ASSESSING THE IMPACT

OF INTEGRATING A MES TO AN ERP SYSTEM

Young B Moon, Varun Bahl

Institute for Manufacturing Enterprises

Department of Mechanical, Aerospace and Manufacturing Engineering

Syracuse University,USA

Keywords: Enterprise Resource Planning (ERP), Manufacturing Execution

Systems (MES), Production Lead Time

Determination, Simulation.

Abstract: Despite the claims by software vendors on positive valu

es of an integrated MES and ERP system, there has

been no systematic study conducted to assess and evaluate the impact of such an integrated system on shop

floor operations. This paper presents a simulation study to evaluate the impact of the MES integration with

the ERP system on production lead times. First, we describe a methodology of using a discrete event

computer simulation to address an inherent problem of the Enterprise Resource Planning (ERP) system of

handling uncertainties and unexpected events. Then, simulation study results comparing the performances

of a manufacturing system with MES and a manufacturing system without MES are presented. The

evaluation metric used in this simulation is the production lead time. However, the results obtained in this

study can be expanded to more general situations with different evaluation metrics.

1 INTRODUCTION

The MES system (MESA 1997) is considered as a

solution that completes an integrated information

system in an entire supply chain by providing real

time information on the shop floor to the ERP

system. Many benefits obtained from connecting the

MES system to the ERP system arise from the

synchronization of data and business processes

encompassing from the shop floor to the top floor of

an enterprise. Indeed large numbers of articles have

been published to report the merits of the integrated

MES system with the ERP system. However, these

articles tend to describe the benefits of MES in a

qualitative manner. (Reed 2001: Ake 2003)

In this paper the impact of MES is evaluated

uantitatively and systematically. Simulation models

have been constructed to compare the performance

measures between two systems, one with the MES

capabilities and other without MES. The evaluation

metric used in this simulation is the production lead

time. However, the results obtained in this study can

be expanded to a more general situation with

different evaluation metrics.

1.1 ERP

The Enterprise Resource Planning (ERP) System

(Fortu 2002) is a software system to support and

automate the business processes of an enterprise,

which spans across various functional departments

such as manufacturing, distribution, personnel,

project management, payroll, and financials. The

ERP systems identify and plan the enterprise-wide

resources needed to take, make, and distribute for

customer orders.

The implementation and maintenance of the ERP

stems is very high, typically ranging between 15

to 50 million dollars. Therefore, the project of

implementing an ERP system is typically the biggest

single project that an enterprise launches in its

lifetime. Despite its high implementation and

maintenance cost, the ERP System has become the

de facto solution in industry to deploy enterprise-

wide information system.

However, the ERP systems have a few serious

mitations. First of all, current ERP systems are

built for transaction book-keeping purposes as

opposed to decision making purposes. Transaction

systems are good at monitoring events, but they are

not designed to help the decision-making process.

300

B Moon Y. and Bahl V. (2005).

ASSESSING THE IMPACT OF INTEGRATING A MES TO AN ERP SYSTEM.

In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 300-304

DOI: 10.5220/0002538403000304

 SciTePress

Second, the ERP systems are not capable of

handling the uncertainties and unexpected events

because the original MRP (Materials Requirement

Planning) logic is still in core. The MRP system

recognizes the differences between independent and

dependent demands. Through a simple logic and

with aid of a computer, the MRP system can

generate a list of material requirements for all the

subassemblies and components. However, the

simple logic has a couple of strong assumptions: (i)

unlimited capacity on the shop floor and (ii) non-

stochastic worldview. The capacity assumption is

addressed to some degree in the ERP system through

a feedback mechanism. Yet, the inability in handling

stochastic situations continues in the ERP systems.

Third, even though there are provisions for taking

real-time data from shop floor, the ERP system

needs additional external systems or devices such as

Manufacturing Execution System (MES) to actually

monitor and collect real-time data.

1.2 MES

The Manufacturing Execution Systems (MES)

provide up-to-the-second critical data about

production activities across the factory and supply

chain via communications networks. The MES can

assist in the decision making processes for an

enterprise by providing real time aspects of the

entire manufacturing process. The MES

accomplishes this task by guiding, initiating,

responding to, and reporting on plant activities in

real time, by using current and accurate data. The

MES can help reducing cycle times, levels of Work

in Progress (WIP), data entry time, paperwork and

scrap through the improvement in utilization of plant

capacity, process control quality, arrangement of

plant activities, tracking of orders and customer

service. (Choi 2002: Feng 2000)

The MES acts as an interface between the

planning level (ERP) and control level (shop floor)

by sending critical real time information to plant

managers. Overall it helps in integrating the entire

supply chain by bringing the shop floor closer to the

enterprise which helps the shop floor to become

more responsive to the business needs.

2 PROBLEM DEFINITION

The main objective of the research presented in this

paper is to assess and evaluate the impact of MES on

an enterprise. We start with a Null hypothesis that

the MES has no impact on the operations of the

enterprise. The alternative hypothesis is that the

MES affects the performance by optimizing the

resources. On the basis of the simulation models the

null hypothesis is tested and the comparative

performance measures are used in making the

conclusion so as to accept the null hypothesis or not.

Though we have a reasonable conjecture that real

time information will make the production system’s

operation more efficient, we want to quantify these

benefits. We analyze this impact of the MES' on an

enterprise by simulating two manufacturing systems,

one with MES capabilities and the other without

MES.

Even though performance measures can be

several including cycle time, WIP inventory,

resource utilization or others, we focused on the

production lead time in our initial study reported

here.

3 A METHODOLOGY FOR LEAD

TIME DETERMINATION

Before we begin the simulation study, we need to

address the problem of the ERP system associated

with its non-stochastic nature. Again, we focus on

the production lead time in this paper.

The lead time for a product is specified as a

fixed, deterministic number in the ERP systems.

However, the actual lead time in the shop floor

varies significantly due to the variances in individual

processing times and a queue in front of a highly

utilized workstation. Such variances are modeled in

a simulation model and their results are fed back to

the ERP system to determine the most appropriate

lead times. The procedure employs bi-directional

feedback between the non-stochastic ERP system

and the discrete event simulation model until a set of

converged lead times is determined.

3.1 The Simulation Model

The ERP systems contain much of the

manufacturing relevant data, so their databases can

serve as data depository for simulation models.

The first step involves feeding the data stored in

an ERP system to the pre-built simulation model.

The simulation model reflects a rather long-term

description of the shop floor. An interface to directly

read the data stored in the ERP database has been

designed which would result in an automated update

of the simulation model. The production data could

be read into the simulation model at specific

predefined intervals (e.g. hourly, end of the shift,

daily, etc). This enables the simulation model to

effectively simulate a near “real-time” production

ASSESSING THE IMPACT OF INTEGRATING A MES TO AN ERP SYSTEM

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environment and to automatically update the

necessary data.

The second step requires the incorporation of

the current shop floor status into the simulation

model. A production data acquisition system can be

utilized for this purpose, such as the MES to trace

the current state of a shop floor. The necessary input

or the company specific strategies are stored in

additional databases or ERP database extensions.

From an ERP system and/or additional databases

connected to the PDA (Production Data Acquisition)

system, we extract all the necessary data to update

the simulation models.

The third step is to run the simulation model to

estimate an expected production lead time. A base

model is developed by a simulation expert in

advance, which only needs to be populated with up-

to-date data from the ERP system and other data

from the shop floor. The data included in the

simulation model are resources, product orders and

parameters, current production state for each order

and each resource, shift system and working

calendar, and maintenance intervals. The template

model can be adapted in special cases to implement

additional strategies. A simulation run generates an

event list that is finally returned to the ERP system

with other relevant data.

3.2 The Iterative Procedure

The feedback routine follows two steps. The initial

lead times originally defined in the ERP system for

the products are used as a starting point. Based on

those initial data, the ERP software would make a

MRP run. This schedule is then fed into the

simulation software to check the validity of the MRP

run in the current shop floor conditions. Ideally the

flow times obtained by the simulation software

should be close to the initial lead times. If not,

changes are required in the parameters of the

production schedule.

The first step is at a macro level and involves

increasing the lead time by a predetermined amount

of time. This is done to take into account an

“allowance factor” for delays. The updated lead

times are fed into the ERP and another MRP run is

then made. If the two lead times are comparable then

we conclude that we have reached an optimal

schedule.

Otherwise we need to move onto the second

step which is at a micro level. It may not be always

possible to increase the lead time by more than a

certain amount of time because the job should not be

finished too late. In such cases we would need to

change some other parameters to ensure that the job

gets completed near on time. The following factors

can be changed:

* Overtime - Machine hours

* Overtime - Labor hours

* Priority for a particular product - Rush Job

The results are automatically fed back into the

ERP system. Once the lead times reach the same

values from the ERP system and the simulation, they

are accepted as realistic lead times.

4 RESULTS

MES’s impact on an enterprise is studied with the

help of the simulation models developed using

ARENA 7.0 software. For comparison purposes, two

manufacturing systems are modelled, one with MES

capabilities and the other without MES. The results

from these two models are compared on the basis of

the production lead time.

A basic scenario of a shop floor is simulated

using ARENA simulation software. We assume a

situation when one of the machines (say a lathe) on

the shop floor breaks down. One of the main

capabilities of MES is to reroute the parts to other

machine to optimize the shop floor resources and

reduce the lead time and inventory. To include this

capability in the model with MES we reroute the

parts through the parallel connected lathe 2 when the

lathe 1 breaks down. MES is able to perform this

rerouting because of its real time connection with

the enterprise level. This timely information about

the machine failure generates an optimum route of

the parts to optimize the parameters. For analysis we

run 10 replication of 1 day length and compare the

results of the two simulation models.

After analyzing the comparative performance

measures we observe that the cycle times for the

MES model are significantly lesser than the normal

model (without MES) for each replication which

supports the fact that Manufacturing Execution

Systems brings improvement on the lead time of the

system. Also the queue length or the number of parts

waiting for the lathe 1 (failed Lathe) indicates that

MES is able to reroute the parts to the other lathe

when lathe 1 fails. This capability of MES helps in

reducing the WIP inventory & leads to roper

resource utilization.

5 CONCLUSION

Based on the comparative performance measures we

can safely reject the null hypothesis and conclude

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REFERENCES

that MES plays an important role in reducing the

lead time of a manufacturing system. The results

show that the production lead time in a system is

reduced by more than 60% by including MES

solutions in the production system. On the basis of

all these results, we conclude that MES makes a

significant impact on an enterprise.

Ake, K. 2003. ERP to MES integration: An integral key to

an Effective Supply Chain, Available in

www.entegreat.com.

Choi, B.K & Kim, B.H. 2002. MES architecture for FMS

compatible to ERP, International Journal of Computer

Integrated Manufacturing, 15(3): 274-284.

Some of the other benefits introduced by MES

are reduced WIP inventory and better resource

utilization. Using the simulation models we

demonstrated that MES has significant potential to

impact the supply chain and to make it more

efficient. MES forms a critical link between the ERP

and shop floor.

Feng, S.C. 2000. Manufacturing Planning and Execution

Software Interfaces .Journal of Manufacturing

Systems 19(1): 1-17.

Fortu, T. 2002. Enterprise Resource Planning –

Integration with Automation Systems, Master’s Thesis,

Helsinki Institute of Technology, Helsinki, Finland.

The presented research framework and

methodology can easily be expanded to more

complex manufacturing systems or an entire supply

chain. Other performance metrics can be included in

the future study.

MESA 1997. MES Explained: A High Level Vision (White

Paper).Pittsburgh, PA, MESA international.

Reed, R. 2001. Aligning the plant floor to the board room.

IIE Solutions.33: 34-47.

ERP

MRP

SIMULATION

MODEL

CHECK

LEAD

TIME

UPDATE

LEAD

TIME

Data

Different

Lead time

Figure 1: Lead time calculation

Comparable

to ERP

OPTIMAL

SCHEDULE

MES

SHOP FLOOR

Optimal Schedule with

downtime for machine

failure

Updated Schedule

ERP

Real time shop

floor status

Machine breakdown

notification

Figure 2: Simulation model with MES capabilities

ASSESSING THE IMPACT OF INTEGRATING A MES TO AN ERP SYSTEM

303

Table 1: Experimental Results

Normal Model MES Model

Cycle Times (in mins.)

Rep. 1 223.74 135.01

Rep. 2 281.95 111.05

Rep. 3 223.41 128.41

Rep. 4 194.36 172.56

Rep. 5 220.79 177.23

Rep. 6 236.91 211.29

Rep. 7 191.91 96.92

Rep. 8 203.86 146.65

Rep. 9 161.52 139.32

Rep. 10 231.68 219.4

Queue Lengths

Assembler 20.58 23.37

Inspector 0 0.01

Lathe 2 0.1 0

Lathe 1 15.93 0.78

Resource Utilization

Assembler 0.84 1

Inspector 0.54 0.66

Lathe 2 0.25 0.68

Lathe 1 0.32 0

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