Marc-André Weber

Tim Jeske

Frank Lennings

Sascha Stowasser
email: .stowasser@ifaa-mail.de

T Weber

F Jeske

Lennings

Productivity Strategies Using Digital Information Systems in Production Environments

Keywords: Productivity Management, Industry 4.0, Design Framework, Digitalization, Strategic Process Development

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Successful companies guide their actions along a basic long-term guideline, the socalled vision. The approach for realizing this vision and thus the organization's orientation towards action in competition is known as a strategy [START_REF] Neumann | Integrative Managementsysteme[END_REF]).

In industrial manufacturing, processing is performed to transform input factors into output. These inputs contain raw materials and auxiliaries, intermediate products and other operating materials, which pass through value-adding production processes, resulting in finished products [START_REF] Günther | Industrie 4.0 in der Metall-und Elektroindustrie[END_REF]. The relationship between output and the use of production factors is called productivity (Wöhe 2002).

The term Industry 4.0 describes the usage of information and communication technologies for industrial production processes to improve value creation (Roth 2016), (ifaa 2016). Digitalized processes in the manufacturing industry open new possibili-ties for productivity improvement. At the same time, digitalization can be used to adjust a company's strategy as well as product and service offerings (Roth 2016).

It is important for companies to have high productivity in order to survive in international competition. Productivity gains are essential to overcome higher costs compared to competitors. All processes in a company directly or indirectly influence its productivity. This applies to direct as well as to indirect areas.

For this reason, it is necessary to monitor productivity and influence it in an effective way. This should be done by planning and implementing a holistic strategic approach including strategic goals that can be measured by performance figures. They need to be continuously steered and controlled. These tasks are described by the term productivity management.

Productivity management is carried out for the continuous improvement of a company's activities with regard to increasing effectiveness and efficiency [START_REF] Gackstatter | Disruptive Innovationen als Weg aus der Krise[END_REF]) and thus goes beyond the mere consideration of the ratio of output to input. The undertaken activities for influencing productivity should have at least a mid-term scope and therefore have to be linked to the company's strategic goals.

The term "productivity strategy" stands for the possibility to choose various options for influencing productivity within the boundaries of a previously defined organizational strategy with its business model and core activities (ifaa 2016).Within the context of strategic productivity management, a company's activities are geared towards long-term influencing and targeted use of productivity(enhancement)strategies. This includes the assurance of positive results by strategic productivity planning (e.g. defined values for selected productivity key performance indicators), the implementation of this planning, a continuous evaluation of the productivity development and its controlling (Dorner and Stowasser 2012), (ifaa 2015).

Basically, productivity strategies can be classified by the aimed change of the ratio of output to input. Approaches to influence productivity focus -in general -either on the reduction of input (material, time etc.) or on the improvement of output (number of goods, customer satisfaction etc.) [START_REF] Weber | Ansätze zur Gestaltung von Produktivitätsstrategien in vernetzten Arbeitssystemen[END_REF]), [START_REF] Ruch | The measurement of white-collar productivity[END_REF]).

Use of Digitalization for Improving Productivity

A productivity strategy is determined by the production strategy chosen to implement the business strategy. The production strategy essentially describes the use of production concepts and technologies, including the production system and the process structure. To design a production system and its processes with an adequate flow of information, the design and implementation of state-of-the-art computer-assisted information systems is highly requested [START_REF] Gackstatter | Disruptive Innovationen als Weg aus der Krise[END_REF]). Improvements of productivity can be reached by using information and communication technologies if they are integrated in the design of industrial production and its processes. The recent developments of digitalization within this decade open up new opportunities for the management and improvement of production (e. g. human-robotcollaboration, data glasses, automated guided vehicles or exoskeletons, among others. See (ifaa 2016) for examples and their application). Mainly the adaptation of produc-tivity strategies using computer-based information management in alignment with the specific surrounding conditions and requirements of the company in scope are promising. The degree of utilization of IT-integration and digitalization already implemented in the company needs obviously to be considered. In order to be able to use additional digital tools and IT technologies, it may be necessary to adapt the IT infrastructure [START_REF] Schlick | Industrie 4.0 in der praktischen Anwendung[END_REF].

Possible outcomes of implementing productivity strategies based on IT are -for example -improved resource allocation by optimizing production, preventive maintenance based on sensor-captured real-time machine data or the use of extensive simulations based on a continuous digital engineering using Augmented or Virtual Reality (Roth 2016).

However, the diversity of techniques, standards and their impact on all business processes makes it difficult to choose and implement them (Samulat 2017). It is therefore necessary to develop strategies for the management of productivity in ITintegrated and digitized work systems and to prepare them for practical application. All potentials of digitalization should be taken into account for the design of work places and processes. The technology can be used to support human performance, or to decrease the consumption of material or energy. There is also a great possibility for decreasing actual process times [START_REF] Weber | Ansätze zur Gestaltung von Produktivitätsstrategien in vernetzten Arbeitssystemen[END_REF]), [START_REF] Meiller | Visual Analytics zur Auswertung von Daten für die Industrie 4.0[END_REF].

Since digitalization allows an improved information provision, it can be used to reduce the complexity of information handling (collection, transferring, processing, providing, usage), while at the same time improving productivity. For this purpose, the necessary steps from data collection to data usage must be coordinated, so that the data can be processed fully-digitized without media breaks and can be used in an integrative manner to influence productivity management strategically.

Framework for Clustering Productivity Strategies

Operational decisions to improve productivity focus on influencing input and/or output factors. One should have the best possible understanding of the interdependencies between those factors (ifaa 2016). Based on the information provided previously, the productivity goal -influencing input or output -and possible applications of digital information management in industrial production can be combined to a matrix. Additionally, a third axis can be added to assign approaches out of the matrix to the tasks performed in different direct production areas -including all material handling and intra-logistic activities -as well as in the indirect areas supporting production, such as scheduling, controlling, management and leadership, training, legal advisory service etc. The resulting cube (compare Figure 1) serves as a framework for the classification and further development of productivity strategies.

Fig. 1.

Framework for systematic design of productivity strategies in digitized working environments.

For each of the twenty cubes of the so-called "organizational and design framework", different productivity strategies can be classified. These strategies can be independently of each other or have strong interactions with one another. Based on this framework, various productivity strategies for industrial production can be structured and evaluated, taking into account the impact of IT and digitalization. To do so, it is important to assess a productivity strategy for its contribution to each cube. Subsequently, fundamental potentials for the design and development of productivity strategies can be derived.

To identify a single cube, we use (α, β, γ) to describe them. α represents the main effect influencing productivity, β stands for the application of digital information management and γ for the corporate application area. α1 and α2 assign the productivity goals of output enhancement and input diminishment. Digital information management is described by using β1 to β5, referring to data collection, data transfer, data processing, data providing and data usage. Finally, γ1 is used for all direct production areas and γ2 for the supporting indirect areas.

For example, (α2, β4, γ1) describes the cube for productivity strategies aiming at a decrease of input (α2) using digitalization for data providing (β4) within the production (γ1). A practical application could be the usage of handheld tablets to support the worker in manufacturing operations -e. g. by showing each single assembling stepwhich will lead to a decrease in time input necessary for performing the task. Other examples are given in [START_REF] Weber | Ansätze zur Gestaltung von Produktivitätsstrategien in vernetzten Arbeitssystemen[END_REF]).

Application of the Framework

The framework serves as a basis to sort and categorize productivity strategies into socalled strategy cubes. On this basis, a systematic design of strategies is supported.

According to the cubic structure, this includes first an integrated analysis of the application of digitalization (from data collection to data usage), second the distinction between direct and indirect production tasks and finally a view on different aims to be reached by the strategies (enlargement of output or decrease in input).

How to Classify Productivity Strategies

The following three-step procedure is generally recommended (other approaches are possible as well) for the classification of existing productivity strategies into the framework:

• Step 1: Identification of columns most suitable categorizing the technical solution based on information and communication technology (determining β). • Step 2: Determination of the area where the technology is applied (determining γ).

• Step 3: Examination of the impacts that can be achieved or that are expected from using the technology (determining α).

Especially in step 1, the distinction has to be made between the aspects offered by the strategy itself, and the components needed for a successful implementation of the strategy that are provided by supporting use of other technologies (e. g. before using a tablet for data providing as a possible main scope of a productivity strategy, these data obviously have to be processed before).

How to Search for Productivity Strategies

Searching a strategy within the framework can be done vice versa using the above mentioned proceeding. For example one can first focus on the application area (γ) in which productivity should be improved. In the second step, it can be regarded what can be done to decrease input, e. g. reducing the need for raw materials (α). Finally an application area for digitalization can be chosen to do so (β).

Other search strategies are possible as well, depending on the scope of search. In general, the company's specific needs should trigger the search for a suitable strategy within the framework. For example, the starting point could be a specific technology already in use (e. g. data glasses) and the searcher may look for increased effectivity to influence productivity. In this case β would be the starting point for this search within the framework.

Combining Productivity Strategies

The applicability of the framework and thus the possibilities for the implementation of selected productivity strategies adapted to specific corporate needs are versatile. This includes the selection of adequate productivity strategies, i. e. a selection of several strategies for a targeted influence on productivity, taking into account the opportunities of digitalization. For example, usage of sensor applications and individualized information provision by means of tablets can be used in parallel and ideally complement each other. The parallel use of several strategies is called strategy mix. A mix will probably be necessary in most cases to cover all needs of the organization to successfully influence productivity on the one hand and to take into account all datarelated aspects needed for the implementation of IT-based strategies on the other hand.

The long-term goal of influencing and developing productivity requires a dynamic adaptation of the strategy mix. This will enable the organization to have always those productivity strategies on hand that are currently required. The current status of usage of digitalization does not need to be the optimal one in another point in time.

Determining the right strategy mix is quite challenging. The non-linear and reciprocal relationships between input and output factors are often difficult to determine. In addition, the company should focus on those factors which it can directly affect (ifaa 2016), (Wenger et al. 2011). Designing individualized productivity strategies and adapting them over time -within the constraints given or hardly to be changed -is an elementary task.

Incorporation of Productivity Goals and Data Management

Strategic approaches to achieve (long-term) productivity goals need to be successively broken down and concretized -e. g. in line with the St. Gallen management model (Rüegg-Stürm 2003) -in order to influence the daily work processes in a targeted manner. There are three major fields that need to be taken into regard (Figure 2): These three major fields to be considered while implementing productivity strategies can be specified as follows:

• Corporation: Productivity strategies using information and communication technologies -regardless of whether direct or indirect areas apply them -need first to be specified for different corporate levels. This means that the corporation as a whole (the macro level) needs to determine actions -as well as interdependencies among them -, and these actions must be broken down for each single division (meso level) and further down to each single work stations (micro level). • Time Horizon: For each application level of a productivity strategy, actions and goals -including key performance figures with their respective target valuesmust be set for several time horizons, namely for the long-term strategic scope, the mid-term tactical scope and the short-term operational scope. Therefore, every application level from macro to micro should have specific tasks and goals to be reached within different time horizons. • Alignment: Finally, every usage of information and communication technology for long-term influencing of productivity must be successfully implemented. This requires that the three aspects technology (in a sense of selection, implementation etc.), organization (in a sense of process design and process interactions) and personnel (in a sense of ergonomics and human-oriented work design) need to be considered in an integrative manner.

For every productivity strategy categorized into the framework, all these aspects need to be considered to ensure a successful implementation.

Conclusion

Important terms like productivity management and strategies as well as an understanding of digitalization are explained in this article with regard to their integration into the strategic, long-term development of business models and processes. Fundamental opportunities of digitalization -also referred to as Industry 4.0, Smart Factory or Advanced Manufacturing among others -are presented. Based on this, a framework for systematic structuring and designing of productivity strategies is developed, in which various productivity strategies can be classified taking into account the use of information and communication technologies. This framework is based on three axes: the first axis addresses the desired outcome like improving output or decreasing input. The second axis describes the scope of the information and communication technique used. The third axis distinguishes direct or indirect areas for application. Aspects for successful strategy implementation are mentioned, mainly that productivity strategies must be in-line with the actions performed by the whole company as well as by single work stations, that goals and tasks need to be specified for the longrun as well as for the short-run and that technological, organizational and personnel aspects need to be considered for designing work.

Researchers can classify IT-related strategies for productivity improvement by using the framework and taking into regard the above mentioned details while designing productivity strategies. Practitioners can first get ideas how to structure their existing approaches and how to search new ones systematically. In the long run, the framework will serve as a basis to list a multitude of productivity improvement strategies and identify good-practice examples. Therefore, the presented cube for structuring approaches of digitally supported productivity management and its integration into the strategic context of an organization needs to be further examined with regard to its operational applicability and practicability. The authors currently carry out several surveys to do so.

Fig. 2 .

 2 Fig. 2. Integrated view on productivity strategy implementation.

Acknowledgments

The presented results are part of the project "TransWork -Transformation of Labor through Digitalization". We thank the German Federal Ministry of Education and Research (BMBF) for funding this research project (grant number 02L15A164).


Rakshith Badarinath
email: rakshithb@psu.edu

Vittaldas V Prabhu

Advances in Internet of Things (IoT) in Manufacturing

Keywords: Industrial Internet of Things (IIoT), Big Data Analytics, Wireless Sensor Networks (WSN), Predictive Maintenance, Energy Aware Manufacturing, SOA, Smart Manufacturing, Industry 4.0

As a promising technology with increased adoption in recent years, Internet of Things (IoT) realizes ubiquitous interconnection of physical devices through internet, opening doors for building powerful industrial applications by leveraging the advances in sensor technology and wireless networks. IoT technologies can be viewed as enablers for smart manufacturing and Industry 4.0. This review paper focuses on applications of IoT in manufacturing, which is also known as Industrial Internet of Things IIoT. To that end, technologies relevant to the application of IoT in manufacturing, such as wireless sensor networks (WSNs), smart sensors, big data analytics, and cloud computing are discussed. A service oriented architecture (SOA) based four-layer model for realizing IoT applications in manufacturing is proposed. Finally, a review of the state of art of IoT applications in manufacturing including shop floor automation, predictive maintenance, energy aware manufacturing, and smart workers is presented with relevant industry use cases.

Introduction

Understanding Internet of Things

Internet of Things (IoT) can be defined as an ecosystem of interconnected, uniquely identifiable physical devices and software components having an ability to exchange data with other devices over a network with limited human intervention. The internet of things Global Standards Initiative (IoT-GSI) defines IoT as "a global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies" [START_REF]Internet of Things Global Standards Initiative[END_REF]. One can visualize IoT as a seamless interconnection of the physical world and cyber worlds, with a pervasive presence around us [START_REF] Atzori | Internet of Things: A Survey[END_REF]. IoT technologies provide means for physical objects to be monitored or controlled remotely over a network. This capability, coupled with ubiquitous interconnection of heterogeneous devices, opens doors for a new class of applications. The physical devices that can be interconnected vary from tiny electronics embedded into products to large automated buildings, vehicles, and everything in-between. An IoT application relies on a foundation created as a result of integration of sensors, actuators, and tracking devices such as RFID tags coupled with information and communication technologies, enabling cooperation among interconnected objects to achieve common goals.

There is an increased interest in using IoT in industries [START_REF] Li | Towards a theoretical framework of strategic decision, supporting capability and information sharing under the context of Internet of Things[END_REF]. The three-year old Industrial Internet Consortium (IIC) comprises over 250 member companies [4]. Applications of IoT appear in a diverse set of industries including manufacturing, agriculture, traffic monitoring, food processing, health monitoring, and personal fitness. Additionally, there has been a sharp increase in the number of publications related to IoT. A search of Web of Science indicated that the number of publications in IoT increased from 0 to 6836 between 2006 and 2016. It is estimated that IoT will encompass 50 billion devices by 2020 [START_REF] Evans | The Internet of Things -How the Next Evolution of the Internet is Changing Everything[END_REF].

This review focuses on identifying existing IoT technologies, architecture requirements, and applications specific to the manufacturing domain. In the current day global competitive environment, manufacturing companies are continuously striving to improve their competitiveness in terms of product price, time to market, efficiency of production processes, costs, etc. New technological paradigms like IoT play a crucial role in keeping companies competitive while meeting continuously changing customer needs. In fact, IoT has a great potential to transform the manufacturing sector [START_REF] Bi | Internet of things for enterprise systems of modern manufacturing[END_REF] by creating novel business opportunities. IoT technologies enable integration of sensor and other electronics into various physical elements in a manufacturing plant where, with cooperative communication, entities can autonomously exchange information leading to processes that can govern themselves by taking intelligent decisions. This provides unprecedented opportunities for the manufacturing sector where resources can be monitored in real time, enabling higher safety and performance. This promising idea has led to several global initiatives such as "the Fourth Industrial Revolution" in German industry ( also called "Industry 4.0"), "Factory of the Future" in the European Union, and "Made in China 2025".

Enabling Technologies for IoT Applications

In this section, technologies which form the backbone of IoT applications in manufacturing are discussed.

Wireless Sensor Networks (WSN)

There are barriers to running cables on a factory floor because of potential safety hazards and associated costs. This is exacerbated as the distances between the connected points grow. WSNs can alleviate many of these barriers by offering numerous spatially distributed sensor nodes1 that are wirelessly connected through gateways to enable remote monitoring of physical or environmental conditions [START_REF]What Is a Wireless Sensor Network? -National Instruments[END_REF]. Table 1 summarizes currently available wireless communication technologies suitable for WSN [START_REF] Bin | Comparison of Internet of Things (IoT) Data Link Protocols[END_REF], [START_REF]IoT Technology 2017 Overview Guide on Protocols, Software, Hardware Network Trends[END_REF].

Up to 125 Kbps

A typical autonomous WSN node consists of radio, battery, microcontroller, analog circuit, and sensor interface [START_REF]What Is a Wireless Sensor Network? -National Instruments[END_REF]. As seen in Table 1, high data rate capability often results in power hungry devices. The selection of communication technology for manufacturing shop floor is largely influenced by the size of the plant, the number of physical assets that need to be monitored, and their location within the plant.

Smart Sensors

Sensors have evolved over the years from bulky dedicated devices to miniature multivariable sensing packages. Sensors are primary sources for any actionable data that can contribute to profitable business decisions. A smart sensor has localized computational capability to turn raw data into actions in real time, allowing fast responses and circumventing network latency issues. While some smart sensors have actuation capability, this function is mainly limited to dedicated hardware. A number of sensors are already being used the manufacturing domain, primarily to monitor machines and processes. A recent survey from PwC reveals that 35% of the manufacturing industry in US is already collecting and using data from smart sensors embedded in machines, to enhance operational efficiencies [START_REF] Pwc | The Internet of Things: What It Means for US manufacturing[END_REF].

Big Data Analytics

Increased adoption of sensors to monitor physical assets such as machinery and end products leads to generation of massive amounts of raw data, estimated to be in the order of 1000 Exabytes (or 1 Zetabyte) annually per plant and expected to increase in coming years [START_REF] Mourtzis | Industrial Big Data as a Result of IoT Adoption in Manufacturing[END_REF]. This massive amount of data, commonly referred to as big data, needs to be analyzed in a computationally efficient and timely manner for identifying trends and patterns among process parameters and outputs. Insights derived from such big data analytics is already changing decision-making in industry and a number of new ventures are targeting related business opportunities [START_REF] Pwc | The Internet of Things: What It Means for US manufacturing[END_REF].

Cloud Computing

Cloud computing is changing the way industries do business by eliminating upfront cost for localized computing resources, and replacing this with lower-cost "pay-as-youuse" computing as a service over the Internet [START_REF] Xu | From cloud computing to cloud manufacturing[END_REF]. Moreover, cloud computing is dynamically scalable, which makes it an ideal platform for big data analytics where powerful computing resources may be used intermittently. Cloud computing's scalability, better resource utilization, and plug-and-play networks of heterogeneous devices have led to the emergence of "Cloud Manufacturing" [START_REF] Xu | From cloud computing to cloud manufacturing[END_REF]. Cloud manufacturing can be viewed as an approach where hardware and software functions are virtualized as a scalable, "elastic", virtual manufacturing enterprises that can be rapidly configured or shutdown. This can be expected to play an important role in realizing the full potential of the IoT paradigm.

Architecture for IoT Applications in Manufacturing

Service Oriented Architecture (SOA) consists of discrete services, which are composable to achieve a higher-level goal. SOA standards allow for a physical device's functionality to be offered as interoperable services independent of vendors, products, and technologies [START_REF] Ivezic | On Architecting and Composing Through-life Engineering Information Services to Enable Smart Manufacturing[END_REF]. Since IoT is based on cooperative communication between heterogeneous devices to achieve a specific goal, SOA can be applied [START_REF] Serm) Kulvatunyou | On Architecting and Composing Engineering Information Services to Enable Smart Manufacturing[END_REF].

There has been a lot of research related to design of multi-layer SOA for IoT in general and also for specific domains like manufacturing. Gubbi et al. [START_REF] Gubbi | Internet of Things (IoT): A vision, architectural elements, and future directions[END_REF] proposed a cloud-based framework for generic IoT that includes three layers: network of things, cloud computing, and applications. Atzori et al. proposed a three-layer model for IoT consisting of an application layer, a network layer, and a sensing layer [START_REF] Atzori | Internet of Things: A Survey[END_REF]. Lee et al. suggested a five-layer architecture model for cyber-physical manufacturing systems consisting of a sensing level for data acquisition; a data-to-information conversion level involving smart analytics for component health; a cyber level for cyber modelling and simulation; a cognition level for remote visualization; and a collaborative-diagnostics and configuration level which involves self-configuration and self-optimization based on learning [START_REF] Lee | A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems[END_REF]. Y. Zhang et al. proposed a real-time information capture and integration framework for IoT applications in manufacturing, comprising 3 layers: event sensing and capturing; manufacturing and data processing; and application services [START_REF] Zhang | Real-time information capturing and integration framework of the internet of manufacturing things[END_REF]. , each level has increasingly sophisticated IoT applications, making it a hybrid of functional and application architecture. In this paper, we build on these to propose a 4-Layer SOA for IoT applications in manufacturing including sensing and data acquisition, network, services, and applications layers as illustrated in Figure 1 along with the specific functionality for each layer. Note that our proposed architecture would be suitable for cloud manufacturing or non-cloud manufacturing. While no industrial implementation of the proposed architecture exists at present in its entirety, the individual elements of the architecture have been successfully realized in practice, and are presented in the next Section.

IoT Applications in Manufacturing

A: Real-time data collected from the shop floor can be used for designing an optimal production systems with automated work flows. Optimal decisions on production scheduling are driven by results of raw data processed through powerful analytics. Industry Example: At Siemens' electronics manufacturing plant in Amberg, Germany, machines and computers handle 75% of the operations autonomously, with roughly 1,000 automation controllers in operation along the production line. Parts being produced communicate with machines by means of a product codethat tells the machines their production requirements and which steps need to be taken next [START_REF] O'marah | The Internet of Things Will Make Manufacturing Smarter[END_REF].

B:

Analyzing real-time data on a machine's health in correlation with equipment process models can lead to predictive maintenance based on the remaining useful life estimation and automated condition-based maintenance strategies, leading to lower machine downtime and increased productivity. Industry Example: GE's digital windfarm (a fleet of IoT-enabled wind turbines) is monitored in real time and the collected data on rotor speed and weather conditions is used to build predictive-maintenance models using GE's Predix™ cloud-based analytics software. "Every day at GE's remote M&D Center in Atlanta, Georgia, GE collects more than 30,000 operating hours of data from a fleet of more than 1,500 assets. Insights drawn from this volume of power generation big data have translated into total customer savings estimated at $70 million in 2014" [19].

C:

The manufacturing sector typically accounts for one-third of the global energy demand [START_REF] Karnouskos | Towards the energy efficient future factory[END_REF]. An IoT application allows for continuous measurement of energy consumption of physical assets on the shop floor. This, when correlated with production plans and states of assets, can lead to dynamic, closed-loop energy regulation algorithms to intelligently regulate assets to the low-energy states, resulting in energy savings while maintaining the throughput level (despite the ramp-up time from a low-energy state to an operational state). This is not only applicable to manufacturing equipment, but also for plant HVAC and lighting systems. Industry Example: At BMW's Spartanburg (SC, USA) production plant, IoT-based smart energy meters constantly measure energy consumption of the facility to predict and identify sources of abnormal or high energy consumption. This led to total energy savings of over 100,000 kWh in the first year, which is estimated to correspond to total energy cost savings of about 25 million euros over next 10 years. Additionally, the system can also identify imminent breakdowns of equipment by monitoring energy usage. For example, if a piece of equipment is using more energy without any change in production parameters, it is likely a result of malfunction or wear and tear of its components[21].

D:

There is a new trend in applying wearable technology to enhance productivity and workforce safety. Industry Examples: Thyssenkrupp, AG has more than 24,000 service technicians who use mixed-reality devices in elevator service operations. Hands-free remote holographic guidance reduces the average length of service calls by up to four times [22]. Intel and Honeywell have collaborated to develop a system of wearable sensors that monitor a worker's breathing, heart rate, posture, and toxic-gas exposure. Data from the sensors is analyzed and displayed on cloud-based dashboards, helping managers better anticipate unsafe working conditions [23].

Mapping the application B to our four-layer SOA model, smart sensors in the sensing and data-acquisition layer continuously monitor the electronic signature of components of a windmill such as rotor bearings, rotor blades, gearbox, etc. This data is transmitted in real time to the service layer through WSNs in the network layer. The service layer may be cloud-based or on-premises proprietary software having analytics capability to sift through the data and detect any anomalies or trends that are indicative of component degradation by comparing the data with inputs from analytical reliability component models. These trends, along with estimated remaining useful life of the component derived by comparison to component reliability models, are presented on user dashboards in the application layer. This can be used to automatically trigger preventive maintenance or to alert service managers to schedule maintenance of affected components, helping to avoid unexpected downtime due to breakdowns and failures. Next, application D comes into action, where service technicians use mixed-reality wearable technology devices to efficiently identify the component and service it.

Conclusions and Challenges

Fig. 1 .

 1 Fig. 1. Proposed SOA-based architecture for IoT applications in Manufacturing

Table 1 .

 1 Comparison of different communication technologies in IoT

Technology Range Frequency Power Consumption Data Rate NFC (IEC 18000-3)

		Up to 0.2 m	13.56 MHz	Low (~50 mA) Up to 424Kb/s
	Bluetooth LE	Up to 100 m	2.4 GHz	Low (~12.5	1 Mbps
	(Bluetooth 4.2			mA)	(Smart/BLE)
	Core Spec)
	Wi-Fi	Up to 100 m (can	2.4 GHZ / 5 GHz	Medium/High	150 -200
	(IEEE	be extended)		(~116 mA)	Mbps (down-
	802.11a/b/g)				load); up to 20
					Mbps for up-
					load
	WiMAX	Up to 30 miles	2 -11 GHz	High	Up to 83 Mbps
				(Depends on	(download);
				range of cover-	up to 46 Mbps
				age ~200 W)	(upload)
	RFID	Distance driven	LF:125-134 kHz	Low	Up to 640
		by operating fre-	HF:13.56 MHz	(Depends on	Kbps
		quency: up to	UHF:850-960 MHz	operating fre-
		200 m for active		quency and tag
		RFID		type)
	Cellular	35 Km max for	1900/2100 MHZ	High	3 -10 Mbps
	(4G/LTE)	GSM, 200 Km		(~184 mA)	(LTE)
		max for HSPA
	Z Wave	30 m	900 MHz	Low	Up to 100
	(ITU-T G.9959)			(~2.5 mA)	Kbps
	ZigBee	Up to 100 m	2.4 GHz	Low (<	250 Kbps
	(IEEE 802.15.4)			10mW)
	EnOcean	Up to 300 m	315 MHz, 868 MHz,	Low
	(IEC 14543-3)	(outdoor) and 30	902 MHz	(Stand By: 100
		m (indoor)		nA)

  This paper reviewed the main enabling technologies for IoT: wireless sensor networks, smart sensors, big data analytics, and cloud computing. Based on prior architectures for such systems, a new SOA-based four-layer architecture was proposed. Successful IoT applications in manufacturing were reviewed and mapped to the proposed four-layer architecture. Integration of smart-sensing and actuation capabilities on equipment with cyber space offers prospects of new levels of productivity, and could become an important dimension of competition in the future. A few of the current IoT applications in manufacturing used cloud computing, but most did not. There are several research challenges limiting widespread adoption IoT applications in manufacturing: (1) Data security/privacy concerns: opening up vast number of connected devices to the cloud offers multiple points of entry for an attacker; (2) Integration of legacy equipment often needs dedicated patches and may not be always feasible;[START_REF] Li | Towards a theoretical framework of strategic decision, supporting capability and information sharing under the context of Internet of Things[END_REF] Need for open standards: Standards help improve interoperability of different systems resulting in sustainable functioning of the system and increased adoption; and (4) Technological barriers: manufacturing applications that require real-time feedback control may not be able to tolerate the data transfer latency that can arise with IoT. Other challenges include the high cost of sensor instrumentation and legal issues of data ownership. Overall, emerging IoT applications in manufacturing open new research challenges that require technology standards and solutions that can harness large streams of real-time data to optimize operations across the entire manufacturing life cycle.

Autonomy refers to devices equipped with sensors capable of self-recovery and making localized decisions.


Volker Stich

Daniel Pause
email: daniel.pause@fir.rwth-aachen.de

Matthias Blum

Analyzing the Impact of different Order Policies on the Supply Chain Performance

Keywords: Order, Supply Chain, System Dynamics, Inventory Management, Design of Experiments

Influenced by the high dynamic of the markets and the steadily increasing demand for short delivery times the importance of supply chain optimization is growing. In particular, the order process plays a central role in achieving short delivery times and constantly needs to evaluate the trade-off between high inventory and the risk of stock-outs. However, analyzing different order strategies and the influence of various production parameters is difficult to achieve in industrial practice. Therefore, simulations of supply chains are used in order to improve processes in the whole value chain. The objective of this research is to evaluate two different order strategies (t,q, t,S) in a four-stage supply chain. In order to measure the performance of the supply chain the quantity of the backlog will be considered. A Design of Experiments approach is supposed to enhance the significance of the simulation results.

Introduction

Today, manufacturing companies are confronted with the influences of a dynamic environment and the continuously increasing planning complexity [START_REF] Brecher | Integrative Production Technology for High-Wage Countries[END_REF]. Reduced time to markets, rising product diversity as well as complex multi-tier and world-spanning supply chains are faced with growing inter-connectivity of production machinery, enterprise resource planning systems and manufacturing execution systems. Due to globalization, the number of market participants rises resulting in a growing competition amongst the individual companies [START_REF] Rushton | The Handbook of Logistics and Distribution Management: Understanding the Supply Chain[END_REF].

In order to remain profitable as a business, the industrial enterprises in high-wage countries must identify cost carrier of the production process in order to reduce unnecessary costs. This is why companies focus on the production steps with a high share of added value and reduce the depth of production [START_REF] Wannenwetsch | Integrierte Materialwirtschaft, Logistik und Beschaffung[END_REF]. A strong trend towards reduced inventory is sensed so that components are delivered "just-in-time" (JIT) for the production. Accordingly, the process of ordering must be designed in a way which ensures a smooth production. As part of inter-company value chains the individual view of ordering is not adequate anymore. Rather, the analysis and optimization of the whole supply chain continues to gain importance [START_REF] Schuh | Produktionsplanung und -steuerung. Grundlagen, Gestaltung und Konzepte[END_REF].

Considering these problems, companies lack on an efficient design of their order processes. In order to create a better understanding of complex interdependencies in the supply chain a simulation model of a four-stage supply chain was designed (sub-supplier, supplier, manufacturer and customer).

In this research t,q and t,S order policies are going to be implemented in a supply chain model. In order to further investigate the interactions of the parameters in the supply chain, a Design of Experiments approach was used. Hereby, the interactions of the parameters are investigated and principles how to design an efficient supply chain are derived. The emphasis is put on the interplant relation instead of the internal production itself.

The remainder of this paper is organized as follows: First, we give a short introduction about the today`s challenges for manufacturing companies and further discuss related work in Section 2. Next, we introduce the methodology of System Dynamics in Section 3. Section 4 focuses on the description of the simulation model and in section 5 we present the results of predefined simulation scenarios. Finally, we conclude in Section 6 and highlight future work.

State of the Art

Due to the close link between ordering, inventory management and production a separate analysis of these sections is not sufficient. Thus, the emphasis is put on the optimization of all companies involved in the value-added process (supply chain). To investigate the cooperation and acting of a supply chain various research approaches already exist. In the following, these approaches are outlined. [START_REF] Moizer | A Decision Support Framework to Assess Grocery Retail Supply Chain Collaboration: A System Dynamics Modelling Approach[END_REF] examine the advantages of a close cooperation between the manufacturer and its suppliers of a retail supply chain and the influence of efficiency and performance. A simulation was used based on a trial group consisting of 12 retailers in the US. It was shown that collaboration can cut costs, risks and inventory for both the retailer and their suppliers [START_REF] Moizer | A Decision Support Framework to Assess Grocery Retail Supply Chain Collaboration: A System Dynamics Modelling Approach[END_REF].

Langroodi and Amiri (2016) investigate the choice of the most appropriate region for order placements in a five stage multi-product supply chain, consisting of a customer, an incorporate retailer, manufacturer, material distributor and supplier, in four different regions using a System Dynamics model. A scenario analysis with varying costs and demands was conducted. The model aims to minimize the costs of orders between two stages consisting of transport, price for the product and order placement and thus choose the best supplier [START_REF] Langroodi | A system dynamics modeling approach for a multi-level, multi-product, multi-region supply chain under demand uncertainty[END_REF]. [START_REF] Hishamuddin | A Simulation Model of a Three Echelon Supply Chain System with Multiple Suppliers subject to Supply and Transportation Disruptions[END_REF] analyze disruptions of supply and transportation on the system's total recovery costs and other performance measures in a three stage supply chain with multiple suppliers. Thus, different scenarios of disruptions were established to evaluate system costs and stock-outs. It was shown that transportation disruptions have more damaging effects than supply disruptions due to the higher lost sales quantity. In addition, disruptions in the earlier stages have a higher negative impact to the supply chain compared to later disruptions [START_REF] Hishamuddin | A Simulation Model of a Three Echelon Supply Chain System with Multiple Suppliers subject to Supply and Transportation Disruptions[END_REF]. [START_REF] Li | A system dynamics simulation model of chemical supply chain transportation risk management systems[END_REF] examine the dynamic risks effects in a chemical supply chain transportation system. Therefore, a System Dynamics model was built and risk scenarios were established regarding the probability and consequence severity in order to compare order fulfillment rate, transportation and inventory level to measure the performance. The major sources of risks transpired among other as breakdown in core operations, inappropriate choice of service provider and lack of inventory management. The researchers used only a questionnaire as the input for various risk scenarios, which could be a source of bias. Thus, it would be necessary to use a more extensive data source [START_REF] Li | A system dynamics simulation model of chemical supply chain transportation risk management systems[END_REF].

This paper continues a previous research which focused on the simulation of s,q and s,S order policies. The previous paper was called "A simulation based approach to investigate the procurement process and its effect on the performance of supply chains" [START_REF] Stich | A Simulation based Approach to investigate the Procurement Process and its Effect on the Performance of Supply Chains[END_REF]. Combining the model components and results of both papers, further research will focus on analyzing and comparing all four order policies as a whole.

Methodology

System Dynamics is a computer-aided approach for modeling, simulating, analyzing and designing dynamic and complex issues in socio-economic systems. Initially called Industrial Dynamics [START_REF] Forrester | Industrial Dynamics[END_REF] the field developed from the work of Jay W. Forrester at the Massachusetts Institute of Technology.

Simulation models based on System Dynamics contain four different types of elements [START_REF] Coyle | System dynamics modelling. A practical approach[END_REF]:

1. Levels represent state variables of the system 2. Flow variables symbolize temporal change of the state variables 3. Auxiliary variables are used for decision rules describing casual relations 4. Constants are parameters to be set for the simulation Supply chains mainly consist of inventory (information or material) as time based variables and flows as activities (transport of material and information). Decisions steer running activities and thus the state of the system [START_REF] Aberle | Management der Logistikkette. Kostensenkung -Leistungssteigerung -Erfolgspotential[END_REF]. A superior aspect to other simulation models is the possibility of feedback loops within the model. Thus, it is suitable for analyzing complex problems [START_REF] Wienholdt | Dynamische Konfiguration der Ersatzteillogistik im Maschinen-und Anlagenbau[END_REF]. A common way to analyze a system's behavior using System Dynamics is a scenario analysis. This was done by using the tool Vensim.

4

Description of the Simulation Model

Model Structure

The model consists of a sub supplier, a supplier, a manufacturer and a customer (Figure 1). Between the respective supply chain partners a material flow and an information flow is taking place. The sub supplier serves as an infinite source of order items and the customer is able to create different demand situations. The supplier and the manufacturer are modelled according to a simplified business structure, which is described in the following paragraph.

The business structure of both the supplier and the manufacturer are composed of an arrival warehouse for incoming goods, a quality check (QC) for the incoming goods, a production and a shipping warehouse from which the products are delivered to the customer.

Fig. 1. Model structure

The parameters contained in the model are shown in Table 1. Conducting a scenario analysis maximum stock level, periodic time, fixed order quantity and production rate were defined for both the supplier and the manufacturer. The customer demand is induced by an Excel based data generator. Thus, based on several parameters different demand situations can be applied to the simulation model. An expected demand and a standard variation need to be specified prior to the simulation as well as the initial situation with regard to a so-called trend, season or a constant demand.

Order Process

In the beginning of the simulation a certain stock is available in the shipping warehouse from which the demand is satisfied. According to a constant time interval a new order is placed at the supplier. The order quantity varies depending on the order policy investigated. This paper puts emphasis on frequent order points (t,S and t,q order policy), because order policies using a variable order point has already been investigated in a previous research paper at APMS 2016. Order policies with frequent order points can have two different modes for determine the order quantity. When using a t,S strategy the order quantity varies due to the stock replenishment up to a maximum stock level. Whereas in the case of a t,q strategy a fix quantity is ordered every time regardless of the maximum stock level [START_REF] Gruen | Grundzuege der Beschaffung, Produktion und Logistik[END_REF].

Results of the Simulation

Two different order policies with frequent order points (t,s and t,q) has been analyzed based on different scenarios of demand situations (seasonal, trend and stationary). The results were compared by evaluating the quantity of the backlog. However, the results of the backlog did not differentiate much in the scenarios of a seasonal, trend and stationary demand pattern. This is why the results presented in this chapter will only be based on seasonal demand pattern. Figure 2 illustrates the backlog of manufacturer and supplier for t,S and t,q order strategy based on a seasonal demand pattern. From the figure, it can be inferred that for the considered parameters, when both manufacturer and supplier implement t,q strategy, backlog of manufacture is high (around 21000 pieces) and backlog of supplier is relatively low (around 2000 pieces) at 440th day. Likewise when both implement t,S strategy backlog of supplier is high (around 7000 pieces) and that of manufacturer is relatively low around 15000 pieces at 440th day.

Fig. 2. Results of the Scenario analysis based on seasonal customer demand structure

Further, it can be observed from the graph of backlog manufacturer that the nature of graph is influenced by the customer demand, which is seasonal in this case. Hence a wavy graph is obtained for both t,S and t,q strategy. The backlog difference between the t,q and t,S along the time increases, hence a gap between both graph increases with time, and it is maximum at 440 th day. The manufacturer backlog is relatively low with t,S strategy as the manufacturer tend to order after every time interval 't" till he reaches his maximum stock 'S', which would lead to more ordered parts at his end to serve the customer demand. In contrary the manufacturer backlog is high with t,q strategy as he would order only fixed order quantity 'q' after every time interval "t' which would put manufacturer in high backlog.

Whereas in the case of supplier backlog, it can be noted that there is an initial backlog as early as day 1 for t,S strategy and then in the course of time it is zero for some time. Then the backlog is observed as the day progresses. It can be seen that, there is a sudden increase and drop of backlog, which is a result of the supplier being able to deliver the order in time for a short duration and then he will be in backlog after some time. This process repeats periodically as seen in figure 2. The same process repeats for t,q strategy, too, but the difference is that the backlog is zero for more than 100 days and then the backlog increases in the course of time with abrupt increase and decrease in between.

Based on the simulation results a Design of Experiments analysis was conducted to visualize and identify the factors that influence the backlog of delivered parts most. The main effects charts in Figure 3 illustrate the examined factors (also see Table 1) and indicates if it has a positive or negative impact on the backlog when changing from a predefined minimum to a maximum value.

Fig. 3. Main effects chart for t,S order strategy with seasonal demand

A horizontal line in Figure 3 implies that there is no effect on the backlog, the steeper the line the greater the influence on the backlog. Hence, there would be almost no effect of production rate of supplier (PL L) and manufacturer (PL H) on supplier and manufacture backlog. It is also observed that the greatest influence on the backlog of the manufacturer and supplier are the periodic time (L, L0) and the maximum stock level (HB, HB 0).

An interaction plot shows the interdependencies between factors when changing specific factor settings. Because an interaction can magnify or diminish main effects, evaluating interactions is extremely important. The lines in Figure 4 are indicating the strength of the interaction between the parameters. If the lines are parallel to each other, no interaction occurs. The more nonparallel the lines are, the greater the strength of interaction is.

Evaluating the Supplier's backlog, almost no interaction between the maximum stock of the manufacturer (HB 0) and the supplier (HB) can be detected. But it can be seen that there is an interaction between manufacturer's periodic time (L0) and maximum stock level of manufacturer (HB 0). Other parameters like production rate of supplier (PL L) and production rate of manufacturer (PL H) do not have much impact on backlog of supplier, as from figure it can be seen that, those parameters are plotted as parallel lines.

Evaluating the manufacturer's backlog, it stands out that there is a great influence between the maximum stock of the manufacturer (HB 0) and the periodic time manufacturer (L0). Other parameters like production rate of supplier (PL L) and production rate of manufacturer (PL H) do not have much impact on the backlog of the supplier.

Due to this fact, it would be advisable that the supplier and the manufacturer plan on their maximum stock for a decreasing backlog.

The main effects charts for t,q order strategy with seasonal customer demand are illustrated in Figure 5. Fig. 5. Main effects chart for t,q order strategy with seasonal demand Similar to Figure 3 there is almost no effect of production rate of supplier (PL L) and manufacturer (PL H) on supplier and manufacture backlog. It is also observed that the greatest influence on the backlog of the manufacturer and supplier are the periodic time (L, L0) and the fixed order quantity (Q, Q0).

In the below Figure 6 an interaction plot for t,q order strategy is illustrated.

Fig. 6. Interaction plot for t,q order strategy with seasonal demand Similar to Figure 4 the lines in Figure 6 are indicating the strength of the interaction between the parameters. Evaluating the supplier's backlog, no interaction between the fixed order quantity supplier (Q) and manufacturer (Q0) can be detected. But it can be seen that there is an interaction between manufacturer periodic time (L0) and the fixed order quantity manufacturer (Q0). Other parameters like production rate of supplier (PL L) and production rate of manufacturer (PL H) do not have much impact on backlog of supplier, as those parameters are plotted as parallel lines. Evaluating the manufacturer's backlog, it stands out that there is a great influence between fixed order quantity manufacturer (Q0) and the periodic time manufacturer (L0). Other parameters like production rate of supplier (PL L) and production rate of manufacturer (PL H) do not have much impact on backlog of supplier, as those parameters are plotted as parallel lines.

Outlook and further Research

In this paper, a simulation of a four-stage supply chain was presented and two order policies were compared. After introducing the basics of ordering and inventory as well as System Dynamics, the model structure was explained and t,S and t,q order policies were chosen for a comparison. It was shown that the periodic time and maximum stock level are main causes for the supply chain's performance. Due to the dependency of the backlog of the supplier by actions of the manufacturer, a close cooperation between all companies involved is advisable. The vendor managed inventory where the supplier manages the inventory and orders of the manufacturer is a method trying to decrease the backlog. In further investigations this method could be considered and implemented in the simulation model. Additionally, other parameter could be added to the model, which might influence the supply chain's performance e.g. costs for inventory and ordering or late delivery charges (liquidation damage) in order to further specify decision rules. Moreover, instead of using the backlog as a main measuring unit for the supply chain performance further objectives such as costs or average inventory level could be considered.

Fig. 4 .

 4 Fig. 4. Interaction plot for t,S order strategy with seasonal demand

Table 1 .

 1 Parameters in the simulation model for the supplier and the manufacturer

	Code	Declaration	Setting Range
	HB	Maximum stock level Supplier	500 -2000 pieces
	HB 0	Maximum stock level Manufacturer	500 -2000 pieces
	L	Periodic time Supplier	2 -14 days
	L0	Periodic time Manufacturer	2 -14 days
	Q	Fixed order quantity Supplier	200-1000 pieces
	Q0	Fixed order quantity Manufacturer	200-1000 pieces
	PL L	Production Rate Supplier	500 -10.000 pieces/day
	PL H	Production Rate Manufacturer	500 -10.000 pieces/day

Acknowledgements. The presented research is a result of the Cluster of Excellence (CoE) on "Integrative Production Technology for High-Wage Countries" funded by Deutsche Forschungsgemeinschaft (DFG). Within the CoE "Integrative Production Technology for High-Wage Countries" several institutes at RWTH Aachen University are conducting research on fundamentals of a sustainable production strategy. The authors would like to thank the German Research Foundation DFG for the kind support within the Cluster of Excellence "Integrative Production Technology for High-Wage Countries.


Jens Heger
email: heger@leuphana.de

Thomas Voss
email: voss@leuphana.de

Optimal scheduling for Automated Guided Vehicles (AGV) in blocking job-shops

Keywords: MILP, job shop scheduling, AGV, cyber physical systems

Promising developments and further improvements of cyber physical logistics systems (CPLS) and automated guided vehicles (AGV) lead to broader application of such systems in production environments and smart factories. In this study a new mixed integer linear program (MILP) is presented for the scheduling of AGVs in a flexible reentrant job shop with blocking. Optimal solutions to small instances of the complex scheduling problem in a make-to-order production, minimizing the make span, are calculated. Different numbers of jobs are considered. Feasible schedules for the machines and the AGVs are generated from different sized instances to evaluate the limits of the mathematical model.

Introduction

The application of cyber-physical systems (CPS) generate new possibilities and challenges for the production planning and scheduling. Thoben et al. [START_REF] Thoben | Industrie 4.0" and Smart Manufacturing. A Review of Research Issues and Application Examples (Mini Special Issue on Smart Manufacturing)[END_REF] state, that establishing a cyber physical logistics system (CPLS) can increase efficiency and flexibility in the production environment. The combination of smart manufacturing and logistics will generate the optimal value stream to fulfill real-time demands. Supporting that statement Auffermann et al. [START_REF] Auffermann | Cyber Physical Systems in der Logistik[END_REF] state, the transportation systems will play an important role in the integration of cyber physical systems. Every production systems needs a flexible and dynamic material handling component. By now forklift trucks are able to identify the transported good and establish a communication to exchange information on the destination where the product wants to go [START_REF]Acatech Study: Industrie 4.0 Maturity Index. Managing the Digital Transformation of Companies[END_REF]. Due to this fact, new decentral approaches and innovative services need to be established for a one piece flow in a manufacturing environment, especially in small and medium sized businesses considering cellular logistics systems. These businesses usually use a job shop layout with a number of jobs, each combining a set of operations. These are processed by assigned machines in a job specific sequence. In nearly all instances, material handling systems are used to move, buffer and store raw material as well as work-in-progress. Those are called Job Shop Scheduling with Material Handling (JSSMH) or Job Shop Scheduling Problem with Transportation (JSPT). Taking into account the type and number of AGVs, picking up materials after an operation is completed and providing it to the next machine, the problem is called Job Shop Scheduling with Autonomous Guided Vehicles (JSP-AGV).

Problem Description

A job shop is given, if a set of 𝑛 independent jobs 𝐽 = {𝐽 1 , 𝐽 2 , … , 𝐽 𝑛 } has to be processed on a set of 𝑚 machines with 𝑀 = {𝑀 1 , 𝑀 2 , … , 𝑀 𝑚 }. Each job 𝐽 𝑖 ∈ 𝐽 , with index 𝑖 for the job and j for the operation, can be described as a sequence of operations 𝑜 𝑖𝑗 on machine 𝜇 𝑖𝑗 𝜖 𝑀 with the processing time 𝑝 𝑖𝑗 . For every operation, a set of machines 𝐴(𝑗) is assigned, which can possibly process it, representing optional parallel machines.

The processing order will be called precedence constraint. Once an operation is started, it cannot be interrupted (called preemption) and only one operation can be processed on a machine at a time. Reentrant processes are also considered. They are typical for semiconductor manufacturers or similar production processes with extremely expensive machines. Operations are processed on the same machine twice, with at least one intermediate step in between. This situation occurs, if a product is tested after assembly, repaired and tested again or more than one layer of coating is applied to the product. Exemplarily, Fig. 1 shows the correct sequence of operations with 𝑂 𝑖,𝑗+2 on machine 𝑀 𝑘 after 𝑂 𝑖 𝑗+1 on Machine 𝑀 𝑘+1 and 𝑂 𝑖𝑗 being processed on 𝑀 𝑘 earlier.

Regular job shops consider infinite buffer space between two intermediate operations, one on each side of the machine. In case of absence of the intermediate storage place, the problem can be considered as blocking environment. In the blocking scenario, machines cannot process any other operation until the last processed object has been cleared/unloaded from the machine. The operation of a processed job has to remain on the machine until the next machine is available. This situation is presented in Fig. 1, with 𝐵 𝑖,𝑗+1 (dashed red box) being the blocking time added to the process duration of 𝑂 𝑖,𝑗+1 . These circumstances will delay the start of the next operation of the upcoming jobs. This situation is well known in industrial environments and commonly found in scheduling train-yards or surgeries in a hospital. setup times [START_REF] Gröflin | A new neighborhood and tabu search for the blocking job shop[END_REF] are known from literature and have been calculated without setup times on a CPU-GPU combination [START_REF] Dabah | GPU-Based Two Level Parallel B&B for the Blocking Job Shop Scheduling Problem[END_REF]. A heuristic solution generating a schedule by particle swarm optimization is provided by Zhao et al. [START_REF] Zhao | An improved particle swarm optimisation with a linearly decreasing disturbance term for flow shop scheduling with limited buffers[END_REF]. Using a single criteria tabu-search [START_REF] Bouibede | A Tabu Search for the Bicriteria Scheduling Problem of Job-Shop with Blocking[END_REF] or multi criteria tabu-search [START_REF] Lakehal | New algorithm of scheduling based on prioriry rules on machines to solve bi-criteria blocking job shop scheduling problem[END_REF] seems to be promising as well.

Adding transport operations to the job shop problem, the formulation to classify the environment used by Graham [START_REF] Graham | Optimization and approximation in deterministic sequencing and scheduling: a survey[END_REF] must be enhanced by material transport operations 𝑇 𝑖𝑗 , representing the transportation when 𝑜 𝑖𝑗 is processed on machine 𝜇 𝑖𝑗 and 𝑜 𝑖𝑗+1 is processed on machine 𝜇 𝑖𝑗+1 . Also empty travel operations 𝑇´𝑖 𝑗 (green dotted box) have to be considered, subject to the same rules as 𝑇 𝑖𝑗 , seen in Fig. 1. Taking into account the transport operation of an AGV and the absence of a buffer at the machine, there is a crucial dependency between the schedule of the machine and the AGV. These can be solved by a nonlinear model provided by Zeng et al. [START_REF] Zeng | Scheduling of no buffer job shop cells with blocking constraints and automated guided vehicles[END_REF], a linear problem considering assembly and batches [START_REF] Anwar | Integrated scheduling of material handling and manufacturing activities for just-in-time production of complex assemblies[END_REF] and a linear model with handover times reviewed by Poppenborg et al. [START_REF] Poppenborg | Online scheduling of flexible job-shops with blocking and transportation[END_REF]. Other mathematical formulations are provided by Caumond et al. [START_REF] Caumond | An MILP for scheduling problems in an FMS with one vehicle[END_REF] considering a single AGV with limited buffer and Fazlollahtabar considering multiple AGVs and turning points for deadlock resolution [START_REF] Fazlollahtabar | Mathematical model for deadlock resolution in multiple AGV scheduling and routing network. A case study[END_REF]. Given that scheduling AGVs in a job shop is considered NP-hard due to the simultaneous scheduling problems (i) of the machines and (ii) the AGVs in the job shop [START_REF] Nouri | A Classification Schema for the Job Shop Scheduling Problem with Transportation Resources: State-of-the-Art Review[END_REF] typical approaches are heuristic algorithms. Scholz-Reiter et al. [START_REF] Scholz-Reiter | Analysis of Priority Rule-Based Scheduling in Dual-Resource-Constrained Shop-Floor Scenarios[END_REF] presented a solution for general dual resource constrained problems dynamically adjusting scheduling rules based on the environment, which can be adopted to AGVs. Another solution was presented by Ulusoy using a Genetic Algorithms (GA) for the simultaneous scheduling of AGVs and a Flexible Manufacturing System (FMS) with 4 workstations [START_REF] Ulusoy | A genetic algorithm approach to the simultaneous scheduling of machines and automated guided vehicles[END_REF]. Different hybrid approaches combining GA with other methods have been tested as well. Another approach presented by Baruwa is based on colored petri-nets which is providing a fast heuristic solution [START_REF] Baruwa | A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles[END_REF]. Graphical solutions are provided by Lacomme using a memetic algorithm on a non-oriented disjunctive graph and Zhang using a shifting bottleneck procedure based on a disjunctive graph [START_REF] Lacomme | Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles[END_REF][START_REF] Zhang | A modified shifting bottleneck heuristic and disjunctive graph for job shop scheduling problems with transportation constraints[END_REF].

Considering the possible optimization criteria in general, the minimization of the make span is sufficient [START_REF] Nouri | A Classification Schema for the Job Shop Scheduling Problem with Transportation Resources: State-of-the-Art Review[END_REF]. Fazlollahtabar et al. [START_REF] Fazlollahtabar | Methodologies to Optimize Automated Guided Vehicle Scheduling and Routing Problems. A Review Study[END_REF] presented the minimization of the make span (Cmax) as a goal criterion, but considered (weighted) tardiness or the mean flowtime as minimization criteria as well.

Scenario

The considered environment in this paper is a 𝐽6, 𝑅2|𝑝𝑟𝑒𝑐, 𝑡 𝑗𝑘 = 𝑡 𝑘𝑗 , 𝑡' 𝑘𝑙 |𝐶 𝑀𝑎𝑥 using Graham's notation enhanced by Knust [START_REF] Knust | Shop scheduling problems with transportation[END_REF]. The scenario and slight variations are commonly used for benchmarking [START_REF] Deroussi | Simultaneous scheduling of machines and vehicles for the flexible job shop problem[END_REF] as well as the RoboCup Logistics League (reference http://www.robocup-logistics.org/ for more information). Four machine groups (MG 1-4) with 2 parallel identical machines in MG 1 and MG 3 as well as 2 single machines in MG 2 and MG4 are feed by two AGVs (R1-R2), used for material movements. Each order consists of 5 transport operations, executed by the 2 AGVs, and 4 machine operations (one on each group). The considered scenario is depict in Fig. 2. Scenarios closely related to this one have been proposed for AGV schedule comparison by [START_REF] Bilge | A Time Window Approach to Simultaneous Scheduling of Machines and Material Handling System in an FMS[END_REF] and used by [START_REF] Baruwa | A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles[END_REF][START_REF] Zeng | Scheduling of no buffer job shop cells with blocking constraints and automated guided vehicles[END_REF]. Instead of combining the load and unload station in one location, different locations, similar to [START_REF] Poppenborg | Online scheduling of flexible job-shops with blocking and transportation[END_REF], have been considered for this contribution.

The products take a certain route (grey and black lines represent different orders), defined by the product type given at the first entrance into the shop. Due to the operation sequence of the given orders, proceeding constrains are assumed. If a machine has processed an operation, it is blocked until the next transport operation unloads the machine. No operation can be substituted by another machine and no operation can be skipped. Machine breakdown and maintenance are not considered. The first operation of each robot is the pickup of a product from a transfer station and making it available to the first machine. As mentioned before, the transport time depends on the layout of the machines. In Fig. 2, transport operations with solid lines are processed by Robot M2 and dashed lines are taken care of by Robot M1. The optimal assignment has to be calculated, see Section 4. The last operation is delivering the final product to the sink (transfer station M9), disposing the product from the shop floor. The movement of the AGV is not bound by any loop or network. The AGV takes the shortest path, calculated on a given map. A transport operation contains the pickup, the transport and the drop off of a product. The loading capacity of the AGV is one object per transport operation. In this paper, the transportation time is considered to be a given amount of time, depending on the position of the machines. Once an AGV has completed the operation and is idle, it stays where it finished the task. Dwell and idle points are not considered in this study. Enhancing the notation of Gröflin [START_REF] Gröflin | The flexible blocking job shop with transfer and set-up times[END_REF] with the ideas of Poppenborg [START_REF] Poppenborg | Online scheduling of flexible job-shops with blocking and transportation[END_REF], leads to a model with reentrant processes, blocking and transfer times as well as the possibility to consider set-up times for each order. The model divides every operation into multiple steps and synchronizes the start of a step with its predecessor's steps. This mixed integer linear model has been used for all following calculations. Fig. 2. In this example the 2 jobs take simple routes through the system, including 5 transport operations.

Experimental results

All calculations are done for a static scenario, where all jobs are known prior to the start. For both models, the same set of jobs was considered. Calculation in this case were done on Intel® Core i7-2600 and solved with Gurobi 7.0.2 [START_REF] Gurobi Optimization | Gurobi Optimizer Reference Manual[END_REF] modelled with AMPL [START_REF] Fourer | AMPL. A Mathematical Programming Language[END_REF]. In Fig. 3 the CPU Calculation times are presented for instances with increasing number of jobs. It can be seen, that with an increasing number of jobs the calculation time increases drastically, as expected.

Taking into account the fact that the solver did not find an optimal solution for 6 and more orders after 48 Hours, the solver time limit for all further calculations was set to 120 minutes. During that time, the solver should be able to find a feasible solution but not the optimal answer. This holds up to 10 jobs of the new model, but not for the reference model. After 120 minutes the solver was not able to calculate a feasible solution for 8 and more orders in the reference model. In Fig. 4 the best solution for Cmax is presented. For all instances with more than 5 orders the feasible solution after 120 minutes (7200 seconds) calculation time and its gap are displayed. The gap is the difference from the last feasible solution to the lower bound, being zero proving an optimal solution. Still these results proof optimal solutions for small instances up to 5 orders. The development of the gap over time can be seen in Fig. 5. In the figure it can be seen that the time to calculate the first feasible solutions seems to depend on the size of the problem. Increasing the number of AGVs, machines or operations will lead to rising calculation times as well, but the impact has not been tested so far. The detailed convergence behavior will be looked at in the future. The effect of applying dynamical influences to process-and transport times has not been evaluated so far. The different feasible solutions and gaps due to the different notation of the models, which can be seen in Fig. 4, have to be analyzed as well.

Outlook and Conclusion

Considering a small process like a flexible manufacturing system with no more than 4 machine groups and 2 AGVs, the developed MILP provides an optimal near real time solution for online applications, up to 4 orders in the system. Considering 5 to 10 orders, feasible but not optimal solutions can be presented within reasonable time frames. This shows the amount of orders as a crucial variable to the problem. The usage of powerful solvers and the use of cloud infrastructure can improve the results to a certain extent. It has to be take into account, that real complex production systems can contain multiple FMS being supplied by more than two AGVs. This leads to the fact, that this approach can no longer be used for the efficient scheduling of large systems. Concluding, the small amount of orders which can be calculated motivates the search for faster algorithms which can cope with larger amounts of orders.

In further research, it has to be evaluated how the program behaves on a rolling time horizon base, to be able to represent the dynamic behavior of a plant, for example, machine failure or priority order. Moreover, in further research other approaches to the larger instances of BJS-AGV problem, such as dynamic rule-based dispatching of AGV's, have to be considered, developed and evaluated. These new approaches, e.g. Heger et al. [START_REF] Heger | Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times[END_REF], can be assessed in comparison to the optimal solution provided by this model.

Fig. 1 .

 1 Fig. 1. Empty travel times and blocking are considered in the new linear model.

Fig. 3 .Fig. 4 .

 34 Fig.3. The calculation time increases drastically with the addition of jobs, especially from 5 to 6 orders, resulting in more than 48 h calculations without an optimal solution.

Fig. 5 .

 5 Fig. 5. The larger the instance the longer the first feasible solution takes to calculate.


Dr Volker Stich
email: volker.stich@fir.rwth-aachen.de

Moritz Schröter
email: moritz.schroeter@fir.rwth-aachen.de

Felix Jordan
email: felix.jordan@fir.rwth-aachen.de

Lucas Wenger
email: lucas.wenger@fir.rwth-aachen.de

Matthias Blum
email: matthias.blum@fir.rwth-aachen.de

Assessment of Counter-Measures for Disturbance Management in Manufacturing Environments

Keywords: Counter Measures, Deviations, Disturbance Management

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Introduction

The amount of data generated in production companies is continuingly growing. One reason for this development is the advancing integration of system control and measurement utilities within the production, due to new cost-efficient, high-performance information technologies. Whereas the number of applications for isolated optimization problems is continuously growing, the analysis of complex processes as found in production systems require suitable data models representing the real world [START_REF] Stich | Big Data Technology for Resilient Failure Management in Production Systems[END_REF].

The research project BigPro addresses this issue by creating a Big Data driven, proactive disturbance management system, capable of processing various data from the manufacturing environment. Within a platform, data will be analyzed for data patterns that indicate possible disturbances in the production system. The results of the analysis is matched with a set of predefined counter-measures and then forwarded to responsible stakeholders. Additionally, generic counter-measures are proposed, if no specific one is available.

This paper deals with a systematic approach to asses counter measures for disturbances in a manufacturing environment. The remainder of this paper is organized as follows: First, we give a literature overview of existing approaches to disturbance management and classification of measures in Section 2. Next, we introduce the concept of the proposed methodology to evaluate counter measure in Section 3 describe the underlying calculation scheme. Finally, we conclude in Section 4 and highlight future work.

Literature Review

Before describing the developed assessment model, the terms of disturbance and deviation in context of manufacturing environments are explained. Furthermore, existing concepts of disturbance management are reviewed.

Disturbance variables in operations management

In manufacturing practice as well as in research the concepts of disturbances and deviations are often used as a synonym. Disturbances interrupt or at least affect a running production or logistics process [START_REF] Meyer | Logistisches Störungsmanagement in kundenverbrauchsorientierten Wertschöpfungsketten[END_REF]. Deviations are unexpected differences between the actual and the planned state, which must not lead to failure but could be indicators for disturbances [START_REF] Meyer | Logistisches Störungsmanagement in kundenverbrauchsorientierten Wertschöpfungsketten[END_REF][START_REF] Schmitz | Big Data im Störmanagement[END_REF].

To measure such disturbances a set of disturbance variables is necessary, which are recorded and observed through time. Disturbance variables are distinguished by their relation to a process, a temporal progress, an impact and a reason [START_REF] Heil | Entstörung betrieblicher Abläufe[END_REF]. Furthermore, deviation variables are divided into exogenous and endogenous depending on the sphere of influence of the affected company [START_REF] Bauer | Instandhaltungsmanagement[END_REF]. Research has shown that less than 30 % of disturbances in manufacturing are exogenous [START_REF] Heil | Entstörung betrieblicher Abläufe[END_REF]. Since the project scope lies on process-related disturbances on the Shopfloor, exogenous disturbances are no longer considered.

Disturbance Management

To cope with the high number of possible deviations in an ever-complex production environment disturbance must be handled systematically. SIMON defines the disturbance management in the context of production control. It is task of the disturbance management to initiate appropriate reactions to significant deviations between logistics aims and conclusion from feedback data [START_REF] Simon | Fertigungsregelung durch zielgrössenorientierte Planung und logistisches Störungsmanagement[END_REF]. FISCHÄDER extends the definition by considering the purpose of disturbance management from an economical perspective. Therefore, goal of the disturbance management is the minimization of costs of the disturbance itself and the taken measures [START_REF] Fischäder | Störungsmanagement in netzwerkförmigen Produktionssystemen[END_REF].

The disturbance management consists of three different steps. Firstly a disturbance is indicated by a deviation e.g. of a defined indicator. Secondly, the derivation is analyzed and decided if a counter measure has to be taken. Depending on affected object and type of derivation a corresponding counter measures has to be selected and implemented. Afterwards it is determined, if the measure has been successful and the disturbance is remedied. Otherwise another measure hast to be taken [START_REF] Heil | Entstörung betrieblicher Abläufe[END_REF][START_REF] Fischäder | Störungsmanagement in netzwerkförmigen Produktionssystemen[END_REF].

FISCHÄDER's approach focusses on the design of production systems and adaptions of the capacities. Those adaption measures are implemented in several models and directly related to logistics KPIs. Although this is very precise depiction of possible options, it stays on a rather theoretical level [START_REF] Fischäder | Störungsmanagement in netzwerkförmigen Produktionssystemen[END_REF].

LEITÃO develops a holonic architecture for disturbance management for flexible manufacturing systems. It comprises of holons for resources, maintenance, quality control, products and the supervisor. The chosen approach allows the detection and diagnoses of disturbances. The taken measure is always a re-scheduling of orders under consideration of available resources, which are evaluated by their price, location and reputation. In addition a prediction of disturbances is described [START_REF] Leitão | A holonic disturbance management architecture for flexible manufacturing systems[END_REF].

PATTIG and THORHAUER present a disturbance management for rescheduling production orders based on the availability of material and potential factors (machines, equipment and personal). The approach focuses on a methodology for the production control and makes the order release depend on the availability of aforementioned production factors. Disturbances within a production process are not addressed explicitly [START_REF] Patig | Flexible Produktionsfeinplanung mit Hilfe von Planungsschritten[END_REF].

MEYER develops a "disturbance variable management", that allows a precise description of disturbances [START_REF] Knüppel | A Universal Approach to Categorize Failures in Production[END_REF]. Measures are stored in a database and linked manually to a disturbance. Thereby the measures mostly are of conceptual character, e.g. doing a 5 Why Analysis or carrying out an FMEA. The measures are linked to the necessary qualification of staff, which is stored separately [START_REF] Meyer | Whitepaper] Schlussbericht zu dem IGF-Vorhaben StöGröM -Nachhaltiges Störgrößenmanagement in produzierenden KMU[END_REF].

Systemization of measures in disturbance management

Disturbance management includes different types of measures to cope with upcoming disturbances. Those haven been classified by several characteristic (see Table 1). Table 1. Systemization of measures in disturbance management [START_REF] Meyer | Logistisches Störungsmanagement in kundenverbrauchsorientierten Wertschöpfungsketten[END_REF] preventive measures reactive measures analytical statistical repulsive withdrawing cause-related measures impact-related measures First of all measures can be classified into preventive and reactive measures [START_REF] Patig | Flexible Produktionsfeinplanung mit Hilfe von Planungsschritten[END_REF]. Preventive measures aim at the identification and elimination of possible deviations or disturbances. Whereas analytical approaches can lead to the cause of a disturbance, statistical methods only apply to the observed effects and therefore the impact [START_REF] Meyer | Logistisches Störungsmanagement in kundenverbrauchsorientierten Wertschöpfungsketten[END_REF]. Applying preventive measures in most cases is associated with financial expenses and personal expenditures [START_REF] Heil | Entstörung betrieblicher Abläufe[END_REF]. Therefore, it has to be clearly determined, if the consequences of the disturbance justifies the costs or if it is more favorable to react. Furthermore, preventive measures should only be taken when a possible scenario concluding of description of the disturbance and the time is available [START_REF] Heil | Entstörung betrieblicher Abläufe[END_REF].

On the other hand, a measure taken after the disturbance has occurred is called reactive measure. Compared to preventive measures the effectivity is decreased. Nevertheless, these can be sensible when the costs of remedy are lower than the cost effects of the disturbance or the disturbance cannot be predicted at all or with the necessary precision. Reactive measures can be divided further into repulsive measure, which aim for a remedy or decrease of impact and so-called withdrawing measures when the only option is to reduce the spread of the effects. [START_REF] Heil | Entstörung betrieblicher Abläufe[END_REF]. Furthermore, the measures can be classified into cause-related and impact-related. Whereas the former aim for the remedy, the latter can only reduce the effects [START_REF] Heil | Entstörung betrieblicher Abläufe[END_REF].

Conclusion

Whereas the proposed systemization for measures allows the assignment of any realworld measure to one of the fields, the presented approaches lack a detailed evaluation concept. This can be explained with the strong focus on production planning and control [START_REF] Patig | Flexible Produktionsfeinplanung mit Hilfe von Planungsschritten[END_REF] as well as supply chain planning problems [START_REF] Meyer | Logistisches Störungsmanagement in kundenverbrauchsorientierten Wertschöpfungsketten[END_REF][START_REF] Fischäder | Störungsmanagement in netzwerkförmigen Produktionssystemen[END_REF]. In these areas, the impact of single measures on indicators often is hard to describe. Another approach deals with the implementation of several solution concepts [START_REF] Meyer | Kompetenzorientiertes Störgrößenmanagement[END_REF]. For the proposed disturbance, management based on big data an assessment for specific and even more general measures is necessary.

Assessment of Counter-Measures for Disturbance Management in Manufacturing Environments

BigPro focusses on disturbance management in manufacturing environments relying on BigData analytics. Therefore, several information sources on the shop floor are connected to a universal messaging bus. Predefined patterns, which are supplemented during operation, lead to error messages. A combination of error messages then is mapped to a disturbance. Based on a list of possible failures derived from FMEA and other process analytics approaches possible counter measures are selected. In many cases, there is more than one counter measure, depending on the level of criticality or only a few can be initiated, since not all prerequisites are met. For a further description of the platform and the integration of the counter-measures see [START_REF] Stich | Big data implementation for the reaction management in manufacturing systems[END_REF].

In the following, the morphology to describe counter measures is presented before explaining the assessment methodology in detail. The application is shown with a real world example from an industrial partner.

Morphology of counter measures

To assess counter measures, a set of description criterions has been developed. The research follows a desk research approach, combining several aspects of existing work in disturbance management. The results are presented in Table 2 and explained in detail in chapter 3.3.

Mathematical model to assess counter measures

To find appropriate measures and to bring them in an order, a numerical calculation is necessary. Table 3 provides an overview of the proposed evaluation scheme to asses counter measures in disturbance management. Each criterion i is subdivided into five different characteristics c. Each possible characteristic is represented by a value, starting from 0 for not feasible at all up to 4 representing the optimal condition. To adapt the criterions to disturbance-specific cases, a simple weighting formula is introduced.

M= ∑ 𝜔𝜔 𝑖𝑖 • 𝑐𝑐 𝑖𝑖 F 𝑖𝑖=1 ∑ 𝜔𝜔 𝑖𝑖 𝐹𝐹 𝑖𝑖=1 (1)
Let M be the priority of the measure, F the number of features ( 6) wi a weighting factor within a range from 0 (irrelevant) to 4 (highly relevant) and ci the valuation of the feasibility of the feature i. The minimum of M can be 0, whereas the maximum is 4. In the following exemplary characteristics are assigned to any criterion.

Explanation and application

The developed morphology and evaluation scheme was applied at an industrial partner of the research project. The use case deals with an assembly of pedelec carts with manual assembly tasks. The disturbance is a scratched type plate provided by a supplier and labeled in-house. Table 4 provides an overview of possible characteristics, which are explained in detail below. For the presented approach, the duration of measure is defined as the timespan between the observation of a disturbance and the (noticeable) effect of the initiated counter-measure. Since the disturbance management foremost handles short-term disturbances, a short duration of measure is desired. A long time span indicates mid-or long-term measures such as process redesign. The given characteristics have to be adapted to the company's requirements. A duration of less than an hour can lead to significant waste and is not acceptable, whereas other processes allow a relative long time for corrections.

As mentioned before, the disturbance management deals with the minimization of costs [START_REF] Fischäder | Störungsmanagement in netzwerkförmigen Produktionssystemen[END_REF]. Therefore, the trade-off is between costs of the disturbance and costs of a measure. It is up to the company to define the exact bounds of the characteristics; the given values only serve as an example and are derived from the use-case.

The impact of the measure relates to the effect of the measure on the deviation variable or the cause of the disturbance. The more sustainable, the higher the stage. Examples for the solely elimination of the effects of a deviation is the implementation of a 100 % quality control. Hereby it is ensured, that no waste is processed any further without addressing the causes. The reason for the fault remains unknown at the first glance and needs further examination.

For an efficient and effective disturbance management, counter-measures should be standardized and therefore documented in a way that allows employees to take the best actions possible. This accounts for extensive measures, especially [START_REF] Heil | Entstörung betrieblicher Abläufe[END_REF]. It is further distinguished between certain degrees of documentation, e. g. it has to be checked, if the documentation consist of visualizations like flow diagrams, illustrations etc. or is only a short summary and difficult to comprehend if never done before.

A measure can either relate on a certain work place (e.g. a disturbed machine with a defined problem state and solution) or affect the whole supply chain. This is taken into account with the organizational escalation level. Later oftentimes requires additional efforts and resources. Therefore, the former option (a local solution) is preferred in any case the desired level impact of measure (see criterion 3) can be achieved.

Whereas the documentation of a feasible solution is always the best option, the desired resolution can only be achieved if qualified staff is available [START_REF] Meyer | Whitepaper] Schlussbericht zu dem IGF-Vorhaben StöGröM -Nachhaltiges Störgrößenmanagement in produzierenden KMU[END_REF]. This requires an appropriate personal capacity planning and a certain degree of qualified employees. The characteristics therefore take into account the mean degree of qualified personal available to fix the considered disturbance.

Table 5 shows a possible application after setting the characteristics of counter measures within a workshop with the industrial use case partner. Weightings have been set as well as the specific value for each criterion. In the result, the measure scores at 1.7 out of 4 and therefore is comparable to other measures for the specific or similar disturbances.

Conclusion and Outlook

The paper presents an approach to assess pre-defined counter-measures. Therefore, characteristics from existing approaches have been combined. The weighting component enables companies to specify borders in context of their processes and requirements. Once the characteristics for each criterion are defined, new measures can easily be evaluated and connected to newly occurring disturbances. This is a crucial step to increase the automation level of the desired disturbance management. Further research is needed for finding ideal types for several kinds of measures (e.g. logistics measures, maintenance tasks, production re-scheduling).

Table 2 .

 2 Relevant criterions to asses counter measures in BigPro disturbance management

	i	criterion	explanation	ref.
	1 duration of measure	time from detection until recovery	[4]
	2	cost of measure	necessary expenses to perform the measure	[14]
	3	presumed/actual impact of measure	impact of the measure in terms of fixing the symp-toms or the reason	[15]
	4 standardization of m.	current degree and quality of documentation to perform the measure	[4, 11]
	5	organizational escalation level	implementation level of the measure (e.g. ma-chine, plant, supply network)	[16]
	6	degree of qualified staff available	Share of staff, which is qualified to perform the counter measure	[11]

Table 3 .

 3 Evaluation scheme for characteristics of measures

	i	criterion	0	1	characteristic c 2	3	4
	no.	description	not feasible	partly feasible	mainly fea-sible	very feasible	optimal

Table 4 .

 4 Possible criterions with characteristics derived from the industrial use case

	i	criterion	0	1	characteristics ci 2	3	4
	1	duration	> 1 month	1 week -1 month		1 day -1 week	1 hour -1 day	< 1 hour
	2	costs [€]	>100.000 >10.000		>1.000		>100	<100
	3	impact of measure	none	dev. impact decreased	dev. impact eliminated	dev. impact re-solved, cause decreased	Cause eliminated
	4	standardiza-tion of m.r		roughly documented	moderately documented	well documented	fully docu-umented
	5	organiza-tional esca-lation level	Supply Chain	Plant		Shopfloor Machine group work place
		coverage
	6	rate of quali-	0 %	<5 % [5 %; 25 %) [25 %; 50 %]	>50 %
		fied staff

Table 5 .

 5 Exemplary calculation of feasibility of a measure

	i	criterion	characteristic weighting 0 1 2 3 4 w	ci•wi
	1	duration of measure	█	3	3
	2	costs of measure [€]	█	4	8
	3	impact of measure	█	2	2
	4	standardization of measure	█	3	9
	5	org. escalation level	█	1	3
	6	coverage rate of qualified staff	█	4	4
			∑	17	29
			M		1.7

Acknowledgements

The BigPro project is funded by the German Federal Ministry of Education and Research (Project number 01|S14011A-I), in the context of the operational program ICT 2020 -Research for Innovation. The project will end in November 2017.

Special thanks go to: Auto Heinen GmbH, Asseco Solutions AG, Cognesys GmbH, i2solutions GmbH, Robert Bosch GmbH, Software AG, FZI Forschungszentrum Informatik, Werkzeugmaschinenlabor der RWTH Aachen.