Managing Production Complexity with Intelligent Work Orders

Ville Toivonen, Eeva Järvenpää and Minna Lanz

Laboratory of Mechanical Engineering and Industrial Systems, Tampere University of Technology,

Korkeakoulunkatu 6, Tampere, Finland

Keywords: Intelligent Work Order, Decentralised Production Systems, Process Configuration, Information Management.

Abstract: Progress of Industrial Internet of Things is rapidly increasing the amount of data collected from manufacturing

operations. This data can be utilized to control and improve production systems in various ways. Production

control systems play a key role in realizing the potential cost savings and productivity increase. Companies

are required to manage increasing complexity while shortening response times to changes. A concept of

Intelligent Work Order (IWO) is proposed to assist in these challenges. It supports local or distributed

decision-making, and decreases integration complexity between different factory IT-systems. IWOs also

increase information visibility at the shop floor. The IWO structure and functionality are described with a

discussion of the benefits of the approach.

1 INTRODUCTION

Production processes are facing increasingly higher

demands for planning and control that start to

resemble one-off production. The Web-based

consumer trade is pushing the requirement of minimal

lot-size down as the customers want and are capable

for selecting or even configuring digital product

orders at the level of detail that so far has been viable

for large companies placing orders for big production

lots or for skilled sales personnel specifying high-cost

products, such as cars or kitchen furniture, for and

according to the customer.

Allowing the increase in the product

configuration variability also increases the need for

more integrated IT and manufacturing systems along

the ordering-production planning-manufacturing

chain. Likewise, more flexible and accurate

monitoring and control systems on the factory floor

are then needed for maintaining the product quality

and efficiency, ensuring the human-machine safety

under the increased variability in the production

process, and managing the significantly more

complex logistics associating each individual product

to its components, production schedule and the

customer data.

The industrial recognition of the need to increase

digitalization and to introduce novel control systems

has led to German Industry 4.0 initiative (Brettel et

al., 2014) and other similar approaches. A survey

conducted in Finnish manufacturing industry

(Järvenpää et al., 2015) showed similar interest from

the companies but also a large gap between the

academic concepts and the status of the control

system implementations. In this paper a concept of

Intelligent Work Order (IWO) is presented, which is

an effort to bridge that gap and show how existing

Manufacturing Execution Systems (MES) can be

extended to meet the future requirements of

production control systems. In Section 2,

decentralized production control systems are

discussed in general and in relation to IWO. Section

3 presents the structure and functionality of IWO,

while Section 4 is dedicated for information systems

integration. Benefits of IWO are summarized in

Section 5, before closing remarks of Section 6.

2 DECENTRALIZED

PRODUCTION CONTROL

Commonly stated reasons for distributing production

control are managing complexity and gaining the

ability to react quickly to production changes or

disruptions in the system. Decentralized control

systems are built on local decision making and lack a

holistic view over the whole system. In agent based

control systems, such as in (Caridi and Cavalieri,

2004), a coordination process is designed to diminish

Toivonen V., JÃd’rvenpÃd’Ãd’ E. and Lanz M.

Managing Production Complexity with Intelligent Work Orders.

DOI: 10.5220/0006507801890196

In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KMIS 2017), pages 189-196

ISBN: 978-989-758-273-8

the negative effects of having a narrow view of the

whole system, while in holonic manufacturing

systems (HMS) the decision makers are only

connected to higher level controllers (McFarlane and

Bussmann, 2000). This means HMS are always

hierarchical, while the form of agent based systems

can vary more freely. These approaches are

applicable both in a single manufacturing site or a

supply chain (Saharidis et al., 2006). Some often

noted benefits and hindrances of centralized and

distributed control policies are listed in Table 1

(Zannetos, 1965) and (Toivonen et al., 2011).

Decentralised control systems are sometimes

viewed to contradict any kind of forward planning of

production. More practical view is to plan on

aggregate level and allow a decentralised control

system enough decision making power to take care of

execution. Another integrating approach between

planning and control is to have the decentralized

control system forecasting future events (Valckenaers

and Brussel, 2005).

3 INTELLIGENT WORK ORDER

Intelligent work order provides one possible

infrastructure for realising an agent based production

control system in real environments. In this case IWO

can be an information agent or sometimes both an

information agent and a controller. Control decisions

(such as resource allocation and job dispatching) are

done based on the information gathered by the

intelligent work order. In order to fully realise a

decentralised control system, IWO needs to fulfil the

following set of minimum requirements:

 Ability to recognize and communicate with

resources

 Ability to create work orders for subtasks

 Ability to communicate with a higher level

controller

 Inclusion of a decision making control logic

Intelligent work order collects information from

several corporate IT-systems and can also be used to

collect and communicate process information for

various purposes.

Table 1: Comparison of decentralized and centralized control hierarchies.

Decentralized Centralized

Benefits Benefits

 Increased flexibility due to self-configuration that

also enables efficient response to changes.

 Provides decision-making power to lower level

managers or workers with better experience on local

processes.

 Efficient development of the local operations and

processes.

 Detailed and up-to-date information.

 Clearness of goals and responsibilities.

 Expanded responsibility and decision-making

authority which often result in increased work

satisfaction, motivation and efficiency.



Lower costs for organizational and transaction

management.

 Fast dissemination of information.

 Fast decision-making due to one author

controller.

 Consistent processes and practices.

 Combined control system that enables fast

review of resource allocation.

Disadvantages Disadvantages

 Higher costs for organizational and transaction

management.

 Possible lack of coordination among autonomous

managers.

 Lower level managers’ decisions could be

damaging, because they do not necessarily have full

understanding of the wider perspective.

 Lower level managers may have goals that are

different from the goals of the entire supply chain.

 Lower level managers may make decisions that are

not in the organization’s best interests.



Inflexibility that is caused by the complexity of

information systems and organization structure.

 Weak ability to respond to changes that is caused

by the complexity of organizations.

 Challenges that result from information validity

and integrity

 In a strongly centralized organization process

optimization is difficult due to the complexity of

the organization’s structure.

 Lack of collaboration that is caused by a

centralized control system which is managed by

a dominant authority.

3.1 IWO Functionality

Traditionally a work order describes either the

process or the end result of the required task. These

work orders are commonly delivered to the factory

floor in paper format (Järvenpää et al., 2015), which

means they have limited information content and are

difficult to maintain. Intelligent work order is digital

and contains up-to-date information of both process

(e.g. instructions, NC-programs) and output (e.g.

specifications, quality control guidance) in a machine

and human readable format, as shown in Figure 1.

IWO is role- and context dependent. It can be

configured based on the operators’ personal

characteristics, preferences and experience. For

instance, the way to present the work instructions may

be modified based on the operator’s native language

and experience.

Figure 1: Principle of Intelligent Work Order.

IWO is created only at the time it is needed, after

which it calls for resources required in the process.

This approach is intended to ensure that changes

made in ISA-95 level 3 and 4 planning systems

(ANSI/ISA-95.00.03-2005) are automatically

considered in the process level. After completion of

the task, process data is aggregated to a desired level

of detail and stored into the memory of IWO. Without

aggregation a lot of the data acquired from the process

might not be directly very useful (e.g. signal values)

to a higher level planning system. The collected

process information can later on be viewed from

resource, time, customer or product point-of-view.

This allows managers to link process information

with businesses processes in a meaningful way.

3.2 IWO Structure

IWO is an information element that has a clearly

defined lifespan from start to the completion of a

specific task. Figure 2 shows a generic IWO structure

and information content that has been deducted from

the requirements. A digital work order is essentially

an information distribution agent that contains all

relevant process information. The additional features

of IWO require a more diverse structure.

Communication interface allows user specific views

to information and the realisation of online

negotiation or alarm systems. Data collection

interface tries to standardise some of the factory floor

data exchange and decrease the related integration

effort. Processing unit contains the local controller in

distributed systems and can be utilised to perform

some operations and reduce complexity of centralised

control systems. Data storage is a short term storage

location for work order data and feedback from the

manufacturing process.

The information content of IWO is closely related

to the element structure. In addition to the process

data, IWO contains metadata defining how

information is shown, collected and distributed:

Parent – Defines the process structure

Process Information – Task specific information

e.g. work instructions and NC-programs

Information Distribution – Defines connections

and integration to other IT-systems

Information Collection – Defines reporting

activities and connections and integration to the

control logics

Processing and Acting – Decision making and

communication block that allows raising alarms,

calling other services etc. This part defines the low

level controlling logic.

The information content should be fairly similar

for any digital work order. The main difference in

IWO is the processing block that allows it to be used

for decision making.

Figure 2: IWO Structure.

3.3 IWO Creation and Storing

IWO constructor is responsible for creating and

configuring work orders. The main information

content of work orders exists in MES and other higher

level information systems but the configuration can

also be based on the status of the production system.

As an example, a work order could be configured to

always link with a machine that has the shortest setup

time.

IWO constructor needs integration to systems that

maintain the work order related information. While

system integration is not discussed in detail in this

Section, differences of the two main approaches

should be noted. Integrating the constructor solely

with MES is cheaper and easier than creating

connections to several higher level systems.

However, it is important to understand the depth of

the existing MES integration. Alarming systems and

other real-time control methods require a deep

integration to ensure up to date information and rapid

response capability. If the existing MES

implementation contains mainly historical data for

aggregate planning or the information content is

otherwise limited, the constructor might require

additional integration effort to shorten the time delay

of information updates.

Table 2 summarises the elements required for

creating IWOs.

Table 2: Key elements for creating IWOs.

Constructor

Contains a tool for defining

IWO content, see IWO

structure for details

Data storage for created IWO

models

Integration to valid IT-

systems for collecting

information

Information

element (IWO)

A digital work order for

production tasks

Short-term storage for

documenting the process

Communication ability to

real-time applications

Distribution cente

A database or a server

Long or mid-term data

storage for process

information

An interface for information

systems to access process

information

After a task has been completed, results are

collected and stored for later use. The proposed

concept does not describe what should be done with

the process data after it has been stored. However, the

information should be made available for different

IT-systems in a way that does not depend of the MES

integration.

Figure 3: Control hierarchy.

4 SYSTEM INTEGRATION

The approach presented here is meant to reduce

integration complexity and improve system

connectivity while the related industrial standards are

still being under discussion and development. In

order to achieve this, the factory floor integration has

been reversed to a bottom-up process as shown in

Figure 3. This means that production process defines

how work orders are brought to and how information

is collected from the process. In addition to the lower

level integration layer, IWOs form a layer of

abstraction between the higher level information

systems and the process data. System complexity

increases in the lower levels of control hierarchy

while the higher level system interfaces stay usually

fairly constant. One important aspect of IWO is to

provide accurate process level information for later

analysis and development purposes. Mapping the

feedback information and sensor data against IWO

combines process plan, end result and resource,

environment and customer data into a single

information object.

The link between two work orders describes a

process chain where a result from a prior task affects

the configuration of the IWO in a later task. The direct

communication between work orders is more relevant

when one is functioning as the controller of subtasks.

The implementation of the system defines how much

of the controlling power is given to IWOs and which

decisions are made by a higher level controller. The

higher level systems are used for planning ahead,

which means that the utilized data is always

somewhat aggregated and delayed.

4.1 Data Collection

In manual reporting the data must often be written

down first and later entered into the system -

sometimes by a different person than the one who

recorded it in the first place. Typographical and

transcription errors are common. Once these errors

become part of the data set, they become difficult to

detect and eradicate, making all the resulting reports

less reliable. At the shop floor of manufacturing

companies, there are thousands of operations without

any link with the current Enterprise Resource

Planning (ERP) and MES solutions. In addition to

that, the relationship between shop floor actions and

data input into MES is in majority of cases off-line.

This means that workers are feeding information of

processed jobs manually and often with a time lag to

the actual events. That is why, traditionally ERP and

MES solutions cannot be used to process real time

information from shop floor or to compare real

actions with planning. The lack of real time

information into the MES and ERP systems are

leading to a very low rate of response from the

management team, which is leading to a high

percentage of scrap, useless consumption of raw

materials and energy, increase of waste in process,

and nevertheless losses for SME’s.

The long and continuing effort of removing waste

from manufacturing systems should be directed to

information management as well. This does not only

involve the system side but even more importantly the

practices of producing and consuming information in

the factory floor. In this approach a large portion of

the information management procedures are defined

in the process, allowing similar streamlining and

continuous process development as has been

executed with Lean methods in manufacturing. This

is different from the typical approach where the IT-

systems are defined top down and users are

constricted to those definitions.

4.2 Integration of Individual

Controllers

The trend of digitalization is rapidly increasing the

amount of available data from the factory floor. At the

same time, the complexity and cost of MES

integration are also increasing in the same fashion.

This means many companies create subsystems or

‘digital islands’ in domains where the direct benefits

of digitalization are greatest. In manufacturing

industry this can be seen for example in supplier

specific automated solutions that can offer great

flexibility but are difficult to integrate under a single

controller. IWOs can be used in two different ways to

improve the situation:

1. IWO can assist in connecting these islands

by providing a common communication

interface. In this case defined information

can be exchanged between individual

systems, but there is no controller linking

them as an integrated system.

2. Both systems provide an integration to allow

IWO to function as a controller. This takes

more effort but allows extended automation

and process control. In such an approach

IWO can be seen as an integration platform

to reduce the complexity and costs of

integration.

In a similar fashion, IWOs can be utilized in

human-machine interfaces to reduce human input to

the machine controller and to communicate the

current status and future actions of the machine to the

worker. Flexible integration is especially important

for increasing the level of automation in industries

where it has been difficult.

5 IWO VALUE PROPOSITION

Some of the main benefits and potential applications

of IWO are listed here as a summary of the concept.

The list is categorised to follow timeline of a process;

planning, execution and reporting. MES

implementations are always company or

manufacturing site specific, which means that the

following list is suggestive at best:

Quality of Information

Several practices have been considered to

improve the information quality of IWOs. IWO

creator is integrated to the original source of

information as closely as possible. This is meant to

enforce ‘one owner policy’ and reduce harmful data

replication to several systems. From the process side

the aim is to reduce manual input by increasing

automated data collection and also to give control

over reporting to the process where information is

created.

Targeted Quality Control

One benefit of the Intelligent Work Order is the

ability to reconfigure work process in real-time. This

can be utilised for example in quality control

practices. In case of deviations, such as appearance of

a defected part, an additional inspection can be

assigned for each task that involves a part from the

same batch. IWO allows to focus corrective measures

quickly and precisely to the orders that might be

affected.

Real-Time Process Control

All information needed to accomplish tasks in the

workstation is delivered automatically to the

workstations without the need of worker

involvement. The information of task status and

realized production is always available for production

planning and control, which enables fast reaction to

possible variations and disturbances. The application

of rapid response in quality control can also be

extended to increase system responsiveness in

general. IWO allows order and task specific process

configuration, which means even individual

production orders can be controlled in real-time.

Worker Specific Work Instructions

IWO allows user based configuration of the

process information. In practice this can mean, for

example, choosing the language of work instructions

based on the recognised worker. Similarly new and

less experienced employees might get more detailed

instructions and be required to sign more additional

verifications during the process. Context aware

applications can also involve physically readjusting

the work place to assist in the operation.

Improved Traceability

Mechanical engineering industry has lagged many

other industries in building traceability chains

through their supply chains. IWOs provide a

systematic approach for recording in-house actions in

detail and as such improve product traceability.

Automated data collection

One key driver of digitalization is to reduce

manual handling of information. This is essential for

reducing mistakes and shortening the time delays

between events and information availability. IWO

supports introduction of novel reporting and sensor

technologies by simplifying their integration to the

MES.

Development and Analytics

The information generated during the production

processes, e.g. recordings, task duration,

measurement and quality data, and other data

collected by various sensors, are linked to the

intelligent work order. Analysing existing products

and processes is facilitated by linking the product,

resource and operational process data. The generated

expressive information object can later be used for

different analytics and for increasing the accuracy and

quality of planning and control.

6 DISCUSSION

We have proposed a concept of intelligent work order

(IWO) to tackle increasing complexity, to improve

real-time control and to allow a better integration

between different factory IT-systems. The trend of

digitalization has increased the interest of

manufacturing industry in similar approaches and

industrial implementations do exist. We believe the

additional effort required for MES development and

integration has a very short payback time in most

manufacturing environments that have a dedicated

production control system in place. The concept

should be applicable to different control hierarchies

and MES implementations, and allow cherry picking

the benefits that are chosen as key drivers for the

investment.

The concept is providing tools for closing the gap

between current MES implementations and the future

needs from the control systems. There is a recognized

need for convergence of factory IT and operational

technology. Tools and methods are required

especially to facilitate integration of legacy hardware

in factories. These should allow both collecting real-

time information of production processes and moving

decision making power closer to the process while

maintaining a holistic view of the production.

This research has started from interests of our

industrial partners and we are hoping for this work to

contribute to industrial adaptation of the presented

ideas and digitalization of manufacturing industry in

general. At the moment, industrial demonstrations are

being planned in order to further advance this

development. Interesting applications could also be

found in rapidly developing fields such as

collaborative robotics.

ACKNOWLEDGEMENTS

This research was carried out as part of the Finnish

Metals and Engineering Competence Cluster

(FIMECC)’s MANU programme in the LeanMES

project.

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