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A supporting tool for improvement of batch manufacturing
European Symposium on Computer Aided Process Engineering - 12 J. Grievink and J. van Schijndel (Editors) ® 2002 Elsevier Science B.V. All rights reserved.
673
A supporting tool for improvement of batch manufacturing Petra Heijnen and Zofia Verwater-Lukszo Delft University of Technology The Netherlands
Abstract Integration of the activities in a complex system as a batch plant will need extra support. The Management Decision Tool, as described in this paper, will give batch plant managers the opportunity to perform a quick scan of the plant focusing on objectives and activities, and their interactions. As a result the most promising activities for improvements are determined, an optimal way for performing product innovations is stipulated and new ideas for process innovations are generated.
1. Introduction and objectives At the Delft University of Technology an interfaculty research project aimed at improving the efficiency of batch process operations was started in 1998. The industrial partners in this research endeavour represent the chemical and metallurgical process industry, the food industry and a number of agro-based companies. The close cooperation with the industrial partners and the identification of their needs for support on integrated plant management result among others in the development of a management decision tool. The objective of this tool is to support management in taking the following decisions: Which improvements at which activities in the plant will contribute to the overall objective of the enterprise? Which interactions between the activities should be improved to increase their effectiveness as well as their efficiency? How to organise product innovations in the existing plant in an optimal way? To answer these questions a thorough analysis is needed of the company's structure, the goals, the desired performance, the way the activities are performed and the expected improvements. System engineering will be a suitable approach for such an analysis. An industrial site can be seen as a large integrated system, which is built from objects (subsystems: departments, key activities etc) linked together by material and information flows in a complex structure. In this paper the implemented ideas for the Management Decision Tool (MDT) will be discussed. The execution of the proposed method will give the plant manager the opportunity to get a quick scan of promising improvements in the batch plant and to manage his plant in an integrated way. Moreover, it will support the plant manager in the creative search for process innovations.
674
2. The Management Decision Tool The MDT is applicable to each batch plant. The key model of MDT uses a so-called standard plant containing activities, which take place in almost every batch manufacturing plants, as well as activities that take place in all kind of production environments. To use the MDT in a dedicated plant, the present models need only to be adapted to the specific situation and do not have to be built from the ground. The MDT consists of four different steps through which the user is guided. After performing all the steps the user has gained more insight in the organisation of the plant with respect to the plant objectives and their relative weights compared to the main objective of the plant, the activities in the plant and their mutual interactions and the most important possibilities for improvement.
3. Objectives of the plant The first step in the MDT covers a thorough analysis of the objectives of the batch plant. For the MDT an objective tree is developed that will be applicable to most batch plants. In an objective tree the objectives are arranged in a hierarchical way starting from the main objective, which characterizes and defines the area of interest, i.e. the system involved (Keeney, 1992 & 1993). The main objective of the standard batch plant is formulated with respect to the long-term existence of the plant. This strategic objective is made operational by dividing it into more concrete subobjectives. The objective tree is split up in this way into three horizontal levels (Keuning, 2000) 1. The strategic objectives as regards the organization of the relations between the plant and its environment. 2. The tactical objectives as regards the choice of a good structure for the organization and management of the resources in the plant in such a way that the strategic objectives are achieved. 3. The operational objectives as regards the use of the resources that are available in the plant in such a way that the tactical objectives are achieved. Figure 1 shows part of the objective tree as available in the MDT. The user of the MDT could adapt the objective tree to the specific situation in the plant considered. The adaptations will in general be carried out at the level of operational objectives.
4. Activities and their interactions In the second step of the MDT the activities, which guarantee that the operational objectives of the plant are achieved in practice, are formulated. For the MDT an activity model is developed in which the activities of the standard batch plant have been modelled by using the so-called IDEFO technique. IDEFO is a method to model activities and their mutual relations in a hierarchical way (FIPS PUBS 183, 1994). In the MDT firstly all activities are defined that may influence the measure in which the operational objectives are achieved. These activities have a high degree of detail and they can be combined into domed activities. After a number of these compositions the
675 so-called context activity will be achieved. In the same way the interactions between the activities will be modelled and combined into interactions with less degree of detail.
W^ Figure J: Part of the objective tree of the standard batch plant The interactions that are distinguished in an IDEFO model are 1. Input; what is transformed by the activity into the output, 2. Output; what is produced by the activity, 3. Control; what controls the activity, i control Im Ic. 4. Mechanism; what performs the objective activity.
11
input
AcnvrrY mechanism
output
676 In the activity model as used in the MDT a fifth flow is being introduced for the objectives to which an activity may contribute. An arrow coming from the activity and pointing upwards will denote this flow. As an example the objective high product quality will be considered. This objective is decomposed into five sub-objectives. For every operational objective several activities are performed to guarantee that the objective is achieved. These activities are denoted by the grey boxes.
good production process
high quahty of materials
high quahty of installation
's~sr
"XX"
sr-iIVfANAGE MAINTENANCE
PURCHASE INSTALLATION
good production personnel
CONTROL PRODUCTION
MANAGE RECIPES
Figure 2: Activities added to the operational objectives
In Figure 3 a small part of the activity model for the standard batch plant is shown, where the interactions between the activities "manage maintenance" and "control production" are central.
i
iL
1
CONTROL
¥
PRODUCTION iV control tools
high quality of mstallation
mamtenance standards and methods
maintenance requests
T
^r
MANAGE MAINTEt^ANC:E
responses & technical feedback
Figure 3: IDEFO diagram of ''Control Production" and "Manage Maintenance"
677
5. Contribution of the objectives to the overall objective In the third step of the MDT the user will be asked to order the objectives in the objective tree to the degree in which they contribute to the main objective. The pairwise comparison method (Saaty, 1980) will be used for the ranking of the objectives. In every group of objectives in the objective tree, i.e. all child objectives of one parent, every pair of two objectives is compared. The user decides for every two objectives which one will yield the highest contribution to the main objective. Doing this for every pair in the group a unique preference list will result. With the formula kwI
m 1 where /:,• = S ~ r=i r
m
j=\
(^)
}
the weight w, of the /th objective in the preference list is calculated. The relative weight of an operational objective can now be calculated by multiplying its weight with the weights of all its parents. As an example we look again at the five child objectives of high product quality. a. Good production process b. High quality of installation c. High quality of materials d. Good insight in quality e. Good production personnel The user is asked to decide for randomly chosen pairs, which of the two is more important to achieve a high product quality. Assume that the user decides to the following preference structure: dycya>-bye. With Equation (1) the relative weights of the objectives are: 1. Good insight in quality, weight: 0.46 2. High quality of materials, weight: 0.26 3. Good production process, weight: 0.16 4. High quality of installation, weight: 0.09 5. Good production personnel, weight: 0.04 When all weights of the operational objectives are known by multiplying the relative group weights with all the weights of their parents, the objectives with the highest contribution to the main objective of the plant are determined. With the results of the previous step the activities that are responsible for these objectives can be found. For improving the degree in which the main objective is being achieved, the user should focus on the improvement of these activities.
678
6. Efficiency of activities and possible improvements In the last step of the MDT the possible improvements of the activities are being mapped. Here a difference is made between the effectiveness of the activity and the efficiency of the activity. The effectiveness of the activity is defined as the degree in which the activity contributes to its objectives. Most operational objectives can be influenced by more than one activity, as shown in the activity model. To be able to measure the degree in which an objective is achieved, a performance indicator will be linked to the objective. To measure the effectiveness by activity the performance indicator of an objective should be split into several sub indicators that each can be influenced by just one activity. The efficiency of the activity is defined as the costs or time needed to transform the input of the activity into its output. As mentioned before the objective of the MDT is to find activities that are good candidates for improvements, i.e. which will yield a high contribution to the main objective of the plant. Measuring the effectiveness of the activity by its performance indicators can determine the actual contribution of the activity to the main objective. Improvement of the effectiveness of the activity may not be performed at the expense of the efficiency of the activity. With the assistance of the activity model the user can determine which input the activity requires to deliver the right output. The right input on the right moment will improve in general the efficiency as well as the effectiveness of the activity. With the results of the MDT the user may 1. determine which activities are good candidates for improvement, 2. determine the actual performance of the activities, 3. improve the activities by better tuning of their interactions.
7. Results and final remarks The MDT is still under development. The modelling phases for objectives and activities are already evaluated and applied in the plants of the industrial partners. At the moment research concentrates on the definition of the contribution of the chosen operational objectives to the overall plant goal. This will be realised by discussions with a selected team of process people (plant managers, quality managers, schedulers and operators) from an industrial plant in the food and chemical industry. Next, several case studies will be performed for further evaluation.
References IDEFO: FIPS PUBS 183, 1994, Standard for Integration Definition for Function Modelling. Keeney, Ralph L., 1993, Decisions with multiple objectives; preferences and value tradeoffs. New York Cambridge University Press. Keeney, Ralph L., 1992, Value-focused thinking; a path to creative decisionmaking. Cambridge, Mass. Harvard University Press. Keuning, D., D.J. Eppink, 2000, Management en Organisatie, theorie en toepassing, Educatieve partners Nederland BV, (in Dutch). Saaty, T.L., 1980, The Analytic Hierarchy Process, McGraw-Hill Book Co, New York.
673
A supporting tool for improvement of batch manufacturing Petra Heijnen and Zofia Verwater-Lukszo Delft University of Technology The Netherlands
Abstract Integration of the activities in a complex system as a batch plant will need extra support. The Management Decision Tool, as described in this paper, will give batch plant managers the opportunity to perform a quick scan of the plant focusing on objectives and activities, and their interactions. As a result the most promising activities for improvements are determined, an optimal way for performing product innovations is stipulated and new ideas for process innovations are generated.
1. Introduction and objectives At the Delft University of Technology an interfaculty research project aimed at improving the efficiency of batch process operations was started in 1998. The industrial partners in this research endeavour represent the chemical and metallurgical process industry, the food industry and a number of agro-based companies. The close cooperation with the industrial partners and the identification of their needs for support on integrated plant management result among others in the development of a management decision tool. The objective of this tool is to support management in taking the following decisions: Which improvements at which activities in the plant will contribute to the overall objective of the enterprise? Which interactions between the activities should be improved to increase their effectiveness as well as their efficiency? How to organise product innovations in the existing plant in an optimal way? To answer these questions a thorough analysis is needed of the company's structure, the goals, the desired performance, the way the activities are performed and the expected improvements. System engineering will be a suitable approach for such an analysis. An industrial site can be seen as a large integrated system, which is built from objects (subsystems: departments, key activities etc) linked together by material and information flows in a complex structure. In this paper the implemented ideas for the Management Decision Tool (MDT) will be discussed. The execution of the proposed method will give the plant manager the opportunity to get a quick scan of promising improvements in the batch plant and to manage his plant in an integrated way. Moreover, it will support the plant manager in the creative search for process innovations.
674
2. The Management Decision Tool The MDT is applicable to each batch plant. The key model of MDT uses a so-called standard plant containing activities, which take place in almost every batch manufacturing plants, as well as activities that take place in all kind of production environments. To use the MDT in a dedicated plant, the present models need only to be adapted to the specific situation and do not have to be built from the ground. The MDT consists of four different steps through which the user is guided. After performing all the steps the user has gained more insight in the organisation of the plant with respect to the plant objectives and their relative weights compared to the main objective of the plant, the activities in the plant and their mutual interactions and the most important possibilities for improvement.
3. Objectives of the plant The first step in the MDT covers a thorough analysis of the objectives of the batch plant. For the MDT an objective tree is developed that will be applicable to most batch plants. In an objective tree the objectives are arranged in a hierarchical way starting from the main objective, which characterizes and defines the area of interest, i.e. the system involved (Keeney, 1992 & 1993). The main objective of the standard batch plant is formulated with respect to the long-term existence of the plant. This strategic objective is made operational by dividing it into more concrete subobjectives. The objective tree is split up in this way into three horizontal levels (Keuning, 2000) 1. The strategic objectives as regards the organization of the relations between the plant and its environment. 2. The tactical objectives as regards the choice of a good structure for the organization and management of the resources in the plant in such a way that the strategic objectives are achieved. 3. The operational objectives as regards the use of the resources that are available in the plant in such a way that the tactical objectives are achieved. Figure 1 shows part of the objective tree as available in the MDT. The user of the MDT could adapt the objective tree to the specific situation in the plant considered. The adaptations will in general be carried out at the level of operational objectives.
4. Activities and their interactions In the second step of the MDT the activities, which guarantee that the operational objectives of the plant are achieved in practice, are formulated. For the MDT an activity model is developed in which the activities of the standard batch plant have been modelled by using the so-called IDEFO technique. IDEFO is a method to model activities and their mutual relations in a hierarchical way (FIPS PUBS 183, 1994). In the MDT firstly all activities are defined that may influence the measure in which the operational objectives are achieved. These activities have a high degree of detail and they can be combined into domed activities. After a number of these compositions the
675 so-called context activity will be achieved. In the same way the interactions between the activities will be modelled and combined into interactions with less degree of detail.
W^ Figure J: Part of the objective tree of the standard batch plant The interactions that are distinguished in an IDEFO model are 1. Input; what is transformed by the activity into the output, 2. Output; what is produced by the activity, 3. Control; what controls the activity, i control Im Ic. 4. Mechanism; what performs the objective activity.
11
input
AcnvrrY mechanism
output
676 In the activity model as used in the MDT a fifth flow is being introduced for the objectives to which an activity may contribute. An arrow coming from the activity and pointing upwards will denote this flow. As an example the objective high product quality will be considered. This objective is decomposed into five sub-objectives. For every operational objective several activities are performed to guarantee that the objective is achieved. These activities are denoted by the grey boxes.
good production process
high quahty of materials
high quahty of installation
's~sr
"XX"
sr-iIVfANAGE MAINTENANCE
PURCHASE INSTALLATION
good production personnel
CONTROL PRODUCTION
MANAGE RECIPES
Figure 2: Activities added to the operational objectives
In Figure 3 a small part of the activity model for the standard batch plant is shown, where the interactions between the activities "manage maintenance" and "control production" are central.
i
iL
1
CONTROL
¥
PRODUCTION iV control tools
high quality of mstallation
mamtenance standards and methods
maintenance requests
T
^r
MANAGE MAINTEt^ANC:E
responses & technical feedback
Figure 3: IDEFO diagram of ''Control Production" and "Manage Maintenance"
677
5. Contribution of the objectives to the overall objective In the third step of the MDT the user will be asked to order the objectives in the objective tree to the degree in which they contribute to the main objective. The pairwise comparison method (Saaty, 1980) will be used for the ranking of the objectives. In every group of objectives in the objective tree, i.e. all child objectives of one parent, every pair of two objectives is compared. The user decides for every two objectives which one will yield the highest contribution to the main objective. Doing this for every pair in the group a unique preference list will result. With the formula kwI
m 1 where /:,• = S ~ r=i r
m
j=\
(^)
}
the weight w, of the /th objective in the preference list is calculated. The relative weight of an operational objective can now be calculated by multiplying its weight with the weights of all its parents. As an example we look again at the five child objectives of high product quality. a. Good production process b. High quality of installation c. High quality of materials d. Good insight in quality e. Good production personnel The user is asked to decide for randomly chosen pairs, which of the two is more important to achieve a high product quality. Assume that the user decides to the following preference structure: dycya>-bye. With Equation (1) the relative weights of the objectives are: 1. Good insight in quality, weight: 0.46 2. High quality of materials, weight: 0.26 3. Good production process, weight: 0.16 4. High quality of installation, weight: 0.09 5. Good production personnel, weight: 0.04 When all weights of the operational objectives are known by multiplying the relative group weights with all the weights of their parents, the objectives with the highest contribution to the main objective of the plant are determined. With the results of the previous step the activities that are responsible for these objectives can be found. For improving the degree in which the main objective is being achieved, the user should focus on the improvement of these activities.
678
6. Efficiency of activities and possible improvements In the last step of the MDT the possible improvements of the activities are being mapped. Here a difference is made between the effectiveness of the activity and the efficiency of the activity. The effectiveness of the activity is defined as the degree in which the activity contributes to its objectives. Most operational objectives can be influenced by more than one activity, as shown in the activity model. To be able to measure the degree in which an objective is achieved, a performance indicator will be linked to the objective. To measure the effectiveness by activity the performance indicator of an objective should be split into several sub indicators that each can be influenced by just one activity. The efficiency of the activity is defined as the costs or time needed to transform the input of the activity into its output. As mentioned before the objective of the MDT is to find activities that are good candidates for improvements, i.e. which will yield a high contribution to the main objective of the plant. Measuring the effectiveness of the activity by its performance indicators can determine the actual contribution of the activity to the main objective. Improvement of the effectiveness of the activity may not be performed at the expense of the efficiency of the activity. With the assistance of the activity model the user can determine which input the activity requires to deliver the right output. The right input on the right moment will improve in general the efficiency as well as the effectiveness of the activity. With the results of the MDT the user may 1. determine which activities are good candidates for improvement, 2. determine the actual performance of the activities, 3. improve the activities by better tuning of their interactions.
7. Results and final remarks The MDT is still under development. The modelling phases for objectives and activities are already evaluated and applied in the plants of the industrial partners. At the moment research concentrates on the definition of the contribution of the chosen operational objectives to the overall plant goal. This will be realised by discussions with a selected team of process people (plant managers, quality managers, schedulers and operators) from an industrial plant in the food and chemical industry. Next, several case studies will be performed for further evaluation.
References IDEFO: FIPS PUBS 183, 1994, Standard for Integration Definition for Function Modelling. Keeney, Ralph L., 1993, Decisions with multiple objectives; preferences and value tradeoffs. New York Cambridge University Press. Keeney, Ralph L., 1992, Value-focused thinking; a path to creative decisionmaking. Cambridge, Mass. Harvard University Press. Keuning, D., D.J. Eppink, 2000, Management en Organisatie, theorie en toepassing, Educatieve partners Nederland BV, (in Dutch). Saaty, T.L., 1980, The Analytic Hierarchy Process, McGraw-Hill Book Co, New York.