Performance improvement in production systems through practiceoriented bottleneck management

Performance improvement in production systems through practiceoriented bottleneck management Johannes Schultheiss*, M. Eng.; Dipl. Wirt.-Ing.(FH) Hamburg University of Applied Science, Berliner Tor 21, 20099 Hamburg, Germany. E-mail: Johannes.Schultheiss@haw-hamburg.de Jochen Kreutzfeldt, Prof. Dr.-Ing. Hamburg University of Applied Science, Berliner Tor 21, 20099 Hamburg, Germany. E-mail: Jochen.Kreutzfeldt@haw-hamburg.de * Corresponding author Abstract: The paper presents a new approach to determine and visualize bottlenecks in production systems. The presented methodological solution uses flows of orders and throughput limiters to describe such systems. This approach enabled the development of a new algorithm, which approximates not only the overall system performance but generates more detailed information on system constraints that can be used to begin optimization. Moreover the findings are visualized as a set of throughput curves. For practical testing purposes, a software module for bottleneck optimization was developed. It was tested during the pilot phase of the project, which took place using the production data from a machine and plant manufacturer. With this information it was possible to take tailored actions to improve the processing of orders and in doing so optimize the overall production system performance. The presented work is carried out in a joint project, in which two discrete part manufacturing companies, two software houses and two universities cooperate. Keywords: Bottleneck, Visualisation, Theory of Constraints, Theory of Logistic Operation Curve, Branch and Bound, Throughput, Throughput Curve 1 Introduction 1.1 Background In many production environments, the system performance is constrained or reduced by bottlenecks. The greatest bottleneck in the system has an impact on every logistical target parameter. Bottlenecks are the cause for queues in front of work stations. Furthermore queues are the reason for extended and distributed lead times, which can cause problems to meet delivery dates. Another effect is a reduction in the order supply of orders to succeeding work stations and the appearance of idle times. Bottlenecks are often the starting point for improvement initiatives. However, which bottleneck must be addressed first, in order to achieve a quick and accurate optimisation of the whole production system? If the production system contains more than one bottleneck, the question arises as to which bottleneck offers the greatest potential for optimisation. For this reason, not only is the identification of bottlenecks important, but also the evaluation of them in terms of their

Johannes Schultheiss; Jochen Kreutzfeldt 2 optimisation potential. Often, bottleneck approaches stemming from Operations Management are viewed in practice as being too complicated and difficult to communicate. Hence, the implementation of bottleneck management, especially supported by modern information technology, in practice still remains poor. The development of an appropriate approach to fulfil these needs is necessary. 1.2 Scope of Paper This article presents a method for both the identification and the evaluation of bottlenecks. In order to identify and improve the management of bottleneck in manufacturing practice, the project DePlaVis 1 (throughput increase in plant engineering and operation through bottleneck-oriented planning and visualization) was launched (Kreutzfeldt 2007). The project is funded by the German Federal Ministry for Education and Research (BMBF). The project is a cooperation between two universities, two systems vendors and three companies in the field of mechanical engineering and manufacturing. Initially, the prevalence of bottlenecks and requirements necessary for an effective bottleneck management in practice were identified. The results are presented in this article. Particularly important for the practical application is the use of a visual method to transmit results. To represent a bottleneck and its potential for optimization, a new throughput curve is introduced and presented through an example. Different from existing concepts the new developed curve allows not only to describe the overall throughput of a production system, but also to indicate the contribution of single work flows to the total throughput. To determine the optimal capacity increase at the bottleneck, the throughput curve has been further extended with a Branch and Bound algorithm. These methods were programmed into a prototype software program and tested with practical data from the cooperating companies. The results are presented based on a case study. The article ends with an interpretation and a conclusion of the applied bottleneck management model. 2 Literature Review 2.1 Bottleneck Management in Production Systems An already solved Problem? The occurrence of bottlenecks in manufacturing systems is a phenomenon that has long been recognised and discussed in the literature. As early as the middle of last century, the bottleneck in production was defined by Gutenberg (1951). He described a bottleneck as a business resource that can only be used up to a certain threshold. The resource with the lowest ceiling represents the bottleneck of the production system. In order to ensure a logical and successful business planning on the shopfloor, the short-term planning must be adjusted around the bottleneck resource. For long-term improvement, an adjustment to the bottleneck across the entire system is required. Building on the idea of a dominant bottleneck resource, Goldratt (1990) proposed a bottleneck-based method for continuous improvement of production systems. He created nine global rules to deal with bottlenecks. However, the algorithm was never published by Goldratt (Haberlandt 1999) and it is unknown how, he identified or evaluated bottlenecks. Haase et al. (1995) proposed a method that combines process planning and batch size planning. The basis of bottleneck control is thereby formed by sequence dependent setup times and capacity constraints. From Banaszak (1997), a bottleneck-driven control based on work-in-progress was developed, whereby several production orders vie on a place in the execution sequence of the bottleneck resource. Dangelmeier (1997) also presented an agent-based, three-tier release process for order planning. In the first step, material resources are planned based on the bill of materials followed by the scheduling of bottleneck resources. Finally, the complete scheduling is carried out. For bottleneck identification, demand and capacity are compared and the most overloaded workstations then auction their capacity to the outstanding production orders. Starting at the bottleneck, a forward and reverse scheduling of contracts then follows. The American Production and Inventory Control Society (APICS) define a bottleneck as a facility, function, department or resource whose capacity is smaller than or equal to the demand (Cox 1998). Only a capacity overload is used as a criterion for bottleneck. 1 DePlaVis in German stands for Durchsatzsteigerung im Anlagenbau und betrieb durch engpassorientierte Planung und Visualisierung.

Johannes Schultheiss; Jochen Kreutzfeldt 3 One method for dynamic bottleneck planning was developed by Trentesaux et al. (1998). Any resource whose capacity is exceeded by demand is regarded as a bottleneck. Based upon the bottleneck an order management is introduced. Firstly, every production order is scheduled on the bottleneck resource. Thereafter, the bottlenecks are once again identified. The goal is to reduce the number of bottlenecks to one. The bottleneck-oriented logistics analysis determines bottleneck using, multi-criteria analysis (Wiendahl and Nyhuis 1998). Using this idea, different operating figures were collected for each work system. Measures were then derived to ensure the improvement of logistical target values. Based on the figures, bottlenecks are then prioritized. This allows a targeted optimization. Depending on the target value and bottleneck perspective under observation, different improvement tools can be used (Nyhuis 2003). Haberlandt (1999) suggests three levels upon which bottlenecks can be observed. These are dependent on the length of the period under observation and the organisational area. It is differentiated between operative, tactical and strategic bottlenecks. Depending on which level the bottleneck is observed, different courses of action are proposed. However the dependencies between different bottleneck stations are not made. Windt (2001) defines a bottleneck as the difference between supply and demand. She distinguishes between bottlenecks types on the basis of throughput, throughput time and scheduling. In a four-stage process, bottlenecks are defined and aggregated on the basis of their deviation from certain key figures. For the calculation, feedback data from the production is used. In the first stage, the bottleneck is determined based on the largest deviation from the target figure. Following this, the bottlenecks are weighted respective of job throughput and are then set in relation to the total throughput. In order to assess the optimization potential, a further multiplication is made with the respective inventory. Finally, the target values are compared to one another and the production bottleneck is identified. By pair wise multiplication of the target values, a multicriteria bottleneck evaluation can be conducted. The goal of this procedure is to determine the bottleneck resources that could be outsourced for future. Löffler et al. (2002) undertook a dynamic classification of bottlenecks, whereby a difference is made between bottlenecks that are static, dynamic and discrete. At static bottlenecks the demand over the total period is greater than the supply, whereby at dynamic bottlenecks this is only partially the case. At dynamic bottlenecks a comparison between supply and demand must carried out continuously. Discrete bottlenecks only then occur at the moment at which the demand cannot be fulfilled. In this case, the time until which the demand is fulfilled is identified as the bottleneck. Wiendahl und Hegenscheidt (2002) developed a model for assembly lines. The bottleneck is defined as the station with the lowest availability. The goal of this approach was to improve the total system capacity utilisation through identification of the correct inventory buffer size. The relationship between utilised capacity and availability is described through analytical functions that operate in a range of extreme cases. The lower boundary is depicted as a system with buffers that are free of inventory; the upper boundary is depicted as a system with the inventory buffers being full. The operating curve of this system, dependant on the buffer size, can then be determined on the basis of a correlating C-Norm function. For parameterization of the function, a simulation model was used. The shifting bottleneck detection method was introduced by Roser et al. (2002). In their approach, they differentiated between instantaneous and average bottlenecks. Bottleneck measurement occurs on the basis of an operating figure that measures the time that the machine is active. During the time that a particular machine is active it causes other machines to wait. The machine that is longest active is regarded as the bottleneck. From a dynamic perspective it is differentiated between static and shifting bottlenecks. However no calculation of absolute values, such as a possible increase in throughput, was carried out. A combination of this approach with Toyota production management methods was undertaken (Herrmann et al. 2005), through which measures to combat bottlenecks were derived and the optimization potential determined. A bottleneck-driven approach to start-up management was suggested by Scholz-Reiter et al. (2006). The start-up curve of a process is analysed and is improved based on potential bottlenecks. Juran et al. (2008) develop a model that can evaluate the effects of moving bottlenecks on traffic network performance by considering the effect of specific objects within the traffic on the traffic flow. The varying size

Johannes Schultheiss; Jochen Kreutzfeldt 4 and speed of objects, such as a truck, within traffic plays an important role in the bottleneck detection. This model seeks to find a set of optimal travelling paths that minimize travelling times. Chen and Chen (2009) discuss the problems of static bottlenecks in a manufacturing environment. They propose a model based on a heuristic bottleneck algorithm, which considers scheduling problems in a flexible flow line. The goal of this approach is to minimize the throughput time. This is achieved through a bottleneck management based on the optimization of job scheduling. Most of the approaches put forward here consider bottlenecks as a phenomenon in which the supply and demand are not balanced. Only a few models consider the effect of bottlenecks on the entirety of the production orders. Nevertheless if an assessment is done, it is not quantified. The effect of a capacity increase on the performance of the entire system is not calculated. Of the available literature that is discussed, none of these approaches or models offers any kind of specific visualisation concept for bottlenecks. Only one approach works with the construction of operating curves. It focuses however on the representation of assembly lines. An explicit, visual comparison of performance at bottlenecks and possible optimization parameters is not available. Furthermore, no direct correlation is offered between input parameters (orders, capacities, work plans) and expected output values. 2.2 Production Management with Operating Curves The relationship between logistic objectives inventory, throughput and performance were first described by Wiendahl (1992) with help of the Funnel Model. The analogy of the funnel describes a work system. The fill level of the funnel depicts the work load of the system. The width of the funnel opening, through which the production orders flow, equates to the capacity. The relationship between these parameters can be found of the throughput diagram, the first visual impression of the logistical parameters of a work system. Input and output are depicted over time. The Logistic Operating Curve (LOC) was developed by Nyhuis (1991, 2006, 2007) (Wiendahl and Nyhuis 2003). As can be seen in Figure 1, it describes the performance and throughput of a production system as a function of the existing order backlog. In the ideal LOC, three different ground states can be distinguished. By partial loading there exists idle time between the production orders. The system is thereby not properly utilised and entails a loss of performance downstream. The performance increases proportional to the loaded inventory. The throughput time consists of the processing time and the minimum transition time. If the work-in-progress (WIP) reaches the so-called minimum inventory level, the performance rises no further and the (maximum) capacity is reached. At this point the throughput time still represents the minimum throughput time however. For the purposes of parameterisation, the working point of the system should be as close to this point as possible in order to achieve optimal results. If the WIP increases beyond the minimum level, no additional performance can be won and throughput time increases due to the build up of work in the queue. The real LOC shows a slightly different picture. As a result of the dynamic bottleneck situation, performance loss can be observed and the utilisation of the total available capacity is not achieved. Even the throughput time begins to go up before the minimum WIP is reached. For this reason Nyhuis (1991) suggests that an approximation calculation should be applied based on a C-Norm function. For this, the empirical ascertainment of the theoretical minimum WIP and the maximum performance is necessary. The Expansion Factor α can be derived using this values. It defines the design of the throughput curve. Because this procedure uses averaged values it can only be applied in static cases. Application on dynamic operative situations is not possible. Wiendahl and Hegenscheidt (2002) use an operating curve to describe the utilisation of assembly lines as a function of the operational availability. Therewith the buffer sizes between the individual workstations can be determined. An operating curve for manufacturing cells was developed by Nyhuis und Ciemniski (2004c). It visualises the interactions between mean inventory levels, performance, throughput, utilisation of the complete manufacturing cell and the utilisation of individual work systems within the cell. A delivery date operating curve was suggested by Nyhuis et al. (2004b). Using a comparison between planned and achieved mean throughput, it produces a correlation between the mean delivery time and the mean inventory level. As was seen with the original LOC, ideal and real operating curves can be derived. Whereas a discrete function describes the ideal delivery date operating curve, the real curve is described through a normal distribution. This approach

Johannes Schultheiss; Jochen Kreutzfeldt 5 serves the controlling of logistic processes. The objective is to regulate the delivery date adherence by changing the WIP. Figure 1. Comparison of ideal and real Logistic Operation Curve (Nyhuis 1991). Use of operating curves in production planning is underpinned by static assumptions. Because they are an excellent tool for illustrating the tension between the four competing logistic objectives - delivery time, inventory, throughput time and utilisation-, they are particularly suitable for positioning of production systems logistically (Nyhuis 2006, 2007). However an operative production control on the basis of operating curves is not yet possible. All approaches regarding LOC have in common, that they are currently not applicable for bottleneck management purposes. 3 DePlaVis Bottleneck Management with Operating Curves 3.1 Bottleneck Management and Performance Improvement with Throughput Curves To improve the operational planning and control of manufacturing, a quantitative description of the production system is necessary. The methods used must clearly portray the link between the logistic success factors. According to Nyhuis et al. (2005), analytical approaches and simulation methods come into consideration. They include: - Simulation models are a widespread and widely accepted tool for describing and optimizing complex systems. A simulation model is a mapping of a real dynamic process. Conclusions found through simulations can then be applied to real situations (VDI 1993). Software systems are a good solution for the mapping of simulation models. They allow the modelling of real systems and their reactions by changing the input parameters. - Another approach is analytical modelling. One classical, analytical model is the queuing theory (Hopp and Spearman, 2000). This model describes the relationship between stochastic arrival, processing and

Johannes Schultheiss; Jochen Kreutzfeldt 6 output of orders. Another analytical approach is the theory of logistic operating curves (Wiendahl and Nyhuis, 2003). The foundations of this theory were already presented above. Simulation models are used if the deductive or experimental description of a real system due to real circumstances or system complexity is not possible. The advantage of simulations lies in the low preparation costs compared with deductive models. However, each simulation model requires validation. To this end, the simulation should be tested with real values and the output parameters compared with the existing system. Thus, a validation is only possible if the system already exists. Planned systems can therefore be reviewed only on the basis of empirical values or in comparison with other models. Given that simulations are a representation of a specific system, they must be re-validated after each change of the model. Furthermore, a simulation gives no evidence as to the global optima. A general statement is not possible. Only after evaluating a number of different simulations, can an iterative improvement be achieved. In queuing theory, the system behaviour depends heavily on the assumed distribution of the average arrival time and the processing time. Changes to these parameters or the dynamic behaviour can be very difficult to depict. In addition, queuing models assume the independence of input and output. This assumption however has proved in practice as being unrealistic as possible capacity expansions of the model are then excluded. The assumed boundary conditions and restrictions, which must be taken when applying the queuing theory, lead to a poor acceptance of the models in practice. Logistic curves are also statistical in nature. They describe the interdependencies between the logistic objectives using static averages. An explicit consideration of discrete events does not take place. The derivation of a logistic operating curve demands a higher effort than in the derivation of a simulation model, due to the parameterisation of the function. However, this workload is reduced in the application phase, as an adjustment can be made by simply changing the system parameter. Furthermore no validation of a specific production system is required. Another advantage of the analytical model lies in its general validity. While discrete-event simulations are often only valid for a specific application, operating curves have a greater validity. By adjusting the input parameters of the system conditions, a rapid adaptation of the changes is ensured. Furthermore, the general approach allows the finding of global optima. This allows potential points of optimization to be targeted and in practice precisely coordinated action can be initiated. In order to determine and to evaluate specific bottlenecks, an analytical approach, combined with a new visualisation method, has been chosen. Visual approaches have a particularly good acceptance in practice (Eppler and Mengis, 2009). To represent the relationship between the input variables (e.g. load, work plans and capacity) and output variables (e.g. performance, utilization and inventory), the throughput curve was selected as the basis for the identification and evaluation of bottlenecks. The calculation of bottlenecks can be carried out analogous to the calculation of an electrical network (Kreutzfeldt, 2007). The first algorithm for calculating the throughput operating curve was described by Kreutzfeldt (1995). To apply it, three assumptions are made: 1. A bottleneck restricts the flow of production orders in the same manner that a resistor limits the flow of electrical current in an electrical network. The probability that an order is processed depends on the ratio of the workload to the capacity at the work station with the greatest workload. This work station is termed the throughput limiter. The throughput limiter is defined as the work station with the greatest ratio of work load to capacity. 2. Thus, a parallel can be drawn between the continuous flow of orders through a production system and an electrical current as it flows through an electrical network. All orders that move through the same limiter become a continuous flow of orders. This flow contains the workload of all orders on all work stations in a period. 3. In this way, just as an electrical network can be described by electric currents and resistors, a production network can be modelled based on bottlenecks and flows of production orders. The analogy of a production system to an electrical network is given in Figure 2. To better understand the calculation of the throughput curve, a small production system is investigated in Figure 3. Here the planning process of a sequential, three-tiered production process can be seen. Each production order has a work content of 3 hours at each work station, as it moves from station A via B to C.

Johannes Schultheiss; Jochen Kreutzfeldt 7 Figure 2. Analogy of a Electrical Network and a Manufacturing System (Kreutzfeldt 2007). Figure 3. Computation of a Throughput Curve.

Johannes Schultheiss; Jochen Kreutzfeldt 8 The demonstration of the bottleneck s effects will be made clear by incremental scheduling of production orders. The first order in step 1 can be scheduled without problem. However, station B is identified as the throughput limiter as it has the highest capacity utilisation - ratio of input load to capacity. Should the loading of the system continue to follow the same pattern, the system bottleneck will appear here. To derive the throughput curve, throughput is drawn as a function of the workload. Since order 1 can be completed, an angle bisector is drawn from the origin to a point at which both workload and throughput are equal to 9 hours per period. Scheduling of the second job in step 2 can also be completed without issue. In the throughput curve both workload and throughput increase along the same line to 18 hours per period. At this point, there is no more available capacity at station B; it is already 100% utilised. Station A still has unused capacity however and production order 3 can be scheduled. Should a third order in step 3 be planned for station B, it will only increase the inventory levels preceding this station as it cannot be processed. Furthermore, workstation C is now underutilised. Accordingly, the content of production order 3 cannot be calculated as throughput. It is therefore advisable not to release work order 3 because it increases both the work-in-progress rate and system queues. Only 18 hours per period of total throughput can be achieved over a possible 27 hours per period of input. With this data the respective throughput curve can be derived. The throughput curve shows two characteristics of bottleneck evaluation. First of all, the difference between input and throughput is depicted as a horizontal line. It visualises the throughput potential upstream and downstream the bottleneck. If the bottleneck is unburdened, a maximum throughput equal to this throughput potential can be realised. If the optimisation objective is to reduce inventory in the system, priority should be given to relieving the throughput limiter with highest throughput potential. In Figure 3 step 3, the throughput potential of three work orders is 9 hours. If the decision is in favour of an increase in capacity at the bottleneck, the question arises as to what increase in throughput can be achieved. If the capacity of the station B is increased by 3 hours per period, the throughput of the entire system increases by 9 hours. To describe and measure this correlation, an increase in throughput is differentiated through its emergence. Work completed at the bottleneck is identified as direct throughput, while work completed at stations upstream and downstream of the bottleneck are labelled indirect throughput. The indirect throughput is indicated by a vertical line at the end of the throughput curve. In Figure 3 this line is light grey in colour. The ratio of indirect throughput to direct throughput is crucial for calculating an increase in the productivity. It is called throughout quotient. The greater the proportion of indirect throughput, the greater the possible overall increase in throughput at a bottleneck. For companies to achieve productivity gains, bottlenecks with a large proportion of indirect throughput should be targeted. In Figure 3, the three work orders have an indirect throughput of 12 hours and a direct throughput of 6 hours. Based on these parameters the throughput curve at every throughput limiter can be drawn. The next step is to extend the throughput curve to a production network that consists of multiple work stations and order flows. An example is shown in Figure 4. It is a production system with four workstations and three material flows.

Johannes Schultheiss; Jochen Kreutzfeldt 9 Figure 4. Example for Computation of Throughput Curves within a Production Network. The first step of the calculation is shown in Figure 5. As previously shown, the bottleneck is determined by comparing the capacity utilisation at each work station. In this case the most overloaded work station is station 3 with a value of 200 percent. Thus Material Flow 2 and Material Flow 3 are combined into a single flow of production orders Order Flow 1. Next, all input loads over both material flows and across all stations are summed to give a value of 80 hours of work per period. The throughput probability the probability that any particular order will be processed at the bottleneck is 50 percent. If the input load is multiplied with this probability, the throughput of the Order Flow 1 is calculated to be 40 hours of work per period. The remaining 40 hours is potential throughput. 20 hours of which is direct throughput; the remaining 20 hours is indirect throughput. Of the indirect throughput, work stations 2 and 4 can each process 10 hours of work. The associated throughput curve is presented in the lower part of Figure 4.

Johannes Schultheiss; Jochen Kreutzfeldt 10 Figure 5. Computation of first Throughput Curve within a Production Network. For the second throughput curve, the influence of Material Flow 2 on Material Flow 1 must be considered for the throughput calculation. It begins with the premise that the optimisation of greater bottlenecks is preferred to lesser bottlenecks. For this reason, the capacity at work station 2 is corrected by deducting the contribution of Material Flow 2. This means that from the original 20 hours of work capacity, now only 10 hours is available for processing of Material Flow 1. In the second step of the calculation, the second greatest bottleneck is elected as a starting point for the next throughput curve. As in Figure 6 shows, this bottleneck is at work station 2 where the remaining 10 hours of free capacity is overloaded and has a value of 150 percent. Just as in the first calculation step, Material Flow 1 becomes Order Flow 2. The input load of work stations 1 and 2 is summed to give 30 hours per period. Since the bottleneck at work station 2 is 150 percent overworked, a throughput probability of approximately 66 percent of the throughput can be calculated. By multiplication of work load and throughput probability, it is 20 hours of throughput per period. The throughput curve of Order Flow 2 is located in the lower part of Figure 6 together with the total throughput curve. The total throughput curve describes the throughput behaviour of the entire production system. It can be calculated as the sum of the two individual throughput curves. Particularly important are the two break points at which the curves change direction. The first break point is located at an input load of 55 hours per period. If all work on all stations were equally loaded, all orders could be completed up to this input load. Beyond this point however, the throughput grows disproportional to the input load. The reason for this effect is bottlenecks in the system. They cause an increase in the WIP rate. From an input load of 73.3 hours per period there will be no further growth in throughput since all order flows are blocked by a bottleneck. Input loads beyond this point are not useful since they only lead to inventory build-up. Based on this curve, the entire static throughput behaviour of the system is presented. th Proceedings of the 4 European Conference on Technology Management

Johannes Schultheiss; Jochen Kreutzfeldt 11 Figure 6. Computation of second Throughput Curve within a Production Network. Should measures to relieve bottlenecks be decided upon, through a capacity increase for example, dynamic effects play a further role (Roser et al., 2002). An approach to address shifting bottleneck behaviour is explained in the next chapter. 3.2 Estimation of Dynamic Bottleneck Behaviour through Branch-and-Bound For the prediction of dynamic behaviour due to a capacity increase, a solution is needed that can calculate the point at which the bottleneck changes its location. This point is important for the implementation of improvement measures. Through an enlargement of capacity at the bottleneck, further effects can be measured on subsequent work stations as load increases. Wiendahl and Hegenscheidt (2002) show that at the major bottleneck in a linear process, only enough capacity should be added up to the maximum capacity of the next highest bottleneck. Any further increases will only create new idle capacity and therewith inefficiencies. Hence, the point of shifting of the bottleneck is important to address targeted improvement measures. This statement may also be applied further to production networks on the whole because any adjustments of the capacity at the bottleneck resource will also affect the flow of orders that do not directly pass through the bottleneck itself. This appears, due to reduced order supply of work stations behind the bottleneck. Due to an increase in throughput at the bottleneck, additional capacity at upstream and downstream work stations is also needed. This in turn can lead to erratic bottleneck behaviour. In complex situations beyond linear processes, such as job shop productions, an implementation method without extensive computing time is necessary. One basis for the application are the known Branch and Bound algorithms (Dakin 1964), through which it is possible to model the whole production network. They reduce the computing time by cutting unnecessary parts of the system from the operation. Starting from the bottleneck

Johannes Schultheiss; Jochen Kreutzfeldt 12 station, the relations between the material flows of the upstream and downstream work stations can be modelled using a tree model (Altfeld 2008). The tree structure for the example (chapter 3.1) is presented in Figure 7. Figure 7. Branch and Bound for Bottleneck on Workstation 3. It is assumed that the capacity at the greatest bottleneck, i.e. work station 3, has to be increased. From there a maximum work of 20 hours should flow onto each of the work stations 2 and 4. However, work station 2 is influenced through the 15 hours of work that arrives from work station 1. Although this flow of orders does not pass directly through the bottleneck, it also affected by the flow from work station 3 to work station 2 and therefore also by a capacity increase at the bottleneck. That means, the orders of different order streams are competing for free capacity. Next step is to cut every branch, which is unnecessary for computation in order to reduce time. Therefore the possible workload of a work station is compared to its capacity. If the workload does not exceed the capacity of the work station, the branch beneath the work station can be removed. In this case study, every work station beneath work station 1 is eliminated. Next, the capacity at work station 3 is gradually increased until a dependant work station obtains a capacity utilisation equal to that of station 3. This calculated increase in capacity at the bottleneck is the critical capacity increase. A further increase at the bottleneck is inappropriate and only causes the bottleneck to jump to a new position, creating new inefficiencies. If the point of the bottleneck shift is detected, the next greatest bottleneck can also be identified. With this knowledge an iterative improvement process can be introduced. These are the foundations of an optimisation of a manufacturing system through a bottleneck-oriented approach. In the next step, the current handling of bottlenecks in practice is investigated in a case study. 4 Case Study 4.1 Data collection To investigate the current handling and deficits of the applied management of bottlenecks, three German companies in the field of mechanical engineering and manufacturing were examined. The participants and their skill areas are summarized in table 1. Thirty managers in the areas of production, work planning, logistics and general management were interviewed. To ensure a high quality of the interviews findings, the focus was on managers with a long experience within their job and the related field. 75 percent of the interviewees had been working in their company for more than 10 years and 80 percent had been working in their respective jobs for more than 6 years. This ensured that the interview participants were professionally qualified and the questions were answered adequately.

Johannes Schultheiss; Jochen Kreutzfeldt 13 Table 1. Companies of the Case Study. Name of Company Size Website Industry GE Inspection Technologies (business unit X-ray) Small www.geinspectiontechnol ogies.com manufacturing of equipment for non-destructive material examination, especially considered X-ray testing machines Harburg-Freudenberger Maschinenbau Large www.harburgfreudenberger.com engineering of manufacturing systems for elastomertechnique (rubber and caoutchouc) as well as production of press systems for food industries Voith Turbo BHS Getriebe Medium www.bhs-getriebe.de manufacturing of high-performance gearboxes, couplings and rotor turning gear units for high-speed and industrial applications To demonstrate the improvements made by the application of the throughput curve, data planning was collected from two of the three companies. The data sets were directly taken from the ERP systems. To calculate the throughput curve, the following data sets used were: - Capacity of each work station per period - Order master data - Planned and scheduled process and setup times - Hierarchy of work stations The work started with the investigation of current bottlenecks management techniques within the manufacturing environment. 4.2 Bottleneck Management in Practice The interviews showed that bottlenecks in production are a widely spread problem but are only poorly solved in practice. Every interviewee admitted being affected and constrained by bottlenecks on a daily business. As this conclusion was valid over all functions and companies, it supports the hypothesis that bottlenecks are a common and unsolved problem in practice. Its importance can be emphasized by the outcome that 80 percent of the interviewees are confronted with bottlenecks daily. In contrast thereto is the management of such bottlenecks. 70 percent of the interviewees admitted using no form of structured bottlenecks prognosis or management. The remaining 30 percent use their own ERP system or self-made scripts, such as spreadsheet programs. One reason for the absence of bottleneck management is that the necessary data is not available. Only 25 percent of those interviewed document, save or evaluate data regarding bottlenecks. Therefore the basis for bottleneck prediction and a continuous improvement of such problems is missing. If it is possible to predict bottlenecks before they occur, there are by far more approaches available to solve these problems than otherwise. 75 percent of all predictable bottlenecks can be solved by internal rescheduling or subcontracting. Should bottlenecks appear spontaneously and randomly, the problem is different. In such cases, the use of subcontracting as a solution decreases from 52 to 25 percent. The reason for this is that outsourcing partners find it very difficult to react spontaneously. Information is needed in advance. The preferred solution then to the issue of bottlenecks is internal rescheduling. However it requires enough buffer capacity to be free or the use of an alternative technology, which can be both costly. Should no internal rescheduling or subcontracting be possible, the last action is a delivery date rescheduling; this can lead to an unsatisfied customer. The different methods available for predictable and non-predictable bottlenecks are shown in Figure 8.

Johannes Schultheiss; Jochen Kreutzfeldt 14 Await bottleneck 7% Delivery date rescheduling 16% Analysis of occurence 19% Internal rescheduling 22% Subcontracting 52% Subcontracting 25% Internal rescheduling 59% Actions taken to address predicted bottlenecks Actions taken to address non-predicted bottlenecks Figure 8. Actions to adress Bottlenecks. These findings state that the management of bottlenecks can be improved through early identification. The throughput curves are an approach to identify and evaluate bottlenecks in advance. With this knowledge in hand more options for solutions are possible because predicted bottlenecks have a bigger probability to be solved by subcontracting. The reason is obvious that subcontractors can react better with a longer preliminary lead time. The most frequent causes for the appearance of bottlenecks are too many production orders or not enough capacity, as Figure 9 shows. With the help of cooperation partners, companies are able to reschedule orders within a production network. This leads for the company to a temporary capacity increase at the company being affected by a bottleneck. This action is only possible however, when the bottleneck is predictable. Furthermore it emphasizes the need of throughput curves to identify bottlenecks. Others 2% Persistent deficit of capacity 17% Material not available 19% Communication failures 26% Temporarily too high workload 36% Other sectors 36% Production 64% Reasons for bottlenecks in production-systems Bottlenecks caused by sector Figure 9. Reasons and Appearance of Bottlenecks. However, a proper approach for the management of bottlenecks should not only concentrate on production processes. Two-thirds of all production bottlenecks are caused by the failures within the production department.

Johannes Schultheiss; Jochen Kreutzfeldt 15 However, 45 percent of all bottlenecks are caused by non-available materials or communication problems, like missing construction drawings or delayed work plans. The next scientific challenge is to expand bottleneck management onto the upstream functions. However this is not part of this paper because the throughput curve is currently only able to cope with production processes. 4.3 DePlaVis Demonstrator A Bottleneck Management Software A demonstration software for the improvement of bottleneck management in manufacturing companies was developed within the project DePlaVis. The objective is to evaluate the practical capability of the approach in special consideration of practical data. Only the investigation of practical data allows a useful conclusion to be drawn about the capability of the throughput curve for manufacturing companies. For the administration of the data, the architecture of the demonstration software is based on Microsoft SQL Server. Likewise, the algorithm was developed as a database application. This allows a fast evaluation of available data. A graphical user interface (GUI) was developed in the.net framework to visualize the bottlenecks for the user. This made a client-server solution possible. The user is able to start the algorithm with the GUI and the computation is then calculated on the server. Through this, both the integration of the demonstration software into an ERP system and the use of practical data is facilitated. The main requirement of the demonstration software was to integrate and test practical data obtained from cooperation partners. A problem, that appeared, was the different types of data from the various ERP systems that are employed in the different organisations. SAP/R3 is used by one of the companies (further information provided on www.sap.com). In this case, the data was integrated by a manual export function. In the other the company the ERP System is a customized solution of PSIPENTA (further information is provided on www.psipenta.com). To ensure a stable and easy access to this data, the connection was made with a Linked- Server interface. Through these methods it was possible to work with actual and historical data from both companies. 4.4 Results The company data, that was used, was collected over period of four weeks. The data was made anonymous but remained still representative for practise. 1,503 production orders were examined that were scheduled on 67 work stations. There were 52 order flows identified that contained a total input workload of 14,547 work hours. The throughput calculated for the period amounted to 10,487 work hours, with a capacity of 23,730 hours. First of all the throughput curves for all order flows were calculated. The result of all throughput curves is shown in Figure 10. The analysis concentrates on cost center 6951 and cost center 6958. For better visualization of results, a bottleneck matrix was developed, shown in Figure 11. For the axes, two relative values are used. On the abscissa is the relative throughput potential. On the ordinate is the relative Leverage effect - the ratio of direct throughput to indirect throughput. Should a stock reduction as optimisation measure be sought, improvement methods should concentrate on throughput limiters with large X-coordinates. In the bottleneck matrix presented in Figure 11, cost centre 6951 has a high throughput potential. This work station is responsible for almost one third of the total throughput potential responsible. However, it has a comparatively low Leverage effect. This means that the throughput increase is probably rather low compared with the increase in capacity. The reason for this is that only a few upstream and downstream process steps are influenced by this bottleneck. Conversely, cost centre 6958 offers a much bigger Leverage effect. This is evident from its high position on the ordinate. However, 6958 has a much lower throughput potential, only 10 percent. Nevertheless, due to its larger influence on other process steps and improvement at this cost centre is easier to accomplish. In the sense of a global optimum, improvement of the throughput limiters with a large Leverage effect and a significant throughput potential should be undertaken firstly. They not only have the greatest potential for optimisation but are also the easiest to implement. In this case it is initially decided to focus attention on cost centre 6958 as an improvement here promises the greatest advancement.

Johannes Schultheiss; Jochen Kreutzfeldt 16 Cost centre 6951 Throughput [h] Cost centre 6958 Workload [h] Figure 10. Throughput Curves of Case Study. Cost centre 6958 Leverage effect [%] Cost centre 6954 Throughput potential [%] Figure 11. Illustration of a Bottleneck Matrix.

Johannes Schultheiss; Jochen Kreutzfeldt 17 The throughput curve of the cost centre 6958 is depicted in Figure 12. The flow of orders that is being limited by this cost centre is made up 223 production orders that are to be processed on 37 work stations. The effect of the throughput limiter on the other work stations will be made explicit once more, through consideration of the throughput. Before the optimization the throughput equates to 810 work hours. The throughput limiter worked on 157 of these and influenced 653 work hours on stations upstream and downstream from its position. The workload load of the order flow at all stations before the optimization amounts to 1218 hours. Therefore a possible potential throughput 408 hours could be achieved. Now it has to be estimated how much of an increase in capacity would be useful. Cost centre 6958 Throughput potential (408 hours) Throughput [h] Indirect throughput 653 hours) Direct throughput (157 hours) Workload [h] Figure 12. Throughput Curve of Cost Center 6958. An increase in the capacity to fulfil the complete demand at the bottleneck does not make sense. As previously stated, the capacity at the throughput limiter should only be increased as not to lead to the formation of new bottlenecks. The results of the case study are shown in Figure 13. The capacity at the bottleneck was increased from 157 to 169 work hours in the period under examination. Thereby an overall throughput increase of 60 hours was achieved. This means that for one hour of additional capacity, five hours of extra throughput could be generated. The advantage of this method is that through preventative bottleneck planning, the necessary increase in capacity can be anticipated. All possible means to expand capacity should then be at the disposal of the production management. This could be, for example, an application for overtime. It is also conceivable to send a forecast of necessary resources to an eligible service provider and thus plan the capacity as a framework for the coming term. A possible solution could be sending an order forecast to a relevant outsourcing partner for a capacity reservation within the coming period. This facilitates the targeted outsourcing of jobs at a later stage.

Johannes Schultheiss; Jochen Kreutzfeldt 18 Capacity [h] total capacity increase: 12 hours total throughput increase: 60 hours Total throughput [h] Capacity Total throughput Legend 13 Before optimization After optimization Figure 13. Optimisation Results of Case Study. This technique gives the production management not only a tool for the early detection and assessment of bottlenecks but also a method that delivers possible measures to unburden bottlenecks. 4.5 Further Research It has emerged through the survey and the use of throughput curve that not all bottlenecks occurring in the production are created in this business area. Often it is the upstream area of engineering that is responsible. If the necessary information for production (e.g. engineering drawings, work plans, NC programs) is not on time or fully available, delays are possible which may have a negative impact on the production and in turn productivity. These problems however cannot be overcome solely through better management of production. An extension of bottleneck management over engineering processes is required. Therefore it remains to be considered whether methods from production management are suitable for engineering management. Another topic to address is the assembly in customer-oriented production systems. Existing bottleneck management approaches discuss only the assembly processes in mass or serial production (Hegenscheidt 2003). The sequence of the assembled components there plays a lesser role since they are substitutable. In strong customer-based order manufacturing this is not the case however. Such effects need to be considered in the bottleneck planning for the assembly. 5 Conclusion Interviews conducted in the three companies showed that there are problems in the practical implementation of bottleneck management. Preventative bottleneck planning, especially with the support of existing software systems, is not widespread. In addition, many employees have a poor understanding of existing results due to the lack of visualization. In the adaptive detection and treatment of bottlenecks lies a large potential for optimization. The throughput curve provides a visually presented, easily understood approach that illustrates graphically the interaction between the scheduled and completed work in a production system. On the basis of planned order data it is possible to identify, evaluate and communicate bottlenecks, as well as their potential for optimisation. The early recognition of bottlenecks that may occur at some future time allows a variety of countermeasures to be implemented. A gain in lead time is important especially in cooperation with partners