case study wiendahl_2005

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Production Planning & Control,Vol. 16, No. 7, October 2005, 634651

Stumbling blocks of PPC: Towards the holistic

configuration of PPC systems

H.-H. WIENDAHL*y, G. VON CIEMINSKIz and H.-P. WIENDAHLz

yInstitute of Manufacturing and Management (IFF),

University of Stuttgart, Nobelstrasse 12, 70569 Stuttgart, Germany

zInstitute of Production Systems and Logistics (IFA),

University of Hannover, Scho nebecker Allee 2, 30823 Garbsen, Germany

Manufacturing companies often complain about the difficulties they face in meeting theircustomers logistic requirements. Many blame the perceived inadequacies of their productionplanning and control (PPC) software for their performance deficits. The paper illustrateswhy this is only a partial view of the causes of the shortcomings. PPC software is just oneof six configuration aspects of the entire PPC system. The authors argue that the configurationof the PPC aspects objectives, processes, objects, functions, responsibilities and tools has to becarried out methodically and consistently in order for the PPC system to function properly.The analysis of examples of so-called stumbling blocks of PPC, inadequate configurationsof one or several of the aspects, supports this claim. The paper closes with the proposal of achecklist that the authors suggest as a first approach to ensure the consistent configuration ofPPC systems.

Keywords: Production planning and control systems; Configuration aspects of PPC systems;Stumbling blocks; Configuration and operation of PPC systems; Actors in PPC

1. Introduction

It is almost 30 years since Orlicky (1975) first described

the material requirements planning (MRP I) algorithm.

To this day the algorithm remains the kernel of many

production planning and control (PPC) systems. Despite

30 years of progress in PPC theory and practice, and

the definition of additional key functions, a large

number of manufacturing companies remain unsatisfied

with the degree of fulfilment of their logistic objectives.

Recent surveys prove that companies still miss theirlogistic targets by a wide margin (Fraunhofer IPT

Institute 2003, Wiendahl 2003a). This applies to the log-

istic performance measures of productionwork-in-

progress levels, throughput times and schedule

reliabilityin the same way as to those of stores:

inventory levels, service levels and delivery delays.

A historical review reveals various causes of the

unsatisfactory logistic performance and, considering

these, the solutions that a holistic configuration of

PPC systems requires. In the past, critical evaluations

of PPC methods identified the limited capabilities of

computer hardware as the principal cause for the insuffi-

cient fulfilment of logistic objectives. These hardware

limitations only allowed a step-by-step development

of PPC algorithms. Due to this, the manufacturing

resource planning (MRP II) algorithm that followed

MRPI is characterised by the successive execution ofits functions. As real situations in manufacturing

companies seldom conform to the rigid assumptions

that are underlying this algorithm, there were calls for

a more realistic consideration of practical conditions.

PPC research therefore concentrated on the develop-

ment of new functions and algorithms (Plossl 1985,

Vollmann et al. 1997) and neglected the analysis of

the required preconditions such as an organisational

framework for PPC (Kraemmerand et al. 2003).*Corresponding author. Email: [email protected]

Production Planning & ControlISSN 09537287 print/ISSN 13665871 online # 2005 Taylor & Francis

http://www.tandf.co.uk/journals

DOI: 10.1080/09537280500249280


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Over time, the remarkable progress of computer tech-

nology facilitated the application of more powerful

planning software such as enterprise resource planning

(ERP), supply chain management (SCM), advanced

planning and scheduling (APS) or manufacturing execu-

tion systems (MES) (Stadtler 2002). All of these systems

carry out a considerably larger number of functionsthan their predecessors. They apply sophisticated

mathematical algorithms to solve multi-variable

optimisation problems and can thus consider numerous

planning restrictions simultaneously. Due to the

immense complexity of the implementation of these

large systems they often fail to produce the substantial

logistic performance improvements the companies

are hoping for (Davenport 1998). In contrast, other

businesses preferred simple PPC approaches. The

increased popularity of just-in-time principles and

Japanese management methods made companies avoid

the application of software and focus on organisational

aspects instead. They achieved remarkable performancegains, e.g. by the introduction of Kanban control cards

(Soder 2004). The contrast between highly sophisticated,

computerised PPC systems whose logistic performance

is insufficient and simple, rules-based control mechan-

isms that achieve astonishing results made researchers

and industrialists realise that the problems of PPC

cannot be solved by more powerful software alone.

There seem to be other causes of the described perfor-

mance deficits, which had been neglected so far.

The standard textbooks on PPC offer detailed

descriptions of the theoretical foundations of the PPC

functions, mainly mathematical models and algorithms

(Plossl 1985, Fogarty et al. 1991, Hopp and Spearman

2000, Vollmann et al. 1997). However, instructions on

the design and implementation of PPC systems are

uncommon or not very detailed. Fogarty et al. (1991)

emphasise that the choice of a logistic strategy should

reflect the nature of the customer demands. The logistic

strategy in turn determines appropriate manufacturing

strategies and the corresponding feasible planning and

control methods. Vollmann et al. (1997) stress the

importance of mapping the planning and control

processes specifically for the purpose of implementing

PPC software to support the planning functions. The

same authors provide a selection of the prerequisitesof the system implementation. Otherwise, there are

only case studies on MRP or ERP system implementa-

tions available that provide some indication on the

critical success factors of PPC systems (see, for example,

Akkermanns and van Helden 2002, Wiers 2002).

In general management literature, important

approaches are being discussed that aim to ensure the

fulfilment of business objectives. Publications on PPC

almost completely ignore these discussions, especially

as far as the role of operational employees is concerned.

Kaplan and Norton (1996) propose the balanced score-

cards as a method to link business strategies to specific

aspects of performance. Miles and Snow (1978) deter-

mine what types of business organisations lead to

above-average levels of performance. Maslow (1987)

and Huczynski and Buchanan (1991) explain the impor-tant influence that human motivation and employee

involvement have on the performance of a business.

Storey and Sisson (1993) discuss the effects of

performance-related pay on the performance of a com-

pany and provide instructions on the effective design of

remuneration systems. In order to ensure that PPC

systems contribute to high levels of logistic performance,

these general methods and approaches have to be

adapted for the specific field of production management.

Publications that transfer these approaches to the field

of PPC have only recently been published (Wa fler 2003,

Wiendahl and Westka mper 2004, Nyhuis 2004).

According to the authors experience it is not only theneglect of above-mentioned important factors but also

the lack of awareness of the correlations between sepa-

rate factors that affect the configuration of PPC systems

and lead to undesirable logistic performance deficits.

These so-called stumbling blocks of PPC are errors in

the configuration of a PPC system as a whole. The

symptoms of these stumbling blocks, insufficient fulfil-

ment of logistic objectives, a lack of transparency and

excessive efforts required, are easily identifiable. Often

though those responsible for PPC on the operational

level are not able to simply remove the stumbling

blocks. On the one hand the interdependencies between

their causes make a final analysis more difficult; on the

other hand, the changes required by the situation can

exceed the competencies of the operational staff

involved. In most cases, only the managing directors

can remove the causes of the stumbling blocks.

Therefore, the objective of this article is to create a

framework for the identification, analysis and removal

of classic stumbling blocks of PPC:

. Section 2 defines the key terms of PPC. The PPC

system, configuration aspects of PPC and

stumbling blocks of PPC.

.

Section 3 describes typical stumbling blocks ofPPC. The descriptions first identify their respective

symptoms, analyse their causes and present

possible solutions to remove the stumbling blocks.

The practical examples included in the discussion

of each stumbling block are based on the experi-

ences the authors gained in industrial projects.

The projects focus on the configuration of PPC

concepts, the selection of suitable software tools

and the implementation of both in practice.

Stumbling blocks of PPC 635


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. Section 4 outlines a framework for the holistic

configuration of a PPC system. It lays the founda-

tions for a coherent and customised composition of

the planning and control functions of manufactur-

ing companies.

. The conclusions in section 5 draw the insights

together in the form of a questionnaire. The holisticconfiguration of a PPC system should consider the

issues that the questionnaire raises in order to

avoid the formation of stumbling blocks.

This merges the aspects of the functions and data of

PPC as well as its processes and responsibilities in an

integrated model that provides a basis for the holistic

configuration of PPC systems.

2. Key terms of PPC

The PPC system is the central logistic control mechan-ism that matches a companys output and logistic

performance to the customer demands. The task of the

PPC system is to plan, initiate and control the product

delivery of a manufacturing company as well as to

monitor and, in case of unforeseen deviations, i.e.

disturbances or order changes, to re-adjust the order

progress or the production plans.

2.1 PPC system

In the context of this paper, the term PPC system

denotes the entirety of functions and tools used for the

planning and control of the logistic processes in a

manufacturing company. The scope of application of a

PPC system includes the three value added processes,

Source, Make and Deliver, in accordance to the termi-

nology of the supply chain operations reference (SCOR)

model (Supply Chain Council 2004) (cf. figure 1a). The

input and output stores of a company are thus subject

matter of a PPC system in the same way as production.

The PPC system crosses company boundaries: It allowsfor the requirements of customers and suppliers since,

following supply chain management principles, the

management of the storage processes takes the delivery

performance of the suppliers as well as the demand

behaviour of the customers into account. According

to this definition, the term PPC system comprises

more than just the PPC software. The software is only

the tool to plan and control the logistic process chain

as well as the storage of production master data and

feedback data.

2.2 Configuration aspects of a PPC system

On the basis of this definition, six configuration aspects

of a PPC system can be distinguished (cf. figure 1b):

. The logistic objectives of a company are situated

at the heart of the PPC system. If necessary, these

have to be differentiated for different departments

of the company.

. The PPC processes determine the logical and

chronological order of PPC planning and control

activities. Thus they define the workflow of order

processing in terms of the information flow along

the logistic process chain. The activities related to

the material flow follow the same logic, but are not

directly a subject matter of the PPC system.

Value-adding processes

Source Make Deliver

(a) (b)

Object Responsibility

ProcessFunction

Objective

Figure 1. Definition of a production planning and control system. (a) Scope of application and (b) configuration aspects.

636 H.-H. Wiendahl et al.


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. The PPC objects are the planning objects of

PPC. The most important objects are the articles

(finished products, components or raw materials),

resources (machinery and personnel) and orders

(customer orders, spare parts orders, sample

orders, etc.).

. The PPC functions define the activities that arerequired to plan and control the logistic processes

in the stores and in production. The fundamental

activities are the definition of local objectives and

targets, forecasting and decision-making, providing

feedback on order progress as well as continuous

improvement.

. PPC responsibilities determine the positionsand

therefore the members of staffthat are in charge

of certain PPC activities. Conventional PPC

systems ignore this organisational view as they

operate on the assumption that responsibilities

are organised by a central entity (see for example

Hackstein 1989, Vollmann et al. 1997).. The five configuration aspects described above

constitute the logical core of a PPC system. The

purpose of the tools for planning and control is

to support the operational order processing by

(semi-)automated PPC activities. This creates stan-

dards for the operational activities and relieves staff

of time-consuming routine tasks. More time there-

fore becomes available for the required planning

and control decisions.

These configuration aspects serve as a theoretical

basis to analyse and remove the stumbling blocks

of PPC.

2.3 Stumbling blocks of PPC

The presence of stumbling blocks of PPC becomes

apparent through symptoms such as the insufficient

fulfilment of logistic objectives, a lack of transparency

of order processing or an unnecessarily high effort of the

staff involved in carrying out PPC activities. The term

stumbling block exclusively applies to internal mistakes

in the configuration of the six aspects defined above.Factors related to the external environment such as

unreliable suppliers or literally chaotic customers are

not considered. The PPC system itself does not have

any control over these factors. Nevertheless, the external

factors represent requirements that have to be consid-

ered when designing the PPC system.

An analysis of the relationships between causes and

effects is required in order to detect and remove the

stumbling blocks.

Ideally, the symptoms can be traced back to a single

cause. In this case, only one configuration aspect is

affected and the mistake in the configuration is easily

detected and removed. An example is the entry of

incorrect planned capacity values into PPC software.

If, for instance, the capacity of a bottleneck work

system has wrongly been set at 18 hours per workingday instead of the correct value of 16 hours, production

overloads arise. This stumbling block can be

easily removed by a simple correction of the planned

capacity value.

In cases where several cause-and-effect relationships

influence or even amplify each other, the removal of

stumbling blocks becomes more complex. Here, several

of the configuration aspects are affected. Even though

their symptoms are as apparent as for the simple

stumbling blocks, their removal is a lot more difficult:

It is necessary to, first, identify the relationships between

the different causes. Secondly, the causes in different

configuration aspects have to be changed simultaneouslyand in a co-ordinated way. Typically, this exceeds the

competence of the operational actors so that their

managers have to understand and remove the stumbling

block.

3. Typical stumbling blocks of PPC

The following examples describe the stumbling blocks

with several causes that are most commonly found in

industrial practice. Each explanation is divided into the

description of the symptoms and the analysis of their

causes. Measures that are used for the removal of the

stumbling blocks follow. The examples are based on real

situations in industrial companies.

3.1 Stumbling block missing positioning in system of

logistic objectives

The first example of a stumbling block of PPC highlights

the importance of defining consistent objectives and of

communicating the responsibilities for fulfilling the

objectives clearly to the staff that plan production

operations or carry them out.In PPC, one can often find conflicts between the

logistic objectives work-in-progress level (WIP level),

utilisation, throughput time and schedule reliability

because they are neither compatible nor locally or

temporally constant (Wiendahl 1995). Accordingly,

one should never maximise or minimise the value of

just one objective, but consider the simultaneous

effects of measures on all logistic objectives. The nature

of these conflicts has been recognised for some time



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(Gutenberg 1951, Plossl 1991). Nevertheless, many com-

panies are not aware of their consequences. Frequently,

production managers are trying to optimise the utilisa-

tion of work systems concurrently to the throughput

time. Detailed investigations demonstrate that such an

approach is not target-oriented because ultimately no

single objective of the optimisation can be defined.Substituting the minimum-cost objective for the logistic

objectives does not resolve the conflict. Instead, compa-

nies should start by setting strategic objectives derived

from the market environment (Ketokivi and Heikkila

2003). Typical examples of such objectives are Reduce

throughput times by 50% or Maintain a delivery relia-

bility of 95%. These objectives serve as the priorities,

which dominate the trade-off that has to be reached with

the remaining logistic objectives. The production oper-

ating curves are a proven methodology for the analysis

of the interdependencies between logistic objectives and

their consequences for PPC. They quantitatively

describe the dependence of the objectives utilisation,throughput time and schedule reliability on the WIP

levels in production and can easily be computed

(Nyhuis and Wiendahl 2003). Figure 2 shows that the

best possible target values for the different logistic objec-

tives do not coincide at the same WIP level of a work

system. A classical example of this phenomenon is the

conflict between short order throughput times and

high work system utilisation that was already

mentioned. Whereas short work system throughput

times can only be achieved at low WIP levels, high

WIP levels are required in order to guarantee a high

utilisation. This in turn leads to excessive throughput

times. The situation requires a trade-off between

the logistic objectives. Companies can achieve this by

positioning their logistic processes at certain operating

points on the production operating curves.

The conflict between objectives described above

only represents a stumbling block if those responsible

ignore it in their day-to-day job. In a typical example,

the managing directors of a medium-sized manu-

facturer of construction components required shortthroughput times to achieve short delivery times.

At the same time, they demanded a high utilisation

of expensive machinery in order to obtain a fast

return on investment. The production operation curves

clarify the conflict that the production department

faced as a result (cf. figure 2): On the one hand,

the objective of short throughput times requires a

low WIP level in production (WIPTTPmin). On the

other hand the objective of a high utilization

necessitates a high WIP level (WIPUmax). The inconsis-

tent directives of the directors are the cause for two

stumbling blocks:

. In day-to-day business, concrete decisions concern-

ing orders have to be taken. Conflicting manage-

ment directives fail to determine the most

important logistic objective. As a result a guideline

for these decisions is missing.

. As the management directives described above are

contradicting in themselves the target values that

are derived from them have to be as well.

Therefore it is impossible for operational planners

to take rational decisions.

The production operating curves are helpful tools to

analyse and remove both stumbling blocks:

. Initially, the curves explain the interdependencies

between the logistic objectives and facilitate their

relative prioritisation (step 1 of the logistic posi-

tioning).

. The remaining target values follow from the value

set for the most important objective. For example,

the desired throughput time determines both the

target utilisation as well as the target WIP level

(step 2 of the logistic positioning).

Taking a strategic decision, the directors regarded

short throughput times as the most important objec-

tive. However, in order to implement the new manage-ment directives, further boundary conditions had to be

considered. The machine operators still tried to

maintain high WIP levels at the work systems so

that they always see a work load in front of their

machines and can reduce setup times by changing the

sequence of orders. Obviously, this strategy also

supports a high work system utilisation. At the same

time it adversely affects throughput time and schedule

reliability.

WIP level

Utilisation

Maximum

SchedulereliabilityMaximum

Minimum

Throug

hputtim

e

WIPUmaxWIPTTPmin0

WIPTTPmin: WIP level at targetthroughput time

WIPUmax : WIP level attarget utilisation

Figure 2. Logistic operating curves as a model of theinterdependencies between logistic performance measures.



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In order to maintain the desired prioritisation of the

logistic objectives, management should take the follow-

ing actions:

. Offer qualification in production logistics to all

relevant employees (including shop floor operators)

and communicate the new priorities of the logistic

objectives.

. Verify the conformance of the logistic objectives

with the interests of the employees. In particular,

management has to ensure that compensation

schemes effectively support the objective of short

throughput times.

The following section describes how those involved

influence production planning and control, and

how their decisions impact on the fulfilment of logistic

objectives.

3.2 Stumbling block divergent stakeholder interests

The second stumbling block confirms the importance of

the consistency between the logistic objectives and the

PPC staff who have the responsibility for meeting the

targets. It also stresses the fact that staff has to be

qualified in order to carry out PPC functions.

In an empirical study on the implementation of ERP

systems, Amoako-Gyampah (2004) came to the conclu-

sion that different levels of the management hierarchy

have different perceptions of the system to be intro-

duced. It is therefore essential that the managing

directors not only provide all future users with adequate

training in the application of the new system, but that

it is equally important they make efforts to convince

staff members of the benefits of the change and of the

necessity to utilise the new system to achieve enhanced

business objectives.

The report by Wiendahl et al. (2002, 2005) on the

introduction of a Kanban control is a prominent exam-

ple for the potential for conflicts between such business

objectives and the individual objectives and interests of

production employees. In this case, production manage-

ment wanted to reduce throughput time and WIP levels

significantly. The central planning department wasresponsible for the design of the Kanban system and

the setting of its parameters. On the basis of customer

demands and target replenishment times the planners

also calculated the number of Kanban cards required.

The production department was briefed about the

changes and a subsequent trial run passed without

problems. The company therefore regarded the

implementation of the new production control system

as a success.

It came as a surprise that the production was not able

to sustain the aspired improvements for more than a

short time after implementation of the new control

system. Rather, both the values of WIP and throughput

times soon rose to old levels again. The detailed analysis

of the production department that was initiated as a

consequence, revealed insufficient consultation with theproduction operators.

The operators pursued the objectives job security

and stable order processing by stockpiling orders for

uncertain times in the future. This leads to unnecessary

safety stocks, permanent changes to the order sequence

and decreasing schedule reliability.

Obviously the pull principle that is underlying the

Kanban control does not conform to these interests of

the production operators: Kanban enforces temporary

idle times for most work systems. In order to counteract

this, the operators added copied Kanban cards to the

Kanban control loops to raise WIP to the previous

levels. Thus, they apparently resolved the conflictsbetween the objectives of the company and their own

individual objectives. Production management only

realised that the unwanted modifications had been

made and understood the exact causes of the modifica-

tions after the analysis of the Kanban control system.

The example highlights the prerequisites for a

sustained successful implementation of the new control

system; thorough qualification of all staff involved and

an incentive system that emphasises the objectives of

due-date oriented order processing (in order to avoid

order sequence modifications) and flexible working

hours (in order to guarantee processing on demand)

rather than promoting the conventional objective of

high resource utilisation.

3.3 Stumbling block missing responsibility

for inventories

The third stumbling block illustrates the consequences

of a lack of coordination of the responsibilities for the

PPC processes and objects. They result in an insufficient

fulfilment of the logistic objectives.

Often, there is no clear dividing line separating onearea of responsibility from another. Typical symptoms

are high inventory levels of purchased components and

finished products, or recurrent discussions on the

binding effect of orders and their reliable fulfilment.

The company described in this section produces

make-to-order machines of medium complexity. Depen-

ding on the customer requirements, this may include

engineer-to-order operations. A detailed analysis

was initiated by the managing directors who were



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dissatisfied with the high inventory levels of purchased

components and finished products, and the uncertainties

caused by sudden changes of due-dates or engineering

changes.

Figure 3a shows parts of the order processing chain.

Figure 3b indicates the problems resulting from an

unclear definition of interfaces: The production isresponsible for the material flow from the start of the

production order (i.e. printing of order documentation)

to the final operation (i.e. input to store). This includes

the responsibility for throughput times and WIP

levels. Subsequently, the finished products are handed

over to the sales department, either to be delivered

immediately or to be stored in the finished product

store. The purchasing department has the responsibility

for all purchased components. However, there is no

responsibility defined for the target inventory levels for

finished products. The company neither deemed it neces-

sary to define nor to regularly monitor them, because

final assembly should not take place before there is acustomer order. This should have prevented finished

products from being stored.

Recurring appeals to cut inventory levels remained

without effect. Instead, purchased component and

finished product inventory levels were steadily growing.

In addition, staff in the shipment area complained about

too small dispatch and storage spaces. The root cause

analysis showed:

. Initially, customers insist on the machines being

delivered as soon as possible. Near completion of

the order, they tend to postpone the delivery date

when they realise that the machine is not needednow, e.g. because of building delays.

. The actual start date of production is delayed

relative to the planned start date. The reasons are

product engineering changes due to changes in

customers requests or design modifications by the

engineering department.

Two issues have to be solved to improve the

interfaces:

1. Placing of orders (information flow): Does the

person who acquires new or altered information

directly benefit or have a quantifiable advantage

from passing it on? Would it be to his/her

disadvantage if he/she did not pass it on?

2. Delivery of orders (material flow): Does the

supplier have a direct, quantifiable benefit from a

timely delivery to his/her successor? Would a late

delivery be to his/her disadvantage?

In our example, a handover deadline was fixed for the

transfer of products to the shipment area, which iswithin the responsibility of the sales department. The

resulting deadlines are realistic, because the calculation

of the order flow includes a capacity check.

. From a production point of view, the sales depart-

ment places fixed orders. The fact that products

are handed over without transferring inventory

responsibility to the sales department is the reason

why the sales department experiences neither an

advantage nor any disadvantage if it fails to pass

on the postponement of customer due dates.

. Likewise, from a sales point of view, the promise

made by production seems to be binding. Butproduction has no fulfilment risk: delivering

Throughput time production order Idle time Idle time

Finishedproducts

... ...

Parts fabrication order I

Assembly order

OP 4

Time

Printing order documentation Input to store

OP 1

Shipment

Parts fabrication order II

Responsibility Production

Start-up

ResponsibilityPurchasing

Purchasing

ResponsibilitySales

(a)

(b)

Purchasedcomponents

Purchasing Production Sales

placesfixed order

deliverson time

placesfixed order

deliverson time

Figure 3. Stumbling block missing responsibility for inventories. (a) Status as planned and (b) actual status.



8/18

goods on time means production is cleared of its

responsibility, without having to worry about

any penalties when orders are completed behind

schedule.

A possible solution may be to add the finished

product store to productions responsibility. This initi-

ates the necessary improvements out of self-interest.

An analysis of the interface between production

and purchasing shows similar results: The required due

date for purchased components is calculated by back-

ward scheduling, before passing it on to the suppliers

with a safety lead time. Production takes no responsi-

bility for inventory levels from the planned start but

only after the order is actually started. This is why

production is merely interested in passing on informa-

tion regarding production orders being pulled ahead

but not about orders postponed. Frequently, the pro-

blem is solved by transferring the responsibility for

material dispatch and material inventory responsibilityto production.

3.4 Stumbling block inconsistent responsibilities

for functions

If the responsibilities for PPC objects, functions and

objectives are defined inconsistently, top management

is obliged to spend a high effort on resolving unneces-

sary disputes as the following example exemplifies.

One of the principal tasks of management is to clearly

define the responsibilities and assign the functions

within a company. It is generally accepted that appro-

priate objectives have to be defined so that responsibil-

ities within the organisation are consistent and therefore

the functions be carried out reliably (Kaplan and

Norton 1996). Unfortunately, reality often rather

reflects the informal organisation, i.e. the power struc-

ture among the persons concerned.

In a company with 300 employees, the right way to

fulfil functions and to accomplish the given objectives

was the subject of heated discussions among the three

departments of dispatch, production and logistics:

Dispatch is responsible for order release, productiontakes on capacity control and sequencing at the

work systems, and logistics is responsible for promising

delivery dates, thus being partially in charge of order

generation. Each department has its own system of

objectives, the priorities differ: The primary objective

of dispatch are short throughput times, the principal

goal of production is a high utilisation, while the top

priority of logistics is a high schedule reliability.

All these objectives were quantified by targets. The

resulting conflicts are illustrated by the following

disputes:

. Dispatch aims to release orders at the latest

possible moment to meet the objective of short

throughput times. Whenever demand is low,

intense debates with production are unavoidable.Production wants orders to be released much

earlier to maintain a high utilisation.

. To meet the objective of high schedule reliability

and ensure that customers receive their products

on time, logistics strives for realistic delivery

promises. It sets the planned start and finish dates

as well as the sequence of orders in accordance with

these promises. As soon as demand rises, however,

the available capacities are not sufficient to keep the

promised delivery dates. Hence, production is

urged to raise capacity. If this is not possible, the

dispatch department is asked to release orders at

an earlier point in time.. Usually, the parties concerned are not able to reach

an agreement. Therefore, often top management is

asked to solve the conflict and decide upon which

orders to release or which to speed up. Necessarily,

the set objectives are missed.

A helpful framework to remove this stumbling

block is Lo ddings (2004) model of manufacturing

control. Its basic idea is to combine the functions of

manufacturing control with the objectives of the

PPC system. Thus, it becomes possible to assess whether

the responsibilities for functions and objectives are

consistently defined. He defines the following fourfunctions (cf. figure 4a):

. Order generation determines the planned input and

output, as well as the planned order sequence.

. Order release determines when orders are released

to the shop floor (actual input).

. Capacity control determines the available capacity

in terms of working time and the number of staff

assigned to work systems, and thus affects the

actual output.

. Sequencing determines the actual sequence of order

processing for a specific work system, and thus

affects schedule reliability.These functions affect the three manipulated variables

input, output and order sequence. The discrepancies

between two manipulated variables lead to the observed

variables of manufacturing control (cf. figure 4b):

. The start deviation results from the difference

between planned input and actual input.

. The WIP level results from the difference between

actual input and actual output.



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. The backlog results from the difference between

planned output and actual output.

. The sequence deviation results from the discre-

pancy between actual and planned sequence.

The observed variables affect the objectives of PPC

described above, i.e. throughput time, WIP level,

utilisation and schedule reliability.

Figure 4b shows the interdependencies connecting

functions, manipulated variables, observed variables

and objectives to each other. The functions define the

manipulated variables, the observed variables resultfrom the discrepancies between two manipulated

variables, and the logistic objectives are determined by

the observed variables.

As a basic principle, conflicts arise when one de-

partment takes the responsibility for a specific ob-

jective when the accomplishment of this objective is

also affected by another department. These conflicts

obviously cannot be resolved by the persons involved.

Figure 5 illustrates how the responsibilities for

objectives and functions are defined by the described

company:

.

The first conflict arises between the production andthe dispatch departments: Although order release

(via actual input) and capacity control (via actual

output) affect the objectives of throughput time

and utilisation, the responsibility for objectives

and functions is not united under one authority.

Accordingly, situations, in which the achievement

of an objective depends on the decisions of the

other department, require a higher authority to

make the final decision.

. The second conflict arises between the production

and the logistics departments: Production affects

the objective of schedule reliability via capacity

control (actual output) and sequencing, whereas

logistics impacts the objective via order generation.

Again the responsibility for objectives and func-

tions is not united under one authority. This inevi-

tably leads to a permanent conflict as described

in the above paragraph and requires a higher

authority to solve each case. For production to

call for an earlier order release to ensure utilisation

even complicates the matter, as a third party, i.e.

dispatch, has to be considered.

To remove this stumbling block the responsibility for

the complete order processing chain must be put into

the same pair of hands. An order management centre

could fulfil this role. Alternatively, it is possible to

divide the order processing chain into sub chains, in

which the responsibilities for objectives and functions

are combined.

3.5 Stumbling block insufficient quality of feedback data

The insufficient quality of feedback data reported in

the following case is a symptom of the lack of

integration of all PPC functions in the tools for planning

and control.

Data quality has recently been identified as one of the

important factors in the configuration of PPC system

(Xu et al. 2002). All purposeful and successful planning

2

13

4

Ordergeneration

Orderrelease

Capacitycontrol

Sequencing

Production

(a) (b)

Disposition

Ordergeneration

Plannedinput

Plannedoutput Backlog

Schedule reliability

Utilization

WIP level

Throughput time

Startdeviation

ActualInput

WIPlevel

Actualoutput

Capacitycontrol

Orderrelease

Plannedsequence

Sequencedeviation

Actualsequence Sequencing

Observed variable

ObjectiveDirection

Manipulated variableFunction

Difference

Figure 4. Model of (a) functions and (b) logistic interdependencies in manufacturing control. Adapted from Lo dding (2004).



10/18

and control depends on a complete, consistent and

current data basis for all planning, control, execution

and performance measurement activities (Wiendahl

et al. 2003a). Besides the production master data, the

production feedback data are especially important for

this purpose:

. Feedback data represent the inputs for the logistic

performance measurement carried out at the end ofa production planning period. Deviations between

the planned and actual values of logistic perfor-

mance measures lead to new control decisions or

the adjustment of target values (see section 3.6).

. For day-to-day business the continuous logistic

performance monitoring is more important.

The deviations between planned and actual order

progress detected by this function have to be

corrected by immediate control measures in order

to make sure that promised or planned due-dates

can be maintained despite order changes or

inevitable disturbances.

There is a range of possible causes for the insufficient

quality of feedback data, of which the IT structure in a

manufacturing company is one of the more significant

reasons. In a survey carried out by the Fraunhofer

Institute for Systems and Innovation Research in 2001,

60% of the companies responded that there is no

hardware connection between the production data

acquisition (PDA) software and the remaining IT

structure (Beckert and Hudetz 2002). A timely and fast

intervention of production control in production is thus

impossible.

At times there is a complex structure of mutual

dependencies that is underlying the symptoms. This is

exemplified by the following example: In the manufac-

turing company considered, the feedback data were

characterised by inconsistencies that resulted from a

substantial delay in recording the data in the PDA

software (only 75% of operations showed a positivethroughput time). However, the actual processing

times matched the standard processing times relatively

accurately. After the introduction of new planning

software the problem disappeared within a period of

six weeks.

A preliminary analysis showed that the feedback data

were only used for the controlling of costs but not for

the ongoing monitoring of the order progress. A second

manual feedback systemlocal inspections by the

foremenprovided the feedback information required

to control the order progress in time. As the feedback

data were not immediately incorporated in the nextproduction plan, the operators did not recognise the

benefit of the plausible and immediate provision of

feedback data. The regular appeals by the production

managers to increase the quality of the feedback data

therefore did not have any effect.

The PPC cycle shown in figure 6 provides a basis for a

detailed analysis of the situation. It consists of a logical

sequence of the activities of production planning and

control. Based on insights from decision theory, the

LogisticsOrder

generation*

Dispath

Production

Throughput time

Utilization

Plannedoutput

Backlog Actualoutput

WIP level

Actualinput

Orderrelease

Dispatch

Capacitycontrol

Production

Sequencing

* promised delivery date

Actualsequence

Sequencedeviation

Plannedsequence

Logistics Schedule reliability

Function

Difference

Objective

Direction

Observed variable

Manipulated variable

Responsibility

Stumbling block

Figure 5. Stumbling block inconsistent responsibilities for functions.



11/18


12/18

requirements planning and one for the capacity require-

ments planning and scheduling. Therefore there are two

relevant types of stumbling blocks:

1. Inconsistent parameters: The scheduling parameters

at different levels of aggregation are inconsistent

(e.g. the offset of a manufactured component is

equal to 3 weeks, the sum of the throughput times

of all operations included in the manufacturing

order is equal to 4 weeks).

2. Unrealistic parameters: The values of the schedul-ing parameters are normally not maintained at the

planned values in reality (e.g. the mean planned

throughout time of manufacturing orders is equal

to 5 days whereas the actual mean throughput time

is equal to 7 days).

Manufacturing companies often underestimate the

significance of correct parameter setting. The scheduling

parameters are merely estimated or derived from

historic data. In this way, a tool manufacturer used a

mean planned throughput time value of 27 working days

for scheduling manufacturing orders. This value was

based on the experience of the production foreman.The actual mean value of the throughput time for the

manufacturing orders was equal to 32.5 working days.

The differences between plan and actual mean values

occurred due to the production bottleneck: a long

operation throughput time of a coating process.

The prerequisite for realistic planned values is

the knowledge of the actual values. The lack of a

performance monitoring function constitutes an obvious

stumbling block in this context. The company consid-

ered did lack this function:

. The feedback dataorder master data and due

dateshave to be recorded at all work systems

on the shop floor (step Collect in the PPC cycle

in figure 6). Subsequently, order throughput times

and other logistic performance measures can be

calculated from these.

. Subsequently, logistic performance measurement

has to determine the accuracy of the planningparameters. This is achieved by comparing the

throughput time parameters set for the scheduling

function of the PPC software with the actual values

measured in production. If necessary, the param-

eters have to be adjusted bearing in mind the

logistic objectives (step Learn in the PPC cycle

in figure 6).

. Only the introduction and regular execution of

the PPC cycle guarantees the accuracy of the

throughput time parameters. The procedure

described equally applies to all other PPC planning

parameters.

Adjusting parameters may lead to another stumbling

block: For the purpose of replenishing the finished

products store, the order dispatch function assumed a

throughput time of 27 working days. Backward sched-

uling runs generated the required production orders

based on this assumption. Thus, the difference of

5.5 days between the planned and the actual

throughput time affected the schedule reliability. The

OPOP

Dispatch level123

IA

B

1

2

3

1 3

Throughput time operation 2

OP2

B

Throughput time production order B

Offset dispatch level 2Time

Time

Time

Material requirementsplanning:BOM explosion and

offsetting

Capacity requirementsplanning:throughput scheduling

Function Parameter

Replenishment time/offset dispatch level

Planned orderthroughput time

Planned operationthroughput time

Aggregation level

Product (mean) purchasing time of entirepurchasing process (external/internal)

Dispatch level (BOM level)

may include one or moreproduction orders

Production order (mean) throughput time ofproduction order

equal to replenishment time ifdispatch level includes only oneproduction order

Operation (mean) throughput time of operationof production order

estimated or calculated(inter-operation time + operation time)

Figure 7. Classical scheduling parameters of PPC.



13/18

production was running into backlog. For this reason,

companies have to be aware not to enter the

vicious circle of production control when modifying

planning parameters. The next section explains

how to act correctly when these modifications become

necessary.

3.7 Stumbling block lack of logistic understanding

Due to a lack of logistic understanding, many manufac-

turing companies fail to make the correct connections

between decisions taken in the PPC functions and

their effect on the degree of fulfilment of the logistic

objectives.

How a production system deals with logistic issues

and how this affects planning and control has been the

subject of discussion for some time, especially in view

of the familiar shortcomings of the MRP approach andthe lack of logistic understanding on the part of the

users of PPC.

The vicious circle of production control is a particu-

larly illustrative example of how little is known about

the actual interdependencies between manipulated and

observed variables (cf. figure 8). In the USA this circle

was first described by Mather and Plossl (1978), while

Kettner (1981) and Wiendahl (1995) explained its

consequences to the German audience. The vicious circle

sets out from the mistaken conclusion that schedule

reliability is poor because the planned throughput

times are too short. This is shown in the throughput

diagram in figure 9a. When increasing the values of

the parameters in the backward scheduling run, the

orders will be released to the shop floor much earlier.

As orders cannot be started in the past, the input curve

takes a leap (one-off load surge), making the WIP levels

at the work systems and hence the length of the orderqueues grow (cf. figure 9b). This implies, on average,

longer waiting times and longer throughput times of

orders, along with an increased variation of throughput

times (Wiendahl 2002).

As a result, the schedule reliability is decreasing

and completing important orders on time is only

possible by means of rush orders and costly expediting

exercises. The vicious circle is spiralling upward to

stabilise at a level where the amount of work pieces

stored as work-in-progress exceeds the storage capacity

(Wiendahl 1995).

The correct logistic analysis would be as follows: The

backlog is the actual cause of the due-date deviation of

orders (cf. figure 10a). This backlog cannot be reduced

by increasing the planned throughput times, but by

temporarily increasing capacities or outsourcing work.

Figure 10b shows the effects of this intervention: From

the present day the backlog will gradually decrease.

As a result, adherence to the planned due dates is

improving, and from a certain point in time planned

and actual output are matched. However, such reactions

call for flexible capacities (Wiendahl 2002).

Outsourcing work for some time basically has the

same effect. However, compared to an increase of

capacity the impact will be delayed (cf. figure 10c).

Throughout

times and their

variation

increase

Length of

queues

increasesPlanned

throughput

times are

increased

Load on

work systems

increases

Orders

are released

earlier

Insufficient

delivery

reliability

Figure 8. Stumbling block lack of logistic understandingcauses vicious circle of PPC. Adapted from Plossl and Kettner.

Present day

Actual output

Input

(planned/actual)Planned

output

TimePresent day

Time

Planned

output

New planned

throughput time

(a)

(b)

Load

surge

Planned input

= Actual input

Due-date deviation

(too late)

Planned

throughput time

Planned

throughput time

New actual

throughput time

Due-date deviation

(too late)Actual

output

Workcontent

Workcontent

Actual

throughput time

Figure 9. Inadequate logistic reaction to interrupt viciouscircle of PPC. Throughput diagrams for (a) initial situationand (b) for an increase in planned throughput times.



14/18

A similar effect may be achieved by deferring make-to-stock orders (orders not related to a customer request)

or rejecting customer orders, though the latter might

have a negative effect on the market.

3.8 Stumbling block inadequate logistic guidelines

The stumbling block described below shows the

consequences of a lack of consistency between the

PPC functions and the process, which the functions

are meant to control.

An important instance of wrong PPC parametersetting is the formulation of inadequate logistic guide-

lines. In this case, the planned throughput times entered

in the PPC software are realistic and match the mean

value of the production throughput times. However,

the variation of the actual throughput times is higher

than the planned tolerance. Hence, the available

planning functionality (throughput time planning

based on mean values) and the actual throughput

time performance (high variation of throughput times)

do not conform. This inconsistency shows analogies to

fluid mechanics (Wiendahl 2003b):

. Throughput time planning based on mean values

assumes that the order stream resembles a steadily

flowing river (a so-called laminar flow of orders).

Only when throughput time variation is very low,

schedule reliability is sufficient.

. If the order stream resembles a mountain torrent

(comparable to a turbulent flow of orders),

the focus has to be on the individual order. The

individual planning of throughput time ensures

schedule reliability despite strongly varying

throughput times.

Such a situation allows for two alternatives:

. On the one hand, logistic turbulences might

be inevitable. Individual throughput times are

necessary and the software must be adapted

accordingly.. On the other hand, the steady-river scenario is

feasible. Orders are processed according to the

FIFO rule (or maximum slack). A low variation

of throughput times ensures the planned schedule

reliability.

The relationship between logistic requirements

and logistic capabilities determines the choice of a

logistic guideline. The requirements depend on the

allowed due-date deviation (tolerance requirements),

the demand fluctuation (flexibility requirements) and

the delivery time (speed requirements), cf. figure 11

(Wiendahl et al. 2002, 2003b):

. Tolerance requirements: Is the planning tolerance

set for a value such as throughput time, smaller

than the actual variation?

. Flexibility requirements: Do the fluctuations in

demand exceed capacity flexibility?

. Speed requirements: Do heterogeneous delivery

times require heterogeneous throughput times?

If the requirements exceed the capabilities, it is

necessary to apply individual throughput times for

each order. Practical experience shows that missing one

of the three requirements is sufficient to increase the var-

iation of throughput times. In most cases, this is due

to varying order priorities or sequence changes meant

to avoid setup times. Accordingly, sufficient planning

tolerances, little demand fluctuations and homogeneous

delivery times allow for order throughput times to be

based on mean values. The same applies vice versa:

Heterogeneous delivery times, considerable fluctuations

in demand and tight planning tolerances call for the

individual planning and control of orders.

Planned input= Actual input

Planned Input= Actual input

Present day

Present day

Time

Time

Actual output

Backlog

Planned output= Actual output

Planned output

Backlog

(b)

(a)

TimePresent day

Outsourcing

(c)

Planned input= Actual input

Planned output= Actual output

Workcontent

Workcontent

Workcontent

Backlog

Figure 10. Adequate logistic reactions to interrupt viciouscircle of PPC. (a) Initial situation, (b) temporary increase incapacity and (c) temporary outsourcing.



15/18

Following a flow-oriented guideline makes it easier to

forecast the throughput time and thus to determine the

delivery date. Traditional PPC methods support this

approach, too. However, strong fluctuations in demand

and unforeseen events make it difficult to providecapacity according to need. This is why it places high

demands on flexible capacities and predictive perfor-

mance monitoring to achieve the ideal of a steady

order stream.

4. Configuration of the production planning

and control system

Many industrial companies are dissatisfied with the

degree to which they fulfil their logistic objectives:

throughput times and inventory levels seem to varyuncontrollably; promised due-dates can only be adhered

to by use of costly expediting exercises. For this reason,

there are controversial views about the potential of PPC

software in academia and practice.

The examples of stumbling blocks of PPC presented

above show that the ever-present demand for improved

software with more powerful algorithms is not always

justifiable. Rather, it is the inconsistent configuration

of the aspects of PPC that affects the fulfilment of

logistic objectives. A holistic (re-)configuration of the

PPC system has to consist of the following three stages:

. Initially management has to determine the logistic

strategy, i.e. the logistic performance that it wants

to offer to the customers. This includes the prior-

itisation of external logistic objectives and the

trade-off between internal logistic target values.

Manufacturing companies have to ensure that the

logistic strategy matches their manufacturing vision

which predetermines the design of its production

systems (Riise and Johansen 2003). In fact, compa-

nies should ideally formulate manufacturing and

logistic strategies simultaneously and also design

production systems and the related PPC system in

parallel.

. The technical concept of the PPC system has to be

built on the basis of the logistic strategy. The basiclogistic configuration has to ensure that the

configuration aspects of PPCprocesses, objects,

functions and responsibilitiesare consistent with

each other as well as the achieved prioritisation of

logistic objectives. The selection of suitable produc-

tion planning and control methods and algorithms

facilitates a partially or fully automated materials

and capacity dispatch. The analyses of the stum-

bling blocks of PPC offer instructions on how to

Minimum delivery time

Mean throughput time

Criterion: Time

Planning tolerance

Variation of throughput times

Criterion: Tolerance

Demand fluctuation

Capacity flexibility

Criterion: Quantity

Delivery/lead time

Lead time

Time

Units/day

Capacityflexibility

Demandfluctuation

Distributionof lead times

Quantity

Requireddelivery

time

Variation ofthroughput times

Quantity

Planningtolerance

Figure 11. Criteria for choice of logistic guideline.



16/18

avoid inconsistencies of the configuration aspects

of the PPC system.

. The third stage is the implementation concept of

the PPC system. This includes the selection of

PPC software that is capable of supporting the

technical concept, the setting of all relevant para-

meters in the PPC software and the development ofa suitable implementation strategy that includes

the qualification of all staff. Case studies confirm

the necessity of a role-specific training-on-the-job

implementation (Wiendahl and Westka mper 2004).

It is not sufficient to configure a PPC system once on

implementation. As a rule, changes to the internal and

external situation of the company require a periodic

verification in accordance with the PPC cycle shown in

figure 6. This ensures that the current configuration, the

methods used and the parameters set are still suitable.

Two types of changes can be distinguished:

. Abrupt changes, such as the introduction or with-

drawal of competitive products, the development of

new technologies or other changes to the market

environment are relatively easy to detect. In such

cases, the need for action is obvious. From a logis-

tic perspective there is no need for new methods or

tools for detecting such changes. Timely indicators

of market or technological changes are desirable.

These, however, are research issues for general

management disciplines.

. Creeping changes are much more difficult to detect.

Step-by-step adjustments of market volumes,

delivery or replenishment times hardly attract theattention of those responsible. However, for the

configuration of PPC systems this type of change

is much more critical because it necessitates

the continuous verification and adjustment of the

chosen configuration in parallel to day-to-day

business. It can be compared to the sharpening of

tools that a good craftsman regularly carries out.

5. Conclusions

The discussion of the stumbling blocks presented above

highlights the importance of a holistic configuration of

PPC systems. Although section 4 outlines the main

phases of a methodical PPC configuration process,

a fail-safe procedure has not been developed in

detail yet. However, as focussed questionnaires are a

way of assessing the appropriateness of management

and production system designs (Barnes and

Rowbotham 2003), the following questions can be

recommended as part of a quick-check to assess the

suitability of a chosen configuration. The questions are

separated into five sections:

Objectives and stakeholder interests:

. Have the logistic objectives been defined and are the

objectives consistent? Is their degree of fulfilment

being monitored?. Is someone responsible for the fulfilment of the

objectives?

. Have the logistic objectives been matched to

customer demands and are they consistent with

the performance targets for the employees on all

hierarchical levels (the stakeholders)?

Logistic guideline and PPC methods:

. Does a logistic guideline exist?

. Do the planning and control methods used match

the logistic guideline?

. Is there a mechanism that ensures the consistency

of logistic guideline, logistic positioning and theplanning and control methods used? Is someone

responsible for this mechanism?

Order processing chain and responsibilities:

. Have the separate process steps of the order

processing chain been defined?

. Has the responsibility for each step been assigned?

. Have the interfaces between the responsibilities

been defined unambiguously?

. Do those who have to fulfil the logistic objectives

have an adequate level of authority for making

decisions?

Data quality and parameter setting:

. Is there a mechanism that ensures the accuracy of

master data and feedback data? Is someone

responsible for this mechanism?

. Are the values of the planned throughput times

consistent across all three scheduling levels of the

PPC system (long-range and intermediate-range

planning, and short-term control)?

. Is there a mechanism for continuously checking,

and adapting if necessary, the accuracy of PPC

parameters? Is someone responsible for this

mechanism?Qualification of employees and logistics audit:

. Do all staff involved in the logistics function under-

stand the fundamental interdependencies between

the logistic objectives, the manipulated variables

and the observed variables? Is there a regular

refresh activity?

. Does a logistics audit form part of the quality

management system?



17/18

From a practical point of view, the answers a com-

pany provides to the questions above directly indicate

areas that the company has to improve in order to

achieve a holistic PPC configuration and to avoid the

stumbling blocks described.

From a scientific point of view, further research has to

be carried out in order to adapt the organisational andhuman aspects of existing performance management

theories to the field of production management and

integrate them into the framework for configuring

PPC systems.

Acknowledgements

This article reports on research activities of the pro-

ject Modellbasierte Auftragsmanagement-Gestaltung

(Model-based Configuration of the Order Management

Process) that is funded by the German Research

Foundation (DFG) under registration WI 2670/1.

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Wiendahl, H.-P., von Cieminski, G., Begemann, C. andNickel, R., Human factors in production planning andcontrol. In Integrating Human Aspects in ProductionManagement, edited by G. Zu lch, H. Jagdev and P. Stock,2005 (Springer: Berlin) pp. 113126.

Wiers, V.C.S., A case study on the integration of APS andERP in a steel processing plant. Prod. Planning & Control,2002, 6(13), 552560.

Xu, H., Horn Nord, J., Brown, N. and Nord, G.D., Dataquality issues in implementing an ERP. Indust. Manage. &Data Syst., 2002, 102(1), 4758.

Hans-Hermann Wiendahl studied Industrial Engineering at the Technical University in Berlin.He has worked at the Fraunhofer Institute for Manufacturing Engineering and Automation(IPA) and at the Institute for Industrial Manufacturing and Management (IFF), University ofStuttgart, since 1996 where he held positions as researcher, department manager and now tech-nical manager Order Management. He completed his PhD under the supervision of ProfessorWestka mpfer and is now working on his habilitation thesis. His main research interests are inproduction management, especially PPC, as well as in the selection and implementation of ERPand MES systems. He was responsible for numerous research and industrial projects and haspublished on these subjects extensively.

Gregor von Cieminski holds a degree in Manufacturing Sciences and Engineering from theUniversity of Strathclyde in Glasgow. He is a research assistant at the Institute of ProductionSystems and Logistics (IFA) at the University of Hannover. As a member of the

production management research group his interests are in the fields of logistic modelling ofproduction processes and supply chain management. He has published several articles on thesesubjects in scientific journals and conference proceedings.

Hans-Peter Wiendahlstudied Mechanical Engineering at the Engineering School in Dortmund, atthe RWTH in Aachen and MIT (USA). Under the supervision of Professor Opitz, he completedhis PhD in 1970 and his habilitation thesis in 1972. Until 1979 he was manager of planning andquality at Sulzer Escher Wyss GmbH in Ravensburg before becoming the head of paper machin-ery design for the same company. He became professor and head of the Institute of ProductionSystems and Logistics (IFA) at the University of Hannover in 1979 and held this position until2003. His main research interests are in production management, factory planning and produc-tion systems. He is the author and publisher of numerous books and articles on these subjects.


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