Download limitations and performance of mrpii/erp systems - ICPR

th
19 International Conference on Production Research
LIMITATIONS AND PERFORMANCE OF MRPII/ERP SYSTEMS –
SIGNIFICANT CONTRIBUTION OF AI TECHNIQUES
J. Oleskow, P. Pawlewski, M. Fertsch
Institute of Management Engineering, Poznan University of Technology, Strzelecka 11 Str., 60-965 Poznañ
Abstract
An important characteristic of any manufacturing system is its ability to maintain an immutable performance
under changing operations conditions, the disturbances and unpredictable events that can influence the
performance of system’s objectives. The determination of how much to produce of each component in each
time period, in a synchronized way, arises in MRPII/ERP systems . The aim of this research is to contribute
to the development of more realistic MRPII/ERP systems.
Powerful tools for solving wide spectrum of non clear defined problems in the field of production optimization
and scheduling are artificial intelligence techniques like genetic algorithms, simulated annealing, tabu
search. In the following paper authors present a review of AI techniques that might be successfully
implemented for all sorts of the MRP problems with special impact to lot sizing problems. The potential
benefits and limitations of certain techniques are being described as well as our research domain.
Keywords:
MRPII/ERP systems, genetic algorithms, simulated annealing, tabu search
1
INTRODUCTION
In today's dynamic and turbulent business environment,
there is a strong need for the organizations to become
globally competitive. This means that, in order to produce
goods tailored to customer requirements and provide faster
deliveries, the enterprise must be closely linked to both
suppliers and customers.
Otherwise it becomes difficult to supply the dynamism
of the market. In the production sector, there is no
possibility to reach the quality and production agility with
traditional methods.
Many companies now realize the need for the
development of world class systems and methodologies,
as well as acquiring the “productivity tool” that will let them
be in a commercial position to offer competitive
manufacturing resource planning that assures customers
of quality goods and services and compliance with
international quality requirements on different industry
fields.
Material requirements planning (MRP) is regarded as
one of the most widely used systems for production
planning and control in industry. It has gained significant
popularity in the 1970s when Orlicky (1975) and Wright
(1984) rigorously advocated the potentials and benefits of
MRP. Orlicky’s book, MRP – The New Way of Life on
Production and Inventory Management, formalized the
fundamental concepts and principles of MRP in 1975.
MRP is a production control program that improves the
production efficiency and service supplied to the customer.
The issues related to material requirement planning
logic (MRP) in large business application has remained
relatively consistent even as systems become more and
more complex and evaluated to
currently available
enterprise resource planning systems (ERP). The key
characteristics include the coordination of assembly and
purchased component requirements through time-phased
order releases and the reduction of setups through the
aggregation of common part requirements. Despite the
sophistication of commercial software using MRP logic it
had been criticized for leading to high work-in -progress
inventory and long lead times [1].
There is a wide range of conflicting perspectives
regarding the usefulness of MRPII/ERP systems, and
popular literature provides many negative accounts
describing the limitations and inaccuracies of existing
MRPII/ERP systems.
Although implementing MRPII/ERP systems can be
very expensive, and require large project teams, they have
become widely used; initially in large companies, and more
recently also in medium-sized companies. Furthermore,
developments in Internet technology have enabled
MRPII/ERP systems to he used for information sharing
with suppliers and customers, and new ERP-based
applications are emerging: for instance, salesforce
automation, customer relationship management, data
mining and supply-chain management systems [2].
An important characteristic of any manufacturing
system is its ability to maintain an immutable performance
under changing operations conditions, the disturbances
and unpredictable events that can influence the system’s
ability to achieve its performance objectives [3].
In existing structures within MRPII/ERP systems, the AI
techniques are recommended to be used to solve
problems and to improve the performance of the software,
so with the new structure combined with AI can adapt the
real life conditions.
2
THE PURPOSE OF MRP SYSTEMS
MRP computer systems or MRP logic comprised by
evolving MRPII/ERP systems serve the organization by
providing the functions below:
In terms of Inventory [4]:
Determine the number of parts, components, and
materials needed to produce each end item.
Determine the right part, right quantity, & right time to
order parts. Provide time schedules for ordering
materials & parts.
Maintain a bill of materials sequencing the assembly
parts of the final product ("schematic, product
structure tree").
Priorities: Order for the right due date, keep the due date
valid.
Capacity: Plan to optimize the use of plant & equipment
capacity, Plan an accurate
Objectives: MRP has the same objectives as any inventory
management system
1. To improve customer service
2. Minimize inventory investment
3. Maximize production operating efficiency
According to fundamental philosophy of Material
Requirements Planning: the materials should be expedited
(hurried) when their lack would delay the overall production
schedule and de-expedited (delayed) when the schedule
falls behind and postpones their need [6].
When MRP systems are implemented properly they
allow firms to realize the following benefits:
ability to price more competitively,
reduce sales price,
reduce inventory,
better customer service,
better response to market demands,
ability to change the master schedule,
reduce setup and tear-down costs,
reduced idle time.
In addition to these benefits, MRP systems also:
Gives advance notice so managers can see the
planned schedule before actual release orders.
Tell when to de-expedite as well as expedite.
Delays or cancels orders.
Changes order quantities.
Advances or delays order due dates.
Aids capacity planning.[4]
Despite of beforehand mentioned advantages we
cannot forget about various shortages which arise from real
world MRPII/ERP applications. We will define them
precisely in the next section.
3
LIMITATIONS OF MRP ASSUMPTIONS
Traditionally, MRP system implementation has been
based on the assumption of a deterministic environment.
Thus, demand and lead times have been assumed to be
deterministic. In a typical manufacturing environment,
nevertheless, this assumption is invariably violated. This
conflict between the assumption and reality in the
implementation of MRP is often advanced as the reason
for the failure of MRP to fulfill its promise.
MRP system which intends to determine the gross and
exact needs of the enterprises in the inventory units seems
to be efficient but has some defective sides. These
defective sides are; determining the best application to
obtain the MPS (Master Production Schedule), determining
the lot sizes, determining the customer demands, capacity
requirement planning, inventory levels and locations.
These uncertainties cause the enterprises to be away from
the appropriate conditions [5].
The APICS literature, cited the following four problems
as the cause of most MRP system failures:
1. Lack of top management commitment.
2. Lack of education of those who use the system.
3. An unrealistic MPS.
4. Inaccurate data, including BOM and inventory
records [6].
The inaccuracy of the bill of materials and inventory
database is a common problem with MRP systems.
Inaccurate bills of materials mean inaccurate material and
capacity plans. Providing a management system that will
facilitate data accuracy will likely require major adjustments
in strategic management approaches
Being able to cope with the uncertainty of the
manufacturing environment is of course not a new concern,
even if it is more and more relevant with regards to the
present industrial context.
Many uncertainties in Material Requirements Planning
(MRP) systems are treated as “controllable” elements, with
a variety of buffering, dampening and other approaches
being used to cope with them [7]. However, such
approaches are often found wanting, forcing enterprises
into emergency measures to ensure delivery performance.
MRPII/ERP systems use fixed lead-time to plan for
material purchase and product manufacture. This ignores
real life uncertainties of supply unavailability and variability
of queue, set-up and run times on the shopfloor.
Net requirement patterns generated do not consider the
availability of resource simultaneously, but identify
resources required as a separate, subsequent activity.
MRPII/ERP systems may be loaded with a predetermined scrap rate. Any increase in this rate will
automatically render due date uncertain unless corrective
measures are taken. Such measures may well impact upon
other products in the system, an effect that is not normally
monitored [8].
Existing traditional methods have also other weakness
which considerable limit their practical applications. For
example they search for optimum considering all possible
cases, therefore when planning horizon is becoming longer
the number of alternative schedules increase dramatically.
Moreover these methods establish lot sizes for individual
items only on one level in the bill of material structure
(BOM) hence have got false assumption of the demand for
the item that is constant.
Summing up our dissertations the most commonly
shortages of MRP systems, from our point of view, are
following:
1. Limitations on the length of the MRP planning horizon
over which optimal order schedules can be found.
Usefulness in practical situations is questionable since
large numbers of alternative schedules would need to
be considered and, in addition, optimal short-term
schedules would not necessarily result in optimization
of inventory over the long term.
2. Limited use in manufacturing industry owing to the
complexity of the procedures required to generate
optimal or near-optimal schedules. These have often
been found to be difficult for operating personnel within
manufacturing organizations to understand.
3. Existing methods treat the lot sizing problem as a
single-stage process, but MRP is a multistage process
and, hence, any lot sizing techniques must consider all
items whose demand is related, both horizontally and
vertically, throughout the bill of material (BOM)
structures [9].
There are conducted various researches and in
particular the following issues are considered as analysis
domains:
a. the impact of forecast processing frequency ,
especially when combined with a variable frozen
period in production planning ;
b. the impact of the MRP procedure running frequency;
c. the impact of lead time uncertainty. [10]
th
19 International Conference on Production Research
Regardless of the types of MRP/ERP systems used
within the MRP-planned manufacture, uncertainty that
could occur during the manufacturing process is indifferent.
For instance, scrap could be caused by the poor quality of
raw material, machine variation or labour mistakes.
Therefore we must enable a complete consideration of all
combinations of uncertainties under an MRP-planned
manufacture for preventing discrete examination on
uncertainty
An uncertainty categorization structure can be
developed using the systems theory to categorize
uncertainty into input and process. This structure has also
addressed the uncertainty that occurs in the supply and
demand chain of the manufacturing process. We
categorize uncertainty in following mode – table 1 and table
2.
Table 1 Uncertainty connected with input
INPUT :
Late Supply
Forecast errors
Customer order changes
Table 2 Uncertainty connected with process
PROCESS:
Interoperation move time
Queue waiting time
Process lead-time
Variability in set-up and run time
Tooling unavailability
Material unavailability
Operator absence
Machine breakdown
Late supply
Variability in resource supply
Capacity loading
Order released prematurely
Engineering changes
Lot-sizing and planning horizon
System uncertainty
WIP lost
Safety stock changes
Record errors
Unplanned transactions
Process yield loss
Ouality variation
Scrap
Allocation not issued in expected quantity
Order released in unplanned quanlily
The most important of which are:
1. There is no evidence for the existence of a detailed
structure of types of uncertainty. This absence results
in past research not identifying the relative significance
of uncertainties and only the effects of uncertainty
being examined.
2. Most of the previous research has studied uncertainty
discretely and only some research has considered
uncertainty
combinations.
Even
within
the
combinations,
only
specific
combinations
of
uncertainty were examined.
3. Input uncertainty has received more research
attention, especially at external demand compared
with other uncertainties. This finding shows a suboptimal approach in coping with uncertainty because
evaluation is not made on the relative significance of
uncertainty by considering all possible types of
uncertainty.
4. Little research has been identified in examining
interactions between uncertainties.
After review of various studies we see that so far many
approaches have been proposed to deal with uncertainties
in an MRP environment. Some specific solutions have
been proposed which are applicable under a given set of
circumstances, but there are no general solution
methodologies that would aid the practicing manager in this
environment. For example, as buffering is performed in
response to uncertainty, reducing uncertainty would lead to
a reduction in buffer size and possibly reduction in the
number of locations. However, reduction in uncertainty can
be obtained by either fixing the MPS or by means of
frequent rescheduling. Uncertainty may exist at all levels in
a product structure, demand uncertainty at the end-item
level, and yield , capacity, lead time as well as supply and
demand uncertainty at the component and sub-assembly
level. [7,11,12,13,14]
An important shortcoming in most of the previous work in
this area has been the limited amount of realism in the
models and approaches. None of the works reviewed
benchmarks the parameters used in the studies with any
industrial data. Given the widespread use of MRPII/ERP
systems, such data to ground models could and should be
used. Because the applicability and usefulness of the
approaches in MRPII/ERP systems would seem to be the
degree of realism built into the approach, it would only be
logical to focus on the models that get closer to the real
environment.
4
AI TECHNIQUES EMBEDDED IN MRPII/ERP
SYSTEMS
Powerful tools for solving wide spectrum of non clear
defined problems are artificial intelligence technique like
fuzzy logic, genetic algorithm, neural networks, simulated
annealing, tabu search etc. hence they can be also applied
in the field of production optimization and scheduling.
Artificial Intelligence is formed from the processes;
recognizing information researching the cause-reason
relationship, recognizing and development of some
comprehension techniques, with a large number of
experiments by using computer [15].
AI uses Computer and Intelligence to help to solution of
problems and provides the operations to be more
productive and work at optimum. AI applications produce
new generation and alternative information. The most
commonly applied AI techniques comprise four parts:
Genetic Algorithms, Fuzzy Logic, Neural Network and
Expert Systems.
Genetic Algorithm (GA) is used in the problems whose
mathematical model can not be produced and solution
area is wide. It takes the evaluation process of the
metabolisms in the nature. The basic of GA is being
randomize and producing successful solutions.
Fuzzy Logic (FL) is used to model the uncertainty of life
problems mathematically. It is related with the degrees of
occurring of the events. The most important property of
fuzzy is the membership function.
Neural Networks (NN) is used in speaking, capturing,
signing, optimization problems, and robotic control
applications and consists of doing work like human activity,
in computer.
Expert Systems (ES) is a computer software which is
used for planning, clarifying, translation, consulting
services, like human expert activities, uses automatically
opinion techniques.[16]
Our example of AI application in the field of MPR
systems is lot sizing problems’ domain. It is one of the
most important problems arising in the field of operation
planning .
One variable in MRPII/ERP system design is the
selection of method to determine how much to order (the
lot-sizing rule), once the MRPII/ERP system has
determined it is now time to order. The determination of
when to order involves the perpetual-inventory logic behind
the MRPII/ERP system, the result of which is timephasing
order requirements. How much to order, within the context
of MRPII/ERP systems, is a current topic of interest to both
production/material planners and academics.
The crucial objective of lot sizing problem is in
MRPII/ERP systems is to reduce the total manufacturing
cost including setup cost, inventory holding cost,
overtiming cost etc., while trying to satisfy the customer’s
requirements with the limited capacity [17].
The sizes of lots have to be carefully chosen in order to
avoid schedule with a large number of lots of very small
size that need large setup time on machines
Lot sizing problem greatly affect the performance of
manufacturers but are often an afterthought in most
factories. MRPII/ERP systems ask users to provide lot
sizes as an input for their calculations, but most people
have difficulties to point the optimal lot-size regarding
following issues: how much of each product should be run
at one time, should lots be split, when the production of
items should start.
The theoretical framework for effective solution of lotsizing problems in MRPII/ERP systems should fulfill the
following requirements:
encompasses the entire product structure –
multilevel dimension
include the ability to deal with multi-item systems
regard capacity constrains
be able to deal with rolling time horizon
deal with varying cost and demand
reduce system nervousness
Based on defined by authors requirements for lot-sizing
technique within framework of MRPII/ ERP system
application the simulated annealing, tabu search and
genetic algorithms seems to be appropriate. More detailed
research we provide with respect to Genetic Algorithms.
Table 3 Comparison of selected AI techniques in the field of lot sizing problems
Method/approach
Area of application
Benefits
Limitations
Simulated
Annealing
-Multi-item
unconstrained lot
sizing problems
- single- and multistage
capacity
constrained
lotsizing
problems
with set up times
and set up cost
- might be used for problem where a
desired global optimum is hidden among
many, poor, local optima
- flexibility, able to manage with e.g. varying
cost, varying demand
- able to deal with complex problems
- the annealing cooling
procedure
is
not
transparent to the user and
might lead to the lost of
insight of lot sizing problem
- the simulated method is
rather slow
- neighborhood must be
defined
(representation
method is required)
Genetic algorithms
-multi-item
lot
sizing problems
- single- and multistage
capacity
constrained
lotsizing
problems
with set-up times
and set up cost
- objective function can consider criteria
other than costs, such as delivery times,
quality levels
- the same procedure can be used
whatever the demand pattern, order or
holding costs, order cycles, lengths of
planning horizon and demand distributions,
- not restricted in the number of planning
periods taken into consideration,
- flexibility,
- ability to deal with a large number of
parameters.
- reduce the computational complexity
-heavy data structure of the
encoding
-indirect encoding
ability
to
handle
unfeasible
solutions
(necessity to adopt a
punishment rule)
Tabu search
Single and multilevel
lot
sizing
problems with and
without constraints
- might be used for complex and large
optimization problems
- quick response time and provide good
optimal and sub-optimal solutions
- structure and memory
length, stopping rule and
aspiration criterion must be
defined in a suitable
manner
- technique is complicated
- handle with unfeasible
solutions is required
- neighborhood must be
defined
(representation
method is required)
The industrial lot-sizing problem determines the best
replenishment strategy that size of replenishment and
th
19 International Conference on Production Research
timing of the production quantities. Finding an optimal
solution for dynamic multistage production systems under
some special assumptions still suffers from computation
complexity. Several heuristic approaches have been
proposed in various aspects of the lot-sizing problem but
they can only guarantee the local optimal solution. The
genetic algorithm approach, simulated annealing and tabu
search may be appropriate tools to overcome this draw
back.
AI techniques can be used to determine the process
how to be made. While giving decision for MRP, AI
techniques can make big additions. The uncertainties in
MRP can be prevented by working MRP integrated with AI
techniques. The uncertainties that can not be modeled
mathematically can be modeled by AI techniques can
make alternative solutions and suggestions.
In the inventory problems for the demand there is an
uncertainty for demand caused from being randomize and
fuzzy. When determining demand; fuzzy logic and
optimization can be used to find a solution.
Our current research domain is optimization of lot sizes
in company with changeable range of goods requiring
complex and specialist machines, tools and special
materials. All these factors have a tremendous impact on
the total costs level. During production process planning
we face up to arising conflict: on the one hand efficient
loading machines and tools is required, on the other hand
we must keep material consumptions at the lowest level.
Lot sizing planning must be carried out quickly and
efficiently using all available data, striving to eliminate or
reduce production standstills. In given company lot sizing
optimization becomes really challenging problem. Actually
we develop solely genetic algorithm-based method for
more effective and less time-consuming problem solution.
A natural extension of this research study (using GAs)
will be to optimize lot-sizes through various AI techniques
as well as to optimize lot-sizes, safety stocks and safety
lead times simultaneously. This should give us an even
lower total cost when using safety lead times and safety
stocks at the same time.
5
SUMMARY
In practice, resources are finite and if optimum benefits
cannot be obtained from the application of traditional
approaches, no significant enterprise improvement will
result by leaving the significant uncertainty within the
system. Hence, the role of artificial techniques is crucial for
more
optimum
application
MRPII/ERP
systems.
·
Enterprises must use the MRP to manage the
production process more efficient. If they realize the
production process more efficient and productive with MRP
can decrease the costs. But the existing MRPII/ERP
software has problems about this issue and causes being
unsuccessful.
The defective sides of MRPII/ERP systems must be
detected and some improvement must be made, so MRP
can be more useful and can decrease the costs. The
uncertainties of existing MRPII/ERP software can be
avoided by using AI techniques. When obtaining MPS,
determining lot sizes, customer demands, material
obtaining and demand time, capacity planning, detecting
the amount and the location of the inventory, some
uncertainties occur. With eliminating these uncertainties by
AI techniques, enterprises can work more efficient,
productive and realistic with the existing conditions.
As a conclusion, it is time to abandon MRP fixes and move
toward a new generation of production control methods
that exploit both the simplicity and robustness of the AI
ideas and the sophistication and power offered by modern
computer technology.
REFERENCES
[1]Enns S.T. (2001): MRP performance effects due to lot
size and planned lead time settings. International Journal
of Production Research, vol. 39, No.3, pp 461-480.
[2]Caldwell, B, and Stein, T, (1998) 'New IT agenda', 30
November: 30-8,
[3] Patty W. Cheng, Effective Use of MRP-Type Computer
Systems to Support Manufacturing, Master of science in
Industrial and Systems Engineering, March 1997,
Blacksburg, Virginia
[4] Chase, R. B., and Aquilano, N. J. Production and
Operations Management. R.D. Irwin, Inc. 1995.
[5] Ersöz S., An Expert System For Production & Marketing
Integration, Doktora Tezi, Gazi Üniversitesi Fen Bilimleri
Enstitüsü, 1998.
[6] Joseph A. Orlicky 1975 Material Requirements
Planning: The New Way of Life in Production and Inventory
Management New York: McGraw-Hill.
[7] D.R. Guide, R.Srivastawa , A review of techniques for
buffering against uncertainty with MRP systems,
Production Planning & Control, 2000, vol. 11, no. 3, 223 ±
233
[8]Buzacott J. A., Shanthikumar J.G., Safety stocks versus
safety lead time in MRP controlled production systems.
Management Science, 40, 1994,1678-1689.
[9] Stockton D.J., Quinn L., Khalil R.A.: Use of genetic
algorithms in operations management Part 1: applications,
Proc. Instn.Mech.Engrs, vol.218, part B:J. Engineering
Manufacture, 2004,315-327
[10] Caridi M., Cigolini R., Managing safety and strategic
stocks to improve materials requirements planning
performance, Proc Instn Mech Engrs Vol 216 Part B: J
Engineering Manufacture, 2002, 1061-1065
[11] BLACKBURN. J. D.. KROPP. D. H. and MILLEN. R.
A.. 1986. A comparison of strategies to dampen
nervousness in MRP systems. OMEGA Internalionat
Journal of Management Science. 32.413-429.
[12 BYRNI;, M. D . and MAPFAIRA, H.. 1998. An
investigation of the performance of MRP planning in an
uncertain manufacturing environment. Proceedings of the
I4th National Conference on Manufacturing Research.
Derby. UK. pp. 211-2I6.
This document was created with Win2PDF available at http://www.win2pdf.com.
The unregistered version of Win2PDF is for evaluation or non-commercial use only.
This page will not be added after purchasing Win2PDF.