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ScienceDirect Procedia CIRP 19 (2014) 39 – 44

Robust Manufacturing Conference (RoMaC 2014)

Event-Based Supply Chain Early Warning System for an Adaptive Production Control E. Genca,*, N. Duffieb, G. Reinharta a

Institute for Machine Tools and Industrial Management (iwb), Technische Universität München, 85748 Garching, Germany b Department of Mechanical Engineering, University of Wisconsin-Madison, Madison,WI 53706, USA

* Corresponding author. Tel.: +49-821-568-8354; fax: +49-821-568-8350. E-mail address: [email protected]

Abstract As a consequence of higher complexity, intra- and cross-company production and logistic processes become vulnerable to unforeseen disturbances. In this paper an Event-based Supply Chain Early Warning System is presented that facilitates real-time identification of critical events within the supply network by using event data. As a result, adaptive situational control of intra-company production processes is enabled. The benefits of this approach regarding logistic objectives are evaluated by using a discrete-event simulation based on a prototypical implementation of a cross-company production scenario.

© B.V.Published This is anbyopen access article under the CC BY-NC-ND license © 2014 2014 Elsevier The Authors. Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of the International Scientific Committee of “RoMaC 2014” in the person of the Conference Selection and peer-review under responsibility of the International Scientific Committee of “RoMaC 2014” in the person of the Conference Chair Prof. Dr.-Ing. Katja Windt. Chair Prof. Dr.-Ing. Katja Windt. Keywords: Production Control; Early Warning System; Supply Chain Event Management; RFID

1. Introduction In recent years, the general conditions for production have become challenging for manufacturing companies [1, 2]. The globalization and the change from a seller’s to a consumer’s market increased the pressure of competition [3]. Hence, companies now often focus on their core competencies and reduce their vertical range of manufacture. Added value is created by a large number of partners organizing themselves in complex supply networks [4–6]. Safety buffers in the form of time and stock are reduced between the network partners and processes are tightly coupled in order to comply with the drive towards efficiency [7]. These developments cause high vulnerability of intra- and cross-company production and logistic processes to unforeseen events (e.g. disturbances) [7]. The ability to react in real-time to disturbances, especially within time-critical processes, becomes more and more important. In this context the availability of real-time data concerning current events in the supply chain is essential. Nowadays, this data is often not available because of a low automation level and a lack of standardization [8].

The network-wide application and integration of modern information technologies such as RFID (Radio Frequency identification) in interconnected manufacturing systems enables an increase in transparency and therefore the ability to handle complexity [9]. Using RFID as a low cost automatic identification technology, it is possible to improve the data acquisition process [10]. Therefore, critical events can be identified and evaluated in real-time by event-based tracking of objects in the network. With early warning information it is further possible to safeguard subsequent processes by determination and implementation of appropriate measures in the field of adaptive production control. In this paper, the focus is on presenting an early warning system (EWS) for production in supply chains and evaluating its benefits regarding logistic objectives by using discrete event simulation.

2212-8271 © 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of the International Scientific Committee of “RoMaC 2014” in the person of the Conference Chair Prof. Dr.-Ing. Katja Windt. doi:10.1016/j.procir.2014.04.076

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E. Genc et al. / Procedia CIRP 19 (2014) 39 – 44

2. Safeguarding of Production Processes 2.1. Supply Chain Management The need for stronger networking in the supply chain is addressed within the field of Supply Chain Management (SCM). In general, SCM focuses on the planning and control of material and information flows in supply chains [11]. These tasks are fulfilled within the strategic level by Supply Chain Design (SCD), the tactical level by Supply Chain Planning (SCP) and the operational level by Supply Chain Execution (SCE). With the availability of modern Information and Communication Technologies (ICT), Supply Chain Event Management (SCEM) has been developed as connector between the SCP and SCE [6]. The main focus of SCEM is to enable the execution of planning specifications by considering current events. Logistic objects (e. g. products) equipped with RFID-tags enable a RFID-based SCEM. Thereby, objectrelated events are generated in real time in the course of the manufacturing of products in the supply chain [6]. The basic functions of SCEM are monitoring, notification, simulation, control and measurement [12]. Monitoring aims at observing current production and logistic processes in the supply chain. When critical deviations related to relevant object features are identified, concerned organizational units are notified. Then, possible measures are evaluated with the help of simulation. Additional activities within SCEM include the implementation and controlling of the chosen measures. Furthermore, the measurement of supply chain performance is part of the functionalities of SCEM. Conceptually, the functions of SCEM correspond to acquisition, analysis and evaluation of information [13]. 2.2. Disruption Management An important challenge in planning and control of the manufacturing in supply chains is the capability to react to unforeseen deviations and disruptions [7]. In this context a disruption can be described as the appearance of an unforeseen temporary state, which has a specific beginning and ending and results in a quantifiable deviation with regard to the target state of a process or object. The timeline of a disruption can be divided into two phases [14]. The latent phase is the time period between the appearance of disruption values (e.g. a traffic jam) and the full consumption of safety buffers. The manifest phase of a disruption starts with the beginning of the disruption impact and ends with its elimination. A further third phase can be added to the beginning of the timeline taking into account signals or patterns that indicate the possible occurrence of a disruption. These three phases are illustrated in Fig. 1. indicative t1

latent

Critical event (t1)

Gain in flexibility

t Costs associated Cost reduction Gain in reaction time CE is identified

Disruption impact begins

t

Fig. 2. Potential of early measure implementation [based on 17]

Beginning of disruption impact (t2)

2.3. Early Warning t

Fig. 1. Timeline of disruptions

Ability to react

CE occurs

manifest t2

Considering these phases is important with regard to the management of disruptions. This involves the structural and process oriented organization of appropriate measures and strategies [15]. Two different strategies can be distinguished in terms of handling disruptions. Prevention strategies aim at avoiding the occurrence of disruptions by eliminating their causes in a long term perspective (e.g. additional maintenance). In contrast, reaction strategies focus on measures that have to be taken in order to minimize the impacts of already existing disruptions (e.g. rescheduling). The selection and realization of measures corresponding to prevention and reaction strategies is fulfilled by using specific information technology (IT) systems. According to their specific tasks in the field of production planning and control (PPC), these systems can be assigned to one of four levels. IT systems on the higher supply chain level (e.g. advanced planning and scheduling systems) offer functionalities for cross-company production planning. Systems of the lower three levels focus on intra-company PPC. In this context Enterprise Resource Planning (ERP) systems fulfill the needs for planning of company resources (e.g. capacity planning). Manufacturing execution systems (MES) extend ERP systems with functionalities for controlling resources and production orders considering real-time data [16]. IT systems on the lowest automation level are in charge of the control of manufacturing machines. Specific assistance systems can be implemented that support users in complex action situations. For this purpose, large amounts of data have to be analyzed by the system in real-time according to the particular application area. When a situation is identified in which there is a need for action, decision makers are assisted in the course of determination and evaluation of appropriate measures with regard to relevant criteria. Hence, software solutions in the field of SCEM represent assistance systems. With regard to Fig. 2, the early implementation of appropriate measures to react to a critical event (CE) is crucial. Costs related to a disruption are reduced by ensuring the access to the entire scope of action [17]. Therefore, research in the field of early warning is becoming more and more important.

Early warning describes activities that identify and inform about critical developments as early as possible. On the basis

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ER2

manufacturing

quality assurance

Tier-1

The reference model is a target information model that allows the integration and use of specific assistance systems in manufacturing companies. By considering the

ER2

transportation

assembly

Logistics

OEM

1InfoBroker

2Event

3.1. Reference Model

ER2

Architecture

IB1

Processes

As a prerequisite for the safeguarding of intra-company processes in cross-company production, the transparency of current events in the supply chain has to be ensured. Based on the continuous analysis of real-time information, it is possible to identify critical events that appear in the course of logistic and production processes upstream of a company. The early warning of the specific organizational unit, which will be affected by a particular critical event, enables determining and taking necessary measures as early as possible and thereby minimizing the potential detrimental effects of a disruption. In the following sections, the concept of an Event-based Early Warning System (eEWS) is introduced, which fulfills the requirements regarding the safeguarding of intra-company production processes. The main components of the eEWS are the Reference Model, the Early Warning System and the Adaptive Production Control.

Event structure

3. Early Warning Based Production Control

specifications of the model, it is ensured that assistance systems can access real-time as well as target data and analyze it with regard to current developments within the supply chain. In this context, relevant elements are the supply chain participants, the value-added processes in the field of production and logistics, the event-based data structure and the reference architecture for the cross-company information management. Companies close to the selling market (e.g. Original Equipment Manufacturers, OEMs) are the focus of the model. Furthermore, necessary processes are specified following the supply chain operations reference model. With regard to production, the essential processes are manufacturing, assembly, disassembly and quality assurance. Concerning logistics, the reference model differentiates between the intraand the inter-company transportation, storage and picking, packing and unpacking as well as receiving and loading. The detailed modeling of the particular process is done using extended event-driven process chains [22, 23]. In the course of objects passing through these particular processes, event data is generated at specific milestones using RFID-technology. The event data follows a particular event structure and is based on the EPCIS standard [24]. In order to describe the specific states of objects in the supply chain, the EPCIS standard was extended by relevant features [9]. The developed event structure contains quantitative and qualitative features, whose expressions allow the description of the individual states of products as well as orders. In particular, features concerning the quality of individual objects (e. g. manufacturing tolerances) can be specified. For the realization of intra- and inter-company information flow, a reference architecture, which covers centralized and decentralized elements, was developed. As shown in Fig. 3, a so called InfoBroker (IB) represents the event manager as the central component of the architecture. Furthermore, each member of the supply chain is equipped with an Event Repository (ER) as a decentralized element. Event data, which is generated within the processes of a specific member of the supply chain, is stored in the company-owned ER. The real-time information exchange between the single supply chain members is organized via the IB according to existing access rights.

Participants

of early warning information, appropriate measures can be initiated in sufficient time in order to minimize the impacts of unforeseen events. In this context an EWS is a specific information system that informs decision makers in companies about developments and events with significance. Thus, the key benefit of using an EWS is the extension of the time available to react to unforeseen events. The process of early warning can be split in several phases. First of all, relevant monitoring areas (e. g. processes) have to be defined in consideration of economic criteria. Then, the technical realization of the acquisition and provision of necessary data has to be organized. Based on the availability of data in the required granularity, the next phase involves the analysis of the present information. The data set is checked considering specific indicators, trends, pattern and experiences in order to identify critical events. A prediction of future developments is made in the following phase. Depending on the level of criticality, early warning information is generated and communicated to affected organizational units. Then, required measures can be determined and implemented. Existing approaches in the context of early warning focus on concepts as part of the strategic controlling [18]. Other approaches are available that address production control in supply chains and companies. Li et al. propose a framework for early warning and proactive control systems in food supply chain networks [19]. Czaja develops an indicatorbased early warning system for quality management in automotive supply chains [20]. Hotz presents a simulationbased early warning system to support the operational PPC [21]. However, these approaches do not allow the determination of specific intra-company measures based on critical events that occur in preceding value-added processes in the supply chain.

Repository

Fig. 3. Overview reference model

On this basis, specific intra-company assistance systems can access the relevant event information. These systems are integrated in the IT-infrastructure of companies on the level of

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MES. Furthermore, access to target data is ensured by using standardized interfaces (e.g. Web Services) to systems on the ERP level. 3.2. Early Warning System The application of the reference model in combination with RFID allows the development of assistance systems as decision support systems. The EWS presented in this paper focuses on safeguarding of intra-company production processes by taking measures in the field of adaptive production control. For this purpose, the main objective of the EWS is to identify critical events within the production and logistic processes upstream of the assisted company and evaluate them with regard to their level of criticality. As a result of the evaluation, the early warning information forms the basis for the determination of measures for handling the critical situation. By identifying critical events as early as possible it is ensured that the entire existing time buffer is available for the management of disruptions. The basis of the early warning concept is the continuous analysis of current processes in the supply chain. In this context, experience from the past is considered in the evaluation of the present event information. With the results of the analysis, prediction of future developments is enabled as illustrated in Fig. 4.

3.3. Adaptive Production Control Based on particular early warning information, specific actions are taken in the course of disruption management. On one hand, affected organizational units are precisely informed about the actual situation. On the other hand, measures are determined and implemented aiming at handling the situation. In the context of the specification of concrete measures, existing external (e.g. working time regulations) as well as internal (e.g. capacity flexibility) restrictions have to be considered first. These restrictions cannot be influenced and therefore limit the scope of action. Based on this limitation, influenceable criteria have to be taken into account. These are represented by internal objectives (e. g. adherence to delivery dates) as well as costs (e. g. production costs). Furthermore, nervousness as a consequence of short term rescheduling has to be considered in the determination of appropriate measures. As mentioned above, relevant strategies in the field of disruption management can be preventive or reactive. With regard to the eEWS, reactive strategies in the short term are the most significant. The eEWS includes a catalog of particular actions with regard to capacity control (e.g. use of floaters) and detailed planning (e.g. rescheduling). 4. Simulation-based Evaluation 4.1. Simulation model

Analysis Experience

Prediction transportation

Past

assembly

Presence

Future

t

Fig. 4. Early warning concept

The evaluation of event data is carried out by a rule-based comparison of current and target values with regard to specific order features. The main focus is on time and quality. With regard to the consideration of past experience, specific performance indicators (PIs) become important. PIs represent operating variables whose determination and observation allow evaluation of work already performed. Generally, PIs are related to single or multiple processes of individual supply chain members. By taking into account the trends in PIs it is possible to identify disturbances that can be potentially critical in the near future. For this purpose, techniques in the field of statistical process control represent appropriate tools (e.g. quality control charts) [25]. Furthermore, the appearance of disruptions can follow signals or patterns [20]. These have to be identified by the EWS in case of recurrence. However, experience-based identification often proves to be difficult because of complex underlying relationships. In the context of the eEWS, a combination of neural networks and fuzzy logic is employed.

The potential of the eEWS to safeguard intra-company production processes was evaluated using a discrete-event simulation. Based on a prototypical implementation at the Institute for Machine Tools and Industrial Management (iwb) of Technische Universität München, a simulation model was designed with the software “Tecnomatix Plant Simulation”. The model represents cross-company production of a high variety product (gearbox) in a hierarchical stable supply chain and enables analysis and quantification of adaptive production control based on early warning. Two Tier-1 suppliers and one OEM represent the relevant supply chain members and are included in the model as illustrated in Fig. 5. According to the make-to-order concept, basic parts of the product are manufactured at the Tier-1 suppliers (the gears and other gearbox components) and delivered shortly thereafter to the OEM for final assembly. The workstations at both OEM and Tier-1 suppliers are organized corresponding to the principle of flow processing. Furthermore, the intra- and cross-company processes, which were defined according to the aforementioned reference model, are tightly coupled and the level of safety buffers is kept low.

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Tier-1 A manufacturing gears

Tier-1 B

OEM assembling gearboxes

manufacturing gearbox components

Fig. 5. Overview of the simulated supply chain

4.2. Simulation scenarios

therefore the beginning of the manifest phase of the disruptions (the mornings of days four and ten). x Scenario 4 differs from Scenario 3 with regard to the time of implementation of the measures. There are no differences between Scenarios 3 and 4 concerning characteristics of the measures. In accordance with the application of the eEWS, the OEM is warned about the missing motor flanges in real-time at the beginning of the disruptions’ latent phase (the evenings of days three and nine) and therefore takes actions one day earlier than in Scenario 3. Table 1 summarizes the four scenarios. Table 1. Configuration of scenarios

Four scenarios were used in the simulation studies. 9600 gear boxes in 54 variants are produced in each scenario. Every simulation scenario covers a 20-day period with eight hours working time per day. Necessary transportation of parts between Tier-1 and OEM and the delivery of gearboxes to the end customer is carried out at the end of each day. In Scenarios 2 to 4, the manufacturing is disturbed at supplier Tier-1 B. The first disruption (D1) occurs at days three and four and the second disruption (D2) occurs at days nine and ten. D1 and D2 affect the daily manufacturing output of gearbox components (motor flange) at Tier-1 B: x As a consequence of D1, only 75% (72 parts) of the daily motor flanges (a gearbox component for one particular variant) can be produced according to schedule on days three and four. The missing 25% (24 parts) are manufactured behind schedule on days five and six. x As a consequence of D2, just 50% (48 parts) of the daily motor flanges can be produced according to schedule on days nine and ten. The missing 50% (48 parts) are manufactured behind schedule on days 11 and 12. The delivery of several motor flange parts to the OEM is therefore delayed and affects the assembly of the particular gearbox variant. The following measures in the field of disruption management are available to the OEM in order to react to disturbances D1 and D2: x Rescheduling: The scheduled assembly sequence is adapted by an exchange of orders (e.g. pull). x Capacity control: The assembly capacity is increased by 5%. For this purpose, an additional working station and floater is integrated in the assembly line. However, this measure needs two days of preparation time. The following, the scenarios were studied: x Scenario 1 serves as the reference scenario and sets the benchmark with regard to relevant objectives. The delivery of the necessary parts between Tier-1 and OEM is carried out without any disruptions. x Scenario 2 represents the worst case scenario. The assembly of the products at the OEM is severely disturbed as a result of the late delivery of necessary motor flanges (disruptions D1 and D2). However, no measures are taken in this scenario to handle disruptions. x Scenario 3 is similar to Scenario 2; however, within this scenario measures with regard to rescheduling and capacity control are implemented by the OEM. The measures are not taken until the detection of missing motor flanges at the receiving inspection of the OEM and

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Disruption occurence

O

X

X

X

Disruption management

O

O

X

X

Early warning information

O

O

O

X

O : no

X : yes

4.3. Results Fig. 6a shows the adherence to delivery dates for all products, and Fig. 6b shows the adherence to delivery dates exclusively for the disturbed product variant in the simulation. For all products, adherence to delivery dates in Scenario 4 is higher than in Scenario 3 by 5% and higher than in Scenario 2 by 6%. For the disturbed product variant however, adherence to delivery dates in Scenario 4 is higher than in Scenario 3 by 29% and higher than in Scenario 2 by 34%. a 1,00

0,80

b 1,00

1,00 0,90

0,91

0,96

0,60

1,00

0,80

0,80

0,60

0,40

0,40

0,20

0,20

0,00

0,46

0,51

0,00 Scenario 1 Scenario 2 Scenario 3 Scenario 4

Scenario 1 Scenario 2 Scenario 3 Scenario 4

Fig. 6. Adherence to delivery dates: (a) overall; (b) disturbed variant

Furthermore, as shown in Fig. 7, the number of products that were delivered too late were reduced by 8.4% from Scenario 2 to Scenario 3 and by 61.9% from Scenario 2 to Scenario 4. 1000 984

800

902

600 400

375

200 0

0 Scenario 1 Scenario 2 Scenario 3 Scenario 4

Fig. 7. Number of gearboxes delivered too late

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The differences between Scenario 2 on one hand and Scenarios 3 and 4 on the other hand illustrate the benefit of the disruption management from the perspective of logistic objectives. By comparing the results of Scenarios 3 and 4, the impact of early warning information in disruption management becomes evident. In Scenario 4, appropriate measures (see Section 4.2) are implemented in real-time enabled by identification of the disruption at the Tier-1 supplier. Thereby, the entire scope of action with regard to internal and external restrictions and flexibility can be accessed. In scenario 3 the measures are not implemented until the beginning of the disruptions’ impact at the OEM. Hence, the limited scope of action reduces the effect of the implemented measures and explains the observed differences. 5. Conclusions This paper presented the concept of an eEWS for safeguarding intra-company production processes. On the basis of a discrete event simulation it was shown that the realtime identification of critical events and early implementation of appropriate measures enables significant reduction of the consequences of unforeseen disruptions. The growing relevance of adherence to delivery dates as a logistic objective in successful global competition requires the integration and use of cross-linked assistance systems. In this context, the eEWS assists companies operating in supply chains in coping with complexity and turbulence. With regard to the eEWS, on one hand, further work is needed on the crosslinking between early warning information and measure determination in the field of adaptive production control. On the other hand, the integration of costs as additional objective in the course of the disruption management needs to be considered. Furthermore, the technology of the eEWS has to be investigated with particular regard to performance (e.g. accuracy and timeliness of early warning information) and cost-effectiveness. In this course, costs related to RFIDtechnology (e.g. tags, reader) and EWS (e.g. horizontal and vertical integration) have to be considered. Acknowledgements This research was supported by the Bavarian Research Foundation within the program for promoting the international cooperation in applied research. Basics of the eEWS were developed within the project “RFID-based Automotive Network” (RAN) funded by the German Federal Ministry of Economics and Technology (BMWi). References [1] Nyhuis P, Münzberg B, Kennemann M. Configuration and regulation of PPC. Production Engineering 3/3/2009. p. 287-294.

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