PRODUCTION MANAGEMENT Adaptive process planning

PRODUCTION MANAGEMENT

Adaptive process planning

Berend Denkena • Leif-Erik Lorenzen • Justin Schmidt

Received: 23 May 2011 / Accepted: 23 November 2011 / Published online: 2 December 2011

� German Academic Society for Production Engineering (WGP) 2011

Abstract The success of companies with job shop pro-

duction strongly depends on their production flexibility. This

is often significantly influenced by the process planning and

production control. Aiming at maximizing production flex-

ibility, this paper presents an approach to further integration

of process planning and production control by combining

and optimizing already existing planning methods. Essen-

tially, in a rough planning stage, all process chains which are

technological relevant to the manufacturing of a given

product are taken into consideration. Applying a dynamic

multi-criteria evaluation to all process chains ensures that the

most appropriate, situation-specific process chain is chosen

for production. This is done based on pre-established pro-

duction targets, which facilitates a flexible response to

incidents and other unplanned production events. The

structure and functionalities of the presented approach are

thoroughly explained in this paper and its feasibility is

demonstrated with an example.

Keywords Process planning � Production control �Flexibility � Analytical hierarchy process

1 Introduction

In a closely interconnected global market, which poses

increasing demands on the productivity of manufacturing

companies, production flexibility is a major success factor

[1]. In particular, frequently changing conditions often

require the ability to respond rapidly to alterations in the

production environment. Such changes include, on the one

hand, promptly adapting the production program to the

customer needs and, therefore, reallocating the available

means of production at short notice [2]. On the other hand,

manufacturers also need to compensate for unplanned

events in the operational production environment such as

machine malfunctions, poor quality or lack of material and

resources. These changes disrupt the production and result

in delays of lead times and inability to meet delivery

deadlines. The ability to respond to these alterations is

significantly influenced by preceding planning activities. In

particular, flexible responses are only possible when the

planning contents can be swiftly adapted to the changed

conditions. Thus, at the operational level, the managing

function of the production planning and control plays an

important role for increasing the production flexibility [3].

When creating product-specific process plans during the

process engineering stage, it is necessary to determine the

sequence of the production steps not only for the manu-

facturing of a given component but also for the allocation

of the needed resources. In order to increase production

flexibility, it should be possible to dynamically adjust the

process planning contents to any occurring unplanned

events. However, this additional planning effort should not

lead to further production delays.

In the manufacturing practice, the process planning

usually follows the product design [4]. Using rigid process

plans often means that because of changes the originally

scheduled resources are no longer available during the

production. Deciding on a response to such disturbances in

the production process is part of the production control. A

survey conducted by the IFW has shown that a subsequent

B. Denkena � L.-E. Lorenzen � J. Schmidt (&)

Institute of Production Engineering and Machine Tools (IFW),

Leibniz Universitat Hannover, Hannover Center for

Production Technology (PZH), An der Universitat 2,

30823 Garbsen, Germany

e-mail: [email protected]

URL: http://www.ifw.uni-hannover.de

123

Prod. Eng. Res. Devel. (2012) 6:55–67

DOI 10.1007/s11740-011-0353-7

adjustment of the process plans is required in up to 50% of

all production orders [5]. Due to the necessity of designing

new process plans, such adjustments result in delays in the

production and lead to bottlenecks because of belatedly

started production orders. As a consequence, in addition to

longer lead times and postponement of delivery, there are

also the costs for the additional planning effort.

Notably, there is a need to increase the flexibility in

those companies, whose product range includes mainly

customized made-to-order production as well as single and

small-scale production. Small batch production entails a

wide variety of different process plans and components as

well as frequently changing products and a variety of set-

up procedures. Thus, a typical form of organization of such

production is based on the job shop organization. Job shop

production is characterized by the fact that jobs with the

same or similar tasks or activities are combined in close

proximity to each other. The resulting complex flow of

materials, however, facilitates the creation of bottlenecks

and requires a flexible planning and careful scheduling of

order and process sequences. This can be achieved through

adequate integration of process planning and production

control. For this purpose, adaptive planning tools that allow

a feedback of current production information, and thus

more flexibility in the process plans are particularly suit-

able. This paper starts by commenting on some possibilities

for increasing flexibility through the integration of process

planning and production control (Sect. 2).

Then, with regards to the implementation, the adaptive

process planning approach developed at the IFW is pre-

sented (Sect. 3) and its functionality explained (Sect. 4).

Finally, the application of the method is illustrated using an

exemplary part (Sect. 5).

2 Production flexibility via integrated process

planning and production control

As already mentioned above, it has become clear that a

flexible reaction to unplanned events and incidents is one

of the major success factors for manufacturers with job

shop production. Various definitions and discussions of the

concept of ‘‘flexibility’’ exist; comprehensive analyses

thereof can be found in [6–8]. The research findings pre-

sented in this paper are based on the notion of flexibility as

defined by Toni and Tonchia [9]: ‘‘Flexibility can be

viewed as the capacity of a system to change and assume

different positions or states in response to changing

requirements with little penalty in time, effort, cost or

performance’’. When transferred to the area of job shop

production and the methods of process planning thereof,

three different types of flexibility can be derived [10]:

1. Flexibility via alternative operations: Definition of

alternative and technologically appropriate operations

(production processes) for machining a given

component.

2. Flexibility via alternative operation sequences: Defi-

nition of alternative operation sequences for machining

a given component.

3. Flexibility via alternative manufacturing resources:

The definition of varying operation and operation

sequences is mainly determined by the available

manufacturing resources (tools and machine tools). A

high degree of flexibility can be achieved by selecting

redundant resources and by the feasibility of choosing

different manufacturing processes for machining a

component.

In the presented context it has been found out that, based

on the different types of flexibility and an evaluation of

relevant studies, a company can attain an increase in

flexibility primarily via preparatory process planning and

production controlling activities [11, 12]. This knowledge,

combined with the three different kinds of flexibility

mentioned above, provides the guidelines of the proposed

methodology for adaptive process planning.

3 The idea of the adaptive process planning

methodology

The main objective of the adaptive process planning

methodology presented in this paper is to increase the

flexibility of the production processes in companies with

job shop production by means of a novel integrated and

responsive approach to process planning and production

control [13]. The adaptive process planning approach

benefits from the advantages of other, already existing,

planning approaches. It is based on a combination of three

conventional approaches to integrated planning and control

of manufacturing processes: the hierarchical, the nonlinear

and the dynamic (Fig. 1).

According to Kreutzfeldt [14], hierarchical approaches

to process planning and production control can be divided

into two stages (Fig. 1, top). First, during the rough plan-

ning stage, the operations sequence is decided. However,

during this stage, it is only decided which operations are to

be executed: An exact assignment of operations to specific

manufacturing resources is made in the second phase,

during the detailed planning stage. Alternative operation

sequences or operations are not planned. Any production

disturbances (unexpected events, conditions or develop-

ments) that happen before the planned start of production

have no impact on the process plan. However, if such

problems appear during the manufacturing process, the

56 Prod. Eng. Res. Devel. (2012) 6:55–67

123

affected operations have to be started anew and the

resources have to be reallocated appropriately.

In contrast, the specific characteristic of non-linear

approaches is the fact that all alternative operation sequences

are planned before the start of the manufacturing process and

represented via a process plan network (Fig. 1, center) [16].

The selection of an operation sequence and the allocation of

the appropriate production resources to the operations occur

just before the actual manufacturing process begins. If any

failures occur during the production process, based on the

current state of completion of the component or batch, the

remaining operation sequence is selected in accordance with

the process plan network. Although hierarchical planning

approaches support only a small degree of flexibility,

defining alternative operation sequences makes it possible to

respond flexibly to unexpected events.

In dynamic process planning and production control

approaches, there is no production planning stage before the

start of the manufacturing process (Fig. 1, below) [17].

Based on the current utilization and the availability of pro-

duction resources, the individual operations are planned and

allocated to the resources directly. The planning of the next

operation is performed immediately after the completed

operation. The process sequence is therefore substantially

influenced by the decisions made during the production

control stage. If failures occur, no additional direct planning

efforts are needed. Such approaches are often combined with

artificial intelligence techniques, such as, for example,

agents or multi-agent systems, in order to achieve a high

degree of flexibility in manufacturing.

The combination of the hierarchical (division into rough

and detailed planning), non-linear (creation of process plan

networks) and dynamic approaches (taking into account the

current production workload and resource availability) and

their respective advantages is the basic concept of the

adaptive process planning. In analogy to methods of

management theory, adaptive process planning approach is

additionally based on the concept of ‘‘rolling’’ or ‘‘gliding’’

planning and control [18]. This means, that planning con-

tents are updated in a short term during production.

Figure 2 shows schematically the construction of the

approach as well as the process planning and production

control activities. These are divided into two phases: rough

and detailed planning. During the preliminary planning all

technologically reasonable operations and operation

sequences are determined and prioritized. During the

detailed planning stage, this hierarchy is evaluated parallel

to (rolling, gliding) the current and prior to the potential

Construction

ProductionTime (t)

Start of Production

Start ofPlanning

Process Planning and Production Control

D

D

D

D

D

D

Hie

rarc

hic

Pro

cess

Pla

nn

ing

No

n-l

inea

rP

roce

ss P

lan

nin

gD

yn

amic

Pro

cess

Pla

nn

ing

D DisturbanceRough Planning Detailed Planning Manufacturing

Lo/47926 © IFW

Fig. 1 Approaches to

integrated process planning and

production control (according to

[15])

Prod. Eng. Res. Devel. (2012) 6:55–67 57

123

subsequent operation. Thus, the final operation sequence is

set dynamically during the manufacturing. This ensures the

integration of respective process planning activities into the

course of production control and a flexible response to

failures whenever they occur.

4 Progress of the adaptive process planning

methodology

The complete sequence of the phases of the adaptive pro-

cess planning is shown in Fig. 3. During the rough plan-

ning stage, the CAD product model data form the basis for

the generation of the non-linear process plans which

include all possible alternatives for the manufacture of a

given component. Following, an evaluation of the alter-

native process chains included in the non-linear process

plan is made whereby process chains are selected and

prioritized according to their technological and economic

relevance for manufacturing the component in question.

The required resources for the priority process chain are

booked in the Enterprise Resource Planning (ERP) system.

In contrast to the rough planning, which is focused on the

entire process chain, the detailed planning is centered on a

single production process. Since the detailed planning

occurs parallel to the manufacturing process, it is necessary

to duly start the planning activities corresponding to the

respective progress of the manufacturing process. In order

to ensure this, the time needed for the planning of a par-

ticular process step parallel to the previous manufacturing

process is estimated in the final phase of the rough planning

stage.

During the detailed planning stage of the individual

manufacturing processes, the process chains which have

been created and prioritized as technologically appropriate

during the rough planning stage are accessed again. These

are already reduced since some of the necessary

process steps have been previously implemented. In order

Lo/47927 © IFW

Construction

ProductionTime (t)

Start ofProduction

Start ofPlanning

Process Planning and Production Control

D

D

Ad

apti

veP

roc

ess

Pla

nn

ing

D DisturbanceRough Planning Detailed Planning Manufacturing

Fig. 2 Adaptive process

planning approach

Detailed PlanningRough Planning

Creation ofnon-linear

Process Plans

Evaluation andSelection of

ProcessChains

Booking ofResources

Estimation ofDetailedPlanning

Times

UpdatingProductionInformation

Identificationand Evaluation

of valid Process Chains

AdjustmentPlanning

CAD-Data

ProcessPlan

ERP MES TNB

ERP: Enterprise Resource Planning MES: Manufacturing Execution System TNB: Technological Knowledge Base

Scd/61881 © IFW

Fig. 3 Flowchart of the

adaptive process planning


123

to respond appropriately to a changed manufacturing situ-

ation, the current manufacturing data regarding production

load, queues and availability of resources are retrieved

from the Manufacturing Execution System (MES) during

the beginning phase of the detailed planning stage of a

production process. However, disturbances which have

occurred in the interim result in previously scheduled

process chains no longer being applicable. Therefore,

based on the current information available, all remaining

process chains relevant to the production of the current

component are to be examined. Due to the changed con-

ditions, a re-prioritization of the remaining process chains

has to be carried out. After re-assessment of these chains,

the next process step necessary for the manufacture of the

component is selected.

Since this may lead to a change in the planned production

sequence and to a reallocation of resources, an adjustment

and itemization are required. This is done in the context of

the adjustment planning. In this case, the process parameters,

as well as the automatic generation and subsequent simula-

tion of the NC program, are modified to suit the current

manufacturing situation. In order to realize this alteration

without delaying the production process, the process

parameters (e.g. cutting velocity, feed rate) are compared

with the values of an experience-based production database

(Technological Knowledge Base—TNB). By using an

experience-based storage for planning data a raise of plan-

ning complexity and costs can be kept to a minimum. Also

the planning of similar product variants can be accelerated

with the help of the stored technological knowledge. The

TNB is based on an object oriented part model leaned on the

STEP-NC standard. For a detailed description see [19].

The individual functions and processes of the adaptive

process planning based on the two stages of process

planning—the rough planning and the detailed planning—

are thoroughly explained as follows. Focal point is the

representation of non-linear process plans and the resulting

dynamic assessment of technologically appropriate process

chains.

4.1 Rough planning stage

The preliminary rough planning stage is essentially divided

into the following steps: creation of process plans, evalu-

ation of alternative process chains and timing of the

detailed planning. These steps are outlined below.

4.1.1 Creation of process plans

The first step of the rough planning stage is to create non-

linear working plans based on the product data of the

component. For this purpose, the process steps which are to

be performed later on are identified. The correlations and

dependencies among the different processes, the sequence

relationships, the alternative processes and the alternative

process sequences can be defined using logical operators

(AND, OR). This can be accomplished via a graphical-

interactive input module (Fig. 4, exemplary for a tool

chuck). At the process level, the basic data of each

machining process can be defined by an input mask.

Based on previous experience and knowledge of the

process planner, it is possible to map all technologically

suitable process chains and their boundary conditions in a

compact notation and save them for later usage. A method

P1Sawing

SM 1

V

VP2MillingMM 1

P3Turning

TM 3

P7Grinding

GM 1

P8Grinding

GM 2

P5DrillingDM 1

P4Turning

TM 2

P6Turning

TM 1

V

V

V

V

Process-IDProcess

Machine-ID

V OR-Connector

V

AND-Connector

Scd/61882 © IFW

Data form

Create new process

Fig. 4 Creation of non-linear

process plans


123

for automatic generation of experience-based process

chains using design features is presented by Denkena [20].

4.1.2 Evaluation of alternative process chains

The next step within the rough planning stage includes the

initial assessment and prioritization of process chains, as

well as the selection of the optimal—for a given set of

specified conditions—starting process chain. Apart from

that, the individual operation sequences are to be evaluated

taking into account specific defined criteria. The foundation

of this weighted and multi-criteria evaluation method is the

Analytical Hierarchy Process (AHP) by Saaty [21]. A key

advantage of the AHP over other assessment methods is

that qualitative and quantitative criteria can be weighted

against each other and evaluated. Basically, the AHP can

be presented as a four-step decision process:

Step 1: Establishment of a decision hierarchy, division of

a decision problem into interrelated decision-

relevant elements (process chains and evaluation

criteria).

Step 2: Conducting paired comparisons of the decision

elements using qualitative and quantitative

information (priority estimation).

Step 3: Calculation of the priority of the decision

elements and verification of the consistency of

the priority estimation.

Step 4: Identification of a weighting function by

aggregating the priority of the decision elements

and determining a ranking sequence of the

alternatives (weighting of targets and activities

in the entire decision-making hierarchy).

The decision elements of the AHP consist of the available

alternatives, on the one hand, and, on the other hand, of the

evaluation criteria used. These are interrelated and are rep-

resented at the present decision problem by the process

chains (alternatives) and the production targets (criteria).

The top level of the decision hierarchy, which is established

based on these criteria, includes productivity-regulating

factors such as time, cost and quality. Alternatively, its lower

levels incorporate the descriptive parameters (Fig. 5) [5, 18].

When considering manufacturing costs, a distinction is

made between personnel and machine related costs. When

defining a quality criterion, it is assumed that each opera-

tion can achieve the required product quality by choosing

the appropriate process parameters. Consequently, the

mean critical process capability and the weighted average

rejection rate are utilized as central criteria for the evalu-

ation of the quality of the process chain. Using the number

of alternatives that are available for rescheduling, the

routing flexibility evaluates the flexibility of a process

sequence for possible changes to the route during the

production. Relevant to the routing flexibility value RF of

each processing chain c is the number of directly sub-

sequent process steps NPS in the process chain tree con-

sidered for every manufacturing stage n (see Fig. 12):

RFc ¼Xn�1

i¼1

ðNPSi � 1Þ

In the time category, a distinction is made between

execution and transition time. While execution time is

composed of processing time and setup time, the transition

time includes wait and transport time before or after the

relevant operation [22]. The criterion ‘‘bottleneck’’ is taken

into account as an additional, flexibility limiting factor. By

applying heuristics, it is analyzed how far a possible

process chain leads to exacerbating bottlenecks in the

production. The decision hierarchy shown is based on a

selection of descriptive production targets [23].

The weighting of the evaluation criteria can be conducted

by the user: For this purpose, a paired comparison is made

between the targets (step 1). In order to conduct a comparison

between any two given sub-objectives i and j of the finite set

A, the user specifies the value aij, which serves as a basis for

their comparison. The following applies:

aij ¼1

aji; for all i; j 2 A

The value aij represents the relative importance of sub-

objective i compared to the sub-objective j. Based on

Meixner [24], the scale value of a can range from 1,

meaning ‘‘equal’’ to 9, meaning ‘‘totally dominate’’. Lower

Target function

Time Quality Costs

BottleneckRouting

flexibilityExecution

timeTransition

timePersonnel

costsMachine-hour rate

Processcapability

Reject rate

Scd/61883 © IFW

Fig. 5 Decision hierarchy for

evaluation of process chains


123

significance is weighted analogously using a reciprocal

value, which results in the following scale (Fig. 6).

Subsequently, all aij values are entered into an evalua-

tion matrix. Since the weighting of the criteria is affected

by a certain degree of subjectivity due to company-specific

requirements or human experience and knowledge, a con-

sistency check is made (step 2). Using the evaluation

matrix, a consistency index is calculated, which can be

compared to empirically determined experience-values,

thus serving as a foundation of the consistency check. The

relative weight w of the evaluation criteria is described

using an eigenvector which is calculated by successive

squaring and normalizing the matrix. The overall weight of

a criterion is calculated by multiplying the eigenvector with

the calculated weight of the superordinate criteria level.

In order to determine the relative feature characteristics

k of the available alternatives regarding the given evalua-

tion criterion, a relative weight can be calculated if the

quantitative correlations are accessible. For example, each

process chain can be assigned a unique value for each

emerging personnel cost. In terms of qualitative evaluation

parameters, a weighting of the alternatives to each other

can also allow paired comparison, analogous to the

weighting of the criteria. Combining the feature charac-

teristics kk and the relative criteria weights wi allows

determination of the priority values of each process chain

(for the time criterion: Fig. 7). If this is aggregated along

the decision-making hierarchy, the result is a vector, whose

lines receive the values of the objective function of each

process chain (step 4).

This allows prioritizing the process chains based on the

value of their objective function and select the process

chain with the highest priority as the start process chain. As

the optimum solution, this start process chain functions as a

basis for future resource planning. In addition, a commu-

nication between the process planning and the ERP-system

takes place. For the prototypical implementation, the ERP-

system has been replaced by a relational database which is

limited to the necessary resource management (e.g.

machines, personnel) extent. The schema of the database is

modeled on the data structure of the ERP software SAP

R/3. The ERP database implementation provides the pro-

duction input data for the rough planning such as past

machine utilization shift schedules and material data.

4.1.3 Scheduling the detailed planning

In the area of production planning and control (PPC), a

temporal decoupling of rough and detailed planning is

fairly standard for the industrial practice [25]. At process

level, the scheduling and capacity planning follow after the

rough planning stage. However, after the start of produc-

tion, the technologically oriented, process related detailed

planning has to be coordinated with the detailed planning

concerning the order scheduling and sequencing. In order

to perform these planning tasks parallel to the previous

manufacturing process, it is necessary to estimate the time

needed for the detailed planning stage as early as in the

rough planning stage. Since any extension of the lead time

due to detailed planning during the production phase must

be avoided, the start time of each detailed planning step is

to be scheduled so that it is completed by the time the

associated manufacturing step starts. Thus, the start time of

each detailed planning step is calculated backwards from

the end of production. Therefore, based on the planned

times for each production step and the consideration of

inter-operation time (TIO, total amount of transport times

and queue times between two processes), the start of each

detailed planning step is scheduled backwards (Fig. 8).

For the evaluation of the planned times in the adaptive

process planning, it is important to know the duration of

the planning activity and thus the planning effort. For this

purpose, the content and the activities of the detailed

planning are to be determined. The identification of the

execution times serves as a basis for the determination of

the start and end point of all planning phases and planning

steps. The following figure shows the basic structure of the

developed method which is based on Schneewind [26] and

which is used in the adaptive process planning for the

assessment of planning times. Characteristic of this method

Fig. 6 Weighting scale for evaluation criteria in AHP

Process chain 1

Process chain 2

Process chain n

Execution time

Transition time

Bottleneck

Process Chain 1

Process Chain 2

Process Chain n

Priority value ek forthe time criterion

Characteristicvalue kk

Scd/61885 © IFW

Fig. 7 Calculating the priority value of the time criterion


123

is that each detailed planning step is divided into five

phases (Fig. 9).

The determination of the influencing variables (1) is the

basis for the evaluation of the planning times. The influ-

encing parameters define the time required by operations

and are affected, for example, by the process properties, the

component properties or the resources used. In order to

estimate the expenditures of time linked to the individual

operations, appropriate methods (2) must be selected, such

as Methods for Time Measurement (MTM). The time

periods associated with the respective operations (3) can be

estimated or calculated. To estimate the required time,

relevant expertise is necessary. This can be ascertained by

an expert survey, for example.

Following the data collection, the data processing occurs

(4). Thereby, the established actual times are transformed

into planned times by a regression analysis [27]. Finally,

there is a presentation of the resulting planned times (5),

which covers all stages of planning.

The rough planning stage is completed with the

assessment of the detailed planning and scheduling of all

detailed planning steps. Information about the current

manufacturing situation is needed in order to begin the

subsequent detailed planning stage, whose first planning

step takes place immediately before the first manufacturing

stage. This is necessary if the system is to respond flexibly

to unplanned events that have occurred in the meantime by

selecting one of the each situation-specific optimal process

chains.

4.2 Detailed planning stage

In order to be able to incorporate current production

information in the context of the detailed planning stage,

t

Start of Production End of Production

Pl. 7 Pl.9Pl.6Pl.4Pl.1

P4 P6P7

09:0008:5008:4008:3008:2008:1008:00

P1

P9

P10P 13

P 14

Pl. 13

Pl. 14

Pl. 10

TIOTIO

Legend: Px: Production step xTIO: Inter-operation time

Pl.x: Planning step xt: Time axis

TIO TIO TIO TIO TIO TIO

Backward scheduling

Scd/61886 © IFW

Fig. 8 Detailed planning for

the production of a pinion shaft

Determination of influencing variables

Determination of parameters influencing time need of planning sections

1

Method selection

Selection of methods determination the planning effort of influencing variables

2

Data collection

Collection of actual times and determination of target times

3

Data processing

Data transformation (actual times into planned times); relevance analysis of influencing variables

4

Presentation of resulting planned times

Fixing of starting and ending of all planning steps5

Detailed Planning

Rough Planning

Scd/61887 © IFW

t

p1… pn

t?

p1… pn

t

Detailed Planning

Production

t1, actual… tn, actual

?

ti, actual

pi

tplanned?

Fig. 9 Methods for evaluation

of the planned times


123

already available systems which are part of the manufac-

turing environment for PPC are re-accessed. In addition to

the production data acquisition (PDA) and machine data

acquisition (MDA) systems, the MES are becoming par-

ticularly important nowadays [28]. They enable a key-

performance-indicator (KPI) based control and monitoring

of the entire production in real time. The acquisition and

integration of these data in the production process ensures

that detailed planning is based on current information on

the available resources and machine utilization. By com-

paring the present information with the process chain

hierarchy established during the rough planning stage, the

process chains suitable for the manufacturing process are

determined on the basis of the current resource availability.

During the detailed planning stage, all remaining valid

process chains are evaluated parallel to the current manu-

facturing process and prior to the potentially following

process (see Sect. 4.1). This is carried out analogously to

the first assessment made in the preliminary planning stage

using AHP. For a repeated assessment, the statistic average

values of the availabilities and the bottlenecks, used in the

rough planning, are replaced with real-time data from the

MES. The machine utilization which has been altered

contrary to the initial production plan is transferred to the

MES via a data interface and can be thus taken into con-

sideration in the detailed scheduling of the production

orders. As a result, the final operation sequence is set

dynamically during the production. Figure 10 shows the

sequence of such a detailed planning cycle.

Here, the current process chain is replaced by the valid

process chain which shows the highest priority value after

the re-evaluation. Having selected the priority chain, the

adjustment planning of the following process step of the

chosen process chain starts parallel to the manufacturing

process. This approach ensures optimal adjustment of the

process plans to the current production situation. Taking

into account the recent data from the MES makes it pos-

sible to react to problems and bottlenecks on short notice

by switching to alternative processing stations.

This specific adjustment of the process sequence

requires a postponement of the detailed adaptation of

planned manufacturing operations on the currently selected

machine so far back that it is completed just before the

immediate start of the production activity. The adjustment

planning consists of five steps (Fig. 11).

During the first step, the process data necessary for the

planning process step are loaded from the TNB (1). These

include the cutting data, the CAM data and the tool data.

Guidelines for these data are the non-linear process plans,

which have been developed during the rough planning

stage. Adjacently, the fixture data (2) and the machine data

(3) selected for that process step are integrated within the

CAM-model. The CAM-model data stored are thus sup-

plemented to provide specific information regarding the

machine kinematics and the performance of the machine.

This allows the creation of the NC program (4). In the last

step, the NC program is validated with the help of the

process simulation (5). By adjusting the process contents

defined during the rough planning stage, it is now possible

to perform the next selected manufacturing process step.

5 Practical potential of the adaptive process planning

A software prototype has been developed in order to test

the method of adaptive planning process and to ensure a

subsequent review of its practicability. This software tool

has been programmed in C# in the development environ-

ment of Microsoft Visual Studio and it includes the fol-

lowing features:

• Definition and graphical representation of non-linear

process plans

• Saving and loading of non-linear process plans

• Representation of all reasonable technological process

chains of nonlinear process plans (process chain tree)

• Weighting of the evaluation criteria of process chains

• Analysis and evaluation of the process chains in

reference to the objective criteria

• Dynamic selection of the optimal process chain

Figure 12 shows the user interface of the software tool

for adaptive process planning. All valid process chains are

listed in the upper area. After manual or automatic start of

the evaluation, the process chains are prioritized with

regard to the respectively assigned value of the weighting

Updating productioninformation

Identification of valid process chains

Evaluation of validprocess chains

CurrentPC = Prio

1?

Set Prio 1 (VPC-List) = current PC

List of VPC

MES

Yes

No

PC: Process Chain Prio: PriorityVPC: Valid process Chains

Adjustment Planning(current PC)

PS x + 1

Scd/61888 © IFW

Sorted Listof VPC

PS x: Current process stepPrio: Priority

Fig. 10 Sequence of a detailed planning cycle


123

function. The first process chain is selected for processing

and then highlighted in the graphical representation of the

process chain tree. A flexible standard interface allows the

storage and reloading of the non-linear process plans in

XML format.

A test scenario has been developed in order to allow a

validation of the adaptive process planning methodology.

Using the material flow simulation software Tecnomatix

Plant Simulation� from Siemens PLM, a manufacturing

scenario has been created which displays the manufactur-

ing structures and processes typical for the job shop pro-

duction (see Sect. 1) in a simulation model. The basis for

this model is the experimental shop floor of the IFW, which

provides an extensive range of cutting machinery and has

the advantage of using real-time machine data. Figure 13

shows a simulation of the job shop production with its

associated material flow model elements. Each machine

tool is represented by a processing station with input and

output buffers. Transport routes link the processing stations

to each other so that necessary manual transport can be

realized by the manufacturing employees. As a represen-

tation of the MES, a scheduling algorithm based on the

actual process plans has been implemented into the simu-

lation model. This ensures the basic function of the MES in

a simplified way.

The job shop production model has been technologically

coupled with the software tool for adaptive process plan-

ning using a Microsoft Excel� interface. Utilizing this

interface allows to assign non-linear process plans to each

product. To simulate the connection to enterprise data

systems like ERP, a Microsoft Access� based database is

applied. Using this software in the context of the simula-

tion as described above, a dynamic evaluation of the pos-

sible process chains and a selection of the optimal process

chain is made. In the simulation process, the selected

process chain is assigned a single processing unit as an

attribute.

A production program consisting out of four different

work pieces (tool chucks) has been selected in order to

realize a prototypical application scenario. These compo-

nents require machining via a combination of turning,

milling and grinding operations. Different non-linear pro-

cess plans have been created for each chuck. Figure 14

shows an example of an extract of a non-linear process plan

for a work piece machining process.

In order to check the performance of the adaptive planning

process, three scenarios have been evaluated using Plant

Simulation�. In the first scenario, the behavior of the pro-

duction system using process plans which remain unchanged

during the whole production time has been considered. An

undisturbed production scenario is assumed. Thus, this sce-

nario represents the ideal case and is a benchmark for the

evaluation of the other scenarios. In the simulation of the

second scenario, non-stochastic disturbances of the produc-

tion process are considered. Selected machines are each

disrupted for a specific period of the simulation process.

Since static process plans are used, this results in production

stagnation. In the third scenario, for the same disturbance

behavior, the process plans are rescheduled with the help of

the adaptive process planning. An evaluation of all possible

process plans is performed for each processing unit before

any machining process. During the initial simulation runs, a

Scd/61889 © IFW

Determination of tool data

Configure tools and verify their availability1

Creation of fixture planning

Develop clamping plan and fix workpiece position2

Determination of cutting data

Determine cutting strategy, cutting paths and cutting values

3

Generation of NC-program

Write the source code4

Process simulation

Simulate machining process with sufficient accuracy

5

N00 G01 Z13N10 …N20 ……

Operation 1

Operation …

n vc ap …

Fig. 11 Planning sequence in

the adjustment planning


123

batch consisting of 30 parts has been processed. In all cases,

the average product cycle time has been measured (see

Fig. 15).

When evaluating the simulation runs, the results of the

three scenarios were compared to each other. In relation to

the lead time, the third scenario shows a significant

reduction of the average lead time of the processing units

by means of the adaptive planning process. Although the

lead time of the undisturbed scenario could not be achieved

due to the needed additional set-up times and the use of

non-optimal processes, the average lead time was

nonetheless shortened by about 22% when compared to

the average lead time of the scenario using static process

plans.

With regard to the above-stated objective of increasing

the flexibility in the production by using the adaptive

process planning methodology, the lead time has emerged

as the key indicator. On the one hand, disturbances that

Selection of theoptimal process

chain

Marking the optimal process chain in theprocess chain tree

Toolbar

Navigation bar

Visualizationbench

Analysis bench

Scd/61890 © IFW

Fig. 12 User interface of the

software prototype

Simulation model withproduction layout

Machine tool

Buffers

Detail: Processing station

Transport route

Machine indicator

Scd/61891 © IFW

Fig. 13 Simulation model of a

job shop production


123

result in production shutdowns have a direct negative effect

on the lead time. On the other hand, all three flexibility

types, mentioned in Sect. 3, have in common that a reaction

to the disturbances can be evaded by providing alternative

routes to the manufacturing order. This results in avoiding

any unnecessary extensions of the order lead times. In the

third scenario it has been shown that this effect can be

achieved using the software tool for adaptive planning

process in order to reschedule the process planning.

6 Conclusion

The development of the adaptive process planning

approach aims to increase the flexibility in job shop pro-

duction companies by promoting improved abilities to

react to disturbances in production. It has been shown that

it is possible to react to disturbances systematically by the

dynamical selection of process plans, which are then

adapted optimally to the company’s production targets. By

providing alternative process plans and evaluating them

repeatedly on the basis of current production information,

non-available resources can be replaced at short notice.

Thus, disturbances can be evaded, which results in a

reduction of lead time.

By means of the application of adaptive process plan-

ning all technologically possible and economically feasible

process chains relevant to the production of a component

can be considered. Postponing the detailed planning for the

period immediately before the start of a process step

ensures maximum flexibility in selecting the optimal situ-

ation-specific process chain. As a result, by adjusting

process plans dynamically, necessary changes to alterna-

tive routes through the shop floor and prompt reaction to

disturbances in the production process are ensured.

The approach presented in this paper marks a new stage

of integrated process planning and production control. On

the one hand the adaptive process planning enables a most

dynamic, responsive and flexible planning and control

which until now only were provided by the idea of dynamic

process planning (see Sect. 3). On the other hand the

method offers a very high degree of applicability in com-

parison to the existing approaches. In the field of produc-

tion control and multi order scheduling further research

will be done. Especially the systems behavior in case of a

highly utilized capacity will be investigated in order to

realize further potential in flexibility and responsiveness in

different production conditions.

Acknowledgments The results presented in this paper were

obtained within the framework of the Collaborative Research Center

(SFB) 653 ‘‘Gentelligent Components in their Lifecycle’’. The

authors would like to thank the German Research Foundation (DFG)

for its financial and organizational support of this project.

10MA2 TurningGildemeister

CTX 420 linear

V

12MA14 TurningGildemeister TWIN32 dc


MD 10S

V11

MA14 TurningGildemeister

CTV 400 linear

6MA18 TurningSchaudt CR 41


CTX 520 linear

9MA11 DrillingGildemeister

DMU 125p

7MA8 DrillingHeller PFV1

12MA5 DrillingIFW Schnelle

Maschine

V1

MA6 MillingHeller MC16

V

Scd/61892 © IFW

Fig. 14 Exemplary tool chuck

and non-linear process plan

0

1.000

2.000

3.000

4.000

5.000

6.000

7.000

9.000

Ideal DeterminedDisturbances

AdaptiveProcess Planning

Ave

rag

e L

ead

Tim

e

[sec]

Scd/61893 © IFW

Fig. 15 Evaluation of the simulation scenarios


123

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