Qin Y. Micromanufacturing Engineering and Technology

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2. It provides other activities of the generic pro-

cesses with suitable interfaces to influence pro-

duction, which is the second prere quisite to

establish overall control loops, including pro-

duction.

Although the specific requirements to be ful-

filled by t he production IT environment differ

from industry to industry on the one hand and

from company to company on the other, t here

exists a set of generic concepts that many indus-

tries have in common: these concepts will be

discussed in the following. The common

approach to define both the scope and responsi-

bility of the production IT landscape is derived

from the scheduling and control hierarchy,

which is based on the organizational structure

of the enterprise, as described earlier. Each level

within the hierarchy has corresponding spheres

of responsibility and planning horizons and is

mapped to corresponding levels or layers of the

IT archit ecture shown in Fig. 24-4 (tasks based

on [2]). On the top-most level, the enterprise

management level, the whole enterprise needs

to be considered. Thus, planning and control

activities on this level cover all sites, have a

response time of the order of magnitude of days,

span multiple weeks or months, and incorporate

the complete list of available orders (strategic

level). The sphere of responsibility of the

manufacturing operations and control level is

limited to single sites. Therefore, the scope of

planning and control activities is limited to this

site, spanning only one or several shifts while

providing a response time in the order of

magnitude of a few seconds to a single shift.

Atthesametime,thenumberoforderstobe

FIGURE 24-4 Production IT landscape – an overview.

CHAPTER 24 Manufacturing Execution Systems for Micro-Manufacturing 381

considered is rather l imited (tactical level ). The

bottom-most level, which reflects the actual

manufacturing processes, is finally concerned

with the execution of single process steps. Thus,

the planning horizon is further limited to the

order of magnitude of seconds or minutes. At

thesametimearesponsetimeoftheorderof

magnitude of milliseconds to seconds has to be

provided. In addition to its specific tasks, each

level has to present a sufficient amount of trans-

parency, i.e. to supply the upper layer with accu-

rate and meaningful information with a defined

maximum delay and with suitable interfaces to

propagate information.

Although the specific shape and the boundaries

of these levels differ slightly from industry to

industry and from solution to solution, the ba sic

concept – including the levels discussed above – is

widely spread throughout many industries.

EXPLORING THE MANUFACTURING

OPERATIONS AND CONTROL LEVEL

Based on the overall pi ctu re of the pr oduction IT

landscape described above, the following sec-

tions will discuss the role of the manufacturing

operations and control layer in more detail –

especially the class of manufacturing execution

systems (MES), which plays a major role in this

context. MES systems bridge the gap between

manufacturing processes – including their con-

trol systems – and the enterprise management

systems as s hown above. They control and mon-

itor operations in production, are targeted to

adjust optimization potentials within produc-

tion, and facilitate the level of transparency

between enterprise management and production

that is required to implement complex process-

integration scenarios, as des cribed above. The

task-oriented view defined by [2] has been

selected as the basic structure for this discussion,

as it gives good insight into the M ES world from

auser’s perspective. The description of the MES

tasks is complemented by a discussion of major

concepts and terms to be considered in the MES

environment.

Operations/Detailed Scheduling

Operations/detailed scheduling optimizes the exe-

cution of available work orders – which have usu-

ally been generated by higher level systems –

based on a set of predefined optimization goals,

while considering the constraints of the produc-

tion under consideration. Generic opt imization

goals are:

1. The generation of a schedule that is actually

executable while considering the availability

of resources and material.

2. The reduction of set-up times, cycle time and

work in progress.

3. An increase of throughput, resource utilization

and on-time delivery.

A major characteristic of this task at the MES

level is its real-time behavior, i.e. the schedule is

continuously adjusted based on events and distur-

bances that occur on the shop floor. These

changes are immedi ately enforced in production.

Thus, the MES is able to handle unforeseen

events, such as resource breakdowns, missing

material, issues with production processes and

quality issues. Generally, there are two

approaches to be distinguished, based on the plan-

ning horizon: the schedu ling approach and the

dispatching approach. The scheduling approach

attempts to generate a plan for assigning a set of

jobs (task in the context of the architecture level

considered) to a set of resources based on given

constraints and optimization criteria within the

range of a scheduling horizon. The dispatching

approach is aimed at the optimization of

resources in real-time by evaluating context infor-

mation based on a given set of rules. Two typical

questions answered by the dispatching sub-system

in the case of job-shop production are:

1. What is next for a given resource? – i.e. which

of the jobs within the job queu e of this resource

should be executed first based on the current

context information and the set of dispatching

rules? Examples for context information are:

resource states, job priorities, time constraints,

delivery dates, etc.

2. Where is next for a given material? – i.e. what is

the best-suited next location for some material

382 CHAPTER 24 Manufacturing Execution Systems for Micro-Manufacturing

after completing a process job on a given

resource? In this case the context to be consid-

ered could be the next step based on the route

assigned to this work order, the state of

resources of the type that is required next, etc.

Due to the ad-hoc nature of decisions in the

case of dispatching, this approach is mainly fol-

lowed in highly dynamic environments as known

from semiconductor manufacturing, for example,

as the large number of disturbances within the

production environment would require very fre-

quent rescheduling. As the schedule generation is

rather resource consuming (in terms of computa-

tional resources) and time consuming, it is diffi-

cult to implement the scheduling approach in this

environment.

To deal with scheduling conflicts that arise due

to disturbances as described above or due to com-

peting optimizati on goals caused by committed

delivery dates and varying job priorities, for

example, is an important aspect of the opera-

tions/detailed scheduling task. The system needs

to provide the capability to handle these conflicts

automatically, if possible, or to supply responsible

people with sufficient information to be able to

take the right decision manually.

Resource Management

Resource management is mainly concerned with

the task of ensuring the availability of the

resources required for executing work orders. In

this context, the term ‘resource’ comprises

machines, equipment, durables and non-material

durables. The former terms were explained earlier

– the latter notion of non-material durables refers

to auxiliary resources such as numerical control

(NC) programs or process programs (PP) in gen-

eral. In several industries process programs are

also referred to as recipes. Recipes are programs

that are executed by the equipment control system

in order to control a specific process. The behav-

ior of the control system can be adjusted by select-

ing different recipes or by adjusting parameters

within given recipes.

The first aspect of the resource-man agement

task comprises the management of resource-

related master data. For each resource a record

of generic information needs to be maintained,

including properties such as the identifier (ID),

the location, and capacities and maintenance-

related data. Furthermore, resources have to be

modeled in sufficient detail in order to support the

selected operations/detailed scheduling approach

and other MES functionalities. Usually, resources

are modeled in the form of a hierarchical structure

that describes relevant components and their rela-

tionship. A simple example would be some cluster

equipment, i.e. a class of items of equipment

which have several process chambers that can be

operated in parallel. Each process chamber offers

specific process capabilities. An internal logistics

system is responsible for the material transport

within the equipment. The mainframe could be

modeled as the root of a tree that contains a set

of load ports and a set of process chambers. If

single chambers are being considered within the

process of detailed scheduling, equipment has to

be modeled down to the chamber level. Each

object within the tree has a specific state model

assigned, including states such as ‘idle’,

‘processing’,or‘locked’ that reflect the current

state of the component at run-time. This state

model is used to specify rules for operating the

resource at run-time. A rule of that type could

define that a resource in state ‘locked’ may not

be used for processing, for example. Furthermore,

resources ha ve a set of capabilities assigned that

define the type of jobs that can be executed. At the

same time, resources are often integrated into an

overall resource hierarchy, such as equipment

groups, areas and facilities, and are linked with

related resources. Machine/tool relations would

be an example of this type of link. In addition to

the equipment model-r elated master data,

resource manageme nt has to cover the sub-tasks

of recipe and parameter management.

The second aspect of resource management is

related to the run-time behavior of resources. The

resource track ing task perfor ms book-keeping on

any relevant dynamic data related to resources,

including information on jobs and recipes exe-

cuted on the resource, state changes based on

the resource model and maintenance-related

CHAPTER 24 Manufacturing Execution Systems for Micro-Manufacturing 383

information. The tracking task makes use of

events that are gathered online from resources

or manual ly logged at user termin als. If the phys-

ical resource starts processing, for example, a cor-

responding event is either automatically or man-

ually logged for the resource, together with a time

stamp. Upon receipt of this event, resource man-

agement will update the corresponding resource-

state model, i.e. transition it from ‘idle’ to

‘processing’, and store the event in the history to

allow for further analysis. The updated state

information is visible for other MES tasks. Up-

to-date resource information is an important

requirement for realizing complex automation

scenarios with the MES, as other MES tasks, espe-

cially the detailed scheduling functionality, rely

on this data. Furthermore, resource management

plays an important role in the process of resource

allocation, especially for the appropriate set-up of

resources before some production job is executed

on the resour ce. The set-up comprises anything

that is required in add ition to the pure resource

in order to process the job, such as the appropriate

tools, tensioning media, recipes and parameters.

In the case of fully automated scenarios the set-up

is performed automatically, e.g. the durables are

requested from connected logistics systems a nd

recipes and parameters are downloaded to the

resource. As soon as the set-up is complete and

material is loaded, remote-controlled resources

can even be automatically started via resource

management.

The third aspect of resource management is

also related to the run-time behavior of resources

and is mainly concerned with the availability of

resources. Resource properties reflecting the qual-

ification state are continuously monitored, such

as the operating hours, the number of production

jobs executed, or even specific resource- or pro-

cess-related properties. If limits defined within the

master data model are exceeded, requalification

of the resource is triggered to ensure the resource

does actually provide the expected process capa-

bility. Similarly, the maintenance state of

resources is monitored. Based on data collected

from the resource and corresponding rules main-

tained in the form of resource master data, pre-

ventive or predictive maintenance strategies can

be implemented in order to control the availabil-

ity of resources to the maximum extent.

With a prevent ive maintenance strategy, main-

tenance activities would be triggered as soon as

predefined threshold values of given resource

properties are exceeded. Exchanging the coolant

after a given number of operational hours would

be an example for a preventive maintenance strat-

egy. If required, the resource is locked for produc-

tive use until the maintenance job has been exe-

cuted. In the case of a predi ctive maintenance

strategy, resource parameters are monitored, cor-

related and evaluated against a set of rules. Main-

tenance tasks are triggered based on the evalua-

tion results at the best-suited point in time before

the resource breaks. The idea of this approach is

to reduce the amount of waste that is potentially

generated by implementing a purely periodic

maintenance strategy, while securing a well-

known level of availability for the set of resources.

For the coolant example, this would mean chang-

ing the coolant only after required material prop-

erties have changed, which are continuously mon-

itored. In the event of a resource breakdown,

which causes an unscheduled downtime in con-

trast to the scheduled downtime triggered by pre-

ventive or predi ctive maintenance activities, a

maintenance request needs to be generated based

on the corresponding resource-state change.

Additional data acquired from the resource can

be linked to the maintenance request in order to

speed up the error-detection-and-repair process.

Material Management

Material management involves all tasks related to

material logistics in production. Special focus is

put on the work in progress (WiP) management.

WiP comprises material that is not residing in

managed inventories, i.e. raw material, partially

completed material and final products. Similar to

resources, material has a set of properties that are

monitored while it is transferred from the raw

state towards the final product. The major prop-

erties of a material are its identifier, its location,

its quality and its quantity. The material ID allows

384 CHAPTER 24 Manufacturing Execution Systems for Micro-Manufacturing

the unique identification of an entity of material,

based on a serial number, for example. Due to the

amount of data that needs to be handled if single

entities of material are individually tracked and

due to the fact that many processes treat multiple

entities of material at the same time, the concept

of lots (batches) is frequently used to group mate-

rials for tracking. Multiple entities of material are

grouped to a lot, which has an ID assigned. In

addition, lots can have similar properties as single

entities of material, such as quality, quantity and

state. Managing lots is part of the material man-

agement domain, which includes the tasks to cre-

ate,tosplit,tomerge or to terminate them. Create

supports the process of building lots from single

entities. Split divides a given parent lot into mul-

tiple children. Merge combines multiple lots into a

single lot. Terminate removes the lot from the

system.

Maintaining the history of lots or single mate-

rial entities is a major task within the material

management domain, which is also referred to

as material tracking or WiP tracking. These terms

describe the process of docume nting the complete

history (genealogy) of lots or single entities of

material within production. The history contains

information such as links to raw material data,

information on the equipment the material has

been processed on, process data that has been

collected while processing the material on the

equipment, information on quality and qua ntity

after each process step and material-related mea-

surement and inspection data. These data are

recorded with time stamps and thus lay the foun-

dation for a compr ehensive material-related audit

trail, which is an important building block for

implementing a traceability strategy.

Like the resource states, the material state,

which is continuously updated while generating

history information, is an important input param-

eter for the detailed scheduling functionality, as it

contains relevant data from an operations per-

spective. Some examples for material state-related

information that needs to be consi dered for opera-

tions are:

1. Material might be locked for further processing

(on hold). The hold status is set if quality pro-

blems are de tected that need further investiga-

tion, for example.

2. Time constraints caused by specific process

properties might require the next process step

to occur within a given time window or after a

minimum waiting time. If such constraints are

violated this might result in scrap.

Furthermore, information on available material

quantities and material locations needs to be eval-

uated for planning and operations. However,

material management does not only monitor mate-

rial movements, it also triggers material move-

ments by internal or external logistic systems.

Labor Management

The task of labour management is closely related

to resource management. It takes the specific

properties of ‘human resources’ into account

and supports the task of allocating sufficient per-

sonnel with the right level of qualification on

schedule for the production. Similar to the

resource-management domain, the run-time func-

tionality is based on a set of master data for single

employees, groups of employees and the organi-

zational structure. Typical attr ibutes of a single

employee are the personnel ID, name, qualifica-

tion or cert ification, etc. Furthermore, the avail-

ability of personnel is maintained in the context of

the deployment scheduling.

Based on the master data described above, per-

sonnel-related status information needs to be

maintained. Time recording allows for gathering

information on the actual availability of staff and

the jobs or tasks executed within this timeframe.

This information is required as a basis to imple-

ment complex work-schedule and wage models,

including flexible working hours, piecework

models, etc. Tracking the association of staff

information with production jobs constitutes

another building block for implementing trace-

ability strategies, as it documents who did what

and whether the person who performed a given

job or task had the right level of qualification

or not. Functionality supporting the resource

deployment gives an overview on the available

staff capacities, provides support to manage these

CHAPTER 24 Manufacturing Execution Systems for Micro-Manufacturing 385

capacities and helps to assign jobs and tasks to

personnel in an efficient and effective way.

Data Collection and Acquisition

The availability of data of sufficient quality with

little delay from production is an important pre-

requisite for all of the MES tasks discussed earlier

(and those to be described later). Data collection

and acquisition realizes the connectivity to the

manufacturing processes with a suitable maxi-

mum delay. The reby, it lays the foundation for

an up-to-date, correct and consistent process

image of the situation in production within the

MES. This image is used to monitor and control

processes in production in real-time – as described

in other MES tasks. The meaning of ‘suitable

delay’ and ‘real-time’ depends on the actual mon-

itoring and control problem to be solved. Usually,

the minimum response time on the MES level is of

the order of magnitude of a few seconds.

Generally, three approaches to data collection

and acquisition can be distinguished: manual,

semi-automated and automated data collection.

In the case of manual data collection, an operator

manually enters data record s using some kind of

input device. Manual data collection is frequently

performed using electronic forms that the opera-

tor has to complete at a user terminal. Semi-auto-

mated data collection needs manual interaction

by a user – however, part of the data is acquired

automatically. Data collection using bar-code

guns or RFID reader s are examples of semi-auto-

mated applications. Automated data collection

requires the implementation of suitable IT inter-

faces for data acquisition. The collection is either

triggered by events that occur within the data

source or by cyclically polling the data source.

Sophisticated mechanisms have been designed to

specify both the amount of data to be collected

and the frequency, based on the current need. The

concept of data collection plans (DCP) allows

users to dy namically determine the data to be

collected for a given process. The user specifies

the collection plan by selecting process variables

to be monitored from a set of available variables

and the desired collection freque ncy. As soon as

the plan is activated on the corresponding

resource, the selected data is available through

the IT interface.

Whenever raw data is collected, it needs to be

checked for plausibility and consistency before

being further processed. These checks are

required independently of the approach used for

data collection. As correct and consistent data is

an important prerequisite for successfully operat-

ing an MES, these checks are considered to be

part of the data collection and acquisition task

in order to prevent the further processing of in-

correct data. After performing the plausibility

and consistency checks, most of the data records

collected have to be preprocessed and consoli-

dated to simplify the downstream tasks. Con-

verting dimensions or evaluating counters to

physical values are two examples of consolidation

and preprocessing steps.

Performance Analysis

Performance analysis targets the evaluation of the

performance on the shop floor, both short term

and long term. Thus, it provides support for estab-

lishing control loops to influence operations on

the one hand and to optimize processes in the long

run on the other. Data that have been collected by

the MES are consolidated to suitable key perfor-

mance indicators (KPIs) that lay the foundation

for further analysis. These KPIs can be compared

against organizational and technical targets: suit-

able corrective actions are derived from the

results, if necessary. The following list provides

a set of examples of key performance indicators

that are used throughout many industries:

1. Equipment utilization. This is the fraction of

time the equipment is performing its intended

function during a specified time period [3].

Measurement of the remaining capa city of

equipment or workplaces provides the ability

to detect bottlenecks.

2. Overall equipment efficiency (OEE). This is the

fraction of the total time that equipment is pro-

ducing effective units at theoretically efficient

rates [4]. This is used widely to measure process

efficiency.

386 CHAPTER 24 Manufacturing Execution Systems for Micro-Manufacturing

3. Cycle time. This is the analysis for all compo-

nents of the cycle time for jobs, including non-

productive time, and the sum of these compo-

nents.

There are numerous ways to make use of per-

formance analysis results and to trigger corre-

sponding actions. In the simplest case the results

are incorporated into management reports and

lay the foundation for further decisions. This is

especially true for the long-term case. For the

short-term case the results of the performance

analysis could automatically trigger actions

within the system or could be fed into the dash-

boards of control centers or information systems

on the shop floor to trigger immediate action by

operational personnel.

Quality Management

The quality management task supports organiza-

tions in reaching the required level of product and

process quality as far as the shop floor is con-

cerned. It comprises the aspects of quality plan-

ning, management of test equipment and quality

inspection. Quality planning requires a variety of

input data from previous process stages in the

product lifecycle on the one hand and overall

quality targets on the other that define the require-

ments for quality inspection. Test plans need to be

transformed into suitable system configurations

to ensure that routes and data-collection plans

are appropriately set up and that the data

acquired from the shop floor are evaluated as

needed. The sampling functionality is responsible

for selecting lots or single products for quality

inspection based on sampling plans that need to

be configured according to the test plan. A simple

sampling plan could define every 50th lot to be

inspected, for example. Dynamic sampling is an

extension that allows for dynamic adjustments of

sampling plans at run-time. The sampling rate

could be automatically increased, if issues are

detected in production that might affect quality,

for example. Quality assurance processes and

measures need to be modeled within the MES

system in order to enforce and to document their

execution. Access-control based on the current

certification level of operators, the ability to issue

directives to operators that need to be confirmed

after reading, and quality gates are examples of

measures to be supported by the MES environ-

ment. Managing test equipment is closely related

to resource management. However, some specific

characteristics of test equipment have to be taken

into consideration. Especially, the support for cal-

ibration traceability needs to be taken into

account.

Another aspect of the quality-management

task is related to documentation, reporting and

control. Depending on the quality-management

system, corresponding documentation has to be

generated which confirms that the defined qual-

ity-assurance measures have been properly exe-

cuted. The quality information collected lays the

foundation for reporting the level of quality

reached and thus allows for implementing

counter-measures early, if issues are detected.

Sample data collected can be evaluated using the

methodology of statistical process control (SPC).

SPC provides the means to monitor processes and

thus to detect possible quality issues and to correct

them in the early stages of the overall production

process for a product. The methodology of fault

detection and classification (FDC) is used to ana-

lyze quality-related data, to detect and recognize

possible issues and to derive suitable actions from

these issues. Example actions that could be trig-

gered automatically are:

1. Put lots on hold;

2. Stop pro cess equipment.

Information Management

Information management is a cross-sectional task

that is built on top of the tasks that have been

described earlier. It makes use of data and context

information available in the system, provides suit-

able views of the data – both for users as well as

for other MES tasks – and complements this infor-

mation by suitable reports and evaluation results.

Furthermore, the task of information manage-

ment has an active component – it responds to

events reported from production in real-time.

Both aspects lay the foundation for establishing

CHAPTER 24 Manufacturing Execution Systems for Micro-Manufacturing 387

complex control loops for production that take a

variety of parameters into account.

Suitable views providing information in the

appropriate context are not only relevant for

management reporting, production management

and control or quality assurance, they also pro-

vide the tools required to implement paperless

production concepts or at least strategies that

require a minimum amount of paper in produc-

tion. Repl acing paper-based lot travelers by elec-

tronic lot travelers is one element of paperless

strategies. A lot traveler contains basic propert ies

of the lot, such as: its identifier and priority infor-

mation; information on the route to be used, i.e.

the process steps to be performed; and additional

information to be considered by the operator.

Furthermore, it is often used to collect feedback

from production, such as the signatures of opera-

tors confirming that certain instructions have

been executed, quality inspection results, process

data, etc. An electronic lot traveler provides users

with the type of information required in a given

context using IT systems. In this case, ‘context’

could mean a given job at a given equ ipment

instance. Other views could provide operators

with specific information on quality trends

achieved at a particular work station which would

help to achieve a constant level of quality. The

capability to access relevant documents in a given

context, such as drawings, manuals and work

instructions, helps operators to work efficiently

and effectively.

The ability to design, execute and monitor

workflows for the shop-floor environment that

allow the guidance of operational sequences on

the shop floor is a major task to be supported by

the MES. These workflows describe sequences of

actions, roles and responsibilities and drive all

operational sequences in a defined way. Imple-

menting this task might require the MES to sup-

port additional classes of master data, if this data

cannot be supplied by other tasks or external sys-

tems. Exception manage ment is an additional

sub-task of information management. It targets

the automatic resolution of exceptions that occur

in production, if possible, or at least the support of

users for resolving exceptions. Semi-automated

exception resolution would provide responsible

users with information on possible corrective

actions and generate except ion notifications for

these users by e-mail or short-message service, for

example. More sophisticated approaches to influ-

ence production based on collected information

are covered by the methodologies of advanced

process control (APC). This term covers a variety

of process-control methods and tools, such as SPC

and FDC as described above, run to run (R2R)

control and others.

MES IMPLEMENTATION

AND INTEGRATION INTO

THE PRODUCTION IT

There exists a variety of approaches to imple-

ment MES solutions supporting the tasks

described above. In most cases these tasks will

not be supported by a single system, but rather

by a collection of cooperating systems. Further-

more, the set of implemented tasks heavily

depends on the requirements of the industry or

group of industries that a given solution targets

and – most importantly – the specific require-

ments of the organization to be supported. The

same holds true for the complexity of the IT

landscape and the enterprise architecture of the

production IT environment. Figure 24-5 shows a

simplified example of a production IT environ-

ment that contains some typical concepts used.

It is based upon the hierarchical structure shown

in Fig. 24-4.

1. The bottom-most layer is the equipment layer.

Equipment provides one or multiple

manufacturing-process capabilities that are

monitored and controlled by internal process-

control systems, such as programmable logic

controllers (PLC), computer numeric control-

lers (CNC), embedded PCs or industry PCs

(IPC). Typically, these control systems are con-

nected to sensors and actuators using standard-

ized field bus systems, such as PROFIBUS and

CANOpen. Depending on the complexity of

the equipment, it might again use a hierarchical

IT infrastructure internally. Exa mples of com-

plex equipment would be cluster equipment,

388 CHAPTER 24 Manufacturing Execution Systems for Micro-Manufacturing

automated lines or transport systems. Equip-

ment is able to autonomously control at least

their basic process capabilities using these

internal control systems. IT interfaces (in Fig.

24-5 marked as ‘Equipment Interface’) expose

selected properties of the internal control sys-

tem of the equipment to the outside world. In

the simplest cases the interfaces provides access

to data available within the internal control

system, such as process variables that contain

sensor results. Furthermore, such interfaces

can prov ide notification of events or alarms

that occur within the equipment, configurable

data collection based on collection plans,

remote control and other capabilities.

Sufficient equipment interface capabilities are

an important prerequisite for supporting the

tasks described above on the MES layer. For

this reason, they provide a broad field for stan-

dardization, which will be described in more

detail in the following section.

2. The equipment automation (EA)/equipment

integration (EI) layer connects equipment to

IT systems on higher layers by using the equip-

ment interfaces discussed earlier. The main

goal of the equipment integration is to provide

a common view of all types of production

equipment to be monitored and controlled

within the factory and to connect equipment

to the factory communication system (usually

FIGURE 24-5 IT Infrastructure (example).

CHAPTER 24 Manufacturing Execution Systems for Micro-Manufacturing 389

Ethernet based). Thus, it implements part of

the resource management tasks. At the same

time, sever al of the data- collection and acqui-

sition-related tasks described earlier are imple-

mented on the EI layer, such as unit conversion

and data verification. Equipment automation

refers to the implementation of equipment

(type)-specific operational scenarios , i.e. it pro-

vides the IT support required to execute pro-

cess steps of equipment in a defined way at a

given instance. These scenarios comprise

equipment-related material logistics, material

verification, set-up instructions, remo te-con-

trol commands and data collection, for exam-

ple. User-interface clients are attached to single

items of equipment or to groups of equipment,

if operational scenarios require user interaction

or supervision. These user-interface clients sup-

port the information-management tasks in the

context of a given item of equipmen t or equip-

ment group. In many installations, the EA/EI

components and the user clients are either

deployed on ded icated PCs or IPCs for single

items of equipment or equipment groups or

deployed on larger server systems within the

factory’s computation al center.

3. The majority of tasks described above are

implemented on top of the EA/EI l ayer. Older

implementations of MES solutions followed a

rather database-centered approach and were

usually based on mainframe t echnologies.

This turned out to be a limitation during

recent years, especially in terms of agility.

One of the non-functional requirements to

be taken into account for implementing

MES solutions is the ability to change, i.e. to

support new requirements. Reasons for

change and new requirements are the intro-

duction of new technologies, new equipment,

new products and process improvements, for

example. Furthermore, there is a need for hor-

izontal integration within the manufacturing

and control level. Due to the variety of tasks

to be supported, there is usually the need to

integrate multiple applications – in many

cases even from different suppliers – in order

to realize the required level of IT support for

production. However, all applications require

a correct and consistent view of the current

situation in production. Both issues were not

sufficiently supported by the old mainframe

solutions. Fortunately, current state-of-the-

art architectures promise to do better. To

use the paradigms of a service oriented archi-

tecture (SOA) looks particularly promising:

the MES tasks described above are implemen-

ted by a collection of independent servic es.

Each service covers a well-defined functional

scope (such as recipe management) and

exposes its c apabilities through public-service

interfaces. In order to implement a given busi-

ness process in production, a corresponding

setofservicesneedstobeorchestrated,i.e.to

be combined, in the right way. This approach

does not only improve agility, as services can

be rewired or complemented by additional

services to support c hanging requirements, it

also greatly simplifies both horizontal as well

as vertical integration within the production

IT landscape.

PRODUCTION

IT STANDARDIZATION

As shown in the preceding sections, establishing a

pervasive shop-floor IT environment requires a

variety of software systems to cooperate – starting

from the enterprise layer down to the equipment

layer. For each type of software system, there

exists a variety of products and suppliers in the

market. Therefore, accepted and implemented

industry standards turn out to be an important

enabler for realizing production IT environments.

Among others, standards are created to serve the

following goals:

1. They provide common definitions of impor-

tant terms and concepts and thus facilitate a

common understanding, which is especially

important for the specification phase of MES

solutions.

2. They define system classes and their scope and

thus create transparency regarding the capabil-

ities of a given system class.

390 CHAPTER 24 Manufacturing Execution Systems for Micro-Manufacturing