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Control of the state of orders on machines
Computers & Industrial Engineering 40 (2001) 35±49
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Control of the state of orders on machines M. Starbek, J. Grum* University of Ljubljana, Faculty of Mechanical Engineering, AsÏkercÏeva 6, 1000 Ljubljana, Slovenia Accepted 24 August 2000
Abstract Many computer-aided production and control (PPC) systems are available on the market and these systems have to be integrated into existing information systems. Experience has proven that commercial PPC systems do not provide the expected results because they do not have a closed production planning and control feedback loop. This article proposes that the basic PPC system model be expanded so as to include a system which enables control, co-ordination and management of the state of orders on machines. Detailed description is given regarding the general production control procedure, which is based on the Deming circle of continuous improvements, and the corresponding procedure for implementation of control of the state of orders, which wait on the treated machines during the selected interval. To implement the practical continuous control of the state of orders a computer software product CONTROL has been developed. q 2001 Elsevier Science Ltd. All rights reserved. Keywords: Production planning and control; PPC system; Flow time of operations; Flow time control
1. Preface The purpose of production control is to harmonise the goals of production with the goals of the company - therefore it covers not only inspection but also the control, co-ordination, and management of production as a whole (Hildebrand & Martens, 1992). Production control should therefore provide for: ² ®nding AS-IS production data; ² analysing deviations from previously established target values; ² planning actions for maximal achievement of target values. * Corresponding author. Tel.: 1386-611771203; fax: 1386-61218567. E-mail address: [email protected] (J. Grum). 0360-8352/00/$ - see front matter q 2001 Elsevier Science Ltd. All rights reserved. PII: S0 3 6 0 - 8 3 5 2 ( 0 0 ) 0 0 05 4 - 1
36
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
Fig. 1. Production system feedback loop.
Production control should deal with the goals, decisions and future outcomes; for the observed data (e.g. state of orders, ¯ow times of orders) it is therefore necessary to de®ne the tolerable deviations of states and ¯ow times from the target values. Operation of the control system within the feedback loop of a machine control (or production system control) is shown in Fig. 1. The production system control process starts when the plan of orders for the selected interval is con®rmed. As internal and external disturbances affect the production system, the planned TO-BE data about orders usually differ from the actual AS-IS data. The actual AS-IS data about the ¯ow of orders during the treated interval are registered by a measuring device, and the discrepancies between the TO-BE and AS-IS data are registered by a controller. If the controller registers a discrepancy, the executor designs actions for control of orders in the next interval. In the ªProduction Systems Laboratoryº at the Faculty of Mechanical Engineering in Ljubljana, a research team was formed in order to develop and test a suitable procedure for production control. On the basis of an analysis of the professional works published in the ®eld of production control (Wirth & Seyfferth, 1995; Wiendahl, 1987; Starbek & Menart, 1998; Bitron & Haas, 1981), it became evident that several researchers have been dealing with various issues in this ®eld. However, we did not succeed to get an insight into the procedure to be used for practical implementation of the production control. This article presents the results of development and the implementation of the production control system with an emphasis on the state of orders.
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
37
Fig. 2. The state of orders control procedure.
2. The state of orders control procedure We developed the basic idea for the design of a procedure for the state of orders control with reference to the Deming circle of continuous improvements (Imai, 1992). Analysis results of the type and sequence of activities within the Deming circle of continuous improvements (research, development, manufacture and sales) led us to the conclusion that the state of orders control can be implemented as a circle of continuous improvements as well. In order to de®ne the type and sequence of activities for the state of orders control, a creativity workshop was organised. Results of the creativity workshop revealed that the procedure for the state of orders control should consist of four activities, which are shown in Fig. 2. The state of orders control procedure therefore consists of the following phases: 1. 2. 3. 4.
the de®nition of target TO-BE values of the mean state of orders, the acquisition of AS-IS state of orders values, the analysis of deviations of actual values from the mean state of orders, the planning and executing the corrective actions to reduce the mean state of orders. In continuation the procedure steps for the state of orders control implementation will be presented.
2.1. De®ning the target TO-BE values of the mean state of orders The basic goal of the state of orders control is that the actual mean state of orders SAIi does not exceed the limiting value STBi on the Mi machine 1 # i # n within the analysed interval P. A sample case of registering the mean state of orders on the Mi machine is shown in Fig. 3. The limiting value of the mean state of orders STBi is the maximum value of the mean state when
38
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
Fig. 3. Limiting value of the mean state of orders on the Mi machine.
production is still pro®table. When the AS-IS mean state of orders exceeds the limiting value some measures should be taken. 2.2. The acquisition of AS-IS values about the state of orders For the acquisition of AS-IS data about the ¯ow of orders Oj 1 # j # m and presentation of the state
Fig. 4. Principle of the funnel model and ¯ow diagram for the imaginary machine Mi.
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
39
of orders on the Mi machine within the analysed interval P, the funnel model is used and the state of orders is presented in a ¯ow diagram (Wiendahl, 1987). The funnel model with the corresponding ¯ow diagram in shown in Fig. 4. Each machine can be represented as a funnel into which orders Oj (instead of water) ¯ow during the analysed interval P - this is the load of the machine. The orders which are currently waiting at the machine represent the state of orders. The funnel has a de®ned diameter of the ori®ce through which the water ¯ows out, and the machine has available resources which represent the planned ef®ciency of the machine. The closer the load is to the ef®ciency of the machine, the lower the level of water in the funnel, or state of the orders at the machine, will be. To get the daily data about the state of orders within the analysed interval P it is necessary to register data about arrival of orders at the machine and departure of orders from it for each machine Mi. The following data have to be collected for each order Oj which came to the machine or left it: ² the order number which came to the machine or left it; ² the term of arrival at and departure from the machine; ² the normalised (effective) contents of work. Having collected the data about the ¯ow of orders across the particular machine Mi during the analysed interval P, one can construct a diagram of the ¯ow of orders which consists of: ² ² ² ²
the initial state of orders ISi, de®ned by the inventory; the course of arrivals at and departures from the machine; the ®nal state of orders FSi; the state of orders area SOi. Having the orders ¯ow diagram for the Mi machine it is possible to calculate the following:
² the mean state of orders SAIi on the Mi machine SAIi
SOi Nh P
SOi the state of orders area on the Si machine. P the analysed interval ² the mean ef®ciency EAIi of the Mi machine EAIi
Ei Eh P
Ei actual departure of orders from the Mi machine within the interval P. ² the mean resources Ci of the Mi machine Ci
Eip Nh P
Eip planned departure of orders from the Mi machine within the interval P.
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M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
² the mean use IAIi of the Mi machine IAIi
EAIi Ci
Accumulation of orders at the machine obstructs the normal production and causes too high funds to be used for production which reduces the pro®t of the company. 2.3. Analysis of deviations of the actual state of orders from the mean state of orders A comparison of the actual AS-IS mean state of orders SAIi with the planned target value STBi may show that: SAIi # STBi no action is needed as the actual mean state of orders is lower than the planned target value SAIi . STBi it is necessary to ®nd out why a deviation occurred and plan actions to reduce the mean state of orders 2.4. Planning and carrying out actions to reduce the mean state of orders When the mean state of orders on the Mi machine is above the planned target value SAIi . STBi it is necessary to propose actions which will reduce the state of orders and carry them out in the next interval P P 1 1: An analysis of the orders ¯ow diagram for the Mi machine (Fig. 4) led us to the conclusion that it is possible to reduce the state of orders only by reducing the state of orders area SOi. After comparing different types of state of orders areas for the Mi machine, it was determined that the state of orders area consists of four partial areas: ² the basic state of orders area BSi, which corresponds to the rectangle with sides of the initial state BISi and the analysed interval P; ² the state area caused by the ¯ow of orders FAi, which arises when the contents of work of the orders that came to the Mi machine within the interval P is greater or smaller than the contents of work of those orders that left the machine; ² the state of orders area due to the control of arrivals CAAi and departures CADi of orders, which arise because the actual course of arrivals and departures does not comply with an ideal course, represented by straight lines; ² the state of orders area due to deviations in the size of the series on arrival DSAi and departure DSDi of orders, which arise due to different times of orders processing. The processing time of the j-th order on the i-th machine is expressed as: OTij TSij 1 mj ´TMij where OTij [Nh] is the processing time of the j-th order on the i-th machine, TSij [Nh] the preparation and
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
41
Fig. 5. Partial state of orders on the Mi machine.
®nishing time of the j-th order on the i-th machine, mj [pieces] the size of the series of the j-th order, TMij [Nh/piece] the time per measurement unit of the j-th order on the i-th machine. The partial areas of the state of orders for the Mi machine within the analysed interval P are shown in Fig. 5. By analysing the causes for partial areas of the state of orders on the Mi machine, the following actions can be undertaken to reduce these areas: ² the basic state of orders area BSi can be reduced by: ± taking into account the FIFO priority rule (the ®rst order that comes to the machine is also processed ®rst), ± better organisation of internal transport, ² the state of orders area due to the ¯ow of orders FAi can be reduced by: ± specifying the machine load which corresponds to the planned machine load, ± specifying the machine load which does not exceed the planned ef®ciency of the machine, ² the state of orders area due to control of arrivals of orders CAAi and departures of orders CADi can be achieved by: ± taking into account the FIFO priority rule when processing orders, ± ®ne arrangement of orders, ² the state of orders area due to deviations in size of series on arrival of orders DSAi and departure of orders DSDi can be reduced by: ± division of series into sub-series, which means that the whole order is divided into several sub-orders, ± reduction of the time needed for preparation and ®nishing activities through the use of the SMED method, ± investment in ¯exible manufacturing systems.
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M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
To reduce the mean state of orders on the Mi machine, it is therefore necessary to ®rst calculate the partial areas of the state of orders within the analysed interval P. The results of this calculation of the partial areas of the state of orders enable the expert technologist to propose actions which will reduce the mean state of orders within the next interval P P 1 1: As quality assurance is required in the mean state of orders control, a computer software product CONTROL was developed. It carries out the required calculations and allows for fast drawing of ¯ow diagrams and calculation of the partial state of orders areas for the Mi machine. The data obtained allow the expert technologist to make fast decisions about actions which will reduce the mean state of orders. 3. Test of the proposed procedure for the control of the state of orders The procedure designed to implement the state of orders control, described above, was tested in a company which produces machines for food-processing and chemical industry. There are four management centres in the company, totalling 55 workplaces (machines): ² ² ² ²
a centre a centre a centre a centre
for components and machines (10 workplaces); for rotational parts (16 workplaces); for non-rotational parts (12 workplaces); for thin sheet metal parts (17 workplaces).
In agreement with the management of the company the following six machines were used in a test implementation of the state of orders control: M1 M2 M3 M4 M5 M6 -
lathe carousel gear milling machine drilling machine grinding machine milling machine
The machines were treated in the interval from 150-th until 190-th workday [Wd]. Acquisition of data about the ¯ow of orders across machines was done manually, as the company does not possess automatic recording devices. The CONTROL computer software was used to draw the orders ¯ow diagrams and to calculate the mean state of orders. In the ®rst step the management of the company de®ned the target TO-BE value of the mean state of orders for the analysed machines: STB 15 Nh=Wd To get AS-IS values of orders (second step of the state of orders control) company employees obtained data about the ¯ow of orders across the six treated machines within the interval from 150 until 190 [Wd]. Part of the orders ¯ow data passing the M6-CNC milling machine is shown in Table 1. The data obtained about the ¯ow of orders across the analysed machines were the input data for the
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
43
Table 1 Part of the data obtained for the M6-CNC milling machine Sequential order number
Order number
Arrival term [Wd]
Departure term [Wd]
Standard work contents [Nh]
1 2 3 4 5 26
35072196 35073819 35067763 35068625 35069325 35066318
137 139 143 143 154 190
156 155 160 160 160 196
33.4 25.0 43.0 28.3 25.7 27.0
computer software CONTROL - it was used to print and draw the following results of calculation: ² orders ¯ow diagrams with calculation of the mean ef®ciency, mean resources and mean use of the machines; ² ¯ow of orders diagrams with partial areas of the state of orders and results of calculations. Flow of orders diagram for the M6-CNC milling machine with the calculated mean ef®ciency, resources and use of the machine is shown in Fig. 6.
Fig. 6. Flow of orders diagram for the M6-CNC milling machine: mean ef®ciency EAI6 p EAI6 C6 EP6 615 1:35 135%: 41 7:5 [Eh]; mean use T6 C 6
E6 P
415:7 41
10:14 [Eh]; mean resources
44
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
Fig. 7. Flow of orders diagram with partial state of orders areas for the M6-CNC milling machine.
Flow of orders diagram for the M6-CNC milling machine with partial state of orders areas drawn in, results of the calculation of partial state areas, and mean state of orders are shown in Fig. 7. Results of the calculation of the mean state of orders and mean ef®ciency of all six machines are shown in Fig. 8. Analysis of the results obtained led us to the following conclusions: ² the actual mean state of orders is above the limiting value STB 15 [Nh] on all six machines, and therefore corrective actions should be undertaken;
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
45
Fig. 8. Histogram of the mean state of orders and mean ef®ciency of the analysed machines.
Fig. 9. Orders ¯ow diagram for the M6-CNC milling machine taking into account the FIFO priority rule: mean ef®ciency EAI6 E6p E6 EAI6 549:4 615 13:4 P 41 13:4 [Eh]; mean resources C 6 P 41 7:5 [Eh]; mean use T6 C 7:5 1:79 179%: 6
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M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
Fig. 10. Orders ¯ow diagram with partial state of orders areas for the M6-CNC milling machine by considering the FIFO priority rule.
² the actual mean state of orders on the machines is 2.4 to 24.5 times greater than the daily availability of machines which is 7.5 [Nh]; ² the mean ef®ciency of the machines differ considerably: from 2.4 to 11.03 [Eh]. The results of the calculation were presented to the management of the company. After they had analysed the results they wanted to know what the mean state of orders and mean ef®ciency of the analysed machines would be if the FIFO priority rule regarding the processing of orders had been taken into account.
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
47
Fig. 11. Diagram of mean states of orders and mean ef®ciencies of the analysed machines by considering the FIFO rule.
The data obtained about the ¯ow of orders across six machines were modi®ed by taking into consideration the requirements of the FIFO priority rule (departure terms were de®ned according to the arrival terms of the corresponding orders). On the basis of the corrected data about the orders ¯ow, the CONTROL software was used to draw the new orders ¯ow diagrams and calculated new mean state of orders and new mean ef®ciencies of machines. New orders ¯ow diagram for the M6-CNC milling machine is shown in Fig. 9, together with the calculation of the new mean ef®ciency, resources and use of the machine; FIFO priority rule regarding orders processing was taken into account in this case. Orders ¯ow diagram for the M6-CNC milling machine with the state of orders partial areas drawn in and calculation results of partial state areas and mean state of orders by considering the FIFO priority rule is shown in Fig. 10. The results of the calculation of new mean states of orders and new mean ef®ciencies of all six machines are shown in Fig. 11. In order to determine the in¯uence of the FIFO priority rule on the state of orders and on ef®ciency of the machines, a comparison of AS-IS mean states of orders and AS-IS mean ef®ciencies of machines was made (the cases when the FIFO priority rule was taken into account were compared with those cases where the rule was not considered). The results of the comparison are shown in Table 2. As can be seen from the results of the comparison, the mean state of orders on the analysed machines would be signi®cantly reduced (by 40.3% on the average) if the FIFO priority rule was taken into account, and mean ef®ciency of machines would increase by 13.2% on average. Due to the lower state of orders, less funds would be needed to ®nance the production, while higher ef®ciency of machines means that resources would be used better. Results of the test convinced the company management that continuous state of orders control is necessary and that it should be included into the basic PPC system model.
M1 M2 M3 M4 M5 M6
113.7 54.1 40.7 17.8 19.5 183.8 Average reduction of mean state of orders
52.8 39.2 20.0 9.7 15.9 100.7
FIFO rule not considered FIFO rule considered
Workplace Mean state of orders
53.5 27.5 50.9 45.8 18.7 45.3 40.3
11.0 11.9 2.5 3.4 4.6 4.8 2.4 2.4 3.2 3.2 10.1 13.4 Average increase of mean ef®ciency of workplaces
FIFO rule not considered FIFO rule considered
Reduction of the mean state Mean ef®ciency of workplaces
Table 2 Comparison of the mean state of orders and mean ef®ciency of the analysed machines
7.5 36.0 3.3 0.0 0.0 32.2 13.2
Increase of the mean ef®ciency
48 M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
49
4. Conclusions There are many computer-aided production planning and control systems available on the market (Fandel & Francois, 1997). It has been proven that these systems do not yield the desired results. The main reasons for this failure are weaknesses in the on-line acquisition of AS-IS data about orders, faulty and insuf®cient analysis of deviations from the previously established target values, and incorrect or incompletely executed actions intended to promote the successful achievement of the desired goals. The article proposes a procedure for the implementation of production control, with an emphasis on the state of orders control on machines. Computer software CONTROL was developed for computeraided construction of orders ¯ow diagrams, calculation of partial state of orders areas, and calculation of the mean state of orders. Possible actions for reduction of the mean state of orders on machines are proposed. The experiment we carried out showed that by applying a simple measure, such as considering the FIFO priority rule, it is possible to considerably reduce the mean state of orders and increase the mean ef®ciency of machines. So far the research has been focused on developing the procedure for implementation of the state of orders control. Further activities will be focused on control of ¯ow time of orders. References Hildebrand, R., & Martens, R. (1992). PPS-Controlling mit Kennzahlen und Checklisten, Berlin: Springer. Wirth, V., & Seyfferth, G. (1995). Baustellen-Controlling, Renningen-Malmsheim: Expert. Wiendahl, H. P. (1987). Belastungsorientierte Fertigungssteuerung. Carl Hanser Verlag, MuÈnchen, 1987. Starbek, M., & Menart, D. (1998). In Stephen, Procter, Kulwart & Pawar, Proceedings of the Third International Conference Managing Innovative Manufacturing, (pp. 359±364), Nottingham, UK. Bitron, G. R., & Haas, E. (1981). Hierarchical production planning. A single stage system. Operations Research, 29, 717±743. Imai, M. (1992). Kaizen der SchluÈssel zum Erfolg der Japaner im Wettbewerb, Langen MuÈller/Herbig: Wirtschaftsverlag. Fandel, G., & Francois, P. (1997). PPS-und integrierte betriebliche Softwaresysteme, Berlin: Springer.
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Control of the state of orders on machines M. Starbek, J. Grum* University of Ljubljana, Faculty of Mechanical Engineering, AsÏkercÏeva 6, 1000 Ljubljana, Slovenia Accepted 24 August 2000
Abstract Many computer-aided production and control (PPC) systems are available on the market and these systems have to be integrated into existing information systems. Experience has proven that commercial PPC systems do not provide the expected results because they do not have a closed production planning and control feedback loop. This article proposes that the basic PPC system model be expanded so as to include a system which enables control, co-ordination and management of the state of orders on machines. Detailed description is given regarding the general production control procedure, which is based on the Deming circle of continuous improvements, and the corresponding procedure for implementation of control of the state of orders, which wait on the treated machines during the selected interval. To implement the practical continuous control of the state of orders a computer software product CONTROL has been developed. q 2001 Elsevier Science Ltd. All rights reserved. Keywords: Production planning and control; PPC system; Flow time of operations; Flow time control
1. Preface The purpose of production control is to harmonise the goals of production with the goals of the company - therefore it covers not only inspection but also the control, co-ordination, and management of production as a whole (Hildebrand & Martens, 1992). Production control should therefore provide for: ² ®nding AS-IS production data; ² analysing deviations from previously established target values; ² planning actions for maximal achievement of target values. * Corresponding author. Tel.: 1386-611771203; fax: 1386-61218567. E-mail address: [email protected] (J. Grum). 0360-8352/00/$ - see front matter q 2001 Elsevier Science Ltd. All rights reserved. PII: S0 3 6 0 - 8 3 5 2 ( 0 0 ) 0 0 05 4 - 1
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M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
Fig. 1. Production system feedback loop.
Production control should deal with the goals, decisions and future outcomes; for the observed data (e.g. state of orders, ¯ow times of orders) it is therefore necessary to de®ne the tolerable deviations of states and ¯ow times from the target values. Operation of the control system within the feedback loop of a machine control (or production system control) is shown in Fig. 1. The production system control process starts when the plan of orders for the selected interval is con®rmed. As internal and external disturbances affect the production system, the planned TO-BE data about orders usually differ from the actual AS-IS data. The actual AS-IS data about the ¯ow of orders during the treated interval are registered by a measuring device, and the discrepancies between the TO-BE and AS-IS data are registered by a controller. If the controller registers a discrepancy, the executor designs actions for control of orders in the next interval. In the ªProduction Systems Laboratoryº at the Faculty of Mechanical Engineering in Ljubljana, a research team was formed in order to develop and test a suitable procedure for production control. On the basis of an analysis of the professional works published in the ®eld of production control (Wirth & Seyfferth, 1995; Wiendahl, 1987; Starbek & Menart, 1998; Bitron & Haas, 1981), it became evident that several researchers have been dealing with various issues in this ®eld. However, we did not succeed to get an insight into the procedure to be used for practical implementation of the production control. This article presents the results of development and the implementation of the production control system with an emphasis on the state of orders.
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
37
Fig. 2. The state of orders control procedure.
2. The state of orders control procedure We developed the basic idea for the design of a procedure for the state of orders control with reference to the Deming circle of continuous improvements (Imai, 1992). Analysis results of the type and sequence of activities within the Deming circle of continuous improvements (research, development, manufacture and sales) led us to the conclusion that the state of orders control can be implemented as a circle of continuous improvements as well. In order to de®ne the type and sequence of activities for the state of orders control, a creativity workshop was organised. Results of the creativity workshop revealed that the procedure for the state of orders control should consist of four activities, which are shown in Fig. 2. The state of orders control procedure therefore consists of the following phases: 1. 2. 3. 4.
the de®nition of target TO-BE values of the mean state of orders, the acquisition of AS-IS state of orders values, the analysis of deviations of actual values from the mean state of orders, the planning and executing the corrective actions to reduce the mean state of orders. In continuation the procedure steps for the state of orders control implementation will be presented.
2.1. De®ning the target TO-BE values of the mean state of orders The basic goal of the state of orders control is that the actual mean state of orders SAIi does not exceed the limiting value STBi on the Mi machine 1 # i # n within the analysed interval P. A sample case of registering the mean state of orders on the Mi machine is shown in Fig. 3. The limiting value of the mean state of orders STBi is the maximum value of the mean state when
38
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
Fig. 3. Limiting value of the mean state of orders on the Mi machine.
production is still pro®table. When the AS-IS mean state of orders exceeds the limiting value some measures should be taken. 2.2. The acquisition of AS-IS values about the state of orders For the acquisition of AS-IS data about the ¯ow of orders Oj 1 # j # m and presentation of the state
Fig. 4. Principle of the funnel model and ¯ow diagram for the imaginary machine Mi.
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
39
of orders on the Mi machine within the analysed interval P, the funnel model is used and the state of orders is presented in a ¯ow diagram (Wiendahl, 1987). The funnel model with the corresponding ¯ow diagram in shown in Fig. 4. Each machine can be represented as a funnel into which orders Oj (instead of water) ¯ow during the analysed interval P - this is the load of the machine. The orders which are currently waiting at the machine represent the state of orders. The funnel has a de®ned diameter of the ori®ce through which the water ¯ows out, and the machine has available resources which represent the planned ef®ciency of the machine. The closer the load is to the ef®ciency of the machine, the lower the level of water in the funnel, or state of the orders at the machine, will be. To get the daily data about the state of orders within the analysed interval P it is necessary to register data about arrival of orders at the machine and departure of orders from it for each machine Mi. The following data have to be collected for each order Oj which came to the machine or left it: ² the order number which came to the machine or left it; ² the term of arrival at and departure from the machine; ² the normalised (effective) contents of work. Having collected the data about the ¯ow of orders across the particular machine Mi during the analysed interval P, one can construct a diagram of the ¯ow of orders which consists of: ² ² ² ²
the initial state of orders ISi, de®ned by the inventory; the course of arrivals at and departures from the machine; the ®nal state of orders FSi; the state of orders area SOi. Having the orders ¯ow diagram for the Mi machine it is possible to calculate the following:
² the mean state of orders SAIi on the Mi machine SAIi
SOi Nh P
SOi the state of orders area on the Si machine. P the analysed interval ² the mean ef®ciency EAIi of the Mi machine EAIi
Ei Eh P
Ei actual departure of orders from the Mi machine within the interval P. ² the mean resources Ci of the Mi machine Ci
Eip Nh P
Eip planned departure of orders from the Mi machine within the interval P.
40
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
² the mean use IAIi of the Mi machine IAIi
EAIi Ci
Accumulation of orders at the machine obstructs the normal production and causes too high funds to be used for production which reduces the pro®t of the company. 2.3. Analysis of deviations of the actual state of orders from the mean state of orders A comparison of the actual AS-IS mean state of orders SAIi with the planned target value STBi may show that: SAIi # STBi no action is needed as the actual mean state of orders is lower than the planned target value SAIi . STBi it is necessary to ®nd out why a deviation occurred and plan actions to reduce the mean state of orders 2.4. Planning and carrying out actions to reduce the mean state of orders When the mean state of orders on the Mi machine is above the planned target value SAIi . STBi it is necessary to propose actions which will reduce the state of orders and carry them out in the next interval P P 1 1: An analysis of the orders ¯ow diagram for the Mi machine (Fig. 4) led us to the conclusion that it is possible to reduce the state of orders only by reducing the state of orders area SOi. After comparing different types of state of orders areas for the Mi machine, it was determined that the state of orders area consists of four partial areas: ² the basic state of orders area BSi, which corresponds to the rectangle with sides of the initial state BISi and the analysed interval P; ² the state area caused by the ¯ow of orders FAi, which arises when the contents of work of the orders that came to the Mi machine within the interval P is greater or smaller than the contents of work of those orders that left the machine; ² the state of orders area due to the control of arrivals CAAi and departures CADi of orders, which arise because the actual course of arrivals and departures does not comply with an ideal course, represented by straight lines; ² the state of orders area due to deviations in the size of the series on arrival DSAi and departure DSDi of orders, which arise due to different times of orders processing. The processing time of the j-th order on the i-th machine is expressed as: OTij TSij 1 mj ´TMij where OTij [Nh] is the processing time of the j-th order on the i-th machine, TSij [Nh] the preparation and
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
41
Fig. 5. Partial state of orders on the Mi machine.
®nishing time of the j-th order on the i-th machine, mj [pieces] the size of the series of the j-th order, TMij [Nh/piece] the time per measurement unit of the j-th order on the i-th machine. The partial areas of the state of orders for the Mi machine within the analysed interval P are shown in Fig. 5. By analysing the causes for partial areas of the state of orders on the Mi machine, the following actions can be undertaken to reduce these areas: ² the basic state of orders area BSi can be reduced by: ± taking into account the FIFO priority rule (the ®rst order that comes to the machine is also processed ®rst), ± better organisation of internal transport, ² the state of orders area due to the ¯ow of orders FAi can be reduced by: ± specifying the machine load which corresponds to the planned machine load, ± specifying the machine load which does not exceed the planned ef®ciency of the machine, ² the state of orders area due to control of arrivals of orders CAAi and departures of orders CADi can be achieved by: ± taking into account the FIFO priority rule when processing orders, ± ®ne arrangement of orders, ² the state of orders area due to deviations in size of series on arrival of orders DSAi and departure of orders DSDi can be reduced by: ± division of series into sub-series, which means that the whole order is divided into several sub-orders, ± reduction of the time needed for preparation and ®nishing activities through the use of the SMED method, ± investment in ¯exible manufacturing systems.
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M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
To reduce the mean state of orders on the Mi machine, it is therefore necessary to ®rst calculate the partial areas of the state of orders within the analysed interval P. The results of this calculation of the partial areas of the state of orders enable the expert technologist to propose actions which will reduce the mean state of orders within the next interval P P 1 1: As quality assurance is required in the mean state of orders control, a computer software product CONTROL was developed. It carries out the required calculations and allows for fast drawing of ¯ow diagrams and calculation of the partial state of orders areas for the Mi machine. The data obtained allow the expert technologist to make fast decisions about actions which will reduce the mean state of orders. 3. Test of the proposed procedure for the control of the state of orders The procedure designed to implement the state of orders control, described above, was tested in a company which produces machines for food-processing and chemical industry. There are four management centres in the company, totalling 55 workplaces (machines): ² ² ² ²
a centre a centre a centre a centre
for components and machines (10 workplaces); for rotational parts (16 workplaces); for non-rotational parts (12 workplaces); for thin sheet metal parts (17 workplaces).
In agreement with the management of the company the following six machines were used in a test implementation of the state of orders control: M1 M2 M3 M4 M5 M6 -
lathe carousel gear milling machine drilling machine grinding machine milling machine
The machines were treated in the interval from 150-th until 190-th workday [Wd]. Acquisition of data about the ¯ow of orders across machines was done manually, as the company does not possess automatic recording devices. The CONTROL computer software was used to draw the orders ¯ow diagrams and to calculate the mean state of orders. In the ®rst step the management of the company de®ned the target TO-BE value of the mean state of orders for the analysed machines: STB 15 Nh=Wd To get AS-IS values of orders (second step of the state of orders control) company employees obtained data about the ¯ow of orders across the six treated machines within the interval from 150 until 190 [Wd]. Part of the orders ¯ow data passing the M6-CNC milling machine is shown in Table 1. The data obtained about the ¯ow of orders across the analysed machines were the input data for the
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
43
Table 1 Part of the data obtained for the M6-CNC milling machine Sequential order number
Order number
Arrival term [Wd]
Departure term [Wd]
Standard work contents [Nh]
1 2 3 4 5 26
35072196 35073819 35067763 35068625 35069325 35066318
137 139 143 143 154 190
156 155 160 160 160 196
33.4 25.0 43.0 28.3 25.7 27.0
computer software CONTROL - it was used to print and draw the following results of calculation: ² orders ¯ow diagrams with calculation of the mean ef®ciency, mean resources and mean use of the machines; ² ¯ow of orders diagrams with partial areas of the state of orders and results of calculations. Flow of orders diagram for the M6-CNC milling machine with the calculated mean ef®ciency, resources and use of the machine is shown in Fig. 6.
Fig. 6. Flow of orders diagram for the M6-CNC milling machine: mean ef®ciency EAI6 p EAI6 C6 EP6 615 1:35 135%: 41 7:5 [Eh]; mean use T6 C 6
E6 P
415:7 41
10:14 [Eh]; mean resources
44
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
Fig. 7. Flow of orders diagram with partial state of orders areas for the M6-CNC milling machine.
Flow of orders diagram for the M6-CNC milling machine with partial state of orders areas drawn in, results of the calculation of partial state areas, and mean state of orders are shown in Fig. 7. Results of the calculation of the mean state of orders and mean ef®ciency of all six machines are shown in Fig. 8. Analysis of the results obtained led us to the following conclusions: ² the actual mean state of orders is above the limiting value STB 15 [Nh] on all six machines, and therefore corrective actions should be undertaken;
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
45
Fig. 8. Histogram of the mean state of orders and mean ef®ciency of the analysed machines.
Fig. 9. Orders ¯ow diagram for the M6-CNC milling machine taking into account the FIFO priority rule: mean ef®ciency EAI6 E6p E6 EAI6 549:4 615 13:4 P 41 13:4 [Eh]; mean resources C 6 P 41 7:5 [Eh]; mean use T6 C 7:5 1:79 179%: 6
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M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
Fig. 10. Orders ¯ow diagram with partial state of orders areas for the M6-CNC milling machine by considering the FIFO priority rule.
² the actual mean state of orders on the machines is 2.4 to 24.5 times greater than the daily availability of machines which is 7.5 [Nh]; ² the mean ef®ciency of the machines differ considerably: from 2.4 to 11.03 [Eh]. The results of the calculation were presented to the management of the company. After they had analysed the results they wanted to know what the mean state of orders and mean ef®ciency of the analysed machines would be if the FIFO priority rule regarding the processing of orders had been taken into account.
M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
47
Fig. 11. Diagram of mean states of orders and mean ef®ciencies of the analysed machines by considering the FIFO rule.
The data obtained about the ¯ow of orders across six machines were modi®ed by taking into consideration the requirements of the FIFO priority rule (departure terms were de®ned according to the arrival terms of the corresponding orders). On the basis of the corrected data about the orders ¯ow, the CONTROL software was used to draw the new orders ¯ow diagrams and calculated new mean state of orders and new mean ef®ciencies of machines. New orders ¯ow diagram for the M6-CNC milling machine is shown in Fig. 9, together with the calculation of the new mean ef®ciency, resources and use of the machine; FIFO priority rule regarding orders processing was taken into account in this case. Orders ¯ow diagram for the M6-CNC milling machine with the state of orders partial areas drawn in and calculation results of partial state areas and mean state of orders by considering the FIFO priority rule is shown in Fig. 10. The results of the calculation of new mean states of orders and new mean ef®ciencies of all six machines are shown in Fig. 11. In order to determine the in¯uence of the FIFO priority rule on the state of orders and on ef®ciency of the machines, a comparison of AS-IS mean states of orders and AS-IS mean ef®ciencies of machines was made (the cases when the FIFO priority rule was taken into account were compared with those cases where the rule was not considered). The results of the comparison are shown in Table 2. As can be seen from the results of the comparison, the mean state of orders on the analysed machines would be signi®cantly reduced (by 40.3% on the average) if the FIFO priority rule was taken into account, and mean ef®ciency of machines would increase by 13.2% on average. Due to the lower state of orders, less funds would be needed to ®nance the production, while higher ef®ciency of machines means that resources would be used better. Results of the test convinced the company management that continuous state of orders control is necessary and that it should be included into the basic PPC system model.
M1 M2 M3 M4 M5 M6
113.7 54.1 40.7 17.8 19.5 183.8 Average reduction of mean state of orders
52.8 39.2 20.0 9.7 15.9 100.7
FIFO rule not considered FIFO rule considered
Workplace Mean state of orders
53.5 27.5 50.9 45.8 18.7 45.3 40.3
11.0 11.9 2.5 3.4 4.6 4.8 2.4 2.4 3.2 3.2 10.1 13.4 Average increase of mean ef®ciency of workplaces
FIFO rule not considered FIFO rule considered
Reduction of the mean state Mean ef®ciency of workplaces
Table 2 Comparison of the mean state of orders and mean ef®ciency of the analysed machines
7.5 36.0 3.3 0.0 0.0 32.2 13.2
Increase of the mean ef®ciency
48 M. Starbek, J. Grum / Computers & Industrial Engineering 40 (2001) 35±49
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49
4. Conclusions There are many computer-aided production planning and control systems available on the market (Fandel & Francois, 1997). It has been proven that these systems do not yield the desired results. The main reasons for this failure are weaknesses in the on-line acquisition of AS-IS data about orders, faulty and insuf®cient analysis of deviations from the previously established target values, and incorrect or incompletely executed actions intended to promote the successful achievement of the desired goals. The article proposes a procedure for the implementation of production control, with an emphasis on the state of orders control on machines. Computer software CONTROL was developed for computeraided construction of orders ¯ow diagrams, calculation of partial state of orders areas, and calculation of the mean state of orders. Possible actions for reduction of the mean state of orders on machines are proposed. The experiment we carried out showed that by applying a simple measure, such as considering the FIFO priority rule, it is possible to considerably reduce the mean state of orders and increase the mean ef®ciency of machines. So far the research has been focused on developing the procedure for implementation of the state of orders control. Further activities will be focused on control of ¯ow time of orders. References Hildebrand, R., & Martens, R. (1992). PPS-Controlling mit Kennzahlen und Checklisten, Berlin: Springer. Wirth, V., & Seyfferth, G. (1995). Baustellen-Controlling, Renningen-Malmsheim: Expert. Wiendahl, H. P. (1987). Belastungsorientierte Fertigungssteuerung. Carl Hanser Verlag, MuÈnchen, 1987. Starbek, M., & Menart, D. (1998). In Stephen, Procter, Kulwart & Pawar, Proceedings of the Third International Conference Managing Innovative Manufacturing, (pp. 359±364), Nottingham, UK. Bitron, G. R., & Haas, E. (1981). Hierarchical production planning. A single stage system. Operations Research, 29, 717±743. Imai, M. (1992). Kaizen der SchluÈssel zum Erfolg der Japaner im Wettbewerb, Langen MuÈller/Herbig: Wirtschaftsverlag. Fandel, G., & Francois, P. (1997). PPS-und integrierte betriebliche Softwaresysteme, Berlin: Springer.