11.1 Fostering energy efficiency by way of a techno-economic framework

M. Putz 1, U. Götze 2, J. Stoldt 1, E. Franz 1 1 Fraunhofer Institute for Machine Tools and Forming Technology, Chemnitz, Germany

2 Chair of Management Accounting and Control, Chemnitz University of Technology, Chemnitz, Germany

Abstract

Aiming for a more benign approach to manufacturing, new technological and logistical approaches to energy sensitive production control have been developed. However, practical experience shows that these are or will usually not be implemented due to unclear or conflicting objectives from the technical and the economic side. A prominent example for this is buffers within production systems. While these should be avoided or at least minimised in order to decrease costs and investments, they may allow for the temporary transfer of production equipment into less energy consuming operation states. This paper reports on joint efforts to reduce interface issues by integrating technical and economic decision making into a consistent procedural framework. Exemplary for its potential application, a specific approach to energy sensitive production control, as well as fundamentals for both a technical and an economical evaluation thereof are presented. Keywords: Energy efficiency; Economics; Production control

1 INTRODUCTION

Energy and resource efficiency have already become a predominant aspect in the industry around the world and will further increase in importance. Production control will no longer only be concerned with output, time, quality, and direct costs but will be sensitive and responsive to the actual energy and resource supply as well as consumption in order to contribute to the competitiveness of companies better still. The recent predominance of energy control in production processes, e.g. in car production, is based on advanced technical and logistic approaches, and solutions in both single process steps and complex process chains. Aside of the technical options and point of view, production control is an important matter for economic targets and decision making. Systematically integrating both points of view (technical and economic) may avoid interface problems during implementation and optimisation, which still appear in practice, thereby providing significant advantages. A first task toward this goal could be to improve the interaction through the combination of technical and economic based planning, as well as the development of novel methods, scenarios, and well-proven approaches. Planning and controlling manufacturing sites is a complex and interdisciplinary task that various stakeholders take an interest in. A significant problem in this regard is that the goals and focus of the concerned decision makers from individual departments diverge. Accordingly, they utilise distinct key performance indicators (KPI) in order to assess the effectiveness of measures and the performance of the production system [1]. Linking these to actual system parameters, however, is difficult; interface-related losses sometimes complicate an optimisation. While various publications discuss methods and approaches to increase energy and resource efficiency [2] few pay attention to the interdisciplinary nature of their implementation. Herrmann et al. suggested an approach which aims to integrate the ecological and the economic process model [2]. However,

this work is strongly focussed on technical solutions and pays little attention to the fundamental economics involved in this field and the interaction of economists and technical adept personnel. With respect to levels of manufacturing activity defined by Duflou et al. (see [2]) it should be noted that the interests of concerned stakeholders vary. For instance, measures on the device level frequently do not concern logistic planners; optimisations on the line level will almost certainly concern them. This paper focuses on the line as well as facility level and describes how a novel approach to energy sensitive production control can be implemented and evaluated using a framework that integrates the technical and the economic view. A review of recent publications shows that several approaches for improving the resource efficiency in manufacturing by altering the production planning and control have been developed [4-6]. These are usually intended for a narrowly defined type of production system or situation. The approach presented in this paper aims to join the benefits of already established production control strategies with new concepts of equipment control, i.e. the ability to remotely shut down individual subsystems and machines, and thus provide a solution applicable in a variety of production systems. In order to establish a holistic basis for its implementation – including both the technical and the economic view – a framework based on the Plan-Do-Check-Act (PDCA) methodology is presented in section 3 after some context for its development has been provided in the following section. 2 CONTEXT AND SCOPE OF THE PRESENTED WORK

This particular work is the result of the on-going effort of the interdisciplinary research project "Energy-sensitive Planning and Control in Factory Operations" (eniPROD-LF2), which is part of the Cluster of Excellence „Energy-Efficient Product and Process Innovations in Production Engineering“ (eniPROD®). The objective of this project is to identify, analyse and, where appropriate, develop or advance

G. Seliger (Ed.), Proceedings of the 11th Global Conference on Sustainable Manufacturing - Innovative Solutions ISBN 978-3-7983-2609-5 © Universitätsverlag der TU Berlin 2013

336

M. Putz, U. Götze, J. Stoldt, E. Franz

strategies for energy sensitive production planning and control in a manner appropriate for the analysed object. In the process, energy efficiency shall be established as a target variable alongside the traditional cost and performance targets (inventory, throughput time, capacity utilisation, scheduling reliability) within the framework of factory operations. Aiming to provide solutions which further the co-operation of all stakeholders in companies, an interdisciplinary research team (figure 1) was composed of specialists in controlling (MAC, Chair of Management Accounting and Controlling), factory planning (FPL, Chair of Factory Planning and Factory Management), and mathematics (ADM, Chair of Algorithmic and Discrete Mathematics) from the Chemnitz University of Technology and researchers from the Fraunhofer Institute for Machine Tools and Forming Technology (IWU). These participants analyse the tasks, methods, and algorithms in production and logistics control to derive energy-relevant strategies. Experts of the “Virtual Reality Center Production

Engineering” (VRCP) aim to develop new energy-related AR-based visualisation and interaction techniques, in order to support operators charged with control tasks. Furthermore, psychologists study (PSY, Chair of Personality Psychology and Assessment) the human factor in control processes (e.g. motivational measures, knowledge distribution), specifically addressing the need for and the design of decision-making support in case of parameter uncertainty or conflicts of objectives.

Energy-efficient plantmanagement

logistics

production control

(ene

rgy-

rela

ted)

KP

I

mat

hem

. opt

imis

atoi

n

FPL

IWU

MA

C

AD

M

visualisation, interaction

actor‘s knowledge & willPSY

VRCP

- methods, algorithms - - implementation -

Figure 1: The eniPROD-LF2 approach. The hereafter presented approach picks up these ideas of eniPROD-LF2 and joins them into a single framework. This interdisciplinary approach will help to significantly improve the identification and exploitation of efficiency potentials. 3 IDENTIFYING AND EXPLOITING EFFICIENCY

POTENTIALS

It has already been mentioned that different departments of a company use distinct KPI. Those with a technical focus usually work with non-monetary figures that can either be measured directly or derived from the afore-mentioned, e.g. the reject rate. On the other hand, economic experts prefer KPI that are closely related to the evaluation of the economic success of a company and the customer-related processes, such as costs per unit. Aiming to exploit efficiency potentials – thus affecting the various KPI – parameters that can be varied in order to influence the production system’s behaviour need to be

identified. Their character, purpose and borders are usually defined by production planners. Connections with individual KPI, however, need to be determined in order to support multi-criteria decision making. For this purpose, the Plan-Do-Check-Act (PDCA) methodology has been adapted as

depicted in figure 2. Its centre features the differing technical/logistic and economic view, as well as the parameters and their connection to the individual views. This concept of multiple views is both the reason and the basis for the hereafter described framework.

Tech

nica

l vie

w

Eco

nom

ic v

iew

Par

amet

ers

P D

CA

Figure 2: PDCA cycle adapted to identify and exploit efficiency potential.

The PDCA cycle – also known as Deming cycle – is a management method which was predominantly developed for identifying, developing and implementing quality measures [7]. Starting from an identified problem, measures are derived and implemented in a process of continuous improvement within four steps: “Plan”, “Do”, “Check”, and “Act”. The main

advantage of the methodology is that it makes use of interdisciplinary teams (here: engineers and economists) for the holistic examination of problems and the identification of viable solutions. In order to adapt this methodology for the identification and exploitation of efficiency potentials in manufacturing, the content of the individual steps of the PDCA cycle has to be altered. Figure 3 depicts an overview of the new process; a comprehensive description is presented thereafter.

Identification of system parameters

Identification of target figures

Determination of target figure to be improved

Association of target figures and parameters

Derivation of relevant parameters

Formulation of appropriate measures

Implementation and test of measures

Assessment of effect on target figures

Ineffective? Effective?

Formalise lessons learned

Standardise and roll-out measures

Pla

nD

oC

heck

Act

Figure 3: Process steps of the new PDCA cycle. The step “Plan” aims for the identification as well as the

determination of target figures from a technical/logistic and economic view. Each identified target figure has to be associated with related parameters of the production system. Based on the intended optimisation for a certain target figure all relevant parameters have to be identified. Their qualitative

337

Fostering energy efficiency by way of a techno-economic framework

impact on the systems performance can be estimated taking the team’s interdisciplinary know-how into consideration. Accordingly, specific measures, i.e. changes to the parameters, have to be devised. Testing the effect of the devised measures deduced from the identified connections between parameters and target figures is the aim of the step “Do”. Different scenarios should be

examined in experiments, which are planned according to established design of experiments (DoE) methods. The actual testing process can be conducted in either a real or a virtual (i.e. simulation model [e.g. 8]) manufacturing system. Especially, the latter can minimise the necessary effort as identified measures can be implemented and analysed in a discrete-event simulation system (if the model is accurate!), not influencing on-going production. Results from the previous step “Do” will be assessed in the

step “Check” in order to determine how effective the devised

measures are. For this purpose, aspects from both the technical/logistic and the economic view need to be considered conjointly. In case of a negative decision being reached, i.e. the measure has to be deemed ineffective, the findings of the step “Check” can be used to devise new

measures and should therefore be preserved. The step “Act” is concerned with acting upon the result of the

previous step. Similar to the original PDCA methodology, measures that have been proven effective should first be standardised and then rolled out to the inspected production system and – if possible – to similar production systems. This approach further increases the efficiency of the described framework because lessons learned can be transferred or referenced in another iteration of the PDCA cycle with little effort. On the other hand, if measures have not been effective a new iteration has to commence in order to devise measures that eventually satisfy the optimisation aims and exploit efficiency potentials. These explanations show that the proposed framework is meant to provide a systematic approach to the interdisciplinary work of various departments. How experts from either the technical/logistic or the economic side go about optimising the system in question is highly dependent on the actual task, i.e. increasing the energy efficiency of a system will typically require different tasks than increasing its degree of automation. Accordingly, the following two sections discuss – based on a case study – methods and approaches which can be used within this framework in order to increase the energy efficiency of a manufacturing site. More specifically, the fourth section focuses on the technical/logistic view, introducing a novel approach to energy sensitive production control and discussing how it can be examined by means of simulation technology. Section 5 elaborates an approach to the economic assessment of the simulation results. 4 CONFIGURING ENERGY SENSITIVE PRODUCTION

CONTROL STRATEGIES

An important step in the implementation and optimisation of novel as well as established production control strategies is the (re-)configuration of parameters in order to reach a predefined system performance. Depending on the system’s

complexity, this is a difficult task which can comprise a multitude of different methods, such as heuristics, simulation, or trial and error. Each of these has certain limits and

benefits, although especially simulation techniques gained noticeable acceptance in various industrial sectors. In this regard, the instrument of choice for discrete production processes is the discrete-event simulation [9]. Hereafter, a novel approach to energy sensitive production control and the configuration thereof are elaborated. The object of study is a section of an exemplary car body production facility where attachment parts (i.e. doors, bonnet, etc.) are assembled and mounted to the otherwise complete frame. It consists of four work stations (WS) where – in order of the material flow – the front doors (FD) and rear doors (RD) for car body variants with five doors, the bonnet and the tailgate, the front doors for variants with three doors (left and right assembled separately), and the wings (left and right produced separately) are mounted. Each work station is supplied by two subsystems (see figure 4) which contain between one and five work groups and are connected via conveyors as well as buffers. The work groups are complex and highly automated robot cells. The four work stations are followed by a so called “light tunnel” where all car bodies are

inspected for imperfections.

WS 1

FD5

RD5

WS 2

Bon-net

Tail-gate

WS 3

FD3left

FD3right

WS 4

Wingsleft

Wingsright

Light tunnel

Figure 4: Structure of the exemplary production facility. This production system has been modelled using Siemens Tecnomatix Plant Simulation. Since this software only allows for the study of material flows, a generic energy-enhancement module – eniBRIC – has been developed to provide additional capabilities [10,11]. Each regarded entity has to be complemented with an instance of eniBRIC which then needs to be parameterised. Energy-relevant parameters include available operation states, required and provided media as well as media consumption in each of the states, the time required for state transitions, and flags influencing the behaviour of individual enhancement module instances. Switching between operation states is possible through standardised interfaces but has to be triggered actively by an additional controlling instance. Furthermore, a configuration and a data collection and evaluation module have been developed but these only have to be instantiated once. Media-supplier-consumer-relationships have to be defined in a central table which is part of the configuration module, along with global setting templates for the flags. Instances of eniBRIC communicate their media demand amongst one another and to the data collection and evaluation module. The latter gathers and aggregates data during simulation time and can visualise or export it according to the users’

requirements [11]. Using the VDA Automotive Toolkit, each work group, work station, conveyor, and buffer has been modelled as a single simulation object. All of these, except for the buffers, have been enhanced with eniBRIC. Regarded media include power (400 V and 690 V), laser light, pressured air (6 bar and 12 bar), lighting, smoulder suction, ventilation, cold water, and cooling water; suppliers for these media have been instantiated and configured. Additionally, methods allowing

338

M. Putz, U. Götze, J. Stoldt, E. Franz

for an energy sensitive equipment control have been implemented. In particular, it is possible to either pause the entire system, or shutdown and start-up individual subsystems [11]. This model has been used to implement and examine a novel approach to energy sensitive production control which is based on the established Kanban principle. This new eniKanban approach enhances the fundamental Kanban procedure with additional rules for powering equipment on or off whenever the buffer after a controlled subsystem reaches a certain thresholds [12]. The basic idea is to shut down and start up equipment as soon as the amount of parts available in decoupling buffers is either sufficient or getting too low. Naturally, this can only work if the controlled subsystem works faster than the subsequent one so that the buffer gradually fills. Suggesting that the cycle time of subsystems is constant, the parameters for this approach are the buffer capacity and the safety stock. The difference between these two (“shutdown difference”) also determines the least time

the controlled subsystem may cease to produce. In order to study their effect on technical/logistic and economic KPI – and thus provide indicators for the configuration –, a number of simulation experiments (each with a duration of 180 days and 50 unique observations) has been made. A preliminary investigation comprised of six experiments and focussed on the effect of the safety stock which determines when equipment should be started up again. For this purpose, the buffer capacity was set to 40 pieces and the safety stock was varied from 15 to 35 in steps of 5. An additional experiment was conducted in order to compare eniKanban with a classic Kanban production control strategy. The general findings in these experiments were that the amount of energy required per car body can be reduced considerably (up to 15% in this study) using eniKanban and that an increased safety stock tends to improve the systems overall output due to fewer supply shortages. Further 24 experiments aimed to examine how the KPIs are affected by a variation of the buffer capacity. For this purpose, four values for the safety stock were chosen based on the previous observations and the buffer capacity was then varied in six steps so that the “shutdown difference”

ranged from 5 to 30. The results made clear that it is possible to achieve an output with eniKanban that compares to the output with regular Kanban, however, this comes at the price of increasing the average stock. Interestingly enough, the amount of energy required per car body remains almost unchanged despite the parameter variation when eniKanban is used. Figure 5 depicts the average output of car bodies for the four examined safety stock (SS) sizes.

149.800

150.000

150.200

150.400

150.600

150.800

151.000

+5 +10 +15 +20 +25 +30

Car

bo

die

s [p

cs.]

Shutdown difference [pcs.]SS 20 SS 25 SS 30 SS 35

Figure 5: Average output dependent on the “shutdown

difference”.

The results from the conducted experiments show that energy sensitive production control can potentially be viable. However, the exact configuration needs to be decided upon considering all identified consequences (e.g. increased average stock, decreased energy consumption, etc.). Simulation experiments can be utilised to provide assistance and input data for the actual decision making process. The economic consequences caused by different configurations can be determined utilising the method introduced in the following section. Hence, the identification of an optimal configuration combining findings from the technical/logistic and the economic view can be supported. 5 ECONOMIC EVALUATION OF ENERGY SENSITIVE

PRODUCTION STRATEGIES

The results of the simulation utilising the eniKanban method show that trade-offs concerning the achievable benefits in comparison with a “traditional” Kanban control exist. Compared to a “traditional” Kanban concept, the eniKanban

control allows for a reduction of the energy consumption but it also implicates a certain decline of the output as well as raised stocks of inventory, depending on the exact configuration. Similarly, the choice of other alternative system parameters – here the size of the safety stocks and the maximum buffer capacities – will usually raise conflicts between these (and possibly other) company objectives. To be able to evaluate the alternative actions and thereby systematically prepare a decision, an economic analysis lends itself to offer a deeper understanding of the respective economic consequences. In the scope of this analysis one solution is to formulate a multi-criteria problem which can be solved by determining, as well as weighting the relevant targets, assessing their fulfilment, and deriving a total utility score from the given data. Another approach is the formulation of a model indicating the (approximate) impact of alternative scenarios on the profit. Since profit is the central economic objective of companies, this paper discusses the latter solution. One potential approach for a profit-oriented evaluation of alternatives will be demonstrated using the example of the comparison between an eniKanban and a “traditional” Kanban control system. This method can also be

used to compare individual parameter configurations. In general, the profit (P) can be formulated as difference between revenues (R) and costs (C):

CRP (1)

For the evaluation of the alternatives – “traditional” Kanban,

and eniKanban –, the analysis can be limited to the variation of profit (∆P) (and thereby also its components ∆R, and ∆C):

CRP (2)

In case of the production system discussed in section 4, primarily the variation of the output (∆output), of the stock of inventory (∆inv), as well as of the energy consumption (∆EC) determines the profitability of the alternatives. Their monetary consequences – the variation of contribution margin caused by alternate output quantities (∆CM∆output), the variation of inventory costs (∆Cinv), and the variation of energy costs (∆Ce) – can be assessed using the following formula:

einvoutput CCCMP (3)

Further differentiating between the quantitative and monetary components of each item as well as types of inventory and

339

Fostering energy efficiency by way of a techno-economic framework

energy carriers (with cm for contribution margin per unit, cinv for inventory costs per average inventory unit, and ce for energy cost per consumed energy or media unit, j as index of inventories (j = 1…,J), i as index of energy carrier units (i =

1,…,I)) leads to:

J

j

I

i

ieijinvj cECcinvcmoutputP ,, (4)

The quantitative components of these values can be derived from the simulation or measurements in the real production system. In contrast, the contribution margin and the cost rates have to be investigated by an economic analysis [e.g. 13]. The contribution margin per output unit expresses the monetary effects of any variation of the output (per unit), e.g. as a consequence of using an eniKanban control instead of a “traditional” Kanban control. It follows: vcpcm (5)

Accordingly, the price (p) and the variable costs per unit (vc) have to be estimated for the basic alternative; thus, the effects on inventory and energy costs do not have to be taken into account here. In many cases price expectations will be available within companies. In this regard, the achievable price itself may depend on the output quantity. However, due to the slight extent of the output variation in the case study the effect can be considered insignificant. For the calculation of the variable costs per unit, methods of direct costing can be applied. Often, the corresponding values can be provided by the company’s cost accounting system. The average inventory costs result from the relevant costs of the physical stock-keeping (cphys stock) as well as the cost of capital tie-up (ccapital), each of which is determined per inventory unit:

capitalstockphysinv ccc (6)

The computation of the relevant costs, i.e. the costs that vary depending on the stock of inventory, requires an analysis of the inventory system and the processes performed therein. In order to determine ccapital the interest rate and the capital tie-up per inventory unit need to be identified. The latter is often assumed to correspond to the purchasing or manufacturing costs of an inventory unit, which may be calculated by means of a company’s cost accounting system. The costs per consumed unit of an energy carrier are highly dependent on the supply channel (make-or-buy, etc.). In many cases, the acquisition price of the purchased energy sources (pext) is complemented by costs for the internal supply (cint), so that:

intcpc exte (7)

The prices for externally procured energy carriers typically consist of fixed and variable price components, which are influenced by various factors, such as type of billing, duration of contract, or load balancing. Cost rates of internal energy supply can be investigated by means of a cost accounting system with energy-related specifications (“energy cost

accounting” [14]). Figure 6 provides an overview of the afore-described profit model, explains relationships between individual variables, and gives examples for influencing factors for these. It has to

be noted, that the latter are incomplete and will have to be considered specifically for any regarded company. Summarising, it can be noted, that it is possible to determine the impacts of the alternatives on the objective “profit” and,

thereby, to identify profitable alternatives under consideration of the mentioned trade-offs. Amongst others, it can be analysed whether an eniKanban control is more economic than a “traditional” Kanban control in a specific production system. The avoidance of buffers propagated in many companies needs to be relativised, if aspects of energy efficiency are taken into account. Admittedly, the concrete calculation of profit variations will raise methodological questions of detail and some simplifying assumptions concerning the cost components have to be made (e.g. of relevant costs including the isolation of costs, or linearity of costs depending on cost drivers). The explanatory power of results will depend on the validity of these assumptions. However, the analysis can be deepened and/or enlarged by including further cost items (e.g. the costs caused by a variation of the maximum buffer capacity), influencing factors and parameters (e.g. cycle times of process steps) or conducting sensitivity analyses in order to estimate the consequences of imperfect data. Nevertheless, the economic analysis can be applied in the Plan, Do and Check phases of a PDCA cycle contributing to a multi-perspective appraisal of policies and actions, promising various benefits. 6 SUMMARY AND OUTLOOK

A method for an integrated analysis, evaluation, and implementation of energy sensitive production control strategies was proposed, consisting of three elements: a PDCA cycle as a framework for systematically identifying and exploiting efficiency potentials, instruments for evaluating control strategies (here, a simulation model was used), and an economic assessment method which directly refers to the results of the technical evaluation. The presentation of the method against the background of the eniKanban control strategy and its configuration revealed the (energy saving) potentials as well as possible conflicts between different company objectives, implying the need for an economic evaluation. The integrated method promises further benefits: It is expected to promote the discussion and operationalisation of objectives and their relevance, to assist the identification of need for action and the reconsideration of system boundaries and viable options. Additionally, the necessity of adjustments caused by a change of economically relevant factors within the PDCA cycle can be identified, and the company-wide acceptance of proposed measures strengthened, because the profitability of policies and actions have been proven in an economic analysis. Thus, the integration of a technical/logistic and economic analysis and control is strongly recommended. However, the method proposed is still in an early phase of its life cycle. As one step, it is planned to enhance the simulation model by a corresponding module for economic analysis to investigate the variation of profit depending on the monetary components.

340

M. Putz, U. Götze, J. Stoldt, E. Franz

7 ACKNOWLEDGMENTS

The presented work summarises outcomes of the research projects InnoCaT® and eniPROD®. The pre-competetive joint research project "Innovation Alliance Green Car Body Technologies" is funded by the "Bundesministerium für Bildung und Forschung (BMBF)" (funding mark 02PO2700 ff) and supervised by "Projektträger Karlsruhe (PTKA)". The authors are responsible for the content of the publication. The Cluster of Excellence "Energy-Efficient Product and Process Innovation in Production Engineering" (eniPROD®) is funded by the European Union (European Regional Development Fund) and the Free State of Saxony. 8 REFERENCES

[1] Schuh, G., Arnoscht, J., Bohl, A., Nussbaum, C., 2012, Integrative assessment and configuration of production systems, Annals of the CIRP, 60/1, pp. 457-460.

[2] Duflou, J. R., Sutherland, J. W., Dornfeld, D., Herrmann, C., Jeswiet, J., Kara, S., Hauschild, M., Kellens, K., 2012, Towards energy and resource efficient manufacturing: A processes and systems approach, Annals of the CIRP, 61/2, pp. 587-609.

[3] Herrmann, C., Bergmann, L., Thiede, S., Zein, A., 2007, Framework for Integrated Analysis of Production Systems, Proc. of the 14th CIRP Conference on Life Cycle Engineering, Tokyo, pp. 195-200.

[4] Bruzzone, A. A. G., Anghinolfi, D., Paolucci, M., Tonelli, F., 2012, Energy-aware scheduling for improving manufacturing process sustainability: A mathematical model for flexible flow shops, Annals of the CIRP, 61/1, pp. 459-462.

[5] Weinert, N., Chiotellis, S., Seliger, G., 2011, Methodology for planning and operating energy-efficient production systems, Annals of the CIRP, 60/1, pp. 41-44.

[6] Neugebauer, R., Putz, M., Schlegel, A., Langer, T., Franz, E., Lorenz, S., 2012, Energy-sensitive production control in mixed model manufacturing processes, Proc. of the 19th CIRP Conference on Life Cycle Engineering, Berkley, pp. 399-404.

[7] ISO 9001:2008, Quality management systems – Requirements.

[8] Herrmann, C., Thiede, S., Kara, S., Hesselbach, J., 2011, Energy oriented simulation of manufacturing systems – Concept and application, Annals of the CIRP, 60/1, pp. 45-48.

[9] Rose, O., März, L., 2011, Simulation, Simulation und Optimierung in Produktion und Logistik, Springer Verlag, pp. 13-20.

[10] Putz, M., Schlegel, A., Stoldt, J., Schwerma, C., Langer, T., 2013, Energy sensitive digital planning and simulation, Proc. of the International Conference on Competitive Manufacturing, Stellenbosch, pp. 365-370.

[11] Stoldt, J., Schlegel, A., Franz, E., Langer, T., Putz, M., 2013, Generic energy-enhancement module for consumption analysis of manufacturing processes in discrete event simulation, Proc. of the 20th CIRP Conference on Life Cycle Engineering, Singapore, pp. 165-170.

[12] Putz, M., Schlegel, A., Stoldt, J., Franz, E., Langer, T., Tisztl, M., 2012, A framework for energy-sensitive production control in MES, Proc. of the 14th International Conference on Modern Information Technology in the Innovation Processes of the Industrial Enterprises, Budapest, pp. 354-366.

[13] Hongren, C. T., Datar, S. M., Rajan, M., 2011, Cost Accounting, Prentice Hall.

[14] Bierer, A., Götze, U., 2012, Energy Cost Accounting: Conventional and Flow-oriented Approaches, Journal of Competitiveness, 4/2, pp. 128-144.

profit

revenues

price variable costsoutput

= contribution margin

inventory cost energy cost (other) fix cost

cost

inventory inventory cost per unit

cost of physical

stock-keeping

cost of capital tie-up

consumption of energy carriers

energy cost per unit

cost of purchased

energy source

cost for internal

energy supply

• − + + +

• •

+ +

output variation

contribution margin•

−

− −

− −cmoutput

demand competitive-

ness capacity

productivity automation

summed up over all types ofinventories

summed up over all types ofenergy carriers

simplifiedprofit

model

profiteffects

influencingfactors

variation of energy cons.

energy cost per unit•

I

i

exti cpEC )( int

energy demand

production equipment

energy supply system

energy price

variation of inventory

inventory cost per unit•

J

j

capitalstockphysj ccinv )(

production-policy

demand structure

inventory system

capital structure

Figure 6: Overview of the described profit model.

341