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Supply Chain Management in a Dairy Industry - A Case Study

July 2009

Authors:

Government Polytechnic, Amadalavalasa Srikakulum dist

Supply chain management is the plan and control of material and information flow among suppliers, facilities, warehouses and customers with the objectives of minimization of cost, maximization of customer services and flexibility. The supply chain of a business process comprises mainly five activities viz., Purchase of materials from suppliers, transportation of materials from suppliers to facilities, production of goods at facilities, transportation of goods from facilitates to ware houses and transportation of goods from ware houses to customers. In this paper, a supply chain model is developed for a dairy industry, located in Andhra Pradesh, India. The supply chain includes four echelons namely raw milk suppliers, plant, warehouse and customers. In this model, emphasis is mainly on production and distribution activities, with a view to find out purchase plan of raw milk, production plan of product mix and transportation plan of the products.

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Supply Chain Management in a Dairy

Industry – A Case Study

K. Venkata Subbaiah , Member, IAENG, K. Narayana Rao K. Nookesh babu

ABSTRACT - Supply chain management is the plan and

control of material and information flow among

suppliers, facilities, warehouses and customers with the

objectives of minimization of cost, maximization of

customer services and flexibility. The supply chain of a

business process comprises mainly five activities viz.,

Purchase of materials from suppliers, transportation of

materials from suppliers to facilities, production of

goods at facilities, transportation of goods from

facilitates to ware houses and transportation of goods

from ware houses to customers.

In this paper, a supply chain model is developed for a

dairy industry, located in Andhra Pradesh, India. The

supply chain includes four echelons namely raw milk

suppliers, plant, warehouse and customers. In this

model, emphasis is mainly on production and

distribution activities, with a view to find out purchase

plan of raw milk, production plan of product mix and

transportation plan of the products.

Index Terms- Supply chain management,

Transportation, Production plan, Customer zones.

I. INTRODUCTION

Supply chain management (SCM) is a

rapidly evolving area of interest to academicians and

business management practitioners alike.

Coordinating the external and internal activities of a

firm is the basic philosophy of supply chain

management. It is about managing the entire process

in a collective and unified fashion.

Most of the manufacturing firms are

organized as networks of manufacturing and

distribution facilities that procure raw materials

transform them into intermediate and finished

K. Venkata Subbaiah is with Department of Mechanical

Engineering, Andhra University, Visakhapatnam, India.

Phone: +91-891-2536486 (R), +91-09848063452 (M)

e-mail: drkvsau@yahoo.co.in

K. Narayana Rao is with Department of Mechanical

Engineering, Government Polytechnic, Visakhapatnam.

K.Nookesh babu is with Department of Mechanical

Engineering, Andhra University, Visakhapatnam.

products and distribute the finished products to

customers. The simplest network consists of facilities

which perform procurement, manufacturing and

distribution. These networks are called value added

chains or supply chains.

A supply chain consists of all stages involved

directly or indirectly in fulfilling a customer request.

The supply chain not only includes the manufactures

and suppliers but also retailers and customers

themselves with in each organization.

A supply chain is an integrated system

wherein a number of various business entities (i.e.

suppliers, manufacturers, industrial customers,

distributors, retailers) work together to address issues

of both materials flow and information flow. A

reference model - the Supply Chain Operations

Reference model (SCOR), has been developed by the

Supply-Chain Council (SCC) [1]. This process

reference model contains standard description of

management process and a framework of relationships

among the standard processes. Ganeshan et.al. [5]

explored the basics of supply chain management from

a conceptual perspective by tracing the roots of the

definition and the origins of the concept from a broad

stream of literature. Pyke and Cohen [6] analyzed the

management of materials in an integrated supply chain

and develop a markov chain model for a three level

production distribution system. Cohen and

Huchzermeier [3] presented a survey of the literature

pertaining to analytic approaches for global supply

chain strategy analysis and planning. The integrated

supply chain network model is developed to capture

the complexities of a multi-product, multi-echelon,

multi-country, multiperiod planning problem for the

optimal choice of facility locations, capacity and

technology used. Sabri and Beamon [4] developed an

integrated supply chain model for use in simultaneous

strategic and operation supply chain planning. .Lee and

Kim [7] proposed a hybrid approach to solve

production and distribution problems in supply chains.

Thomas and Griffin [8] define the categories of

operational co ordination, buyer and vendor,

production and distribution, inventory and distribution.

Arntzen et.al [9] provide the most comprehensive

Proceedings of the World Congress on Engineering 2009 Vol I

WCE 2009, July 1 - 3, 2009, London, U.K.

ISBN: 978-988-17012-5-1

WCE 2009

deterministic model for supply chain management

with an objective function containing the cost and

time elements. Even though supply chain

management is relatively new, the idea of co-ordinate

planning is not new. The study of multi-echelon

inventory/distribution systems began as early as 1960

by Clark and Scarf [2]. Since then many researchers

have investigated multi echelon inventory and

distribution systems. Less research has been aimed at

co-ordination of procurements, production and

distribution systems. In this paper an attempt has

been made to develop a coordinated supply chain-

planning model with procurement, production and

distribution systems.

II. MODEL FORMULATION

The proposal of the model is to find an optimal

strategic plan for an integrated supply chain model.

Notations

MCtvp Cost of material p purchased by

vendor v at time t

PCtfg Production cost of goods g produced

by facility f at time t

VFTCtvp Transportation cost of material p

Transported from vendor v to

facility f at time t

FWTCtfwg Transportation cost of goods g

transported from facility f to ware

house w at time t

WZTCtwcg Transportation cost of goods g

transported from ware house w to

customer location c at time t

FMICtfp Inventory cost of mterial p

of facility f at time t

FGICtfg Inventory cost of goods g

of facility f at time t

WICtwg Inventory cost of goods g of

warehouse w at time t

BOMgp Amount of material p needed for

producing goods g

AVtvp The amount of material p

purchased by vendor v at time t

Rtvfp The amount of material p which

vendor v Transported of facility f at

time t.

AFtfp The inventory of material p in the

facility f at time t

Rtfg Amount of good g which facility f

produced at time t

AFtfg The inventory of goods g at facility

f at time t.

Rtfwg The amount of goods g which

facility f transported to ware house

w at time t.

AWtwg The inventory of good g in the

ware house w at time t.

Rtwcg The amount of good g which ware

house w transported to

customer c at time t.

ACtcg The demand of goods g by

customer c at time t.

Assumptions

1. Capacities of vendors are fixed.

2. Demand is deterministic.

3. Variable cost per unit production is constant

Mathematical model

This model consists of four echelons namely

Suppliers, Plants, Distribution Centers (DCs), and

Customer zones (CZs).

A multi-objective function is formulated to

minimize cost subject to supplies, plant and

distribution capacities, production and distribution

through put limits and customs demand requirements.

Total cost includes fixed costs of production and

distribution, variable costs of production, distribution

and transportation.

Various costs involved in the supply chain are

1. Material Cost = tvp

tvp

vpMC *

∑

2. Production cost = tfg

tfg

fgPC *

∑

3. Transportation cost =

** *VFTC R FWTC R WCTC R

twcg twcg

tvfp tvfp tfwg tfwg

vfp fwg wcg

∑∑ ∑

4. Inventory cost =

***

tfp tfg twg

FMIC AF FGIC AF WIC AWtwg

tfp tfg

ft fg wg

∑∑ ∑

The objective function of the model is to minimize the

total cost associated with the supply chain which

includes material, production, transportation and

inventory costs.

Minimize Z=

** *

***

tfp

tfg twg

MC AV PC R VFTC R

tvp tvp tfg tfg tvfp tvfp

vsp fg vfp

WTC R WCTC R FMIC AF

twcg twcg

tfwg tfwg tfp

fwg wcg ft

FGIC AF WIC AWtwg

tfg

fg wg

∑∑∑

++ +

∑∑

Proceedings of the World Congress on Engineering 2009 Vol I

WCE 2009, July 1 - 3, 2009, London, U.K.

ISBN: 978-988-17012-5-1

WCE 2009

The above stated problem is solved subjected to the

following constraint set.

1. Upper – Lower bound restrictions

0 ≤ Rtvfp ≤ Rtvfp _ UPbound

0 ≤ Rtfwg ≤ Rtfwg _ UPbound

0 ≤ Rtwcg ≤ Rtwcg _ UPbound

Rtfg _ LPbound ≤ Rtfg ≤ Rtfg _ UPbound

etvf =1; ettw =1; etvc=1;

2. Flow Conservative restrictions

tvfp tvp

LV t v p=∀

∑

() *

tfp t et vfp gp tfg

FR BOMR

−

+−

∑∑

(1) ,,

tfp

Ftfp

=∀

() (1)

fw fwg twcg

twg t et t wg

AW R R AW

twg

−+

+−=

∀

∑∑

() ,,

tet wcg tcg

AC t c g

−=∀

∑

III. CASE STUDY

The above developed model is applied to Visakha

Dairy situated in Andhra Pradesh, India. The above

dairy has six vendors located at Vsiahapatnam,

Vizianagaram, Tuni, Ramabadrapuram, Narsipatnam

and Srikakulam. It has two facilities located at

Visakhapatnam and kakinada to meet the customer

demands. Five warehouses are situated at

Visakapatnam, Vizianagaram, Srikakulam Kakinada

and Rajahmundry. Its customer locations are situated

at Visakhapatnam, Vizianagaram, Srikakulam,

Kakinada and Rajamundry.

The input data required for the design of supply chain

for the above stated industry is given below.

Input Data

Material Cost

1 2 3 4 5 6

1 1140 1160 991 1135 872 1056

Vendor to facility transportation cost

1 2 3 4 5 6

1 3.26 26.33 36.24 28.56 84.8 33.35

2 32.98 6.02 66.24 55.56 103.8 3.26

Facility to ware house transportation cost

For facility1

1 2 3 4 5 6

1 3.26 3.26 3.26 3.26 3.26 3.26

2 11.9 11.9 11.9 11.9 11.9 11.9

3 13.6 13.6 13.6 13.6 13.6 13.6

4 8.9 8.9 8.9 8.9 8.9 8.9

5 7.56 7.56 7.56 7.56 7.56 7.56

For facility 2

1 2 3 4 5 6

1 10.9 10.9 10.9 10.9 10.9 10.9

2 4.52 4.52 4.52 4.52 4.52 4.52

3 3.26 3.26 3.26 3.26 3.26 3.26

4 19.8 19.8 19.8 19.8 19.8 19.8

5 18.4 18.4 18.4 18.4 18.4 18.4

Ware House to customer transportation cost

1 2 3 4 5

1 0 11.9 13.16 8.9 7.56

2 13.26 0 1.26 3 4.1

3 9.96 1.3 0 4.26 5.56

4 6.74 3 4.26 0 1.24

5 4.3 4.34 5.36 1.24 0

In the above table the transportation cost for good 1 is

shown and the same table repeats for the remaining

goods.

Inventory carrying cost at the facility for the raw

material and goods are considered as Zeros.

Inventory const at warehouse

1 2 3 4 5 6

1 0.07 0.05 0.07 0.05 0.07 0.05

2 0.07 0.05 0.07 0.05 0.07 0.05

3 0.07 0.05 0.07 0.05 0.07 0.05

4 0.07 0.05 0.07 0.05 0.07 0.05

5 0.07 0.05 0.07 0.05 0.07 0.05

Proceedings of the World Congress on Engineering 2009 Vol I

WCE 2009, July 1 - 3, 2009, London, U.K.

ISBN: 978-988-17012-5-1

WCE 2009

Capacities of Vendors (in thousands)

1 2 3 4 5 6

1 178 43 325 45 22 20

Capacities of facilities for producing different goods

1 2 3 4 5 6

1 25000 11000 7500 4000 4000 32000

2 4000 15000 0 150 4000 30000

Capacities of ware house to hold different products

1 2 3 4 5 6

1 25000 100000 7000 3000 4000 25000

2 1500 5000 0 0 1000 20000

3 1500 4000 0 500 2000 8000

4 1000 3000 0 0 1000 6000

5 0 500 500 500 300 5000

Demands for different goods at different customer

locations

1 2 3 4 5 6

1 23760 10903 7000 3199 3624 23726

2 1072 4624 0 0 304 16684

3 1254 3564 0 106 2076 7352

4 962 3234 0 0 806 6210

5 0 195 0 158 193 4659

The Problem is solved using LINGO student version

package.

IV. RESULTS AND DISCUSSIONS

The optimal solution for the model is

Table I: Quantities of material to be procured version

different vendors.

1 2 3 4 5 6

1 106565 43000 32500 0 22000 29000

Table II: Quantities of goods transported from vendors

to facilities

1 2 3 4 5 6

1 106565 9850 32500 0 22000 0

2 0 33150 0 0 0 29000

Table III: Amounts of goods produced at both the

facilities

1 2 3 4 5 6

1 23048 105920 7000 3313 2003 28631

2 4000 1500 0 150 4000 3000

Table IV: Amounts of goods transported from facilities

to warehouse

From Facility F1

1 2 3 4 5 6

1 23048 105920 7000 3199 3003 23726

2 0 0 0 0 0 0

3 0 0 0 0 0 0

4 0 0 0 0 0 0

5 0 0 0 114 0 4905

From Facility F2

1 2 3 4 5 6

1 712 3383 0 0 621 0

2 1072 4819 0 44 497 16684

3 2216 6798 0 106 2882 13316

4 0 0 0 0 0 0

5 0 0 0 0 0 0

Table V: Amounts of goods to be transported from

ware houses to customer Zones.

From ware house 1

1 2 3 4 5 6

1 23760 109303 7000 3199 3624 23726

2 0 0 0 0 0 0

3 0 0 0 0 0 0

4 0 0 0 0 0 0

5 0 0 0 0 0 0

Proceedings of the World Congress on Engineering 2009 Vol I

WCE 2009, July 1 - 3, 2009, London, U.K.

ISBN: 978-988-17012-5-1

WCE 2009

From Ware House 2

1 2 3 4 5 6

1 0 0 0 0 0 0

2 1072 4624 0 0 304 16684

3 0 0 0 0 0 0

4 0 0 0 0 0 0

5 0 195 0 44 193 0

From ware house 3

1 2 3 4 5 6

1 0 0 0 0 0 0

2 0 0 0 0 0 0

3 1254 3564 0 106 2076 7352

4 962 3234 0 0 806 5964

5 0 0 0 0 0 0

From ware house 5

1 2 3 4 5 6

1 0 0 0 0 0 0

2 0 0 0 0 0 0

3 0 0 0 0 0 0

4 0 0 0 0 0 246

5 0 0 0 114 0 4659

Table I represents the procurement plan which

indicates quantities of raw materials to be procured

from different vendors. As both the material cost and

transportation costs to both the facilities is high from

vendor 4 (i.e., Ramabadrapuram) and the demand for

the raw material can be fulfilled by the remaining

vendors, raw material should not be procured from

the vendor 4. Table III represents the production plan

for the optimal product mix. It gives us the quantities

of material to be produced by both the facilities

considering the demands of the customers and their

transportation cost.

Table II, IV and V represent the transportation plans

for the plant. Table II shows the quantities of material

to be transported from different vendors to both the

facilities. Table IV shows the quantities of different

goods to the shipped to the warehouses from both the

facilities. Table V shows the quantities of different

goods to be transported from different warehouse to

all the customer locations. The Transportation cost to

ware house 4 is very high from both the facilities and

it is also very far away from all the customer zones,

so the warehouse 4 is discarded from the plan. From

the above obtained plans the total cost of the supply

chain is calculated as Rs.27, 41,039/- per one time

period (i.e.12 hours). The obtained value is

Rs.2,02,539/- less than the existing cost.

V. CONCLUSIONS

In this paper supply chain network is

designed for a dairy industry. This network includes

material purchase plan, production plan, inventory

plan and transportation plan. From the results it is

observed that the total cost of the supply chain is 9.8

percent lesser than the existing cost. This model can be

extended to varying demand and costs. This can also

be applied to fast moving consumer goods.

REFERENCES

[1.] Supply-Chain Council, Inc., 1998, Overview

of the SCOR Model V2.0, www.supply-

chain.org.

[2.] Clark, A. J., and Scarf, H., 1960, Optimal

Policies for a MultiiEchelon Inventory

Problem, Management Science, Vol. 6,475-

490.

[3.] Cohen, M. A., and Huchzermeier, A., 1998,

Global Supply Chain Management: A survey

of Research and Applications, Quantitative

Models for Supply Chain Management,

Kulwer academic publishers .

[4.] Ehap H. Sabri and Benita M. Beamon 2000,

A Multi-Objective Approach to Simultaneous

Strategic and Operational Planning in Supply

Chain Design, Omega Vol. 28, NO.5, 581-

598.

[5.] Ganeshan, R , Stephens, P., Jack, E . , and

Magazine, M., 1999, A taxonomic review of

supply chain management research,

Quantitative models for supply chain

management. The Netherlands: Kluwer

academic publishers, 839 - 879.

[6.] Pyke , D.F . , and Cohen, M . A . , 1994,

Multi product integrated production

distribution system , European journal of

operations research. Vol 74 , No I , 18 - 49.

[7.] T. H. Lee and S.H . Kim. , 2000,optimal

production distribution planning in supply

chain management using a hybrid simulation

Analytic approach.

[8.] Thomas D. 1. and P. M. Griffin. , 1996, Co

coordinated supply chain management.

European journal of operation research, 94: 1-

15.

[9.] Arntzen, B. C., G. C. Brown, T. P. Harrison

and L. Troflan, 1995, Global supply chain

management at digital equipment corporation.

Interfaces: 25, 69-93.

Proceedings of the World Congress on Engineering 2009 Vol I

WCE 2009, July 1 - 3, 2009, London, U.K.

ISBN: 978-988-17012-5-1

WCE 2009

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This study investigated the impact of disaster risks on the performance of Zimbabwe’s dairy supply chains. The study was initiated on the premise that Zimbabwe is at high risk and is highly vulnerable to disasters such as drought, cyclones, floods, animal diseases, and crop pests. This study employed a mixed method approach that involved the use of structured questionnaires, semi-structured interviews and observations. A combined total of 92 dairy farmers and milk processors were sampled from a target population of 122 from major milk producing regions in the country, with a response rate of 85%. Key informants, comprising 18 dairy authorities and 30 retailers were purposively sampled. Chipinge, Mutare, Harare, Gweru, and Bulawayo, were the regions under investigation, covering the greater part of the country. The findings computed through Ordinary Least Square regression analysis, indicated that an overall index of disaster risks impacted negatively on dairy supply chain performance. Major indicators of the disaster impacts were job losses, food insecurity, reduced milk productivity, and the general retarded growth in dairy businesses. The results were also corroborated by outcome of interviews with key informants. This study recommends that both the private sector and the government should invest in disaster reduction strategies, to enhance the effective and sustainable performance of dairy supply chains in Zimbabwe. The findings from the study contribute to literature on disaster risks, which has largely been under-explored in developing countries, will also assist practicing managers, particularly in dairy sector, in dealing with disaster risk issues. Similarly, the study findings will assist policy makers to consider enacting policies, and improve existing ones, to assist communities faced with disaster risks.

Organizational Performance through Dairy Supply Chain Management Practices: A Winning Approach

Chapter

Full-text available

Jan 2016

Rajeev Kumar

A Taxonomic Review of Supply Chain Management Research

Article

Full-text available

Jan 1999

In the previous chapters, we focused largely on quantitative approaches to solving Supply Chain Management (SCM) problems including such issues as: inventory management, supply contracts, information flow, product variety, and international operations. In this chapter, we will broaden our focus to include other approaches to

Global Supply Chain Management at Digital Equipment Corporation

Article

Full-text available

Feb 1995
INTERFACES

Digital Equipment Corporation evaluates global supply chain alternatives and determines worldwide manufacturing and distribution strategy, using the Global Supply Chain Model (GSCM) which recommends a production, distribution, and vendor network. GSCM minimizes cost or weighted cumulative production and distribution times or both subject to meeting estimated demand and restrictions on local content, offset trade, and joint capacity for multiple products, echelons, and time periods. Cost factors include fixed and variable production charges, inventory charges, distribution expenses via multiple modes, taxes, duties, and duty drawback. GSCM is a large mixed-integer linear program that incorporates a global, multi-product bill of materials for supply chains with arbitrary echelon structure and a comprehensive model of integrated global manufacturing and distribution decisions. The supply chain restructuring has saved over $100 million (US).

Optimal Policies for a Multi-Echelon Inventory Problem

Article

Full-text available

Dec 2004

In the last several years there have been a number of papers discussing optimal policies for the inventory problem. Almost without exception these papers are devoted to the determination of optimal purchasing quantities at a single installation faced with some pattern of demand. It has been customary to make the assumption that when the installation in question requests a shipment of stock, this shipment will be delivered in a fixed or perhaps random length of time, but at any rate with a time lag which is independent of the size of the order placed. There are, however, a number of situations met in practice in which this assumption is not a tenable one. An important example arises when there are several installations, say 1, 2, …, N, with installation 1 receiving stock from 2, with 2 receiving stock from 3, etc. In this example, if an order is placed by installation 1 for stock from installation 2, the length of time for delivery of this stock is determined not only by the natural lead time between these two sites, but also by the availability of stock at the second installation. In this paper we shall consider the problem of determining optimal purchasing quantities in a multi-installation model of this type.

Optimal Production-Distribution Planning In Supply Chain Management Using A Hybrid Simulation-Analytic Approach

Article

Full-text available

Jan 2001

Production-distribution planning is the most important activity in supply chain management (SCM). To solve this planning problem, either analytic or simulation approaches have been used. However these two approaches have their own demerits in problem solving. In this paper, we propose a hybrid approach which is a specific problem solving procedure combining analytic and simulation methods to solve production-distribution problems in supply chains. The machine capacity and distribution capacity constraints in the analytic model are considered as stochastic factors and are adjusted by the proposed specific process according to the results from an independently developed simulation model which includes general production-distribution characteristics.

Global Supply Chain Management: A Survey of Research and Applications

Article

Jan 1998

Today’s global economic environment is in a state of transition. The principal changes include: 1) worldwide reduction of trade barriers and development of regional, multi-country economic zones, 2) converging consumer expectations for increased product value, variety and availability in all markets, 3) financial obligations to meet new standards for product safety, environmental protection and product recycling that are being adopted internationally, and 4) increased volatility in financial/currency markets. These developments coincide with the adoption of a new competitive strategy by leading multinational companies. This strategy is one of global supply chain management based on enhanced integration of suppliers and customers as well as increased coordination across multiple value-adding processes within the firm (MacCormack et al., 1994; Ohmae, 1995). Such strategies require firms to maintain core competency on a global scale for fundamental processes such as order fulfillment, supply management and new product development, (Majchrzak and Wang, 1995; Womack and Jones, 1996). The global supply chain strategy also requires that the flow of information, cash and material be managed on an international basis, (Porter, 1996; Preiss et al., 1996). As a consequence, the global supply chain strategy involves both operational and financial decisions. Its successful implementation can lead to more effective risk management and the leveraging of both firm-specific and location-specific advantages to yield lower costs and higher revenues.

A Multi-objective Approach to Simultaneous Strategic and Operational Planning in Supply Chain Design

Article

Oct 2000
OMEGA-INT J MANAGE S

In this research, an integrated multi-objective supply chain (SC) model is developed for use in simultaneous strategic and operational SC planning. Multi-objective decision analysis is adopted to allow use of a performance measurement system that includes cost, customer service levels (fill rates), and flexibility (volume or delivery). This measurement system provides more comprehensive measurement of supply chain system performance than do traditional, single-measure approaches. Moreover, this model incorporates production, delivery, and demand uncertainty, and provides a multi-objective performance vector for the entire SC network. The model developed here will aid in the: (1) design of efficient, effective, and flexible supply chain systems and (2) evaluation of competing SC networks.

Co-ordinated Supply Chain Management

Article

Oct 1996
EUR J OPER RES

Historically, the three fundamental stages of the supply chain, procurement, production and distribution, have been managed independently, buffered by large inventories. Increasing competitive pressures, and market globalization are forcing firms to develop supply chains that can quickly respond to customer needs. To remain competitive, these firms must reduce operating costs while continuously improving customer service. With recent advances in communications and information technology, as well as a rapidly growing array of logistics options, firms have an opportunity to reduce operating costs by coordinating the planning of these stages. In this paper, we review the literature addressing coordinated planning between two or more stages of the supply chain, placing particular emphasis on models that would lend themselves to a total supply chain model. Finally, we suggest directions for future research.

Multiproduct integrated production--distribution systems

Article

Feb 1994
EUR J OPER RES

In many firms the goals of the production staff are in conflict with those of the marketing staff. Often the resolution of the conflicting goals is to require a stockpile of finished goods to hold large amounts of inventory. The two functional areas thus are buffered. It is our opinion that an integrated view of the entire production-distribution system may generate significant savings by trading off the costs associated with the whole, rather than minimizing production and distribution costs separately. In this paper we develop a model of an integrated production-distribution system comprised of a single station model of a factory, a stockpile of finished goods, and a single retailer. The concepts and solution methodology, however, show promise for more complex networks. Multiple products with stochastic, independent demand are produced at the factory, stored at FG, and distributed to the retailer where demand is met or backlogged. In addition, FG may order an expedite batch of a particular product if its stock level decreases to a specified expedite reorder point. Expedite batches have nonpreemptive priority over normal replenishment batches. We approximate the distributions of key random variables to compute costs and service levels for all products across the supply chain. We find that our approximations are often quite accurate. Our limited tests of the performance of the near-optimization algorithm indicate that it provides solutions very close to the optimal. We also provide results that yield insight into the behavior of this integrated system.

Jan 1960
475-490

A J Clark
H Scarf

Clark, A. J., and Scarf, H., 1960, Optimal Policies for a MultiiEchelon Inventory Problem, Management Science, Vol. 6,475- 490.

Overview of the SCOR Model V2.0, www.supplychain.org

Jan 1998

Supply-Chain
Council
Inc

Supply-Chain Council, Inc., 1998, Overview of the SCOR Model V2.0, www.supplychain.org.

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