Methodology for evaluating modular production concepts

Chemical Engineering Science 155 (2016) 153–166

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Methodology for evaluating modular production concepts Stefan Sievers, Tim Seifert, Gerhard Schembecker, Christian Bramsiepe n TU Dortmund University, Department of Biochemical and Chemical Engineering, Laboratory of Plant and Process Design, Emil-Figge-Str. 70, D-44227 Dortmund, Germany

H I G H L I G H T S








A methodology for evaluating modular & non-modular production scenarios was developed. Aspects unique for modular production are considered. Supply chain & process simulation is performed in a single software implementation. Economic performance and sensitivity analyses can be calculated rapidly. Drivers and barriers of modular & non-modular production concepts can be identified.

art ic l e i nf o

a b s t r a c t

Article history: Received 19 January 2016 Received in revised form 9 June 2016 Accepted 5 August 2016 Available online 6 August 2016

A more flexible and efficient production of chemicals is a requirement for further strengthening the competitiveness in the chemical industry. An approach proposed to achieve this is modular plant design. It offers new opportunities for the supply chain and combines production flexibility and efficiency. However, modular facilities are expected to be built at comparably small scales and loss of economy of scale is a major concern. There is a need to know under which conditions a modular plant design is a beneficial option. Addressing this it would be helpful to have a methodology that includes modeling of production scenarios in a holistic way including supply chain and process simulation and thus allowing a meaningful evaluation. For that reason we developed such a methodology, using the F3 factory concept as an example for modular plant design. Demonstrating the methodology's feasibility an exemplary implementation in a software tool was established enabling comparative simulation and evaluation of batch, continuous and the modular F3 factory production. As unique feature supply chain and process simulation is combined in a single software implementation allowing for statistical analysis to automatically evaluate the economic performance of production concepts under different boundary conditions of the process and the supply chain. The incorporation of those boundary conditions is usually not part of process simulation and goes beyond state of the art approaches. In this paper, the methodology implemented will be presented and the application will be demonstrated using two production scenarios as examples. For the examples investigated, it was found that compared to the conventional production concept the modular F3 factory concept is economically robust concerning the choice of design capacity with regard to diverse market conditions. & 2016 Elsevier Ltd. All rights reserved.

Keywords: Modularization Modeling Operating mode Supply chain Economic performance Statistical analysis

1. Introduction 1.1. Continuous and batch operation Future development of global chemical market is characterized by diversification and fragmentation; technological improvements n

Corresponding author. E-mail addresses: [email protected] (S. Sievers), [email protected] (T. Seifert), [email protected] (G. Schembecker), [email protected] (C. Bramsiepe). http://dx.doi.org/10.1016/j.ces.2016.08.006 0009-2509/& 2016 Elsevier Ltd. All rights reserved.

and new fields of application make customers demanding for tailor made products (Buchholz, 2010). This leads to an increasing number of products, decreased production volumes of individual products, delocalized product demand and shorter product life cycles resulting in more volatile markets and the demand for flexible production (Buchholz, 2010). Additionally, there is increasing pressure on product prices and a trend of increasing raw material prices. To keep up with these future challenges chemical production must become more flexible and more efficient. Fulfillment of both, flexibility and efficiency, is hardly possible applying existing production concepts. Traditionally, the main economic driver is economy of scale which is exploited by the

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Table 1 Characteristics of world scale continuous and multi-purpose batch production (Buchholz, 2010; Rauch, 1998). Characteristic

Dedicated Continuous

Multi-purpose batch

Operating window Efficiency

Narrow High at design capacity

Capacity extensions

Usually related to capital expenditure High Difficult

Broad, dependent on schedule and occupancy Low, due to lack of heat integration, automation, recycles and partially not optimal process conditions Not necessarily related to capital expenditure, dependent on schedule and occupancy Moderate Often easy, dependent on schedule and occupancy

Investment risk in volatile market environment Product change-over, introduction of new products

production concept of continuously operated world-scale plants (Buchholz, 2010). The other dominant concept is the multi-purpose batch plant, providing high flexibility and the ability to satisfy very special customer demands as main economic drivers (Rauch, 1998). Table 1 presents typical characteristics of world scale continuous and multi-purpose batch production. 1.2. The F3 factory concept A solution proposed to combine the advantages of both existing production concepts is the F3 factory concept (Buchholz, 2010). F3 stands for flexible, fast and future production plants and describes a modular and continuous operating mode. Key element of this concept is the continuous operation of modules, designed to fit into standard ISO containers. By this means a beneficial combination of both efficiency and flexibility can be obtained. The overall F3 factory plant is made up from two modular structures, PEAs (Process Equipment Assembly) and PECs (Process Equipment Container), both equipped with standard interfaces. The structure is schematically illustrated in Fig. 1. Modular unit operations (the PEAs) can be placed into standard containers which represent the modular superstructure (the PECs) and are connected to a shared backbone facility that ensures basic supply with utilities and energy. By the modular design, flexibility is obtained regarding both portfolio and capacity. Portfolio changes can be realized by adaption of the PEC's and PEA's setup (Fig. 1a) while capacity changes can be reached by implementing parallel production lines (Fig. 1b). Applying this concept additionally paves the

way for distributed production. Through this a set of benefits is expected. For comparison with the characteristics of conventional production technologies as presented in Table 1, a selection of these benefits is summarized in Table 2. For more detailed information and examples, refer to (Buchholz, 2010; Seifert et al., 2012; Bramsiepe et al., 2012). However the main disadvantage of the modular approach is the loss of economy of scale. Specific investment costs of small to medium scale production plants will be comparably high and additional investment for the backbone facility will be needed. Assuming technical feasibility, the decision about the use of the production concept is dominated by the economic performance, which mainly bases on operating and investment costs. Usually process simulation is used to determine these costs. As can be taken from Table 2, the advantages of the F3 factory concept arise from a big variety of impact factors ranging from technological aspects to the opportunity to increase production capacity closely linked to market development by following a sequential investment strategy. These aspects cannot be covered with an individual state-of-the-art process simulation tool. Therefore a suitable evaluation method must go beyond determining operating and investment costs. The economic performance evaluation must include the following aspects.


Local production conditions can be exploited. Examples for local








conditions are wage levels, work efficiency, availability and quality of utilities, energy and waste costs, taxes and depreciation regulations. Specific site conditions are important for the construction of a plant; e.g. available structures like existing facilities or buildings have to be distinguished from greenfield conditions. Supply routes for products and raw materials are affected by the selection of the production location. Production can follow the market requirements, which means an adaption of the production capacity may pose a need for adding or removing modules. New options for investment strategies are available; e.g. implementation of a single multi-product strain vs. multiple parallel dedicated strains. Modular plant design may have an impact on the structure of investment and operating costs.

To include all these aspects listed above, a new strategy for the evaluation of the production concept is required. In this article, such an evaluation strategy is developed and applied to an example production scenario. An additional example will be used to emphasize the potential impact of a shift away from dedicated large scale production to distributed production and step-by-step investment. 2. Economic model Fig. 1. Illustration of F3 factory modular elements and concept idea.

As basis for economic evaluation a model of the modular F3

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Table 2 Characteristics of F3 factory production and expected benefits (Buchholz, 2010; Seifert et al., 2012). Characteristic Operating window Efficiency

Benefit/Difference to dedicated continuous/multi-purpose production


Operating window is wide, due to numbering up ability.
No scale-up issues.
Continuous operation allows for high efficiency by inclusion of recycles, heat integration, high degree of automation,


Capacity extension Investment risk in volatile market environment Product change-over, introduction of new products











low personnel demand. Further efficiency may be gained using small dimensions offering a large field of application for process intensification technology. Mobility enables cost efficient production fence-to-fence at the site of a customer or raw material supplier, saving transportation costs, supporting market entry. Step-wise (sequential) capacity extension possible. Safe extension by copying functional production lines. Reuse of modules possible. Decreased investment risk because initial investment starts at low volumes, around pilot scale. In case of low market performance ability to stop production without losing a large investment. Reuse of modules possible. Module construction under workshop conditions and less work in the field reduce specific investment cost. Flexibility through interchangeable structures and option to avoid production scheduling and cleaning by parallel operation. Quick response due to standardized modules & simulation models, reducing engineering effort. Pre-documented startup procedures. From lab to production scale a small factor applies, reducing scale-up issues.

factory concept is required. There are two main simulation requirements that need to be fulfilled; simulation of the F3 factory concept's key features (quick response, scaling ability, flexibility, efficiency, decentralized production etc., see Table 2) and a combined consideration of process simulation, supply chain and market conditions. We propose an economic model consisting of the following six elements: 1. 2. 3. 4. 5. 6.

Process Production line Production site Customers Raw material suppliers Transportation routes

By specifying and combining these elements, production scenarios can be created and processed. Some combinations of these elements also allow the description of non-modular production concepts, which is essential for concept comparison. Hereafter model elements and their use are described. 2.1. Process The element “process” describes the process units necessary to manufacture the product. A process has a specified design capacity i.e. product output. Raw material, energy and utility consumption are calculated applying simulation programs. The process design is fixed by the set of unit operations used. The design capacity determines the dimensions of the unit operations. A process can be operated in batch mode or continuously. For batch processes a fixed batch-time is considered and the operations within the batch process are run one after the other (scheduling is not considered in this element). A process is always assigned to a production line. 2.2. Production line In this element investment costs of conventional batch, continuous and modular production plants are calculated. This is the foundation of the economic evaluation and a major input of the holistic evaluation strategy. In case of non-modular production concepts a conventional batch or continuous plant is considered as a single production line. In case of a modular production concept, a production line can be composed of one or more PECs (compare Fig. 1), containing the process equipment (“PEAs”). Equipment items and dimensions are derived from the “process” element and

represent the basis for investment cost calculation. A modular production plant is composed by one or more production lines and the backbone facility (compare Fig. 1). Based on the production line specifications, investment costs of the backbone facility are calculated in this element. The procedure is explained in more detail later in this article. It is important to distinguish between the process and the production line because not every process requires additional investment. If technically feasible, more than one process can be assigned to a production line. By such assignment a multi-product plant producing several products can be modeled. In this case a scheduling strategy has to be applied to determine which product is produced at which time. Production lines are always assigned to a production site. 2.3. Production site The “production site” element is essential to take special economic drivers like distributed production into account as it describes the geographical location of production plants as well as local production conditions. These are local wages, prices for utilities, energy and waste, profit independent taxes and insurance, income tax and site related fix costs (e.g. site administration). Local wage level determines the costs for logistics, like loading and unloading activities as well as for the distribution organization. Additionally, specific local conditions at the site affecting production and construction, like local labor productivity or existing infrastructure, must be considered. Herewith cost differences between greenfield investments and the extension of existing structures can be described. This has to be considered during investment calculation of the “production line” element. 2.4. Customers This element specifies the market conditions. “Customers” determine the target place for product transportation. Additionally, for each product the development of the demand and the selling price is specified. Herewith, the time-dependency of the market development is taken into account throughout the time-horizon considered. 2.5. Raw material suppliers Besides customers also raw material suppliers are elements of the supply chain. By this element, raw material location and prices are defined. The latter are either constant or time-dependent in

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analogy with the product prices. 2.6. Transportation routes The “transportation routes” element defines routes between raw material suppliers, production sites and customers as well as the characteristics of these routes. This element allows to take economic drivers like distributed production into account. Route definitions determine which customers are served by a production site. The sum of product demands of customers served by a production site determines its total amount of production. As characteristics of the routes the specific transportation costs are used. These differ between sea and overland transportation.

3. Implementation Modular and non-modular production scenarios should be investigated using the same economic model to guarantee a consistent and comparable evaluation. The economic model must be implemented in a suitable software environment to enable simulation. For the software environment following requirements apply:


In the software environment process simulation must be con-





sidered to calculate mass and energy balances which are the basis for investment and manufacturing cost calculation. Also batch process simulation must be considered. This reduces the variety of suitable software environments to chemical engineering simulation tools. All model elements must be implementable. The evaluation strategy developed should be implementable. It bases on the simulation results and without using a single software tool considerable time and effort must be spent on data collection, transformation and interface generation.

Applying some modifications fulfillment of these requirements is achievable using Inosim Professional 9.0 as software environment. Inosim Professional 9.0 was designed for batch process modeling and includes a programming environment that offers numerous interfaces to the simulation, which allow for modifications (INOSIM Software GmbH). Using this flexibility the feasibility of the methodology for evaluating modular processes is demonstrated by an exemplary implementation in a software tool based on Inosim Professional 9.0. In the following the conceptual implementation approach is presented and details of the economic model simulation and the evaluation methodology are given. 3.1. Conceptual approach The software tool developed is called ProMoT.1 The overall structure of the exemplary implementation is presented in Fig. 2, showing the split into the six elements described in chapter 2, enabling a description of different operating modes and diverse production scenarios. The process simulation implemented in Inosim Professional 9.0 (Fig. 2: (A)) is used to calculate the mass and 1 ProMoT stands for Production Mode selection Tool. The name was resolved by the F3 Factory consortium in 2009 (Final report F3 Factory Project). We explicitly point out that there are existing software tools with the same name but different focus developed by Tränkle et al., (2000), Mirschel et al. (2009) and Ginkel et al. (2003) or Mangold et al. (2014) it should not be confused with. At the time of publication the tool is at the stage of a development object and to be considered as a software prototype with a clear focus on functionality rather than ease of use, yet it can be made available on request.

energy balance, the equipment dimensions and batch cycle times. For batch processes the process simulation that comes with Inosim Professional 9.0 can be used. In Inosim Professional 9.0 processes can be modeled recipe-based and process simulation takes place using a discrete event-driven simulator. Mass and energy balances are calculated using short-cut models for the implemented unit operations. Besides detailed mass and energy balances, the batch cycle time is a main result of process simulation. For continuous processes simulation some modifications must be implemented as Inosim Professional 9.0 was not designed to perform continuous process simulation. Unit operations are set up in recipes as in the case of batch process simulation. The eventdriven simulator is bypassed by starting calculation for each unit operation of the process simultaneously. To get a steady state solution of the mass and energy balance that meets to the specified design capacity, starting values (e.g. raw material streams) are adjusted using Newton's method. To consider recycle streams, a Wegstein iteration was applied ensuring high convergence speed for steady state solution (Finleyson, 2006). This is a pragmatic solution for a stand-alone prototype implementation, which is intended to be applied in early phases of concept evaluation and process development. Often in these phases calculations are done using Excels. Results from such calculations can be used in the prototype implementation easily with the built-in Excels interface that the software platform Inosim Professional 9.0 provides. However, in later phases the resulting data of the mass and energy balance and the equipment dimensions elaborated using one of the established steady state process simulators can also be imported using the Excels interface. The data is transferred towards the “process” element which serves as part of the economic model as described in chapter 2. The characteristics of the economic model elements “Process”, “Production plant”, “Production site”, “Customers”, “Raw material suppliers” and “Transportation routes” shown in Fig. 2 have been described in chapter 2 and were implemented according to that description. As described in chapter 2 production scenarios can be modeled by specifying and combining elements. To derive results about the economic performance of a production scenario a simulation of the economic model must be performed (Fig. 2: (B)). For simulation of the economic model some default values are necessary such as cost model parameters, discount rates or cost factor values for FCI calculation. These are summarized in a “general information” element (Fig. 2: (C)). In the following chapter the economic model simulation procedure is described. 3.2. Economic model simulation 3.2.1. Time discretization In chapter 1 aspects are listed that have to be covered by a suitable evaluation strategy. The flexibility aspect of the modular production concept (compare Table 2) implies inclusion of time dependency of e.g. production rates, product demand, plant setup, investments or the overall economic performance. Therefore, for implementing the methodology for evaluating modular processes a certain period of time needs to be simulated; typically the product life-time i.e. a range of years. The discretization of time needs to range between years, which is a too long period for accounting flexibility aspects and weeks or days, which increases computational effort excessively. Thus, one month was chosen as discrete element of time. Nevertheless for some special events even a month is a too long period for exact representation; e.g. for the start-up period of a continuous process or the time of a batch. For the implementation in this case we propose that simulation takes place using discrete time elements according to the event-list of the event-driven process simulation and results are condensed

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Fig. 2. Main structure of the implementation of the methodology for evaluating modular processes.

and assigned to the corresponding month to be displayed. 3.2.2. Cost calculation In the methodology proposed the comparison of different production concepts is based on the economic performance. Therefore, economic key performance indicators are calculated by the implemented economic model simulation. Typical key indicators are the net present value (NPV), internal rate of return (IRR), the payback period (PBP) or return on investment (ROI) (Baerns et al., 2006). For all key indicators, it is necessary to determine all cash flows that are incoming (sales) and outgoing (all kinds of costs). In the economic model simulation implemented this calculation follows the scheme proposed by Baerns et al., (2006) (see Fig. 3). In the following, further details are given for the determination of the variable, fix and investment costs as well as sales, which represent the main cost and income groups. The variable costs are determined by the production rate; the more product is manufactured the more raw material, energy and transportation costs accumulate (compare Fig. 3). The actual values for raw material, energy and utility consumption are taken from the mass and energy balance of the process simulation and are dependent on the load of the plant. Costs of transportation of product and raw material are calculated by the required quantity to be shipped and the corresponding routes. Default values for specific overland and oversea transportation costs are taken from (Gudehus, 2007). Fix costs and depreciation depend on the fixed capital investment (FCI, compare Fig. 3). The FCI is calculated based on delivered purchased equipment costs. The delivered purchased equipment costs can be determined based on characteristic dimensions using cost models. For conventional equipment cost models proposed by Woods (2007) are used. On that basis FCI is calculated using a factor method approach. For modular plants this approach takes the impact of modularization on investment costs into account (compare Table 2) and considers the modular design according to the F3 factory concept using PEAs, PECs and a backbone facility (see Fig. 1). The necessary size of the operational team is estimated

based on cost models, too. Here, the type and number of equipment is used to determine the number of workers necessary for operation and maintenance as proposed by Kölbel and Ulrich (Kölbel and Schulze, 1982; Ulrich, 1984). Achievable sales are defined by the customer's product demand. As the goal of the proposed methodology is to analyze the economic performance of the modular production concept under different boundary conditions such as different developments of customer demands, it is useful to characterize the customer demand by a small set of parameters, facilitating (automatic) analysis. Therefore, a customer demand characterization was developed and implemented taking into account the time of the demand start and the slope of its increase, the maximum demand and starting time and slope of the demand's decrease. For specification of product prices and their changes during product life cycle the same procedure of curve characterization was implemented. To analyze the economic performance of the modular production concept under typical developments of customer demands template demand curves were implemented, for example pharmaceutical product life cycle demand patterns, determined by Bauer and Fischer (2000). The actual sales that can be generated depend on the customer's demand and the production rate of the supplying plants. Three cases of production to demand ratio are important to distinguish for economic model simulation.


Demand is lower than minimum plant utilization. In this case



the production rate corresponds to minimum plant utilization and stocks are filled until they are full. Demand is between minimum and full plant utilization: In this case the production rate corresponds the demand. Demand is higher than maximum plant capacity: In this case the production rate corresponds the maximum plant capacity and stock inventory is reduced until stocks are empty. If the demand very clearly exceeds maximum capacity, installation of one or more additional production lines may be reasonable. Details for simulating investment decisions are explained in the following.

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Fig. 3. Scheme for economic performance calculation (Baerns et al., 2006).

3.2.3. Investments An aspect of flexibility of the modular production concept is the possibility for step-wise capacity expansion by installing further production lines (compare “capacity expansion”, Table 2). In the economic model simulation implemented for each investment a realistic point in time must be determined, which usually corresponds to the point in time by which an optimum economic result can be achieved. A method that is typically used in case of steadily growing sales is the break-even calculation (Baerns et al., 2006). With this method the earliest possible point in time for a single investment can be determined. Production scenarios to be investigated using the economic model simulation require a more advanced consideration because first not only steady market conditions are considered and second modular production scenarios can include more than a single investment. Consequently, an advanced method for decision making on investments was applied. We developed an approach inspired by real options analysis method that is used in the implemented economic model simulation. Real options analysis is a technique to make capital budgeting decisions under uncertainty (Copeland and Antikarov, 2001). The key idea is to consider the fact that an investment decision at a certain point in time is not only characterized by the result of a positive decision but also by the results of the option not to make the decision or to shift the decision to a future point in time. This method requires a statistical description of the effects of a decision. For detailed explanation refer to Lier et al. (2012) who applies real options analysis for a stepwise expansion of a modular plant by copying complete production lines. Seifert et al. (2015) extended this approach by taking expansions of single unit operations into account. Like Lier he used three decision points in time. Because one month is the discrete time element of the economic model simulation, each month simulated may represent

an investment decision option if the flexibility of modular plants is taken into account. However, taking all of the options on a monthly basis into account results in an immense computational effort. For that reason we developed a simplified approach similar to real options analysis. First, the earliest possible point in time of an investment is calculated. For that, the minimum utilization of a production plant is used. It is either set by technical limits obtained from process simulation (e.g. utilization of continuous equipment, a detailed discussion is given e.g. in (Seifert et al., 2014)) or by economical limits, which means the break-even point of utilization (Baerns et al., 2006). Comparing the product demand for a specific production site and the minimum utilization provides the point in time of production start. For multi-product plants, minimum utilization applies to the sum of demands of all products produced in that plant at that time. The results are the initial times of investment. Then the options of shifting these investments to later times or skipping of investments are investigated for each single month following the initial time of investment to the end of the period under consideration. For each option the net present value (NPV) at the end of the period under consideration is calculated. The NPVs calculated by the simplified real options approach are compared to each other to determine the most economical solution (maximum NPV). So in the economic model simulation the simplified real options approach helps to decide whether an investment is profitable and when is the right time for investment. Fig. 4 shows the effect of applying this method to a scenario in which maximum two investments are allowed in a given timeframe of 120 months. 3.2.4. Scheduling In a continuous multi-product production line one product can be produced at a time only. This makes a suitable production

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Fig. 4. Example of the application of real option approach for investment decision. The earliest possible point in time of an investment into a production plant (A) is determined by the lead time period and the minimum utilization at the plant's start-up (B). The product demand is sufficient to ensure minimum utilization of a second production line that consequently can be installed (C). By applying the simplified real options approach later points in time for the second investment are investigated leading to the result that skipping the second investment is the most economical solution during the period considered, comparing NPV with (D) and without second investment (E).

schedule and stocks strategy indispensable for meaningful simulation of production. Similar to strategies for multi-product batch production a strategy is required to find an optimal lot size. The lot size is the period of production for one product for the continuous operating mode respectively the number of consecutive batches for one product to be produced for the batch mode of operation. To find the optimal lot size, the dynamic Silver-Meal method is applied (Günther and Tempelmeier, 2012), which bases on minimizing inventory and product change-over costs while fulfilling the product demand. After determination of an optimal lot size for every single product, the product to be scheduled for production needs to be selected. Therefore, for each product it is determined whether stocks are sufficient to fulfill market demand in the next scheduling period or not. The product with the highest expected production lot value is scheduled for production. By that procedure, at low computation cost a production plan is set up that is flexible, ensures optimum economy and thus represents a meaningful simulation of production. An example for a resulting production plan of a continuous multiproduct plant for three products under constant market conditions is shown in Fig. 5. Here, resulting production rates and corresponding stocks inventory are displayed. The production plan shows that initially production cycles are short. The requirement to fulfill product demands while initially stocks are empty and simultaneous production is not possible leads to many product change-overs establishing inventory holdings for all products in that phase. After that phase, production cycles get more regular because progressively increasing stocks levels serve as buffer, compensating suspended production. Within the considered time horizon there are periods without production (month 82-84) in which inventory holdings are reduced. This is a consequence of the requirement that during operation full utilization shall be achieved. 3.3. Evaluation methodology The implemented methodology for evaluating modular processes allows for the consideration of both, modular production concepts and conventional production and calculates KPIs representing the overall economic performance. To determine parameters with great impact on overall economy, significance analysis can be used (Morris, 1991). Applying significance analysis

Fig. 5. Production rate and stocks inventory for a generic continuous multi-product production. Production starts after construction phase (first 24 months).

to a whole production scenario including various production plants, market development and the supply chain can be used to reveal economic drivers and barriers of each concept and thus support decision making for or against a production concept. In the evaluation methodology screening and identification of crucial parameters is performed using Morris one at a time method (Morris, 1991). Compared to other screening methods like super-saturated designs or group screening like Andres IFFD (Kleijnen et al., 2005) or CSB-X (Wan et al., 2006), for this application Morris one at a time method provides the following advantages:


Investigation of simulation objects with a complex response surface.


Significance of results even if many input parameters have great influence on model response.


The number of simulation runs increases just linearly with the number of input parameters. Depending on model complexity, scenarios we have investigated were characterized by 20–160 model parameters. Applying Morris' method, first the parameter is selected which impact shall be investigated. The investigated parameter and other parameters (the input parameters) are then selected randomly within a given uniform interval and simulation results are

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Fig. 6. Effects of exemplary input parameters on economic key indicators.

generated. It is important to mention that there is a predefined interval staging for selecting the input parameter value so that only certain values can be selected, but which one is determined by random. For example, from a 3-staged uniform interval with values from 0 to 9 only the values 0, 3, 6 and 9 can be selected. In a second step the value of the investigated parameter is changed within its given interval and after generating simulation results the impact of this change is calculated and recorded. This two-step procedure is repeated several times so that the effect of the investigated parameter on the economic key indicators under different boundary conditions (different sets of randomly selected input parameter values) becomes available. This effect is called the mean effect. For each parameter investigated the calculation of the mean effect is performed. The algorithm developed by Morris guarantees, that a minimum of computational effort is reached (Morris, 1991). Using this method, the statistical significance and degree of nonlinearity of each individual parameter's mean effect on the economic key indicators can be identified independently on the other parameters’ current value (Morris, 1991). From a mathematical point of view the mean effect of a parameter represents the unstandardized regression coefficient in a multiple linear regression model (Kleijnen et al., 2005). Thus, the dimension and magnitude of a mean effect depend on the unit of measure of the corresponding input parameter. Consequently the absolute strength of mean effects of different parameters cannot be compared directly, which disagrees with the objective of the procedure. To enable direct comparison of the impact of different parameters in a production scenario and to facilitate interpretation, the mean effects are standardized according to the procedure for standardized regression coefficients resulting in dimensionless standardized mean effects that can be compared directly (Hox, 2010). However, standardized mean effects denote the effect size relative to one other within a single production scenario and may mask the absolute difference between production scenarios. For example, a parameter may have a relative strength of 90% in two scenarios to be compared but varying the parameter by the same percentage may have a different absolute impact on both production scenarios (e.g. in [€]). This essential information for comparing two production scenarios gets lost through standardization (Hox, 2010). To overcome this, the difference of a KPI between two production scenarios is considered as additional KPI before standardization, which allows to evaluate a parameters’ impact on the difference between production scenarios. A simple and generic example is used to explain the interpretation of calculation results. For two different production modes Fig. 6 shows the standardized mean effects of three parameters “Equipment costs”, “Product price” and “Construction time” on the NPV and its difference between the modes (ΔNPV¼ NPVmode 1  NPVmode 2) at a reference point in time. The standardized mean effects on the KPI ΔNPV indicate differences of the impact of each input parameter between both modes.

Therefore, the impact on ΔNPV is used to rank the results when comparing operating modes. Fig. 6 shows that the effect of the parameter “equipment costs” is negative for modes one and two. A negative effect means, that the NPV decreases in case of increasing equipment costs. From all parameters, the parameter “equipment costs” has the biggest impact on ΔNPV, i.e. the difference in economic performance between both modes is most sensitive to equipment costs. Mode two is more sensitive to this parameter than mode one. The product price has the biggest impact on the economic result of both modes. The difference of impact is rather small so that both modes are affected in a comparable way by this parameter. Construction time has a rather small impact, but concept one is more sensitive to this parameter than concept two. The evaluation methodology presented offers a systematic approach for selection of production concepts. The difference of sensitivity of each concept to external influences (e.g. the market development) provides a new parameter for evaluating and comparing production concepts. Finally, this method can be used to identify and focus on crucial parameters in process development.

4. Demonstration The following example production scenarios will briefly demonstrate the benefits of the capability to model and compare production concepts holistically by applying the implemented methodology for evaluating modular processes. Two examples of different scales and focus will be used. The first example focuses on flexibility in process conditions and in production schedule at a single site. Assuming that both the continuous modular process and the batch process have the same supply-chain infrastructure, location aspects of the supply chain are not considered. Comparing two continuous processes the second example focuses specifically on aspects of the supply chain and on flexibility in investment decisions for a distributed production under fluctuating market conditions. 4.1. Example 1 As first example a conventional batch production and a continuous production in a modular plant are compared. For this purpose a small scale multi-product production was defined in which three different specialty chemicals are manufactured. Depending on the product to be produced 10-14 processing steps are performed consecutively. Fig. 7 shows the processing steps for each of the three products and the equipment allocated. The process example used was developed in the F3 factory project (Final Report F3 Factory Project). As shown in the right-hand column of Fig. 7, for the batch process, some processing steps can be performed in the same equipment (e.g. extraction and distillation). In the modular process

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Fig. 7. Overview of process steps for production of three products and allocated equipment for the continuous F3 factory process and the batch process. The product price is 550 €/kg for each product.

each step is performed in a dedicated, continuously operated apparatus. The plant is designed as a single multi-product production line. For manufacturing of different products, specific process steps are skipped (see left-hand column Fig. 7). For the lithiation, nitration and oxidation step micro-channel reactors are used. Hydrogenation is performed in a monolith loop reactor. Much faster dosing of reagents and optimized concentration profiles lead to conversion and selectivity improvement as shown in the middle column in Fig. 7. Here STY, X and S are the relative changes of space time yield, molar conversion, and selectivity in the continuously operated process compared to batch processing. Shortcut models of the corresponding unit operations were implemented in Inosim Professional 9.0 and the processes were simulated. According to the resulting streams and the retention times, equipment sizes were calculated (compare Fig. 7). Based on these, investment costs were calculated using given cost correlations. Investment costs of the batch plant amount to 7.3 M€ and 10.4 M€ for the modular plant. The modular process is designed as a single multi-product production line. If sales are constant throughout the period under investigation, conversion and selectivity improvement of the

Fig. 8. NPV curves of compared concepts under constant market conditions. During operation overstocking is prevented by schedule adaptations for the consecutive production of the three products, which results in uneven development of NPV curves shown.

continuous F3 factory process lead to decreased raw material consumption, which over-compensates higher investment costs for continuously operated equipment. In Fig. 8 the resulting NPV curves of both concepts are shown. After two years of operation (Fig. 8B), the continuous F3 factory plant is more beneficial than the batch plant even though the continuously operated plant requires higher investment costs than the batch plant (Fig. 8A). At the end of the period of investigation the F3 factory plant would gain a higher NPV compared to the batch plant (Fig. 8C). Next, the production flexibility of both concepts is investigated to see if flexibility difference is a decisive factor, affecting the economic result. For batch operation there is no technical limit of minimum utilization and the order of batches can be adapted to the customer demand. This means a high degree of flexibility and a comparably low investment risk; capital investment is not bound to a specific product and thus independent from the individual product's demand. The implemented evaluation methodology was used to check, whether this flexibility is a decisive factor or not. Different market demand patterns were considered for each product and the economic performance of the different operating modes was investigated. In Fig. 9 an increasing demand for all products is shown (scenario 1). At the time of investment, the demand corresponds to 80% of the design capacity and increases linearly up to 2.5 times

Fig. 9. Scenario 1: Economic evaluation of batch and F3 factory operating mode in a scenario with increasing market volume.

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the design capacity during the period of investigation. Because only one product can be produced at a time, in both modes stocks are used to compensate suspended production of the other products. The batch and the continuous plant must be cleaned during product change-over and corresponding costs are considered. During the start-up phase of the continuous plant there are additional costs for off-spec production and corresponding raw material consumption. These costs do not occur in the batch plant. To estimate these costs it is assumed that after three residence times of the largest equipment a steady state and in-spec production is reached again. At an identical change-over rate additional costs and time loss by the change-over procedure lead to both decreased production output and increased specific production costs of the continuous plant compared to the batch plant. Thus change-over is less favorable and performed more seldom if possible. In the investigated scenario of constantly increasing product demand, this is not possible. The reason for this is that the demand is higher than the possible product output preventing buildup of stocks for all products which would be necessary to decrease change-over rate. As a consequence the continuous plant's total sales are lower and manufacturing costs are higher compared to the batch plant, which leads to a lower NPV. In month 110 the demand is high enough so that a second production line can be installed, which however does not change the result within the considered time horizon. Scenario 2 describes an increasing market which then collapses (Fig. 10). Initially results are similar to scenario 1; in the phase of increasing demand production costs of the continuously operated production plant are higher than those of the batch production. After demand collapse operation is not performed at maximum load any longer. Therefore, stocks can be build up that allow for a decreasing change-over rate. As a consequence production output of both the continuous and the batch plant meet the product demand. The efficiency of the continuous production is higher at identical production rates which results in a more cost effective production and a higher NPV at the end of the period under investigation. In scenario 3, flexibility in a more diverse market is investigated (Fig. 11); for product one and two a slow demand increase is assumed, while product three starts at a high demand level but after four years demand decreases until the end of the considered period. In both concepts production can be adapted fulfilling demands. There is no significant difference in flexible response to demands between both concepts but higher process efficiency leads to better economic performance of the continuous plant. In this example flexibility is not a crucial issue for the decision about the operating mode. Nevertheless, it should be mentioned, that a later decrease of demand for product three would result in decreased performance of the continuous plant as effects as explained for scenario 1 would take effect more and more. In the results shown in Figs. 8–11 it can be observed that the curves are always rather close to each other. This is a consequence of similar economic boundary conditions regarding raw material and product prices, product demand and the supply chain. The differences are a consequence of slightly different conversion efficiency, TCI and flexibility. However, such close results require a

Fig. 11. Scenario 3: Economic evaluation of batch and F3 factory operating mode in a product specific market volume scenario.

critical consideration regarding their significance. For this purpose confidence intervals added to the curves shown can be used. Yet, the main result of the evaluation methodology is not a particular NPV curve or final NPV but the concepts’ sensitivity to boundary conditions i.e. their different reaction to different input parameter values. This reaction is not influenced by stochastic effects but deterministically generated. Using the same model and input parameters will always give the same result. Therefore, the indication of confidence intervals is not effective in this case. However, to evaluate the significance of the results, instead, the results are checked at different parameter value combinations. In this way the statistical significance of the individual parameter's mean effect on the economic key indicator selected can be identified and the concepts’ different reaction to different input parameter values can be evaluated (compare Section 3.3). Next, such evaluation is performed for the example investigated. The evaluation strategy described in Section 3.3 is applied to determine most influential parameters affecting economic comparison by significance analysis. 37 input parameters were varied (each on a 10-staged uniform interval) and the effect on NPV at the reference point (year ten) was determined. A selection of results is shown in Fig. 12. The significance analysis reveals that a certain set of parameters generates most of the impact on NPV. If a decision for a certain operating mode has been taken already, this result helps to focus on most important influencing parameters for further process development. If a decision on the operating mode is still open, a comparative result like in this case helps to reveal and judge differences between influencing parameters as well. Fig. 12 shows that equipment costs have the greatest standardized mean effect on difference of NPV between both operating modes (ΔNPV). This means their impact is most significant to ΔNPV; increasing equipment costs will have a stronger negative effect on the batch process than on the continuous process. On the long-term lower manufacturing costs of the continuous process lead to a better compensation of increased equipment costs and thus to a decreased sensitivity to this parameter. The parameter with the second most significant impact on ΔNPV is the estimate accuracy of design capacity. The effect of an accurate estimate is positive for both modes (e.i. a better NPV result), but less positive for the continuous, which means a weaker dependency on this parameter. So a wrong estimate of the design capacity has lower impact on the economic result of the continuous plant. This is a consequence of the scaling ability of the F3 factory concept. For the decision about the operating mode, this would mean a lower risk of investment, if the market volume is uncertain. This additional result would not be disclosed without applying the newly developed evaluation strategy. 4.2. Example 2

Fig. 10. Scenario 2: Economic evaluation of batch and F3 factory operating mode in a collapsing market volume scenario.

The second example is a 100 kt/a production of i-butyraldehyde from propylene and syngas, described by Seifert et al. (2014) (see Fig. 13).

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Fig. 12. Significance analysis of input parameters. ΔNPV ¼ NPV F3 factory – NPVBatch; Accuracy of design capacity estimate ¼ design capacity / market volume; Quality of market development ¼ decrease, stagnation or increase of demand after 60 months.

In the F3 factory process the main reactor is replaced by a modular jet-loop system and columns C1 and C2 are replaced by a modular membrane separation system. By these changes first a modular setup of the most costly parts of the plant is achieved and second the raw material and energy costs are reduced by 7%. A detailed description of the process and the equipment and an evaluation of the modular approach's impact on the process is given by Seifert et al. (2014). We consider a production scenario, in which the conventional production is placed at a central production site in Europe and delivers the product to ten globally distributed customers while the F3 factory plants are located at the sites of the customers (see Fig. 14). Every 6 months an additional customer enters the market with a product demand that increases linearly up to 10 kt/a during a period of three years. The chronological order of customers entering the market, the product delivery distances to the customers and wage levels and tax rates at the customers’ locations are shown in Table 3. As a consequence of decentralized production using the F3 factory process raw material, energy and product delivery costs can be reduced. Capital and personnel costs on the other hand are significantly higher as ten production lines with an individual

capacity of 10 kt/a have to be installed at ten remote locations. The centralized plant requires an investment of 27.4 M€ including initial catalyst loading, while an F3 factory plant of the same size accounts for 27.3 M€ (Seifert et al., 2014). According to the findings by Seifert et al. (2014) on investment costs of the modular setup, installation of ten individual F3 factory production lines with a capacity of 10 kt/a each requires an overall investment of 32.3 M€ (3.23 M€ per individual F3 factory production line). The lead time of one F3 factory production line was calculated with 13 months compared to 22 months for the conventional, centralized plant. First, the NPV development throughout product life cycle is investigated for the market development expected. The economic comparison of both concepts in this scenario is displayed in Fig. 15 showing that the F3 factory concept would be preferable. The minimum plant utilization was set to 60% (Seifert et al., 2014) corresponding to a start of production at 60 kt/a for the conventional plant and 6 kt/a for each F3 factory production line. This results in an earlier market entry for the F3 factory production (month 21 vs. month 52). The number of operational production lines increases with time according to the customer demand. Sequential investments result in early revenues which partially

Fig. 13. Flowsheet of the medium scale i-butyraldehyde production process (Seifert et al., 2014).

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Fig. 14. Overview of locations of the centralized and decentralized production scenario.

Table 3 Key data for supply chain structure, market development and local conditions. Customer / order of market entry

1 2 3 4 5 6 7 8 9 10

Delivery distances (cenPersonnel tralized plant to customer) costs

Income tax level

Land [km]

Sea [km]

[103 €/Person] [% op. inc.]

332 229 1.828 2.330 1.341 229 229 229 229 399

0 0 0 0 0 1.095 6.094 20.086 18.996 20.086

70 70 70 28 28 70 70 8,75 8,75 8,75

35 25 30 20 20 28 30 25 25 25

compensate additional investment costs. The loan amount and pay-back time is smaller for the F3 factory production approach, so that the risk of investment is reduced compared to conventional production. This characteristic gains particular importance in case of unexpected market development. Therefore, in the next scenario we assume that the market does not develop as expected: customers eight, nine and ten will not order the product. In Fig. 16 this scenario is illustrated. Simplified real options approach leads to the decision to skip the investments for the last three plants and thus not to build all production plants that had originally been planned. Therefore, capital costs are reduced compared to the conventional centralized production, which additionally does not reach full utilization as it was designed for a too big capacity. An opposite scenario is shown in Fig. 17. Here an unexpected market growth is assumed; for customers one, two and three the originally expected demand is exceeded and demand development continuous up to 20 kt/a. In this case, the conventionally designed plant´s capacity reaches its limit and the extra market development cannot be exploited. In case of the F3 factory approach three additional production lines are installed. Additional investments at later points in time are justified by the additional revenues that can be gained by the extra sales. The study of these simple examples reveals that if market development and expansion of local production capacities go well together, distributed production can provide advantages despite higher fix costs and investment costs. This conclusion should be consolidated by consideration of

Fig. 15. Economic evaluation of a central conventional continuous and a decentralized F3 factory production.

Fig. 16. Economic evaluation of a central conventional continuous and a decentralized F3 factory production under the condition of unexpected market drop.

Fig. 17. Economic evaluation of a central conventional continuous and a decentralized F3 factory production under the condition of unexpected market growth.

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Fig. 18. Significance analysis of input parameters. Sorted by ΔNPV ¼ NPV modular distributed – NPV centralized.

even more scenarios in a systematic way. Hence, the significance analysis described in Section 3.3 is applied next to determine most influential parameters affecting economic comparison. 23 input parameters were varied (each on a 10-staged uniform interval) and the effect on NPV at the reference point (year ten) was determined. A selection of the resulting standardized mean effects is shown in Fig. 18. The significance analysis shows that the number of customers has the greatest effect on ΔNPV (difference of resulting NPV between both operating modes). The economic result is less dependent on the number of customers in case of the decentralized F3 factory production. Additionally, the influence of the minimum utilization required for start-up is lower for the F3 factory production. Both effects show, that the scaling ability of the F3 factory concept results in an improved robustness against uncertain market conditions, which would mean a lower risk of investment. Under the boundary conditions given, these effects are more significant than those resulting from the differences in production site conditions and the supply chain. For example, for the decentralized production changing personnel costs do have a stronger effect compared to conventional production or changes in sea and land transportation costs do affect the conventional production more than the decentralized production. However, in both cases the absolute difference of the impact on NPV is less than that resulting from changing market conditions described above. This result confirms that despite higher fix costs and investment costs the adaptability in the distributed production scenario considered would mean a lower risk of investment that would need to be taken into account for the decision about the operating mode.

5. Conclusion and outlook As an example for a modular production concept key aspects of the F3 factory concept have been identified and modeled. The model was implemented in a custom made software tool so that a broad range of boundary conditions and their impact on overall process economy can be investigated both for modular and conventional non-modular production. A new evaluation strategy for production concepts was implemented that takes product life cycle into account and allows for the incorporation of aspects like flexibility and future market

opportunities. The incorporation of these additional aspects is usually not part of process simulation and goes beyond state of the art approaches. Hereby economy and process conditions can be investigated in a combined way e.g. by performing comparative studies and sensitivity analyses. Carrying out those studies is enabled by the use of a single software implementation and would otherwise not be possible unless considerable time and effort would be spent on data collection, transformation and interface generation. The implemented methodology for evaluating modular processes was applied to study two examples. Here, simple processes, market and production supply chain structures were used with the objective to demonstrate the capability of systematically testing production concepts under different conditions. In both examples a lower risk of investment under uncertainty of the market conditions was identified for the F3 factory concept. For the selection of the production concept such a result represents an important information demonstrating a major benefit of applying the implemented methodology for evaluating modular processes. The procedure presented uses economic performance indicators as results. However, in the very early phase of process development many non-economic factors are crucial for decision making and existing data often is not certain. Only if non-economic factors are not determining feasibility a focus on economic performance is practical. Hence, before carrying out a detailed economic investigation using the implemented methodology it must be ensured that this is the case. In future works this could be enabled by expansion of the methodology with a set of heuristics that take non-economic factors into account for selecting the production scenario. Currently, the methodology is intended to be applied in early phases of process development and the focus of the implemented methodology lies on evaluation of production concepts developed during that phase rather than optimization of process and supply chain structures. However, in future works the field of application of the developed methodology could be expanded if the calculated results would be used for applying a suitable optimization approach.

Acknowledgment The research leading to these results has received funding from

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the European Community’s Seventh Framework Programme (FP7/ 2007-2013) under Grant agreement no 228867.

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