Intelligent Supply Chain Management

doi:10.4236/jsea.2010.34045

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J. Software Engineering & Applications, 2010, 3: 404-408

doi:10.4236/jsea.2010.34045 Published Online April 2010 (http://www.SciRP.org/journal/jsea)

Intelligent Supply Chain Management

Mohammad Zubair Khan1, Omar Al-Mushayt1, Jahangir Alam2, Jorair Ahmad1

1Faculty of Computer Science and Information System, University of Jizan, Jizan, Kingdom of Saudi Arabia; 2University Women’s

Polytechnic, Faculty of Engineering and Technology, Aligarh Muslim University, Aligarh, India.

Email: [email protected], [email protected], [email protected]

Received December 27th, 2009; revised January 31st, 2010; accepted February 5th, 2010.

ABSTRACT

Fuzzy Logic is used to derive the optimal inventory policies in the Supply Chain (SC) numbers. We examine the per-

formance of the optimal inven tory policies by cutting the costs and increa sing the supply chain management efficiency.

The proposed inventory policy uses multi-agent and Fu zzy logic, and provides managerial insights on the impact of the

decision making in all the SC numbers. In particular, we focus on the way in which our agent purchases components

using a mixed procurement strategy (combining long and short term planning) and how it sets its prices according to

the prevailing market conditions and its own inventory level (because this adaptivity and flexibility are key to its suc-

cess). In modern global market, one of the most important issues of the supply chain (SC) management is to satisfy

changing customer demands and enterprises should enhance the long-term advantage through the optimal inventory

control. In this paper an intelligent multi-agent system to simulate supply chain man agement has bee n dev elo ped .

Keywords: SCM Supply Chain Management, Customer Agent, RFQ, Component Agent

1. Background and Introduction

1.1 Supply Chain Management

Supply chains encompass the companies and the business

activities needed to design, make, deliver, and use a

product or service. Businesses depend on their supply

chains to provide them with what they need to survive

and thrive [1-3]. Every business fits into one or more

supply chains and has a role to play in each of them [3,4].

A multiechelon supply chain is illu strated in Figure 1. It

includes different types of flows i.e. financial flow, In-

formation flow and material flow. Third party logistics

(3PL) services providers (3PL is an organization that

manages and executes a particular logistics function,

using its own assets and resources, on behalf of another

company.) handle the inbound and outbound logistics for

the shipper (The person or company who is usually the

supplier or owner of commodities shipped). The inbound

logistics take care of the material management while the

outbound logistics deal with physical distribution of final

products. The term “supply chain management” arose in

the late 1980s and came into widespread use in the 1990s.

Prior to that time, businesses used terms such as “logis-

tics” and “operations management” instead. Some defini-

tions of a supply chain are offered below:

 “A supply chain is the alignment of firms that bring

products or services to market.”—Lambert, Stock, and

Ellram (1998).

 “A supply chain consists of all stages involved, di-

rectly or indirectly, in fulfilling a customer request. The

supply chain not only includes the manufacturer and

suppliers, but also transpo rters, warehouses, retailers, and

customers themselves.”—Chopra and Meindl (2001).

 “A supply chain is a network of facilities and distri-

bution options that performs the functions of procure-

ment of materials, transformation of these materials into

intermediate and finished products, and the distribution

of these finished products to customers.”—Ganeshan and

Harrison (1995).

Master strategist and a skillful general Napoleon once

remarked, “An army marches on its stomach.” Which

holds true in business, as it moves on physical flow of

materials, another saying that goes is also valid for the

business, “Amateurs talk strategy and professionals talk

logistics” [5-7].

Thus, we can say that Supply chain management is a

set of approaches used to efficiently integrate suppliers,

manufacturers, warehouses, and customers so that mer-

chandise is produced and distributed at the right quanti-

ties, to the right locations, and at the right time in order

to minimize system wise costs while satisfying service-

level requirements.

Supply chain management (SCM) is the task that

moves in a process from supplier to manufacturer to

wholesaler to retailer to consumer [1,4]. Supply chain

management involves coordinating and integrating these

Intelligent Supply Chain Management 405

SUPPLIERS MANUFACTURERS DISTRIBUTORS RETAILERS

CUSTOMERS

Inbound LogisticsO utbound Logistics

Material Mana gementPhysical Distribution

3PL Services Providers

Flow of Information

Flow of Goods

Flow ofFinancealon g with Information

ULTIMATE

SUPPLIERS

Figure 1. The supply chain process

flows both within and among companies [2,8]. It is said

that the ultimate goal of any effective supply chain man-

agement system is to reduce inventory (with the assump-

tion that products are available when needed).

Supply chain management flows can be divided into

three main flows:

 The product flow

 The information flow

 The finances fl ow

The need of Supply chain Management in today’s

scenario is:

 Millions of dollars at stake!

 Excess Inventory costs

 Excess freight charges

 Lost sales/Stock outages

 Wasted time and energy

 Extra staff

 Listings/Delisting

 Customer dissat isfaction – p rivatization

 Capital costs

 Real Estate Costs

 Static and unresponsive SC policies

 Large inventories

 Unreliable deliveries

• Underperformance

1.2 Fuzzy Logic

Fuzzy logic is a form of multi-valued logic derived from

fuzzy set theory to deal with reasoning that is approxi-

mate rather than precise. Just as in fuzzy set theory the

set membership values can range (inclusively) between

any values, in fuzzy logic the degree of truth of a

statement can range between any values and is not

constrained to the two truth values {true, false} as in

classic predicate logic [1]. And when linguistic variables

are used, these degrees may be managed by specific

functio n s, a s discussed be l ow.

Fuzzy Set Theory defines Fuzzy Operators on Fuzzy

Sets. The problem in applying this is that the appropriate

Fuzzy Operator may not be known. For this reason,

Fuzzy logic usually uses IF-THEN rules, or constructs

that are equivalent, such as fuzzy associative matrices.

Rules are usually expressed in the form: IF variable IS

property THEN action.

For example, an extremely simple temperature regulator

that uses a fan might look like this: If temperature IS

very cold THEN stop fan. If temperature is cold THEN

turn down fan. If temperature IS normal THEN maintain

level. IF temperature IS hot THEN speed up fan. Notice

there is no “ELSE”. All of the rules are evaluated,

because the temperature might be “cold” and “normal” at

the same time to different degrees. This paper proposes a

multi-agent system to simulate supply chain management

using the concepts of fuzzy logic. The rest of the paper is

organizes as follows—Section 2 presents the proposed

model, Methodology adopted conducting the experi-

ments and results so obtained have been discussed in

Section 3. Section 4 concludes the paper.

2. Proposed Model

The proposed SCM model has been depicted in Figure 2.

In our model we are introducing the customer agent and

Customer RFQ (Request for Quantities). The customer

agent receives customer RFQ requesting a quantity of a

particular commodity for delivery on a specified day.

The customer agent is the key component while compo-

nent agent is responsible for dealing with the component

suppliers and aims to ensure that there are always suffi-

cient components in stock to address the customers’

changing demand for finished products.

In our model we propose customer agent only and

leave the component agent for future studies.

2.1 The Customer Agent

The customer agent is the key component in our model

(because we believe that offering the appropriate price at

the right time is vital for success). If the price is too low,

the agent will receive a low profit and if it is too high it

will fail to win any orders (because the natural tendency

of customers is to always choose the lowest offer price

among those they receive). Given this, the key challenges

are to determine which customer RFQ to bid for and at

Intelligent Supply Chain Management

406

Figure 2. The SCM model

what price. To achieve this, we use fuzzy reasoning [9,10]

to determine how to set prices according to the agent’s

inventory level, the market demand and the time into

consideration.

2.2 Choosing Customer RFQ’s

The customer agent receives customer RFQs requesting a

quantity of a particular commodity for delivery on a

specified day [2,8,11]. When selecting which RFQs to

respond to, Our ISCM rates them according to the poten-

tial profit that they may bring and according to the in-

ventory it holds. The latter inventory driven strategy of-

fers customers commodity according to what is currently

available and also what can be produced given the deliv-

ery date of pending components orders. In more detail,

suppose a customer RFQ is represented as a tuple (i, q,

pres, cpenalty, pbase), where i is the type of commodity

the customer wants, q > 0 is the quantity, pres > 0 is the

reservation price (maximum it will pay), cpenalty > 0 is

the fine paid if the computers are not delivered on time,

and ddue is the desired delivery date. On each day, the

customer agent receives a bundle of such RFQs and sorts

them in the decreasing order of the profit margin of the

type of commodity requested. Here pbase is the cost of

buying components (sum of the buying price for each

component). The intuition is that the agent will first serve

customers with high profit margins and low penalties.

This is because the higher the pres, the more profit will

be made (compared to selling the same product to a cus-

tomer with a low pres). At the same time, the agent also

wants to avoid getting high penalty orders because of the

inherent uncertainties that exist in the scenario.

Profit Margin = Pres-Pbase-(cpenalty/q)

3. Methodology

We are applying the Fuzzy logic if-else algorithm using

the following rules and simulated it using available data.

Here we define fuzzy rules and then revisited the rules

for our g oa l.

3.1 Fuzzy Rules for Calculating Offer Prices

 R1: if D is high and I is low then r1 is very-big

 R2: if D is medium and I is high then r2 is

very-medium

 R3: if D is low and I is high then r3 is small

3.2 Fuzzy Rules Revisited

 If D is high I is High E is far then r1` is big.

 If D is high I is High E is in between then r2` is big.

 If D is high I is High E is near then r3` is big.

3.3 The Component Agent

The component agent is responsible for dealing with the

component suppliers and aims to ensure that there are

always sufficient components in stock to address the cus-

tomers’ changing demand for finished products. In doing

so, it addresses a challenge that is common to all supply

chains facing dynamically changing customer demand.

That is, it must procure components at a low cost, whilst

simultaneously maintaining a minimal component inven-

tory in order to reduce the daily storage cost and also the

possibility of being left with redundant stock if customer

demands changes.

Types of procu re me nts

 Far future proc urement

 Near Future Procurement

3.3.1 Far Future Procurement

Of the two strategies, the far future procurement one is

the simpler. For this, the agent assumes that in the far

future, there will be a daily minimum need, and thus

checks whether there is sufficient current and pending

Intelligent Supply Chain Management407

component inventory to meet this need. If not, it submits

an RFQ for a fixed amount to the relevant supplier, re-

questing delivery on the date at which the predicted in-

ventory falls below the daily minimum need.

3.3.2 Near Future Procurement

The near future procurement strategy is more complex. It

consists of two elements, a daily demand predictor that

predicts the future demand of components, and a market

tracker that generates the RFQs to be sent to the suppliers,

both to order actual components required and to test the

market to discern the most profitable order lead time and

set appropriate reserve price

3.4 Demand Predictor

As described above, the agent buys components for the

near future based on a prediction of customer demand.

Now, according to the game specification, the number of

RFQs that each agent receives from the customers is de-

scribed by three independent random walks; one for each

market segment (finished products are classified into

three such segments: high, mid and low range). In more

detail, the number of RFQs that an agent receives, within

a single market segment, on day d is denoted by Nd, and

is drawn from a Poisson distribution whose expected

value is given by the parameter, Qd. Thus, for each mar-

ket segment, Nd = Poisson(Qd)

Having predicted the number of RFQs that will be re-

ceived, within each market segment, on each day within

the near future, the agent then calculates the expected

daily usage of each component type (Did).

3.5 Price Tracker

The price tracker acts to maintain an estimate of the cur-

rent market price of the components. Due to the behav-

iors of the competing agents, this market price depends

on the due date with which components are requested.

For example, if the competing agents are ordering com-

ponents with very short lead times, then the supplier will

have little spare capacity, and thus, the corresponding

offer prices that the agent receives will be greater than

those of orders wit h l ong l ead t im e s.

3.6 Factory Agent

One of the main challenges for the factory agent is

scheduling what to produce and when to produce it (i.e.,

how to allocate supply resources and factory time).

This strategy involves manufacturing commodities

according to customer orders and satisfying orders with

an earlier delivery date. Now, since the computers stored

in the factory will be charged storage cost, each order

will be delivered as soon as it is filled. The agent builds

the commodity according to the customers’ orders it has

obtained (which has the advantage of ensuring that the

factory always produces the needed computers on time).

However, if on any day, there are still free factory as-

sembling cycles available, and the numbers of finished

PCs in stock are below a certain thresho ld, then the agen t

produces additional PCs of each kind uniformly (subject

to the availability of components) in order to maximize

the factory utilization. It is critical that this threshold is

set appropriately; a high threshold will lead to excessive

finished PC inventory, which may be hard to sell if de-

mand is low.

3.7 Simulated Results

Profit

Margin Inventory End of sea-

son Predictors

Decision

High High Far yes

High High In Between yes

High High Near yes

High Medium Far yes

High Medium In Between yes

High Medium Near no

High Low Far yes

High Low In Between no

High Low Near no

Medium High Far yes

Medium High In Between yes

Medium High Near yes

Medium Medium Far yes

Medium Medium In Between yes

Medium Medium Near no

Medium Low Far no

Medium Low In Between no

Medium Low Near no

Low High Far no

Low High In Between no

Low High Near yes

Low Medium Far no

Low Medium In Between no

Low Medium Near no

Low Low Far no

Low Low In Between no

Low Low Near no

4. Conclusions

This mixture of baseline and opportunistic purchasing

behavior is a common strategy in this domain and the

technology we develop for achieving this can be readily

transferred. Second, we believe our pricing model tech-

nology will also be useful in real SCM applications

where just undercutting competitors’ prices can signifi-

cantly improve profitability. Specifically, to apply our

model in other domains, the designers of the rule base

would need to adapt the fuzzy rules to reflect the factors

that are most relevant. Now we believe that customer

demand and inventory level are highly likely to be criti-

cal factors for almost all cases and thus these rules can

remain unaltered.

By using different rule bases, different factors can eas-

ily be incorporated (as we did here, in order to handle the

additional need to reduce inventory towards the end of

the season). The purpose model also will use in this area

Intelligent Supply Chain Management

408

focuses on the component agent. We would like to im-

prove it so that it can adap t the quantity for far fu ture and

near future procurement automatically between the sea-

sons according to the procurement behaviors employed

by the opponents.

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