 9Alternative 9Alternative 9Alternative 9

Product AcquisitionProduct AcquisitionProduct AcquisitionProduct Acquisition Total Cost($ MM)

Changefrom

Baseline

Total Cost($ MM)

Changefrom

Baseline

Total Cost($ MM)

Changefrom

BaselineComponent $792.9 $792.9 $792.9


$260.6 $260.6 $260.6

Variable Rate Labor $49.1$49.1$49.1$49.1 ($0)($0)($0)($0) $49.1$49.1$49.1$49.1 $0.0$0.0$0.0$0.0 $49.1$49.1$49.1$49.1 $0.0$0.0$0.0$0.0

Constant Rate Labor $22.3$22.3$22.3$22.3 $0.1$0.1$0.1$0.1 $22.4$22.4$22.4$22.4 $0.1$0.1$0.1$0.1 $22.4$22.4$22.4$22.4 $0.1$0.1$0.1$0.1

InbInbInbInbound Transportationound Transportationound Transportationound Transportation

Supplier to DC $5.6$5.6$5.6$5.6 ($0.5)($0.5)($0.5)($0.5) $5.4$5.4$5.4$5.4 ($0.7)($0.7)($0.7)($0.7) $5.3$5.3$5.3$5.3 ($0.8)($0.8)($0.8)($0.8)

Assembler to DC $1.2$1.2$1.2$1.2 $0.1$0.1$0.1$0.1 $1.2$1.2$1.2$1.2 $0.1$0.1$0.1$0.1 $1.2$1.2$1.2$1.2 $0.1$0.1$0.1$0.1

Supplier to FCC $0.2$0.2$0.2$0.2 $0.2$0.2$0.2$0.2 $0.9$0.9$0.9$0.9 $0.9$0.9$0.9$0.9 $1.1$1.1$1.1$1.1 $1.1$1.1$1.1$1.1


DC & Cross Dock Center $20.0$20.0$20.0$20.0 ($0.9)($0.9)($0.9)($0.9) $19.3$19.3$19.3$19.3 ($1.7)($1.7)($1.7)($1.7) $19.2$19.2$19.2$19.2 ($1.8)($1.8)($1.8)($1.8)

FCCs $0.9$0.9$0.9$0.9 $0.9$0.9$0.9$0.9 $1.6$1.6$1.6$1.6 $1.6$1.6$1.6$1.6 $1.8$1.8$1.8$1.8 $1.8$1.8$1.8$1.8

Outbound TransportationOutbound TransportationOutbound TransportationOutbound Transportation

DC to DA $13.0$13.0$13.0$13.0 ($1.0)($1.0)($1.0)($1.0) $11.8$11.8$11.8$11.8 ($2.3)($2.3)($2.3)($2.3) $11.4$11.4$11.4$11.4 ($2.6)($2.6)($2.6)($2.6)FCC to DA $0.5$0.5$0.5$0.5 $0.5$0.5$0.5$0.5 $0.5$0.5$0.5$0.5 $0.5$0.5$0.5$0.5 $0.7$0.7$0.7$0.7 $0.7$0.7$0.7$0.7

DADADADA

Handling and Delivery $43.4 $43.4 $43.4

Total CostTotal CostTotal CostTotal Cost $1,209.9 $1,209.2

$1,209.5

Optimized ValueOptimized ValueOptimized ValueOptimized Value $113.0$113.0$113.0$113.0 ($0.7)($0.7)($0.7)($0.7) $112.3$112.3$112.3$112.3 ($1.4)($1.4)($1.4)($1.4) $112.6$112.6$112.6$112.6 ($1.2)($1.2)($1.2)($1.2)

% Difference% Difference% Difference% Difference ----0.6%0.6%0.6%0.6% ----1.2%1.2%1.2%1.2% ----1.0%1.0%1.0%1.0%

Transportation AveragesTransportation AveragesTransportation AveragesTransportation Averages

Inbound Miles / Days Inbound Miles / Days Inbound Miles / Days Inbound Miles / Days 647.0 / 1.3 647.0 / 1.3 648.6 / 1.3

Outbound Miles / Days Outbound Miles / Days Outbound Miles / Days Outbound Miles / Days 753.9 / 1.5 753.9 / 1.5 708.8 / 1.42

Total Miles 1400.9 1400.9 1357.4



Suppliers • Current Suppliers• Closures Allowed• Volume Increase Limited to 3x Baseline Production

IB transport • Loads Adjusted for Projected Growth• New Rates Estimated for Adjusted Loads• Supplier to DA Routes Rated from LTL Tables Supplier to DA Routes Rated from LTL Tables Supplier to DA Routes Rated from LTL Tables Supplier to DA Routes Rated from LTL Tables

• Same as above

• FCCs in NJ and LAFCCs in NJ and LAFCCs in NJ and LAFCCs in NJ and LA • FCCs in NJ, LA &FCCs in NJ, LA &FCCs in NJ, LA &FCCs in NJ, LA &DFW DFW DFW DFW

DC

• FCCs Handle Local SuppliersFCCs Handle Local SuppliersFCCs Handle Local SuppliersFCCs Handle Local Suppliersand Local DAs and Local DAs and Local DAs and Local DAs • FCCs Handle All Suppliers andFCCs Handle All Suppliers andFCCs Handle All Suppliers andFCCs Handle All Suppliers andLocal DAs Local DAs Local DAs Local DAs

OB transport • Same as above


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SSSSSSSSSSSSeeeeeeeeeeeelllllllllllleeeeeeeeeeeecccccccccccctttttttttttteeeeeeeeeeeedddddddddddd RRRRRRRRRRRRDDDDDDDDDDDDCCCCCCCCCCCC SSSSSSSSSSSScccccccccccceeeeeeeeeeeennnnnnnnnnnnaaaaaaaaaaaarrrrrrrrrrrriiiiiiiiiiiioooooooooooossssssssssss ––– 222000000333 VVVOOOLLLUUUMMMEEE PPPRRROOOJJJEEECCCTTTIIIOOONNNSSS Cost ComponentCost ComponentCost ComponentCost Component Alternative 5Alternative 5Alternative 5Alternative 5 Alternative 6Alternative 6Alternative 6Alternative 6

Product AcquisitionProduct AcquisitionProduct AcquisitionProduct Acquisition Total Cost($ millions)

Changefrom

Baseline

Total Cost($

millions)

Change fromBaseline

Component $792.9 $792.9


$260.6 $260.6

Variable Rate Labor $49.2$49.2$49.2$49.2 ($0.0)($0.0)($0.0)($0.0) $52.3$52.3$52.3$52.3 $3.1$3.1$3.1$3.1

Constant Rate Labor $22,3$22,3$22,3$22,3 ($0.0)($0.0)($0.0)($0.0) $12.9$12.9$12.9$12.9 ($9.4)($9.4)($9.4)($9.4)


Supplier to DC $4.7$4.7$4.7$4.7 ($1.5)($1.5)($1.5)($1.5) $4.4$4.4$4.4$4.4 ($1.7)($1.7)($1.7)($1.7)

Assembler to DC $1.1$1.1$1.1$1.1 ($0.1)($0.1)($0.1)($0.1) $0.8$0.8$0.8$0.8 ($0.4)($0.4)($0.4)($0.4)


DC & Cross Dock Center $26.$26.$26.$26.8888 $5.9$5.9$5.9$5.9 $26.1$26.1$26.1$26.1 $5.2$5.2$5.2$5.2

TransshipmentTransshipmentTransshipmentTransshipment

DC to DC $3.8$3.8$3.8$3.8 $3.8$3.8$3.8$3.8 $3.3$3.3$3.3$3.3 $3.3$3.3$3.3$3.3


DC to DA $6.7$6.7$6.7$6.7 ($7.3)($7.3)($7.3)($7.3) $6.7$6.7$6.7$6.7 ($7.3)($7.3)($7.3)($7.3)

DADADADA

Handling and Delivery $43.4 $43.4

Total CostTotal CostTotal CostTotal Cost $1,211.4 $1,203.4

Optimized ValueOptimized ValueOptimized ValueOptimized Value $114.5$114.5$114.5$114.5 $0.8$0.8$0.8$0.8 $106.5$106.5$106.5$106.5 ($7.2)($7.2)($7.2)($7.2)

% Difference% Difference% Difference% Difference 0.7%0.7%0.7%0.7% ----6.3%6.3%6.3%6.3%


Inbound Miles / Days Inbound Miles / Days Inbound Miles / Days Inbound Miles / Days 790.1 / 1.58 593.3 / 1.19593.3 / 1.19593.3 / 1.19593.3 / 1.19

Outbound Miles / Days Outbound Miles / Days Outbound Miles / Days Outbound Miles / Days 443.0 / 0.89 443.0 / 0.89443.0 / 0.89443.0 / 0.89443.0 / 0.89

Total Miles 1233.1 1036.31036.31036.31036.3Open SuppliersOpen SuppliersOpen SuppliersOpen Suppliers 107 73


Suppliers • Current Suppliers• Volum Volum Volum Volume Increase Limited to 3x Historicale Increase Limited to 3x Historicale Increase Limited to 3x Historicale Increase Limited to 3x Historical

Production Production Production Production• Suppliers Ship to Closest DC

IB transport • Loads Adjusted for Projected Growth• Supplier to RDC Based on Projected Loads• Rates Estimated for Adjusted Loads

DC • DC/SB in Columbus• New DCs in NJ & LANew DCs in NJ & LANew DCs in NJ & LANew DCs in NJ & LA• Unlimited Capacity

• Historic Handling CostTransshipments • Required DC to DC for All Products not SourcedRequired DC to DC for All Products not SourcedRequired DC to DC for All Products not SourcedRequired DC to DC for All Products not Sourced

Locally Locally Locally Locally OB transport • DAs Served from Closest DC DAs Served from Closest DC DAs Served from Closest DC DAs Served from Closest DC ....

• RDC to DA Routes Rated from LTL Tables


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SSSSSSSSSSSSeeeeeeeeeeeelllllllllllleeeeeeeeeeeecccccccccccctttttttttttteeeeeeeeeeeedddddddddddd RRRRRRRRRRRRDDDDDDDDDDDDCCCCCCCCCCCC SSSSSSSSSSSScccccccccccceeeeeeeeeeeennnnnnnnnnnnaaaaaaaaaaaarrrrrrrrrrrriiiiiiiiiiiioooooooooooossssssssssss ––– 222000000333 VVVOOOLLLUUUMMMEEE PPPRRROOOJJJEEECCCTTTIIIOOONNNSSS ((CCCOOONNNTTTIIINNNUUUEEEDDD)) Cost ComponentCost ComponentCost ComponentCost Component Alternative 12Alternative 12Alternative 12Alternative 12 Alternative 13Alternative 13Alternative 13Alternative 13 Alternative 10Alternative 10Alternative 10Alternative 10

Product AcquisitionProduct AcquisitionProduct AcquisitionProduct Acquisition TotalCost($

millions)

Changefrom

Baseline

Total Cost($

millions)

Change fromBaseline

Total Cost($

millions)

Changefrom

Baseline

Component $792.9 $792.9 $792.9

Gift Set Acquisition $260.6 $260.6 $260.6

Variable Rate Labor $49.2$49.2$49.2$49.2 ($0.0)($0.0)($0.0)($0.0) $52.9$52.9$52.9$52.9 $3.7$3.7$3.7$3.7 $53.7$53.7$53.7$53.7 $4.5$4.5$4.5$4.5

Constant Rate Labor $22.4$22.4$22.4$22.4 $0.1$0.1$0.1$0.1 $11.4$11.4$11.4$11.4 ($10.8)($10.8)($10.8)($10.8) $10.9$10.9$10.9$10.9 ($11.4)($11.4)($11.4)($11.4)


Supplier to DC $8.0$8.0$8.0$8.0 $1.9$1.9$1.9$1.9 $5.6$5.6$5.6$5.6 ($0.5)($0.5)($0.5)($0.5) $5.2$5.2$5.2$5.2 ($0.9)($0.9)($0.9)($0.9)

Assembler to DC $0.7$0.7$0.7$0.7 ($0.5)($0.5)($0.5)($0.5) $0.8$0.8$0.8$0.8 ($0.4)($0.4)($0.4)($0.4) $0.6$0.6$0.6$0.6 ($0.6)($0.6)($0.6)($0.6)


DC and SB $21.2$21.2$21.2$21.2 $0.2$0.2$0.2$0.2 $21.1$21.1$21.1$21.1 $0.2$0.2$0.2$0.2 $20.7$20.7$20.7$20.7 ($0.3)($0.3)($0.3)($0.3)

TransshipmentTransshipmentTransshipmentTransshipment

DC to DC $0.5$0.5$0.5$0.5 $0.5$0.5$0.5$0.5 $0.5$0.5$0.5$0.5 $0.5$0.5$0.5$0.5 $0.2$0.2$0.2$0.2 $0.2$0.2$0.2$0.2


DC to DA $6.7$6.7$6.7$6.7 (((($7.3)$7.3)$7.3)$7.3) $6.7$6.7$6.7$6.7 ($7.3)($7.3)($7.3)($7.3) $6.7$6.7$6.7$6.7 ($7.3)($7.3)($7.3)($7.3)

Handling and Delivery $43.4 $43.4 $43.4

Total CostTotal CostTotal CostTotal Cost $1,205.6

$1,196.0 $1,195.0

Optimized ValueOptimized ValueOptimized ValueOptimized Value $108.7$108.7$108.7$108.7 ($5.1)($5.1)($5.1)($5.1) $99.1$99.1$99.1$99.1 ($14.6)($14.6)($14.6)($14.6) $98.1$98.1$98.1$98.1 ($15.6)($15.6)($15.6)($15.6)

% Difference% Difference% Difference% Difference ----4.5%4.5%4.5%4.5% ----12.9%12.9%12.9%12.9% ----13.7%13.7%13.7%13.7%


Inbound Miles /Inbound Miles /Inbound Miles /Inbound Miles / Days Days Days Days 1521.9 / 3.0 593.3 / 1.2 486.4 / 1.0486.4 / 1.0486.4 / 1.0486.4 / 1.0

Outbound Miles /Outbound Miles /Outbound Miles /Outbound Miles /

Days Days Days Days

443.0 / 0.9 443.0 / 0.9 443.0 / 0.9443.0 / 0.9443.0 / 0.9443.0 / 0.9

Total Miles 1964.9 1036.3 929.4929.4929.4929.4



• Suppliers Ship to All DCs Suppliers Ship to All DCs Suppliers Ship to All DCs Suppliers Ship to All DCs

• Current Suppliers • Suppliers Ad Suppliers Ad Suppliers Ad Suppliers Added ded ded ded

Suppliers

• Proportional Volume Increase• Current Product Mix• No Closures

• Volume Increase Limited to 3xVolume Increase Limited to 3xVolume Increase Limited to 3xVolume Increase Limited to 3xHistorical Production Historical Production Historical Production Historical Production

• Closures Allowed Closures Allowed Closures Allowed Closures Allowed

IB transport • Loads Adjusted for Projected Growth• Supplier to RDC Based on Projected Loads

• Rates Estimated for Adjusted LoadsDC • DC/SB in Columbus

• New DCs in NJ & LA• Unlimited Capacity• Historic Handling Cost

Transshipments • Optional DC to DC for All Products not Sourced Locally Optional DC to DC for All Products not Sourced Locally Optional DC to DC for All Products not Sourced Locally Optional DC to DC for All Products not Sourced Locally

OB transport • DAs Served from Closest DC.• RDC to DA Routes Rated from LTL Tables


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The point most observable in the result tables is that the cost that was alterable in

the model for optimization was only $113.6 million in a $1.2 billion supply chain.

Even in such a situation this is a clear demonstration of the capabilities of

operations research techniques and their help in designing an improved supply

chain. In the best case it has helped improve costs by a reduction of $15.6

million. This represents a 13.7% improvement over the current operational

standards. The recommended best case by the model also happens to be the best

situation in terms of service to the customer, as the response time form a regional

distribution center will be much lower than the response from a central

distribution center.

Recommendations

The exercise done so far was aimed at designing the best supply chain possible

given the operational constraints of the real world situation of the retail company.

The improvement of 13.7% is significant and needs to further worked on before

realizing it as real savings. It must be acknowledged that the model in spite of the

best efforts will still assume some ideal conditions and this could dampen the

resulting savings slightly.

The key output from the modeling exercise is that there are savings available in

considerable amounts if the retail chain is willing to go regional. The main

emphasis here is that not only should distribution be made regional, but

manufacturing or in our case sourcing must go hand in hand with the movement

in distribution. Some of our alternatives which were evaluated prove that the

savings start immediately with movement of production of some of the high

volume west coast production to be spread evenly with source availability on

both coasts.


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Key Recommendations

• Move the production of items that are sourced solely on the west coast to

be evenly available on both coasts.

o The high volume items of some categories are produced only in

the west coast manufacturing facilities. This results in high

transportation cost to the east coast markets which form about

80% of the retailer’s revenue source. The movement of sourcing

will result in better service levels and lower costs.

• Move the distribution capabilities to the east west coast gradually in a

worked plan as evaluated in the exercise.

o Key distribution facilities should be located in New Jersey,

Columbus and Los Angeles.

o These centers should be capable of handling all products as is the

center in Columbus today.

o After moving distribution capabilities, a complex product

assembler should be commissioned in the west coast area to serve

those markets locally.

• Consolidation of transportation volumes will yield incremental benefit

over current volumes and costs.


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C h a p t e r 6

FUTURE WORK REQUIREMENTS AND RECOMMENDATIONS

The exercise of modeling has produced some excellent insights into the working

of the specialty retail sector. The case we have analyzed has a long way to go

before realizing the huge margin of benefit related in the study. Some of the key

future requirements will lie in further evaluating the alternatives in a more

product wise specific sense. As specified earlier the model broadly reduced the

products from over 6000 to a mere 30 groups. The first step to look from here is

to analyze the groups leading to the ones the model has suggested to be moved.

These products can be further studied in sub-models and the plan gradually

implemented over the next few years.

After further studies for the solidity of the present recommendations and growth

areas have been made, a structured implementation plan has to be built. This plan

will look into the specifics of where to have facility locations, movement of

manufacturing and inventory requirements and service level maintenance during

the transition time and other important factors.


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BIBLIOGRAPHY

1. The United States Council of Logistics Management, Oakbrook, IL,defines logistics:

“Logistics is the process of planning, implementing, and controlling the efficient,effective flow and storage of raw materials, in-process inventory, finished goods,services, and related information from the point of origin to the point ofconsumption (including inbound, outbound, internal and external movements) forthe purpose of conforming to customer requirements.”

2. Fortune, November 28, 1994.3. Ratliff, Donald H. and Nulty, William G. Logistics Composite Modeling .

CAPS Logistics Technical White Paper Series, 1996.

4. Anupindi, R. and S. Tayur. Managing Multi-Product systems: Models, Measure,and Analysis. J.L. Kellogg Graduate School of Management, NorthwesternUniversity, Evanston, IL 60208, 1995

5. Swaminathan J. and S. Tayur. Managing Broader Product Lines though DelayedDifferentiation using Vanilla Boxes. Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, PA 15213., 1996.

6. Tayur, Ganesan, And Magazine.7. Quantitative Models for Supply Chain Management. Kluwer Academic

Publishers, Boston., 1999.8. Fransesco, Luna and Stefansson, Benedikt. Economic simulations in Swarm :

agent-based modeling and object oriented programming. Kluwer Academic

Publishers., Boston 2000.9. Dobson, G. The Economic Lot Scheduling Problem : Achieving Feasibility UsingTime-Varying Lot Sizes. Operations Research, 35(5), 764-771. 1987.

10. Glasserman, P. and S. Tayur The Stability of a Capacitated, Multi-EchelonProduction-Inventory System under a Base-Stock Policy. Operations Research,42(5). 1994.

11. L'Ecuyer, P., N. Giroux, and P. W. Glynn Optimization by Simulation: Experiments with a Simple Queue in Steady State. Management Science, 38.1992.

12. Ross, Sheldon M. Introduction to Probability Models . Academic Press, SanDiego, CA. 1999.

13. CAPS Logistics. Supply Chain Designer, User Training Manual. CAPSLogistics, Atlanta GA, 1999.


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APPENDICES


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A p p e n d i x 1

OUTPUT GRAPHICS FROM THE MODELING EXERCISE

Baseline Output

The output of the optimized solution from alternative 0 or the baseline, where

the central distribution center was operational is displayed here. The suppliers and

delivery agents have been colored and sized by their supply and demand volumes

respectively for all the graphic output screens of the model.


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Alternative 2: Optimized Central Distribution. The distribution from the west

coast has been clearly reduced in this scenario output.


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Alternative 3: Regional Distribution Scenario. The distribution centers act as

regional hubs. New Jersey being the core production area, intensively supplies to

the other 2 regions.


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Alternative 7: Flow Control Center Concept – Here we see movement is

heightened in all regions. This will lead to increased costs.


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Regional Flow Center with Dallas Alternative Option – Here the Dallas center

did not make a large impact on the supply chain.


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Alternative 11: The strategic Sourcing Scenario. The flexibility is heightened and

the costs have been reduced in this scenario. It is also evident that a large number

of manufacturing facilities have been eliminated in favor of a strategic few, that

enable growth of the retailer.


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A p p e n d i x 2

SAMPLE OF OPTIMIZATION CODE

Included here is the mathematical optimization problem for the strategic sourcing

alternative. This formulation has over ten thousand variables and over 25,000

constraints for the various flows, costs, and capacities associated with the model.

Minimizeobj: 0.035251 F0.0 + 0.018883 F0.1 + 0.025898 F0.2 + 0.021558 F0.3+ 0.030911 F0.4 + 0.015287 F0.5 + 0.007511 F0.6 + 0.013427 F0.7+ 0.019875 F0.8 + 0.032187 F0.9 + 0.018777 F0.10 + 0.032134 F0.11+ 0.018263 F0.12 + 0.02953 F0.13 + 0.018263 F0.14 + 0.028467 F0.15+ 0.02953 F0.16 + 0.019627 F0.17 + 0.018919 F0.18 + 0.021558 F0.19+ 0.019131 F0.20 + 0.010505 F0.21 + 0.025473 F0.22 + 0.017431 F0.23+ 0.022001 F0.24 + 0.021363 F0.25 + 0.024517 F0.26 + 0.011266 F0.27+ 0.007033 F0.28 + 0.031762 F0.29 + 0.02604 F0.30 + 0.03309 F0.31+ 0.028857 F0.32 + 0.010505 F0.33 + 0.023595 F0.34 + 0.018653 F0.35+ 0.028183 F0.36 + 0.030097 F0.37 + 0.019716 F0.38 + 0.020247 F0.39+ 0.019415 F0.40 + 0.013729 F0.41 + 0.013729 F0.42 + 0.0186 F0.43

+ 0.022571 F0.44 + 0.084372 F0.45 + 0.01275 F0.46 + 0.059748 F0.47+ 0.0804 F0.48 + 0.070755 F0.49 + 0.05648 F0.62 + 0.095566 F0.66+ 0.034041 F0.68 + 0.067782 F0.69 + 0.068235 F0.70 + 0.026886 F0.75+ 0.090262 F0.76 + 0.125363 F0.77 + 0.154128 F0.80 + 0.015247 F0.81+ 0.080099 F0.86 + 0.304571 F0.87 + 0.03279 F0.88 + 0.030436 F0.102+ 0.066607 F0.103 + 0.068426 F0.111 + 0.040802 F0.115 + 0.023244 F0.116+ 0.127652 F0.117 + 0.088463 F0.124 + 0.018539 F0.126 + 0.020451 F0.127+ 0.042682 F0.130 + 0.034041 F0.131 + 0.010889 F0.133 + 0.102202 F0.136+ 0.013388 F0.138 + 0.086302 F0.139 + 0.02803 F0.140 + 0.06661 F0.142+ 0.099869 F0.148 + 0.017004 F0.158 + 0.012506 F0.159 + 0.098897 F0.162+ 0.067617 F0.163 + 0.0303 F0.165 + 0.045688 F0.169 + 0.032641 F0.173

+ 0.040522 F0.175 + 0.090685 F0.176 +

designing an optimal supply chain for a fast growing specialty retail chain

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