Toolbox for Increasing Resource Efficiency in the European Metal Mechanic Sector

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Peer-review under responsibility of the scientific committee of the 24th CIRP Conference on Life Cycle Engineering doi: 10.1016/j.procir.2016.11.247

Procedia CIRP 61 ( 2017 ) 40 – 45

ScienceDirect

The 24th CIRP Conference on Life Cycle Engineering

Toolbox for Increasing Resource Efficiency in the European Metal

Mechanic Sector

Stefan

Blume

a,

_{*, Denis Kurle}

a

_{, Christoph Herrmann}

a

_{, Sebastian Thiede}

a

a_{Institute of Machine Tools and Production Technology, Technische Universität Braunschweig, Langer Kamp 19 b, 38106 Braunschweig, Germany} * Corresponding author. Tel.: +49-531-391-7168; fax: +49-531-391-5842. E-mail address: stefan.blume@tu-braunschweig.de

Abstract

Strategies to improve the economic and environmental performance of companies are usually pursued from a local perspective, hardly considering interactions between different value chain actors. Thus global improvements are not necessarily reached. Against this background, the authors present an approach for an integrated improvement strategy covering both perspectives to reveal hidden resource saving potentials. Moreover, a “decision-making toolbox” has been developed, allowing for an identification of company-internal and cross-company improvements as well as resulting trade-offs. Applicability and benefits of the approach are underlined by a use case application from the metal mechanic industry. © 2017 The Authors. Published by Elsevier B.V.

Peer-review under responsibility of the scientific committee of the 24th CIRP Conference on Life Cycle Engineering. Keywords: Resource Efficiency, Decision-Making Toolbox, Value Chain Analysis, Metal Mechanic Industry

1. Introduction

Energy and resources are an indispensable basis for manufacturing. Considering the predicted global increase in manufacturing over the next twenty years a further increase in material resources and its associated embodied energy by 40% is expected, provided that no considerable policy changes are accomplished [1,2]. Thus, the environmental significance of reducing the resource demand per company is an important step. In addition, economical aspects e.g. resulting from rising gas, coal and oil prices entail the cost pressure on manufacturers [3]. To cope with the challenging situation, manufacturers are inclined to analyze their factory more attentively to mitigate risks related to energy and resource demands, its fluctuating prices and to comply with more and more strict legislative carbon emission constraints [3,4]. Adequate analyses in that regard require a decomposition of a factory into various levels ranging from process/machine to process chain towards the complete factory level on the one hand and a value chain level comprising multiple factories on the other hand.

To make the subject of energy and resource demands more tangible several approaches have been established to focus either on different time scales, factory levels, resources and its

associated environmental impacts. Some of these acknowledged methods conduct Material and Energy Flow Analyses (MEFA) often also enabling Life Cycle Assessments (LCA) to provide information associated to environmental impacts of products, processes or combined system levels as well as accounting aspects. Another established method rooted in the lean manufacturing domain, Value Stream Mapping (VSM), systematically analyzes process chains to reveal time, stock and quality related inefficiencies [5]. Extended versions also incorporate further aspects such as energy demands of processes and supporting services (Energy VSM/EVSM) [6]. To capture the dynamics of the interactions of levels and resources simulation has proven to be a promising method [7].

However, all of these approaches represent standalone methods usually executed in an isolated manner for a specific purpose. Thus, each method uses different data and varying Key Performance Indicators (KPIs) which hampers the comparability of the respective results. This situation is further exacerbated when entire value chains with multiple factories and their individual behavior are considered because decisions at one factory level may have repercussions on up- and downstream factories. Figure 1 illustrates these two perspectives for a single factory (P1) and an entire value chain (P2) as well as potentially resulting trade-offs between

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different value chain entities if their activities are not streamlined. Typical questions decision makers have to take into account comprise diverse departments:

x Does it make sense to produce smaller batches and reduce the existing inventory? (production planning and control) x Would an alternative raw material improve our product

quality? (quality control and management)

x Is a redesign of our product favorable with respect to cost savings? (design and development)

x Should we use renewable energies to reduce our environmental impacts? (procurement related issues) x Is the factory layout suitable for increasing the production

capacity? (factory planning)

x Do process changes alter the products properties? How does this affect our customers? (manufacturing planning)

Fig. 1. Impacts of isolated value chain perspective

Against this background, the paper proposes a toolbox to enhance resource and energy efficiency for single factories and entire value chains alike. It builds upon a common database resulting in comparable KPIs for all employed methods incorporating EVSM, simulation as well as MEFA and LCA into one seamless environment. The applicability of the toolbox is exemplified by a case from the metal mechanic sector. 2. Background

2.1. EVSM

Value Stream Mapping provides a static method to represent the product’s value stream and all its related processes emphasizing value and non-value adding activities. This method has been extended to assess the impact of different product characteristics on the respective manufacturing processes [8] as well as energy associated aspects either on an average or machine state basis [6] to include the machine’s dynamic behavior. Besides the direct energy of the processes further approaches also incorporate indirect energy consumers which account for a significant share of the overall energy demand [9] by allocating indirect energy demands within the value stream [10]. However, these approaches are only valid for single but not multiple products potentially blocking each other’s resources and thus leading to longer lead times and

higher energy demands. To overcome the shortcomings of the static EVSM character, a combination of EVSM and simulation can be applied [11,12], which helps analyzing the effects of multi-product situations in terms of energy and time subject to varying production planning and control information.

2.2. Manufacturing system simulation

Simulation in general is a widely applied method to analyze a real world system behavior over time which has been used in many ways in the context of manufacturing [13,14]. Some authors focus on simulating the energy demand on process/machine and component level [15,16], whereas others move the energy orientation [7,17,18] or job scheduling of process chains into the spotlight [19]. Another research stream considers the coupling of different simulation models developed in diverse software programs and for varying system levels [20,21]. In that regard not only simulation models but also a multi-level simulation framework including favorable coupling recommendations are proposed [22]. In industrial application, the linkage of manufacturing system simulation with corporate resource planning systems as demonstrated by Li et al. [23] is of particular high relevance in order to continuously optimize the manufacturing system performance.

2.3. MEFA and LCA

MEFA is a method to systematically assess the flows and stocks of materials and energy within a system as well as the flows exchanged with the environment [24] such as raw or auxiliary materials, (pre-) products, waste or emissions. Basing on input-output-models of the system components, various approaches have been developed allowing for both economic and environmental system evaluations [25]. However, MEFA is a static approach, basing on average or cumulated values whereas the considered material and energy flows in a real world system like for instance a manufacturing system are usually highly dynamic and interdependent [26]. Additionally, the MEFA approach is usually not able to provide a complete environmental evaluation considering all impacts caused due to its limited system boundaries, not taking into account any previous or subsequent actions (“gate-to-gate” approach) [24]. In contrast, the methodology of LCA expands the analysis to a life cycle perspective, taking into account all life cycle stages from raw material extraction, manufacturing, utilization until end-of-life (“cradle-to-grave” approach). Hence, all environmental burdens connected with a product or service can be assessed and problem shifting between different life cycle phases as well as between different environmental impact categories can be made transparent and thus avoided [27,28]. Typically, specific software tools and Life Cycle Inventory (LCI) databases are used to estimate the impacts caused by previous or subsequent actions, as primary data can usually not be collected for the whole life cycle of all relevant system flows. Due to the complexity of an LCA analysis and existing methodological freedom, LCA results are often hardly to compare and may also lead to contrary results between

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different studies [29]. The method of LCA is standardized through ISO 14040 and 14044 [30,31], while diverse methodological extensions have been made to include also the economic and social dimensions of sustainability [32,33].

2.4. Overview and Research Gap

Figure 2 summarizes qualitatively the aforementioned main methods regarding different relevant criteria in the context of single factory modeling and evaluation. These criteria have been chosen to take holistic, timely, quality and user-focused requirements towards an integrated approach into consideration. Li et al. [23] as well as Thiede et al. [35] presented an integrated approach combining MEFA, LCA, VSM and simulation as a result of the joint project “Sustainability Cockpit”, which was funded by the Australian Research Council (ARC). However, this approach omits to take the value chain perspective into account. Regarding the modeling and evaluation of value chains Heinemann et al. present a MEFA model chiefly focusing on an existing aluminum die casting value chain [33]. Yet, this approach is an inductively derived approach lacking a simple applicability to other value chains or industries.

Thus, no approach has been found which combines the benefits of the aforementioned methods into one coherent environment that is capable of evaluating a single factory as well as value chains in a flexible, parametrizable manner. Only this could alleviate challenges regarding problem shifts between companies and/or between departments inside one factory.

Fig. 2. Classification of described methods (adapted from [35]) 3. Concept

Motivated by the shortcomings of existent approaches, the objective to develop a tool for holistic evaluations of single factories and value chains basing on a common data basis has been derived, combining the complementing methods presented beforehand. In contrast to the tool developed by Thiede et al. and Li et al. [23,35], who rather focused on continuous application within companies using live data from resource planning systems, the presented approach aims at

one-time assessments of single factories and whole value chains in particular, usually carried out by external consultants. In a first step, general requirements have been derived as basis for development: A multi-criteria analysis shall make trade-offs between different KPIs visible, considering technical (lead times, throughput etc.), economic (material, energy, labor costs etc.) and environmental aspects (global warming, harm to human health etc.) over the product life cycle. By following a

multi-level modelling - ranging from process up to value chain

level - an impact assessment of local decision-making on other affected areas shall be enabled. Prediction functionalities refer to the ability to assess both current and possible future states of the regarded systems. Decision support functions shall guide the user through the tool application and propose suitable solutions. Building upon these requirements, a concept for the toolbox has been derived (see Figure 3), inspired by the system design of the existing “Sustainability Cockpit” [23,35]: x A Data Layer, collecting, consolidating and preparing data

needed for using the aspired methods.

x A Logic Layer, applying the presented methods to convert the input data into the desired outputs, reached by building up virtual models of the production systems, which can be coupled to constitute a value chain.

x A User Interface, presenting the results of the analyses on both local level (Single Factory Module) and global level (Value Chain Module) in a comprehensible manner.

Fig. 3. Toolbox concept

From the user perspective, three different application paths can be distinguished: An EVSM, a simulation and a MEFA & LCA path providing different results depending on their type of calculations (static or dynamic), evaluation perspective (economic, environmental, technical) and focus (production stage or whole life cycle). Based on the user’s objective, either one path can be followed independently or several paths can be used in a complementary way, whereby the results are then combined. In a first step, analysis results are displayed for each

criterion ME FA LCA _EVSM Si mu la ti o n

dynamic system behavior economic performance environmental performance technical performance changes in product/material flow simplicity (time & knowledge) life cycle phases decision support degree of application

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factory in the respective Single Factory Module. In the case that an evaluation from the value chain perspective is required, the user can automatically aggregate and transfer the results to the

Value Chain Module. Decision Support functions are

implemented into both modules to support the user in terms of result interpretation and selection of suitable improvements by providing suitable information from a Knowledge Database. The Single Factory Module with its three application paths as well as the Value Chain Module and the Decision Support functions are further described in the following.

3.1. Single Factory Module: EVSM

The approach within this path is based on the EVSM methodology, which has been extended by several additional KPIs. Each process is represented by a separate box, containing specific KPIs regarding capacity, flexibility, quality, resource consumption and costs. Furthermore, total values are visualized under the process boxes, allowing to quickly estimate the processes relevance. This approach provides relevant key figures at a glance and helps to reach a better understanding of the system and its interdependencies, allowing for a first identification of hot spots and possible fields of action.

3.2. Single Factory Module: Simulation

The simulation path allows for analyzing dynamic aspects that are either related to the dynamic behavior of machines regarding states and media demands or interactions between the respective system elements (products, jobs, machines). These system elements have been realized following a discrete-event simulation (DES) and agent-based (AB) approach also using dynamic systems (DS). The DES ensures to include all relevant changes between machine states e.g. ramp up, idle, processing while the DS calculations continuously imitate conditions over time to compute e.g. the energy demand of machines. The AB approach allows for an individual machine placing and product flow definition also incorporating aspects such as diverging and converging product flows, batch and single process types. As a result, this path provides information regarding time and energy related planning and scheduling aspects subject to the dynamic interactions between the involved system elements. To assess the performance of the system key figures such as value and non-value adding times, energy demands per product and job, system or machine load profiles and failure/maintenance statistics are employed.

3.3. Single Factory Module: MEFA & LCA

The MEFA and LCA modeling is carried out in the software

Umberto, which provides an automated calculation of

connected energy and material flows along a modelled system of transformation processes. By varying input/output balances of system elements, scenario oriented experiments are possible to calculate and compare the resulting energy and material flows [26] as well as related environmental impacts such as global warming potential, eutrophication or resource depletion.

Petri net based models are used to describe the factories. To allow for an easy build-up and adaption of models, pre-defined standard modules for various kinds of processes and Technical Building Services (TBS) have been developed, which can be inserted into a model per drag & drop.

3.4. Value Chain Module

One critical issue regarding the global improvement of a value chain’s performance is the asymmetric information distribution between the partners, which can be explained using the principal agent theory. Pursuant to that the Value Chain

Module extends the scope of analysis from an intra-company to

an inter-company perspective in order to facilitate an improved information exchange and a higher degree of transparency between value chain partners. Accordingly, this module receives aggregated data from all Single Factory Modules which are part of the value chain, calculating global KPIs and showing the respective shares of the actors regarding production costs, environmental impacts or lead time. By assessing the consequences of local decision making or stochastic events from the global perspective, benefits and drawbacks of these decisions can be allocated to all involved partners and trade-offs become visible.

3.5. Decision Support

To improve usability and acceptance of the toolbox, the user is supported by the system through the following decision-making functionalities:

x A Knowledge Database with rule-based and case-based knowledge such as improvement approaches as well as average values for different factory elements to overcome gaps in the data basis and allow for plausibility checks. x A configurable KPI Monitor, comprehensibly visualizing

KPIs and sustainability indicators to enable a multi criteria assessment of the company or value chain.

x A Regulatory Module, providing information about regulatory constraints for the considered processes, e.g. by describing the general framework of the regulation and by indicating legal thresholds for emissions into soil, water and air. The database covers France, Germany and Spain and is designed to be regularly updated.

x A Product Quality Check, revealing interdependencies between different processes concerning product related aspects, e.g. the influence of a changed raw material quality. x A Scenario Analysis, allowing for an easy comparison of alternative options and evaluation of suitable business models on value chain level.

4. Application

In the following the application of the toolbox is demonstrated by means of a case study from the European metal mechanic industry. The general setup of the value chain, comprising three factories, is depicted in Table 1.

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Table 1. Setup of the assessed value chain

Factory number: #1 #2 #3

Main processes: - Grinding

- Hardening - Hot Rolling - Leveling - Peeling - Activating - Chr. Plating - Degreasing - Etching - Grinding - Cutting - Polishing

The case product is a medium sized hard chrome plated piston rod made from steel billets (42CrMo4), which is widely used in hydraulic components such as caterpillars or cranes. Starting with the steel billets, supplied by a local company, factory #1 mainly performs reshaping and separating processes as well as thermal treatment to increase the mechanical properties. Factory #2 focuses on surface treatment processes by applying a chromium layer, whereby the surface roughness is the most important quality requirement. Factory #3 then performs the final preparations before distribution of the piston rod to the customers. Extensive primary data has been collected in the factories to assess the current factory and value chain performance. Figure 4 (a) shows an excerpt from the EVSM visualization of factory #1, giving a first overview about the process chain. Figure 4 (b) depicts the cost distribution basing on the MEFA analysis, taking into account material, energy and labor costs. Costs for raw steel are excluded here due to readability, but it can be stated that they account for ~75 percent of the total production costs and are therefore the main cost driver. Beyond that, three processes are highly cost relevant and can be regarded as economic hotspots.

Fig. 4. Production cost calculation for factory #1, steel inputs excluded By applying the simulation path, knowledge about the dynamic behavior of the factories has been gained. Figure 5 (a) displays the cumulated electrical load profile of the involved machines in factory #1. Two specific processes are responsible for significant load peaks and could therefore be of interest regarding load management strategies. Figure 5 (b) shows the waiting time of one specific product in front of all machines passed, indicating that the product had to queue in front of several machines, increasing its lead time.

Fig. 5. Selected simulation results of factory #1: (a) Electr. load profile of the process chain; (b) Waiting times in front of machines for a specific product

Fig. 6. Cross company KPI visualization in Value Chain Module The results from the three single factories have been transferred to the Value Chain Module to receive a global assessment of the current situation. Figure 6 presents the distribution of selected KPIs across the factories, showing that factory #1 is dominant regarding production costs and environmental impacts (here: CO2 emissions). However,

factory #2 is responsible for 66 % of quality related losses through rejects. Therefore, it also has a significant influence on the total costs and eco-impacts, as the previous factory #1 is forced to produce excess parts to compensate later defects in subsequent steps. The relevance of factory #3 is rather low for all displayed KPI. Basing on these finding, possible improvement measures have been evaluated with the toolbox. One approach to overcome the inefficiencies in factory #2 due to quality related losses is to improve the quality of raw steel used in factory #1. Such a higher steel quality would increase raw steel costs by ~10 %, hence there has been no incentive for factory #1 to change the material so far. The toolbox is now able to make local and global benefits and drawbacks of this measure transparent. Figure 7 gives an impression of the scenario analysis as part of the Value Chain Module for this specific measure. Here, the base scenario and an improved scenario are compared using spider web diagrams, covering six different criteria. It can be stated that the choice of a “better” material causes higher costs for factory #1, but significantly improves the quality rate and therefore also costs, environmental impacts and energy intensity in factory #2. Factory #3 is not affected by the changes and is therefore not depicted. From value chain perspective, a total cost reduction of ~1.4 percent can be achieved and energy intensity and environmental impacts are reduced due to lower quality related losses. However, a suitable business model should be found to first encourage factory #1 for an implementation and second to compensate the additional local costs caused by this change, which account for ~7.6 percent per part for factory #1.

Fig. 7. Comparison of base scenario and improved scenario from single factory and value chain perspective

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5. Conclusion

The presented toolbox allows for a holistic value chain analysis, assessing both the local perspective of each factory involved but also the global value chain perspective. Suitable complementary methodologies have been combined in one environment, using a common data basis and facilitating the comparability of key figures among the assessed systems. The tool has been designed to be applicable also for non-experts in modeling and simulation of production systems. It is expected, that significant saving potentials can be identified and assessed using the toolbox in the European metal mechanic sector. Further, a transferability of the presented approach to other industry sectors is generally given and might be followed as soon as the benefits have been proved in industrial application. Acknowledgements

The research leading to the presented results has received funding from the European Union’s Horizon 2020 Programme under grant agreement no. 636926 with the title “MEMAN - Integral Material and Energy flow MANagement in MANufacturing metal mechanic sector” (www.meman.eu). The authors also want to acknowledge TECHNOFI as partner responsible for the development of the “Regulatory Module”. References

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