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Turbulence Germs and their Impact on Planning and Control – Root Causes and Solutions for PPC Design
Turbulence Germs and their Impact on Planning and Control – Root Causes and Solutions for PPC Design 1
H.-H. Wiendahl1 Institute of Industrial Manufacturing and Management (IFF) – University Stuttgart, Germany Submitted by G. Spur (1), Berlin, Germany
Abstract Abrupt and often surprising changes characterize the market situation of production companies. Important aspects for the design of production planning and control (PCC) are those which potentially cause turbulence and lead to schedule deviations. The paper describes a method to capture these changes qualitatively and assess them quantitatively, while identifying solutions for PPC design. Its theoretical fundament draws on an analogy to physics and develops the morphology of turbulence germs. A case study illustrates its application in detail, starting with a company-specific turbulence profile, analyzing the root causes quantitatively and identifying solutions for improving PPC. Keywords: Management, Planning, Turbulence
1 INTRODUCTION Manufacturing companies are faced with constantly changing market and supplier conditions which greatly affect logistics [1]. This requires adjusting the Production Planning and Control (PPC) system appropriately [2]. Experience shows that factors which potentially cause turbulence are the main challenge for PPC design. The paper introduces a method to handle this problem in three steps: qualitatively capturing the requirements to describe the company-specific turbulence profile, quantitatively assessing the root causes, and identifying a consistent solution for PPC design.
3 TURBULENCE GERMS IN PPC Applying these findings to PPC operation, triggers for turbulence must be identified. These so-called turbulence germs take effect in planning and control and can be clustered in five groups: During planning, variations, fluctuations and plan adaptations are relevant. Control functions deal with unexpected deviations that jeopardize the promised delivery. As mentioned above, inconsistent tolerances represent a cause cluster of their own. Turbulence germs do not necessarily lead to plan deviations: in analogy to fluid mechanics, it is the relationship between requirements and capabilities which determines the relevance of a germ [5].
2 PPC AND TURBULENCE Manufacturing companies act in a network of customersupplier relationships [2]. This entails planning activities because customers expect reliable delivery promises, while purchase orders need to be placed in time with suppliers. These tasks are performed by PPC whose primary function is to allocate items, processes and (human and manufacturing) resources to orders in terms of time and quantity. For this purpose, the planning function plans the production flow in advance for a certain period of time, typically based on mean values for lead times. Then, the control function has to implement the plan in the best possible way in spite of changes and disturbances [3]. In doing so, the control system split up the macroscopic planning results into a microscopic view of single events. In general, such a plan can never be executed precisely. Therefore – similar to the quality control approach – a tolerance for permissible deviations has to be specified. In order to deal with turbulence in PPC, a definition has been developed in analogy to physics [4]: Turbulence exists in PPC when actual values deviate significantly from mean-based plan values [5]. To detect a significant deviation, tolerances denote allowed deviations between actual and planned values. Therefore heterogeneous tolerances are an often neglected trigger for turbulence. Controlling turbulence takes two views [5, 6]: Objectively, turbulence can be identified by relevant deviations. Subjectively, the expectations of the actors involved have to be considered, introducing psychological aspects.
3.1 Morphology of Turbulence Germs PPC covers the processes Source, Make and Deliver. Setting them against the five cause clusters results in the morphology of turbulence germs in PPC [7], figure 1.
Annals of the CIRP Vol. 56/1/2007
Variations Variations represent heterogeneous requirements within the same period. The relation between variations resulting from these heterogeneous requirements (delivery / lead / replacement times) and permissible deviation (planning tolerance) defines the relevance of turbulence germs. From a delivery view, heterogeneous customer requirements complicate planning: If they lead to heterogeneous order lead times, they will violate the basic assumptions of the successive planning concept MRP II with its meanbased lead time planning approach. If variations exceed planning tolerances, the smooth order flow is disrupted, resulting inevitably in plan deviations. From a make point of view, the necessity of large batches for technological reasons can also create this critical heterogeneous order flow. Heterogeneous replenishment conditions (e.g. longlead items) have a similar effect on sourcing. Fluctuations Fluctuations represent heterogeneous requirements within different periods. Main requirements are the variability of demand in terms of total quantity (delivery view) or order mix (make view): the more varying the quantities in relation to the available quantity flexibility in manufacturing, the greater the turbulence. Fluctuating sourcing conditions (times or quantities) have a similar effect. -443-
doi:10.1016/j.cirp.2007.05.106
Source
Cluster
Make
Deliver
Heterog. sourcing conditions • times • quantities
Heterog. order flow • batch creation • heterogeneous order mix
Heterog. delivery requirements • times • quantities
Fluctuation
Fluctuations in sourcing • times • quantities
Fluctuations in production • fluctuating order mix • fluctuating availability
Fluctuations in demand • times • quantities
Adaptation
Sourcing adaptations • sourcing deviations • product cycles (sourcing)
Production adaptations Demand adaptations • capacity demand deviations • forecast deviations • technology cycles (production) • product cycles (final products)
Variation
Inconsistency
Sourcing tolerances Material deviations (sourcing) Unreliable suppliers • deadline • quantity
Deviation
Production tolerances
Planning
Delivery tolerances
Quality deviations (production) Unexpected stoppages • machine breakdown • staff shortage
Changes to final product Order changes • deadline • quantity
Tolerance
Control
Figure 1: Morphology of turbulence germs in production planning and control. Plan adaptations Planning should anticipate future. Rolling planning approaches are used to deal with changing delivery and supply situations. Frequently changing circumstances complicate planning, e.g. forecast deviations or short life cycles of end products (deliver), as well as sourcing adaptations by the supplier or short life cycles of materials and purchased parts (source). From the make perspective, demand adaptations after finalizing the engineering or the short technology cycles can cause turbulence.
anytime within a specified week, while just-in-time delivery directly to the assembly line must arrive on the exact day or even shift. If external requirements differ, the valid internal planning tolerance must be decided beforehand. 3.2 Turbulence Profile To analyze the impact of turbulence germs in practice, it is advisable to generate a turbulence profile. Its application is described for a company unsatisfied with its logistical performance. The company covers eight production areas. For improvement a two-step procedure was agreed on: Firstly, PPC design and implementation of software for the pilot area of Mechanical Forming; secondly, concept adaptation and implementation for the other areas. In doing so, two questions were vital: "Which procedure guarantees a fast and reliable identification of necessary solution modules for PPC design?" and "How can differing PPC requirements in the other production areas be identified effectively?" Figure 2 shows the investigated turbulence profile; planning germs on average are rated higher: Heterogeneous delivery quantity requirements and demand fluctuations dominate here. After order release (control view), unreliable manufacturing processes and deviations from the required material specification are rated highest. The following paragraphs reflect main discussion points and consequences for PPC design in control and planning.
Deviations Deviations from planned values occur when unexpected events after order release necessitate interventions. From a delivery perspective, changes of orders in terms of end product, order quantity or deadline require an intervention by control. From the make perspective, a distinction into unexpected quality deviations as well as staff shortages and machine breakdowns makes sense. During sourcing, unexpected material deviations (technical specification) and unreliable suppliers can result in missing parts. Inconsistent tolerances Logistical requirements also affect the interface design between planning and control. Here, the requested delivery tolerances are of particular relevance. For instance: It could be sufficient for a stock delivery to arrive
heterogeneous delivery requirements (quantity)
8 6
business demand fluctuations
subjective evaluation by technical experts
changes to technical specifications
10
delivery modifications (deadline/quantity)
4 2
turbulence germs planning
batch creation mix fluctuations
unreliable manufacturing processes
0
fluctuating sourcing conditions 1 low impact 10 high impact
deviations in material quality
heterogeneous replenishment (times/quantity)
average rating for planning
turbulence germs control
unreliable suppliers (deadlines)
6,0
4,9
average rating for control
Figure 2: Turbulence profile for the Mechanical Forming area.
-444-
average calculated as mean value of the germs
4 CONSEQUENCES FOR CONTROL The pilot area is characterized by job-shop production with 30 capacity-relevant work centers and 250 workers; work-in-process of 1,800 orders with 11,000 operations in production; simple product structures (1-3 levels). Sometimes the qualitative evaluation of turbulence germs is detailed enough to generate solutions. In this case 'unreliable suppliers' represent an example for this category: The experts agreed on missing parts – due to the poor delivery performance of internal suppliers – as an important germ (figure 2, right). The ensuing discussion revealed that up to now the material availability check was based on planned dates. Thus, an obvious improvement step is to change the software algorithm and in future check the physical material availability before order start.
100
operation time uncertainty
4.1 Root Causes Typically, however, a quantitative analysis is required to identify root causes and improvement steps. The highest ratings got the above-mentioned germs 'unreliable processes' and 'material quality deviations'. Internal experts fear that these germs prevent a successful software-based PPC. A deviation analysis of planned and actual operation times proves the impact on PPC:
[min]
actual operation time
F C 25
A
0
10
600
100
Splitting Splitting A A wire wire splitting splitting B B slitting slitting Forming Forming C C stamping stamping Cutting, Cutting, volume volume production production D D CNC CNC milling milling E E CNC CNC surface surface grinding grinding FF CNC CNC drilling, drilling, small small G G milling, milling, small; small; manual manual Cutting, Cutting, single-part single-part production production H H CNC CNC milling milling II flat flat grinding grinding K K CNC CNC drilling, drilling, small small LL milling; milling; conventional conventional Finalizing Finalizing M M PVD PVD coating coating N N quality quality control control
work center slows down
operation time uncertainty 1)
L H N
K
G M
B I
D
50
E
F C 25
A
planning tolerance ± 2 work days
0 -3,0
-2,0
-1,0
0,0
1,0
30
x Assure daily rescheduling (non-fixed operation dates) to compare promised to planned or actual order flow. Introducing an MES (Manufacturing Execution Software) will not eliminate the causes. But the proposed automated rescheduling will enable early warning alerts, indicating whether delivery dates are at risk or not.
Figure 3: Calculation of operation time uncertainty.
[%]
[h]
x Record setup and operation procedures and compare critical and non-critical work centers to find bestpractice procedures for manufacturing.
planned operation time
work center speeds up
E
4.2 Solutions In order to assess the influence of turbulence germs on PPC the lead time deviation is helpful: It is calculated as difference between output date and input date schedule deviation [8]. The average of all work center operations quantifies its mean influence on delay. Figure 5 shows a weak correlation to operation time uncertainty. Logisticscritical work centers slow down order flow by more than two days. Therefore, the following steps were agreed on:
CNC milling nall 818 operations nout 663 operations OT uncertainty 81% 200
D
and for some operations there are no actual times at all (operators aggregate feedbacks of operations). This is proven by the relation between operation time uncertainty and mean planned operation time, figure 4. The next steps identified the main causes: Planners only estimate operation times instead of calculating them. New parts and test orders typically have little lots. And operators often produce more if manufacturing runs well.
tolerance r 30%
0
I
50
identification selected work centers: figure 5
Figure 4: Operation time uncertainty of work centers.
[%]
0
G
M B
mean planned operation time
x More than 50% of all operations are outside the defined tolerance range of r 30% of acceptable deviation (machining at technological limits). Figure 3 visualizes the results for CNC milling, sub area cutting single parts. The distribution of operation times reveals: Short ones deviate more often from planned values
200
H K N
[%]
0
x Work centers exceed the planned operation times regularly; on average, about 30% (of all operations).
600
splitting forming cutting, volume cutting, single finalizing
L
2,0
lead time deviation
3,0
4,0
[work days]
6,0
1) 2)
2)
operations outside tolerance (r 30%) work center influence on delay
Figure 5: Operation time uncertainty versus lead time deviation for the Mechanical Forming area.
-445-
5 CONSEQUENCES FOR PLANNING In planning, the experts mainly discussed three germs:
80
A
x Heterogeneous delivery requirements: Customers order different quantities, resulting in heterogeneous production lot sizes and obstructing a smooth flow of orders. To reduce this impact, the cutting area was split in two groups: one for volume and one for single parts (large and small lot sizes).
20 0
300
high
[%]
500
6 CONCLUSION Turbulent markets require a fast PPC alignment in an effective and efficient way. The method supports this goal in various ways: It allows a structured and qualitative capturing of requirements, derives consistent solutions, and encourages discussions of logistical interactions and their effects on PPC design and operation. Of course, a qualitative evaluation does not replace quantitative analysis. But the effort for data collection, analysis and verification can be substantially reduced. Experience from more than 35 case studies proved its practicability. 7 ACKNOWLEDGMENTS The author expresses his sincere thanks to Profs. G. Spur and H.-P. Wiendahl and to his colleague M. Bornhäuser for support. The results are part of the ongoing research project "Model-based Order Management Design for discrete manufacturing" which is funded by the German Research Foundation DFG (WI 2670/1). 8 REFERENCES [1] Monostori, L., Váncza, J., Kumara, S.R.T., 2006, Agent-Based Systems for Manufacturing, Annals of the CIRP, 55/2:697-721. [2] Wiendahl, H.-P., Lutz, S., 2002, Production in Networks, Annals of the CIRP, 51/2:573-586. [3] Hopp, W., et. al. 1996, Factory Physics Foundations of Manufacturing Management, Chicago, Irwin. [4] Massey, B. S., 1998, Mechanics of Fluids, 7. ed., London: Van Nostrand Reinhold. [5] Wiendahl, H.-H., Roth, N., Westkämper, E., 2002, Logistical Positioning in a Turbulent Environment, Annals of the CIRP, 51/1:383-386. [6] Mintzberg, H., 1994, That’s not Turbulence, Chicken Little, It’s Really Opportunity, Planning Review, 11/12:7-9. [7] Wiendahl, H.-H., 2006, Auftragsmanagement im turbulenten Umfeld, wt online, part 1: 96/4:183-189. [8] Nyhuis, P., Wiendahl, H.-P., 2003, Logistische Kennlinien, Berlin et. al., Springer.
x Record 'sequence discipline' to monitor behavior. An MES supports these measures. Finite capacity planning approaches obtain a binding character since any relevant deviation necessitates interventions.
quantity
adequate
200
Some results and solutions were obvious to the experts. However, this case study pointed out three benefits: Firstly, thanks to the qualitative approach, the debate focused on main aspects. Secondly, its clear illustration revealed the logistical interrelationships between various phenomena that were previously regarded as unrelated. Thirdly, the available results are structured and can be compared now to the PPC requirements of other areas. On the whole, the visualization and logical derivation of necessary actions facilitated communication considerably right up to the managing directors.
x Install a staff pool for ‘cutting’ to increase flexibility.
1
100
Figure 7: Internal logistical capability of work centers.
x Record the material availability at initial work centers.
0
low
0
relative WIP Level
x Install WIP-oriented order release and backlogoriented capacity control to support a steady flow.
10
K
identification identification selected selected work work centers: centers: figure figure 55
N
x Differentiate promised, planned and actual dates; their differentiation allows to identify bottlenecks and indicates the need for planning or control activities.
20
F
B
5.2 Solutions This leads to proposals concerning PPC methods, production flexibility and behavior of the people involved:
tolerance
L
H CE
1034%
40
5.1 Root Causes A strategic decision on the logistical guideline is necessary to identify a solution; i.e. what order flow to create through the factory: Opting for the flow- or the turbulenceoriented strategy? The flow-oriented strategy creates a steady order flow and allows a 'mean order progress PPC' in accordance with classical ERP Software based on the MRP approach [5]. In this case, the management chose the flow-oriented strategy. Flow-oriented strategy requires low variation of lead times compared to planning tolerance. The internal logistical capability CLI judges the ability to achieve this strategy [5]. CLI reflects the share of orders for which the lead time deviation from the modus (value of highest quantity) exceeds more than half of the tolerance. Figure 6 shows one example. The quantitative analyses revealed: Firstly, at 20% the potential to reduce lead times through WIP reduction is atypically low for a job-shop production. Compared to the benchmark of 200-300% relative WIP (absolute through ideal Work-in-Process [5,8]) many work centers are well positioned, figure 7 shaded area. Secondly, all work centers have a CLI > 25%, i.e. a wide lead time distribution. Further analyses showed: Some are caused by high WIP levels, some by missing parts, and some by WIP fluctuations due to fluctuating demand. Thirdly, capacity flexibility in general is high enough and often only restricted by staff and not by machine capacity.
[%]
D
CLI
x Demand and mix fluctuations: They create dynamic bottlenecks which result in schedule deviations. Today, planners schedule in a semi-automated way; they are supposed to check capacity to avoid an overload.
40
M
G V
[%]
CNC milling n 818 operations P 6.5 work days V 8.2 work days CLI 56%
9
17 [work days] 33 >33 operation lead time within tolerance outside tolerance
Figure 6: Calculation of internal logistical capability CLI.
-446-
H.-H. Wiendahl1 Institute of Industrial Manufacturing and Management (IFF) – University Stuttgart, Germany Submitted by G. Spur (1), Berlin, Germany
Abstract Abrupt and often surprising changes characterize the market situation of production companies. Important aspects for the design of production planning and control (PCC) are those which potentially cause turbulence and lead to schedule deviations. The paper describes a method to capture these changes qualitatively and assess them quantitatively, while identifying solutions for PPC design. Its theoretical fundament draws on an analogy to physics and develops the morphology of turbulence germs. A case study illustrates its application in detail, starting with a company-specific turbulence profile, analyzing the root causes quantitatively and identifying solutions for improving PPC. Keywords: Management, Planning, Turbulence
1 INTRODUCTION Manufacturing companies are faced with constantly changing market and supplier conditions which greatly affect logistics [1]. This requires adjusting the Production Planning and Control (PPC) system appropriately [2]. Experience shows that factors which potentially cause turbulence are the main challenge for PPC design. The paper introduces a method to handle this problem in three steps: qualitatively capturing the requirements to describe the company-specific turbulence profile, quantitatively assessing the root causes, and identifying a consistent solution for PPC design.
3 TURBULENCE GERMS IN PPC Applying these findings to PPC operation, triggers for turbulence must be identified. These so-called turbulence germs take effect in planning and control and can be clustered in five groups: During planning, variations, fluctuations and plan adaptations are relevant. Control functions deal with unexpected deviations that jeopardize the promised delivery. As mentioned above, inconsistent tolerances represent a cause cluster of their own. Turbulence germs do not necessarily lead to plan deviations: in analogy to fluid mechanics, it is the relationship between requirements and capabilities which determines the relevance of a germ [5].
2 PPC AND TURBULENCE Manufacturing companies act in a network of customersupplier relationships [2]. This entails planning activities because customers expect reliable delivery promises, while purchase orders need to be placed in time with suppliers. These tasks are performed by PPC whose primary function is to allocate items, processes and (human and manufacturing) resources to orders in terms of time and quantity. For this purpose, the planning function plans the production flow in advance for a certain period of time, typically based on mean values for lead times. Then, the control function has to implement the plan in the best possible way in spite of changes and disturbances [3]. In doing so, the control system split up the macroscopic planning results into a microscopic view of single events. In general, such a plan can never be executed precisely. Therefore – similar to the quality control approach – a tolerance for permissible deviations has to be specified. In order to deal with turbulence in PPC, a definition has been developed in analogy to physics [4]: Turbulence exists in PPC when actual values deviate significantly from mean-based plan values [5]. To detect a significant deviation, tolerances denote allowed deviations between actual and planned values. Therefore heterogeneous tolerances are an often neglected trigger for turbulence. Controlling turbulence takes two views [5, 6]: Objectively, turbulence can be identified by relevant deviations. Subjectively, the expectations of the actors involved have to be considered, introducing psychological aspects.
3.1 Morphology of Turbulence Germs PPC covers the processes Source, Make and Deliver. Setting them against the five cause clusters results in the morphology of turbulence germs in PPC [7], figure 1.
Annals of the CIRP Vol. 56/1/2007
Variations Variations represent heterogeneous requirements within the same period. The relation between variations resulting from these heterogeneous requirements (delivery / lead / replacement times) and permissible deviation (planning tolerance) defines the relevance of turbulence germs. From a delivery view, heterogeneous customer requirements complicate planning: If they lead to heterogeneous order lead times, they will violate the basic assumptions of the successive planning concept MRP II with its meanbased lead time planning approach. If variations exceed planning tolerances, the smooth order flow is disrupted, resulting inevitably in plan deviations. From a make point of view, the necessity of large batches for technological reasons can also create this critical heterogeneous order flow. Heterogeneous replenishment conditions (e.g. longlead items) have a similar effect on sourcing. Fluctuations Fluctuations represent heterogeneous requirements within different periods. Main requirements are the variability of demand in terms of total quantity (delivery view) or order mix (make view): the more varying the quantities in relation to the available quantity flexibility in manufacturing, the greater the turbulence. Fluctuating sourcing conditions (times or quantities) have a similar effect. -443-
doi:10.1016/j.cirp.2007.05.106
Source
Cluster
Make
Deliver
Heterog. sourcing conditions • times • quantities
Heterog. order flow • batch creation • heterogeneous order mix
Heterog. delivery requirements • times • quantities
Fluctuation
Fluctuations in sourcing • times • quantities
Fluctuations in production • fluctuating order mix • fluctuating availability
Fluctuations in demand • times • quantities
Adaptation
Sourcing adaptations • sourcing deviations • product cycles (sourcing)
Production adaptations Demand adaptations • capacity demand deviations • forecast deviations • technology cycles (production) • product cycles (final products)
Variation
Inconsistency
Sourcing tolerances Material deviations (sourcing) Unreliable suppliers • deadline • quantity
Deviation
Production tolerances
Planning
Delivery tolerances
Quality deviations (production) Unexpected stoppages • machine breakdown • staff shortage
Changes to final product Order changes • deadline • quantity
Tolerance
Control
Figure 1: Morphology of turbulence germs in production planning and control. Plan adaptations Planning should anticipate future. Rolling planning approaches are used to deal with changing delivery and supply situations. Frequently changing circumstances complicate planning, e.g. forecast deviations or short life cycles of end products (deliver), as well as sourcing adaptations by the supplier or short life cycles of materials and purchased parts (source). From the make perspective, demand adaptations after finalizing the engineering or the short technology cycles can cause turbulence.
anytime within a specified week, while just-in-time delivery directly to the assembly line must arrive on the exact day or even shift. If external requirements differ, the valid internal planning tolerance must be decided beforehand. 3.2 Turbulence Profile To analyze the impact of turbulence germs in practice, it is advisable to generate a turbulence profile. Its application is described for a company unsatisfied with its logistical performance. The company covers eight production areas. For improvement a two-step procedure was agreed on: Firstly, PPC design and implementation of software for the pilot area of Mechanical Forming; secondly, concept adaptation and implementation for the other areas. In doing so, two questions were vital: "Which procedure guarantees a fast and reliable identification of necessary solution modules for PPC design?" and "How can differing PPC requirements in the other production areas be identified effectively?" Figure 2 shows the investigated turbulence profile; planning germs on average are rated higher: Heterogeneous delivery quantity requirements and demand fluctuations dominate here. After order release (control view), unreliable manufacturing processes and deviations from the required material specification are rated highest. The following paragraphs reflect main discussion points and consequences for PPC design in control and planning.
Deviations Deviations from planned values occur when unexpected events after order release necessitate interventions. From a delivery perspective, changes of orders in terms of end product, order quantity or deadline require an intervention by control. From the make perspective, a distinction into unexpected quality deviations as well as staff shortages and machine breakdowns makes sense. During sourcing, unexpected material deviations (technical specification) and unreliable suppliers can result in missing parts. Inconsistent tolerances Logistical requirements also affect the interface design between planning and control. Here, the requested delivery tolerances are of particular relevance. For instance: It could be sufficient for a stock delivery to arrive
heterogeneous delivery requirements (quantity)
8 6
business demand fluctuations
subjective evaluation by technical experts
changes to technical specifications
10
delivery modifications (deadline/quantity)
4 2
turbulence germs planning
batch creation mix fluctuations
unreliable manufacturing processes
0
fluctuating sourcing conditions 1 low impact 10 high impact
deviations in material quality
heterogeneous replenishment (times/quantity)
average rating for planning
turbulence germs control
unreliable suppliers (deadlines)
6,0
4,9
average rating for control
Figure 2: Turbulence profile for the Mechanical Forming area.
-444-
average calculated as mean value of the germs
4 CONSEQUENCES FOR CONTROL The pilot area is characterized by job-shop production with 30 capacity-relevant work centers and 250 workers; work-in-process of 1,800 orders with 11,000 operations in production; simple product structures (1-3 levels). Sometimes the qualitative evaluation of turbulence germs is detailed enough to generate solutions. In this case 'unreliable suppliers' represent an example for this category: The experts agreed on missing parts – due to the poor delivery performance of internal suppliers – as an important germ (figure 2, right). The ensuing discussion revealed that up to now the material availability check was based on planned dates. Thus, an obvious improvement step is to change the software algorithm and in future check the physical material availability before order start.
100
operation time uncertainty
4.1 Root Causes Typically, however, a quantitative analysis is required to identify root causes and improvement steps. The highest ratings got the above-mentioned germs 'unreliable processes' and 'material quality deviations'. Internal experts fear that these germs prevent a successful software-based PPC. A deviation analysis of planned and actual operation times proves the impact on PPC:
[min]
actual operation time
F C 25
A
0
10
600
100
Splitting Splitting A A wire wire splitting splitting B B slitting slitting Forming Forming C C stamping stamping Cutting, Cutting, volume volume production production D D CNC CNC milling milling E E CNC CNC surface surface grinding grinding FF CNC CNC drilling, drilling, small small G G milling, milling, small; small; manual manual Cutting, Cutting, single-part single-part production production H H CNC CNC milling milling II flat flat grinding grinding K K CNC CNC drilling, drilling, small small LL milling; milling; conventional conventional Finalizing Finalizing M M PVD PVD coating coating N N quality quality control control
work center slows down
operation time uncertainty 1)
L H N
K
G M
B I
D
50
E
F C 25
A
planning tolerance ± 2 work days
0 -3,0
-2,0
-1,0
0,0
1,0
30
x Assure daily rescheduling (non-fixed operation dates) to compare promised to planned or actual order flow. Introducing an MES (Manufacturing Execution Software) will not eliminate the causes. But the proposed automated rescheduling will enable early warning alerts, indicating whether delivery dates are at risk or not.
Figure 3: Calculation of operation time uncertainty.
[%]
[h]
x Record setup and operation procedures and compare critical and non-critical work centers to find bestpractice procedures for manufacturing.
planned operation time
work center speeds up
E
4.2 Solutions In order to assess the influence of turbulence germs on PPC the lead time deviation is helpful: It is calculated as difference between output date and input date schedule deviation [8]. The average of all work center operations quantifies its mean influence on delay. Figure 5 shows a weak correlation to operation time uncertainty. Logisticscritical work centers slow down order flow by more than two days. Therefore, the following steps were agreed on:
CNC milling nall 818 operations nout 663 operations OT uncertainty 81% 200
D
and for some operations there are no actual times at all (operators aggregate feedbacks of operations). This is proven by the relation between operation time uncertainty and mean planned operation time, figure 4. The next steps identified the main causes: Planners only estimate operation times instead of calculating them. New parts and test orders typically have little lots. And operators often produce more if manufacturing runs well.
tolerance r 30%
0
I
50
identification selected work centers: figure 5
Figure 4: Operation time uncertainty of work centers.
[%]
0
G
M B
mean planned operation time
x More than 50% of all operations are outside the defined tolerance range of r 30% of acceptable deviation (machining at technological limits). Figure 3 visualizes the results for CNC milling, sub area cutting single parts. The distribution of operation times reveals: Short ones deviate more often from planned values
200
H K N
[%]
0
x Work centers exceed the planned operation times regularly; on average, about 30% (of all operations).
600
splitting forming cutting, volume cutting, single finalizing
L
2,0
lead time deviation
3,0
4,0
[work days]
6,0
1) 2)
2)
operations outside tolerance (r 30%) work center influence on delay
Figure 5: Operation time uncertainty versus lead time deviation for the Mechanical Forming area.
-445-
5 CONSEQUENCES FOR PLANNING In planning, the experts mainly discussed three germs:
80
A
x Heterogeneous delivery requirements: Customers order different quantities, resulting in heterogeneous production lot sizes and obstructing a smooth flow of orders. To reduce this impact, the cutting area was split in two groups: one for volume and one for single parts (large and small lot sizes).
20 0
300
high
[%]
500
6 CONCLUSION Turbulent markets require a fast PPC alignment in an effective and efficient way. The method supports this goal in various ways: It allows a structured and qualitative capturing of requirements, derives consistent solutions, and encourages discussions of logistical interactions and their effects on PPC design and operation. Of course, a qualitative evaluation does not replace quantitative analysis. But the effort for data collection, analysis and verification can be substantially reduced. Experience from more than 35 case studies proved its practicability. 7 ACKNOWLEDGMENTS The author expresses his sincere thanks to Profs. G. Spur and H.-P. Wiendahl and to his colleague M. Bornhäuser for support. The results are part of the ongoing research project "Model-based Order Management Design for discrete manufacturing" which is funded by the German Research Foundation DFG (WI 2670/1). 8 REFERENCES [1] Monostori, L., Váncza, J., Kumara, S.R.T., 2006, Agent-Based Systems for Manufacturing, Annals of the CIRP, 55/2:697-721. [2] Wiendahl, H.-P., Lutz, S., 2002, Production in Networks, Annals of the CIRP, 51/2:573-586. [3] Hopp, W., et. al. 1996, Factory Physics Foundations of Manufacturing Management, Chicago, Irwin. [4] Massey, B. S., 1998, Mechanics of Fluids, 7. ed., London: Van Nostrand Reinhold. [5] Wiendahl, H.-H., Roth, N., Westkämper, E., 2002, Logistical Positioning in a Turbulent Environment, Annals of the CIRP, 51/1:383-386. [6] Mintzberg, H., 1994, That’s not Turbulence, Chicken Little, It’s Really Opportunity, Planning Review, 11/12:7-9. [7] Wiendahl, H.-H., 2006, Auftragsmanagement im turbulenten Umfeld, wt online, part 1: 96/4:183-189. [8] Nyhuis, P., Wiendahl, H.-P., 2003, Logistische Kennlinien, Berlin et. al., Springer.
x Record 'sequence discipline' to monitor behavior. An MES supports these measures. Finite capacity planning approaches obtain a binding character since any relevant deviation necessitates interventions.
quantity
adequate
200
Some results and solutions were obvious to the experts. However, this case study pointed out three benefits: Firstly, thanks to the qualitative approach, the debate focused on main aspects. Secondly, its clear illustration revealed the logistical interrelationships between various phenomena that were previously regarded as unrelated. Thirdly, the available results are structured and can be compared now to the PPC requirements of other areas. On the whole, the visualization and logical derivation of necessary actions facilitated communication considerably right up to the managing directors.
x Install a staff pool for ‘cutting’ to increase flexibility.
1
100
Figure 7: Internal logistical capability of work centers.
x Record the material availability at initial work centers.
0
low
0
relative WIP Level
x Install WIP-oriented order release and backlogoriented capacity control to support a steady flow.
10
K
identification identification selected selected work work centers: centers: figure figure 55
N
x Differentiate promised, planned and actual dates; their differentiation allows to identify bottlenecks and indicates the need for planning or control activities.
20
F
B
5.2 Solutions This leads to proposals concerning PPC methods, production flexibility and behavior of the people involved:
tolerance
L
H CE
1034%
40
5.1 Root Causes A strategic decision on the logistical guideline is necessary to identify a solution; i.e. what order flow to create through the factory: Opting for the flow- or the turbulenceoriented strategy? The flow-oriented strategy creates a steady order flow and allows a 'mean order progress PPC' in accordance with classical ERP Software based on the MRP approach [5]. In this case, the management chose the flow-oriented strategy. Flow-oriented strategy requires low variation of lead times compared to planning tolerance. The internal logistical capability CLI judges the ability to achieve this strategy [5]. CLI reflects the share of orders for which the lead time deviation from the modus (value of highest quantity) exceeds more than half of the tolerance. Figure 6 shows one example. The quantitative analyses revealed: Firstly, at 20% the potential to reduce lead times through WIP reduction is atypically low for a job-shop production. Compared to the benchmark of 200-300% relative WIP (absolute through ideal Work-in-Process [5,8]) many work centers are well positioned, figure 7 shaded area. Secondly, all work centers have a CLI > 25%, i.e. a wide lead time distribution. Further analyses showed: Some are caused by high WIP levels, some by missing parts, and some by WIP fluctuations due to fluctuating demand. Thirdly, capacity flexibility in general is high enough and often only restricted by staff and not by machine capacity.
[%]
D
CLI
x Demand and mix fluctuations: They create dynamic bottlenecks which result in schedule deviations. Today, planners schedule in a semi-automated way; they are supposed to check capacity to avoid an overload.
40
M
G V
[%]
CNC milling n 818 operations P 6.5 work days V 8.2 work days CLI 56%
9
17 [work days] 33 >33 operation lead time within tolerance outside tolerance
Figure 6: Calculation of internal logistical capability CLI.
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