|
|
ENDÜSTRİ MÜHENDİSLİĞİ
|
SUGGESTIONS FOR ENHANCING
RESPONSIVENESS
IN SUPPLY CHAINS USING KNOWLEDGE
MANAGEMENT
Vishnu A.S., A. Subash Babu, N.L. Sarda1, Azer ÖNEL2
1 Indian Institute
of Technology Bombay, India
2 Atılım Üniversitesi, Ankara
|
ÖZET İşletmeler, üretim maliyetlerini daha fazla
düşüremedikleri durumlarda, rekabetle başedebilmek için değişik stratejiler geliştirmişlerdir.
Bunlardan biri de müşteriden tedarikçiye ve üreticiye kadar uzanan yelpazede
tedarik zincirinin tüm katmanlarının planlama sürecine katılabilmelerini
sağlamaktır. Başarılı bir zincir kurulabilmesi için önemli olan, planlama
bilgilerinin tüm tedarik zincirinde anında paylaşılması ve ortaklaşa planlama
yapabilmektir. Bu makalede tedarik zincirinin daha etkin çalışabilmesi için
kavramsal bir çerçeve önerilmektedir. Önce farklı üretim yerlerinde üretilen
ve dağıtım ağının farklı yerlerinde depolanmış ürünlerin, müşteriye zamanında
ulaşmasını sağlayan perakendeci-üretici-müşteri üçgeninden oluşan bir
sistemin karşılaştığı temel sorunlar tartışılmaktadır. Bu sistemin kavramsal
alt yapısı sunulmakta, daha sonra sistemin etkinliğinin artırılması için
gereken bilgi teknolojisi ve doğru bigiye ulaşabilmek için bilgi yönetimi ve
veri madenciliği konularından söz edilmektedir. ABSTRACT This paper presents the salient details of an
integrated system, which is under development for enhancing the responsiveness
of supply chains. The system is based on modern information systems and is
intended to be capable of dealing with various temporal and spatial decisions
that are required for effectively managing a supply chain. To equip the
system with better decision-making capability and ability to respond to
customer needs faster, the concept of knowledge management supported by data
mining tools is identified as an integral part of the system. Key Words: Supply chain, responsive, data mining, knowledge management |
INTRODUCTION
A supply chain is a network of facilities and
distribution options that performs the functions of procurement of materials,
transformation of materials into finished products and delivery of products to customers.
Supply chains exist in both service and manufacturing organizations, although
the complexity of the chain may vary greatly from industry to industry and from
firm to firm.
In recent years it has become clear that many
companies have reduced manufacturing costs as much as possible. The rules of
the game in the world of trade, both within a country and across countries are
quickly changing to the 'survival of the fittest and future for the agile
organizations'. Many organizations have used various innovations to win the
game. These days the power of information technology has become almost
inseparable in every new technology and innovation. The study by Bradley [1],
which covered a survey of more than 300 supply chain-related executives, found
that 92% of those surveyed were planning to implement one or more supply chain
initiatives. One of the commonly found approaches is to decentralize the value
adding activities by outsourcing and developing a virtual enterprise. Since
suppliers are located all over the world, it is essential to integrate the
activities both inside and outside of an organization.
Supply chain management is defined as the
integration of key business processes from end user through original suppliers
that provides products, services, and information that add value for customers
and other stakeholders [2]. According to Simchi-Levi et al. [3], supply chain
management is a set of approaches utilized to effectively integrate suppliers,
manufacturers, warehouses, and stores, so that merchandise is produced and
distributed at the right quantities, to the right locations, and at the right
time, in order to minimize system wide cost while satisfying service level
requirements. This integration implies an information system for sharing information
on various value adding activities along the supply chain.
This paper introduces an integrated system for
managing supply chain, which has been designed as part of an ongoing research
study. The system can help managing supply chains of organizations dealing with
products that are made at different sources, stocked at a number of locations
at different echelons of the distribution network and delivered to the end
customers through the appointed dealers of the organization. This system is
intended to provide different categories of management support to realize
responsive supply chains.
RESPONSIVE SUPPLY CHAINS
Making the supply chain effective is increasingly
regarded as very essential for enhancing the overall organizational
competitiveness. When the supply chain management is made effective through
increased responsiveness, a number of benefits will follow: (1) Increase in
revenues, improvement in profitability, and a stable or increased market share
will be observed. (2) Operating and administrative costs will be reduced. (3)
Inventory turnover can be increased, which will reduce both inventory carrying
costs and overall product cost base. (4) Products will be available easier due
to reduction in stock-outs.
Such benefits will not accumulate easily though;
the process of integrating different components in the supply chain is a
complicated undertaking. There are several crucial supply chain management
issues that affect the range of a firm's activities and its competitiveness in
the market. One such issue is the design of a logistic distribution network.
The network consists of suppliers, warehouses, distribution centres, retail
outlets, and inventory of raw materials, work-in-process and finished goods.
There are normally four stages in the design of a logistic distribution network
[4]. The arrangement stage refers to the geographical arrangement or
layout of the distribution network. At this stage the number, location and size
of facilities and assignment of customers and suppliers to warehouses are
determined. The second stage is the deployment stage that starts from
the network arrangement and tries to find an optimal distribution of inventory
and final assembly activities among the available facilities. Each product type
is assigned to one or more locations where it should be kept in stock and, if
applicable assembled. In the flow stage the required inventory levels,
safety stocks, replenishment batch sizes and order frequencies are determined. This
permits a thorough evaluation of the proposed supply chain. The operations
stage is the final stage and covers issues involved in operating a supply
chain. It requires determination of ordering procedures, detailed vehicle
routing algorithms or customer delivery scheduling algorithms.
Another crucial issue in supply chain management is
the variability of consumer demand information as one moves up the supply chain
away from the retailer. If demand information is distorted, the chain may be
subjected to undue pressure and may be forced to reduce its responsiveness over
a period. Many researchers, under the topical interest called bullwhip effect,
have discussed the issues associated with this problem [5].
The bullwhip effect refers to the phenomenon where
orders to the supplier tend to have a larger variance than sales to the buyer
(i.e. demand distortion) and this distortion magnifies upstream in the supply
chain. The distorted information implies that different stages in the chain
will have different demand estimates and this will lead to excess costs and
reduced responsiveness.
Main factors contributing to the bullwhip effect
are as follows [5]:
1. Demand Signal Processing. Demand
distortion may arise when the retailers, manufacturers and suppliers forecast
demand separately and do not share information. Because each stage in the chain
makes its own forecast based on orders and not on real sales figures, a small
change in customer demand becomes magnified as orders move up the chain.
Different forecasting methods may contribute to further fluctuations in
ordering and demand distortion.
2. Rationing Game. Information
distortion can arise out of the practice of 'gaming' where the retailer orders more
than the actual consumer demand thinking that the manufacturer will allocate
less than what he requests. The net effect is a distorted demand. Distortion
also arises during shortage periods when the retailer places large orders and
goes back to standard orders when the shortage is over.
3. Batch Ordering. Firms may
batch order at certain times of the month or the year in order to reduce fixed
costs of ordering and transporting. However, ordering in large lots leads to an
erratic order stream that adversely affects the manufacturer.
4. Price Variations. Pricing
policies can also lead to the bullwhip effect. If prices fluctuate, retailers
often attempt to buy in large quantities during the discounting or promotion
periods.
Identification of the above forces aids the
development of strategies to reduce or eliminate the damaging impact of the
bullwhip effect. The problems caused by demand signal processing can be reduced
by providing manufacturers with demand data at the retail stage, allowing a
single stage of the supply chain to perform forecasting and ordering for other
stages, and shortening the lead-time. The impact of rationing game can be
diminished by allocating scarce products in proportion to the past sales
records, sharing production and inventory information with each stage in the
supply chain and using a contract that restricts the buyer's flexibility. The
batch ordering problem can be reduced by using electronic data interchange
based order transmission systems that help reduce ordering costs and lot sizes.
Retailers can be entitled to order a variety of products to fill a truckload
and offered the same volume discount. The problem due to price variations can
be taken care of by eliminating promotions or by placing limits on the quantity
that may be purchased during a promotion.
Another critical issue in effective supply chain
management is managing the flow of information between the stages in the chain.
Information provides managers with the facts to make decisions. It is essential
to understand how information is gathered and analysed. Using information
technology systems to collect and analyse information provides a firm a
competitive advantage in the market. Information makes it possible to reduce
the variability in the chain, to make better forecasts and promotional and
market changes, to improve coordination of manufacturing and distribution, to
enable retailers to offer better service to the customer by quickly reacting
and adapting to supply problems, and to reduce lead times. It is important,
however, that IT should be integrated with the components of the supply chain
so that these benefits are realized.
THE SYSTEM FOR MANAGING SUPPLY CHAINS
This paper proposes an integrated system shown in
Figure 1 to enhance the responsiveness of the supply chain. The system, using
the power of modern information systems, is capable of supporting an
organization dealing with multiple products, which include both in-house and
subcontracted products sold under the brand name of the company.
The distribution network in the system spans
different geographical areas. Each regional office (RO) coordinates with the
branch offices (BO) in the respective regions. Dispatches are made directly to
branches, which in turn supply products to dealers. Only in case of emergency,
the RO's stock goods. There are two policy groups, namely the Policy Analysis
Centre (PAC) and the Policy Implementation Centre (PIC). These function as the
central nerve centres for planning, implementing and controlling the entire
system.
Customer orders are received and fulfilled by the
dealers, who in turn are served by the respective branches. Dealers and
branches are responsible for managing the information, material and cash flows.
A regional office coordinates all the commercial activities of the branches and
the dealers in the region with the help of a divisional marketing office. The
central policy groups (CPG) PAC and PIC control all these constituents. While
making supply chain related decisions, the CPG considers the availability of
finished goods and work-in-process inventory of both in-house and factored
products, and the actual production at all sources. The PAC determines policies
on production scheduling, allocation, replenishment, stocking, returning and
transportation of the in-house and factored products. The PIC is responsible
for implementing these policies. Both groups are connected to all the other
physical locations of the system through the company intranet whereas the
customers are linked to the dealers through the Internet.
Figure 1. The architecture of the system
From the operational point of view, the system is
to integrate all the constituents of the supply chain using modern information
systems. Since a typical supply chain encounters a number of decision-making
situations, there is a need to drive the system to its desired effectiveness.
For this reason, it is envisaged that the supply chain should be incorporated
with a number of decision support tools to deal with various conflicting and
compromising scenarios. A concept called Logistics Application Integration
proposed by Vishnu et. al [6] can be used to integrate various functions of a
supply chain system. The core of the LAI is a framework called Logistics
Function Deployment (LFD), which facilitates integration of different actions
and/or tasks and the expectations of the supply chain. LFD uses a methodology
based on fuzzy number theory to evaluate various strategies. It is also
possible to include in this proposed system various time tested commercial systems
to evaluate and optimize decisions.
Even a rational decision at one stage of the supply
chain is bound to create dynamics of various natures at different stages.
Especially, in a system that depends upon knowledge workers to contribute to
the value-adding activities, this problem can become menacing if not dealt with
adequate care. Therefore, an effective look-ahead facility -if made available
as a part of the system- will help overcome the ill effects of uncertainty and
thereby help enhance the responsiveness of the system. This requires a systemic
approach or framework for educating and training workers to become effective
members of teams and to be innovative. This is where the concept of knowledge
management appears in the picture. The capability of the integrated system can
be enhanced to the level of a knowledge management system for realizing
responsive supply chains.
KNOWLEDGE MANAGEMENT SYSTEMS FOR ENHANCING RESPONSIVENESS
Knowledge management is concerned with recognizing
and managing an organization's intellectual assets to meet its business
objectives. As Duffy states [7], knowledge management is a business process and
a professional discipline and describes a set of business practices and
technologies used to assist an organization to obtain maximum advantage from
one of its most important assets - knowledge. It is concerned with effective
utilization of an organization's knowledge resource in a systematic way.
Organizations are redesigning their internal structure and their external relationships,
creating knowledge networks to facilitate improved communication of data,
information, and knowledge, while improving co-ordination, decision making, and
planning [8]. Knowledge networks allow their participants to create, share, and
use strategic knowledge to improve operational and strategic efficiency and
effectiveness. Partnership is critical to the creation and spread of knowledge,
and creation and diffusion of innovation [9]. An integrated system proposed
above may provide a good base for partnership.
A supply chain is an integration of various
business processes to deliver what is of value to the customers. Enterprises
that work sufficiently long time generate considerable knowledge in terms of
expertise that are virtues of an organization. This repository of expertise can
be stored, retrieved and deployed as desired with the help of what is called as
knowledge management systems. Especially in an integrated system such as supply
chain, the essence of capturing knowledge is more critical considering the
scope and the spread of this system covering a number of business processes at
various stages. Knowledge discovery tools like data mining can be used along
with decision support systems to improve the responsiveness of the system. In
the proposed framework, it is envisaged to use data mining tools for knowledge
discovery.
Data mining has become a vital tool in trying to
analyse markets, and to predict supply and demand uncertainties in supply chain
systems. It can be used along with the decision support systems to improve the
overall results of the system. The major stages for the data mining process for
knowledge discovery include goal definition, data selection, data preparation,
data exploration, pattern discovery, pattern deployment, and pattern validity
monitoring [10]. The commonly used data mining tools are artificial neural
networks, decision trees, and genetic algorithms, nearest neighbor method, rule
induction, and data visualization [11].
The fundamental goals of data mining are prediction
and description. Prediction makes use of existing variables in the database in
order to predict unknown or future values of interest. Description focuses on
finding patterns describing the data and making them available for user
interpretation. There are several data mining algorithms, which are used to
solve specific problems or objectives [10]. These are categorized as
associations, classifications, sequential patterns and clustering. The basic
idea behind associations is to find all associations such that the presence of
one set of items in a transaction implies other items. Classification
generation develops profiles of different groups. The method of sequential
patterns identifies sequential patterns subject to a user-specified minimum
constraint. Clustering segments a database into subsets or clusters. There is
evidence in the published literature that data mining rules can be effectively
integrated with applications built using relational data base systems [12].
CONCLUSION
Managing supply chains spread over different
geographical areas is a complex issue especially when demand is uncertain.
Effective information support can help mitigate the problems associated with
the bullwhip effect and poor customer service. If the decision-making hub is
properly integrated with the various constituents of the supply chain, and made
available with dynamic information, the supply chain can be managed more
effectively with the use of appropriate decision-making tools. The applications
developed on relational database systems, if properly integrated with data
discovery tools such as data mining, can significantly enhance the
responsiveness of the supply chain. This paper proposes a conceptual framework,
which makes use of information support, knowledge management and data mining to
manage a supply chain.
REFERENCES
1- Bradley
P. (1999). Managers look to supply chain to cut costs, Logistics Management and
Distribution Report, 38(1), pp. 21-22.
2- Bowersox D.J. and Closs D.J. (2000). Logistical Management: The Integrated
Supply Chain Process, Tata McGraw-Hill Edition, India.
3-
Simchi-Levi D., Kaminsky P. and Simchi-Levi E. (2000). Designing and Managing
the Supply Chain: Concepts, Strategies and Case Studies, McGraw-Hill
International Edition, Singapore.
4-
Alvarado U.Y. and Kotzab H.(2001). Supply Chain Management, The Integration of
Logistics and Marketing Management, 30(2), pp. 183-198.
5- Lee
H.I., Padmanabhan V. and Whang S. (1997). Information Distortion in Supply
Chain: The Bullwhip Effect, Management Science, 43(4), pp. 546-558.
6- Vishnu
A.S., Sarda N.L. and Subash Babu A. (December 2002). An Integrated System of
Decision Models for Supply Chain Management, Proc. of ICORD 2002, Chennai.
7- Duffy J. (2000). The KM Technology
Infrastructure, The Information Management Journal, 34(2), pp. 62-66.
8-
Warkentin M., Sugumaran V. and Bapna R. (2001). E-Knowledge networks for
inter-organizational collaborative e-business, Logistics Information Management,
14(1&2), pp. 149-162.
9- Jutla
D., Bodorik P. and Dhaliwal J. (2002). Supporting the e-business readiness of
small and medium-sized enterprises: approaches and metrics, Internet Research:
Electronic Networking Applications and Policy, 12(2), pp. 139-164.
10- Vishnu
A.S. and Sarda N.L. (2001). Data mining in Supply Chain management: Concepts
and Applications, Technical Report, CARE, IIT Bombay.
11-
(1999). Introduction to Data Mining and Knowledge Discovery, Data Mining:
Technology Report, 3rd Edition, Two Crows Corporation.
12-
Sarawagi S., Thomas S., and Agarwal R. (2000). Integrating Association Rule
Mining with Relational Database Systems: Alternatives and Implications, Data
Mining and Knowledge Discovery, 4(2), pp. 89-125.