A knowledge management approach for managing uncertainty in manufacturing
| Date | 01 April 2006 |
| Published date | 01 April 2006 |
| DOI | https://doi.org/10.1108/02635570610661561 |
| Pages | 439-459 |
| Author | S.C.L. Koh,A. Gunasekaran |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
A knowledge management
approach for managing
uncertainty in manufacturing
S.C.L. Koh
Management School, University of Sheffield, Sheffield, UK, and
A. Gunasekaran
Management Department, University of Massachusetts-Dartmouth,
North Dartmouth, Massachusetts, USA
Abstract
Purpose – This paper proposes a knowledge management approach for managing uncertainty in
manufacturing enterprises.
Design/methodology/approach – The knowledge management approach consists of a
knowledge-enriched manufacturing system, which is modelled using SIMAN simulation language
and programmed using Visual Basic applications. A knowledge-based planning module and an
execution platform are simulated so that signals could be transferred, and configuration to the planned
parameters could be made, in order to minimise variations due to uncertainties. A reference
architecture and intelligent agent are created to store tacit knowledge and create explicit knowledge,
respectively.
Findings – Manufacturing enterprises should use both tacit knowledge about uncertainties and
buffering and dampening techniques, simultaneously with the explicit knowledge that is generated by
the intelligent agent, for managing uncertainty. The design of the knowledge management approach
enables easy integration with material requirements planning, manufacturing resource planning or
enterprise resource planning systems, and complements with the adoption of advanced technology.
Originality/value – A new concept – management by valued-added urgency, emerges that
underpins the knowledge management approach. It is grounded from the previous literature on
managing uncertainty classified into: masking approach; standardising approach; prioritising
approach; and optimising approach and extended Westbrook’s priority management theory.
This concept focuses selectively on value-added changes that need to be made to counteract
variations caused by significant uncertainty.
Keywords Uncertainty management, Knowledgemanagement, Manufacturing industries
Paper type Research paper
1. Introduction
The complexity and competitiveness in a twenty-first century supply chain have
changed the way that manufacturing enterprises sustain and manage their operations
(Bose, 2006). Owing to the combinatorial factors, e.g. increasing trends of business
process outsourcing (Franceschini et al., 2003), use of complex production planning and
control systems (Chung and Snyder, 2000), and unpredictable internal and external
events in manufacturing enterprises (Koh et al., 2000); it is increasingly difficult to plan
The current issue and full text archive of this journal is available at
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The authors would like to thank the Production Director and Planning Manager of the case
company. This project was partially funded by the ORS Award from the Council of Vice
Chancellors and Principals UK.
Knowledge
management
approach
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Industrial Management & Data
Systems
Vol. 106 No. 4, 2006
pp. 439-459
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/02635570610661561
and control manufacturing operations. In this paper, these phenomena can be referred
as uncertainty, which can be defined as any unpredictable event in manufacturing
environments that disturbs operations and performance of an enterprise (Koh and
Saad, 2002). Uncertainty can be measured by the frequency of its occurrence, and
analysing the relative contribution and resulting effect on delivery performance can
quantify whether the impact is minor or major.
Various buffering and dampening techniques are used to tackle the effect of
uncertainty, e.g. overtime production, subcontracting, outsourcing, holding safety stock,
and keeping safety lead-time. These techniques are adopted to minimise the effect of
uncertainty on delivery to customer. Today’s manufacturing enterprises must be
responsive andbe able to tackle uncertainty quicklyand robustly in order to sustain and
enhance business competitiveness. In orderto respond to uncertain demand, supply and
productionprocess, the role and performanceof a production planningand control system
within a manufacturing enterprise willbe challenged. Since, such a productionplanning
and control systemacts as the back-office informationbackbone to integrate withsupply
and demand(Davenport, 2000), itis vital that their fitness underthe effect of uncertaintyis
understood.This paper focuses onmaterial requirementsplanning (MRP), manufacturing
resource planning (MRPII) and enterprise resource planning (ERP) systems as the
production planning and control systems used in manufacturing enterprises. The
constraint of the study is thatthese systems are used for production-planning purposes.
This defines the scope of thisstudy to exclude the examination of other modules in ERP
with the exception ofthe production-planning module.
MRP, MRPII and ERP have been widely implemented for controlling
production-planning activities in modern manufacturing enterprises. They become
the central systems in manufacturing environments within which production data such
as demand, supply, product, inventory, accounting, costing, lead-time and routing are
kept in an integrated manner. The same MRP logic is used in MRPII and ERP in their
production-planning modules (Enns, 2001), thus their inability to cope and respond to
uncertainty is still prevailing given that the planned order release (POR) schedules are
indifferent to those generated from an MRP system (Koh and Saad, 2003a).
2. Literature review
2.1 Buffering and dampening techniques
The term buffering and dampening techniques are used in this paper to describe the
actions taken to recover from the effects of uncertainty. Buffering technique is referred
as a more physical arrangement, e.g. inventory buffer; whilst dampening technique is
referred as a relatively intangible arrangement, e.g. safety lead-time (Koh et al., 2002 ).
Through a comprehensive literature review, Guide and Srivastava (2000) found that
safety stock and safety lead-time were the key robust techniques used by many
researchers. This justifies the research effort in applying safety stock or safety
lead-time to manage uncertainty. Sridharan and LaForge (1989) reported that more
nervousness might be produced when using safety stock. This finding aligns with
the conclusion from Ho et al. (1995). Buzacott and Shanthikumar (1994) found that the
use of safety lead-time is preferred against the use of safety stock when it is possible to
make accurate forecasts of future required shipments over the lead-time. These
findings limit the robustness of safety stock and safety lead-time with the constraint of
the lead-time variation information.
IMDS
106,4
440
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