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The supply chain event management application: a case study
Available online at www.sciencedirect.com
9th IFAC Conference on Manufacturing Modelling, Management and Control Berlin, Germany, August 28-30, 2019
The supply chain event management application: a case study ScienceDirect
Fanny Palma*. Jania A. Saucedo*. J.A. Marmolejo-Saucedo** Roman Rodriguez-Aguilar*** IFAC PapersOnLine 52-13 (2019) 2698–2703 * Universidad Autónoma de Nuevo León, Monterrey, México, e-mail: [email protected]; The supply chain event management application: a case study [email protected]. **Universidad Panamericana, Facultad de Ingeniería, Augusto Rodin 498, México, Ciudad de México, 03920, México; eFanny Palma*. Jania A. Saucedo*. J.A. Marmolejo-Saucedo** Roman Rodriguez-Aguilar*** mail: [email protected]. *** Universidad Panamericana, Escuela de Ciencias Económicas y Empresariales, Augusto Rodin 498, México, Ciudad de México, 03920, México; e-mail: [email protected] * Universidad Autónoma de Nuevo León, Monterrey, México, e-mail: [email protected]; [email protected]. **Universidad Panamericana, Facultad de Ingeniería, Augusto Rodin 498, México, Ciudad de México, 03920, México; email: [email protected]. *** Universidad Panamericana, Escuela de Ciencias Económicas y Empresariales, Augusto Rodin 498, México, Ciudad de México, 03920, México; e-mail: Abstract: A deviation from planned processes reveals [email protected] consequences that a disruption can cause in any area, even more a disruption with Ripple Effect (RE), which implied for the global Supply Chain (SC) when depends essentially from the stakeholders in all levels and chains. The learnt lessons from these tragical events show that many companies could not assess the impact and its side effects, therefore they cannot respond adequately, prolonging the crisis and expanding the disruption. A disruption evaluation model would allow to know from the beginning the potential impact to check carefully into the critical Abstract: A deviation from planned processes reveals the consequences that a disruption can cause in any events to provide the necessary resources to control it. Hence, it is relevant that SC managers know and area, even more a disruption with Ripple Effect (RE), which implied for the global Supply Chain (SC) employ the right tools to focus on the control and the solution; as well to evaluate the impact and the when depends essentially from the stakeholders in all levels and chains. The learnt lessons from these disruption criticality level to mitigate and control the impact with total certainty that the appropriate actions tragical events show that many companies could not assess the impact and its side effects, therefore they are being taken according to the problematic. Copyright © 2019 IFAC cannot respond adequately, prolonging the crisis and expanding the disruption. A disruption evaluation model would allow to know from the beginning the potential impact to check carefully into the critical events to provide the necessary resources to control it. Hence, it is relevant that SC managers know and © 2019, the IFAC (International Federation Automatic Control) Hosting by Elsevier Ltd. the All impact rights reserved. employ right tools to focus on the of control and the solution; as well to evaluate and the disruption criticality level to mitigate and control the impact with total certainty that the appropriate actions are being taken according to the problematic. Copyright © 2019 IFAC © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Keywords: Supply chains andFederation networks;ofRisk Management; Modelling Supply Chain © 2019, IFAC (International Automatic Control) Hosting by Elsevier Ltd.Dynamics. All rights reserved. even higher for enterprises without planification. A disaster or a disruptive event do not notify, is not predictable. However, Catastrophes that have had numerous consequences in the SC the impact could be mitigated if they provide risk management chains and networks; Risk 2011, Management; Modelling Supply Chain and solution models; being anDynamics. opportunity for companies to we have asKeywords: an exampleSupply what happened in Japan in March an event known by many as the RE disrupted not only to build strategies to ease these disruptions. manufacturing companies in the country but many around the even higher for supposed enterprises to without A disaster or Digitalization be aplanification. radical change in the 1. INTRODUCTION world which they were suppliers, the automakers were one of information a disruptive processing event do not notify, is not predictable. However, and use, allowing a significant storage the many affected, the supply problems did not take long to be the impact mitigated if they provide risk management in acould shortbe space, nevertheless, the revolution relays in Catastrophes that have had numerous consequences in the SC as well present affecting the availability of some products in the and solution models; being an opportunity for companies to we have as an example what happened in Japan in March 2011, the potential strategies of the generated information in the market, as a final balance, the fall in shares of some affected build strategies chainstoofease thethese SC disruptions. and their accessibility. The SC an event known by many as the RE disrupted not only to different companies followed the crisis (Park et al. 2013). Nowadays, information digitalization implies new ways to obtain benefits, manufacturing companies in the country but many around the Digitalization supposed to be a radical change in the the geographical borders had disappeared for the SC and its complementing with tools or methodologies to support the world which they were suppliers, the automakers were one of information processing and use, allowing a significant storage chains, increasing its competitive advantage and the management. There are reactive strategies centered in the the many affected, the supply problems did not take long to be as well in a short space, nevertheless, the revolution relays in complexity and vulnerability to handle it as well, considering process design when a disruption is produced and how it is present affecting the availability of some products in the the potential strategies of the generated information in the it requires collaboration of third parties and intermediaries. managed. The Supply Chain Event Management (SCEM) is a market, as a final balance, the fall in shares of some affected different chains of the SC and their accessibility. The SC Besides, a domino effect disruption with an economic tool employed for the disruption. SCEM consists in companies followed the crisis (Park et al. 2013). Nowadays, managing information digitalization implies new ways to obtain benefits, performance impact is more latent for a global SC. In short, an timely identification of the disruptions happening along the the geographical borders had disappeared for the SC and its complementing with tools or methodologies to support the RE occurs when a disruption does not remain located or SC; providing alerts from the digital information to being chains, increasing its competitive advantage and the management. There are reactive strategies centered in the contained at one level of the SC, being expanded in into other aware of what kind of disruptions been occurred to notify complexity and vulnerability to handle it as well, considering process design when a disruptionhave is produced and how it is levels and impacting the SC performance (Dolgui et al. 2018). on time to the decision makers, assessing the criticality level, it requires collaboration of third parties and intermediaries. managed. The Supply Chain Event Management (SCEM) is a the scenario building with the potential contingency and Besides, a domino effect disruption with an economic Despite the fact the companies have an effective planification managing tool employed for the disruption. SCEM consists in activities, and finally, the elaboration of control performance impactstrategies, is more latent global SC. In short, with contingency they for area not exempted from corrective an timely identification of the disruptions happening along the activities to recover the SC operability (Heusler et al. 2006). RE occurs when a disruption does not remain unexpected disruptive events. The impact of theselocated events or is SC; providing alerts from the digital information to being contained at one level of the SC, being expanded in into other aware of what kind of disruptions have been occurred to notify levels and impacting the SC performance (Dolgui et al. 2018). on time to the decision makers, assessing the criticality level, Copyright © 2019 IFAC 2758 Despite the fact the companies have an effective planification the scenario building with the potential contingency and with contingency strategies, they are not exempted from corrective activities, and finally, the elaboration of control activities to recover the SC operability (Heusler et al. 2006). unexpected disruptive events. TheFederation impact ofof these events is Hosting 2405-8963 © 2019, IFAC (International Automatic Control) by Elsevier Ltd. All rights reserved. 1. INTRODUCTION
Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2019.11.615 Copyright © 2019 IFAC 2758
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2. LITERATURE REVIEW Nowadays, the management within the SC is not limited to a company; this is expanded to their stakeholders, giving the opportunity to create the concept Supply Chain Management (SCM). For Hugos (2010), is the processes coordination such as production, inventory and transportation through all the SC participants; achieving a better responsibility and efficiency composition for the targeted market. This management could turn complex due the to the natural implication of it based on the SC members relationship; being exposed to suffer unexpected events despite an adequate planification, impacting its performance and the customer satisfaction as well. Financial losses arise because of the damage fixing, and if the disruption is not controlled on time, the damage would be even higher to many levels in the SC; denominating this a Ripple Effect. If the RE is not mitigated opportunely it could spread to other levels, where the impact cost increases and even take to companies to bankruptcy when they cannot recover after a disruption (Hendricks & Singhal 2005). Dolgui et al. (2018) were the pioneers exploring the term RE in depth and defining it as: the result of the disruptive spread of an initial interruption to other stages of the SC, supplying, production and distribution networks. An exhaustive literature review about the disruption managing within the SC and the RE in the work of Palma (2018), conducted us to the SCEM, being the proper deviations identification to the SC processes; the alerts and detouring analysis of the potential failings or taken place, the control strategies building to recover the SC operability and the proactivity to avoid future disruptive events through a fivefunction cycle. The relevance of risk analysis to disruption events has a range of applications in different sectors with their respective specificities, such as the case of disruptions in supply of electricity that have a huge number of distinctive features. One of the ways to avoid disruptions in such kind of supply chains is multi-supplier power grids (Popov, Krylatov, Zakharov and Ivanov, 2017). 2.1 Supply Chain Event Management (SCEM) SCEM is a tool used as a link between the planification and the SC execution as a recovering strategy in the face of disruptions that may happen (Ivanov et al. 2016). It is integrated by five main functions: process monitoring, disruption notification, possible corrective actions simulation, selection, launching of a control action and measuring to eliminate a future deviation based on Key Performance Indicators (KPI) adjust (Ivanov et al. 2016). Ijioui et al., (2007) defines SCEM as the processes and systems that alert companies to unplanned changes in supply lines or other events so that they can respond with alternatives with support of a five set integrated functionalities.
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For SCEM, the disruption is a key part; being the start for the monitoring, built on the status information about logistical entities managed in SC processes by time and space. The disruption happens comparing the available digital information from the sequence state of the logistical process with the planned, considering a defined tolerance. If the real course is turned from the planned state beyond the tolerance level, there is a disruptive event; forcing to an action impulse (Heusler et al., 2006), being not only an insufficient point of this, but also an excess (Stölzle 2004) as shown in Fig. 1.
Fig. 1. SCEM approach, monitoring and notification (adopted from Heusler et al., 2006). Monitoring The monitoring function is a continuous activity, which involves detecting relevant disruptions in real time; to carry out this functionality, an observation and comparison must be carried out in the execution of planned processes against the real. The particularity of SCEM points to a timely identification of such disruptions, which for Zimmermann (2006) involves four categories of relevant situations: alert trigger: an alert that there is a disruption is issued by the SC member who detects it; status request: an explicit request is made for information on the status of an order. Hence, a process is observed and evaluated; if a disruption occurs as planned; random trigger: orders or processes are randomly selected and then monitored; probabilistic trigger: enough information is available to predict a high probability that the order will be affected by a disruption. Notification For the medical area it was frequent to observe that pain was not treated in time and effectively to certain types of patients, which if not treated properly could predispose to increased complications and infections that ended in surgical interventions (Riker and Fraser, 2011). Many researchers in the field have agreed on the use of protocols and algorithms that promote evidence-based behavior for the correct treatment of the patient, such as pain assessment scales (Chanques et al, 2006). Similarly, once a disruption has been detected in the SC, it is important to notify the members of the SC by means of a protocol to act through the previous evaluation of the disruption itself on a critical scale, in this way, the necessary resources and the way to treat the eventuality will be available in time. Once the disruption has been detected, immediate notification to those responsible is essential for the execution of immediate
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actions. This function seeks to facilitate the flow of information between SC members (Giannakis and Louis, 2011) and consists of the following steps: establish a level of criticality according to the disruption, identify the recipients and send the notification. Fuzzy Logic (FL) supported this phase, FL is a method that tries to reduce the difference between the natural mechanism of human thought and the classical logic for decision making with unclear decision rules. FL was drawn up by Lotfi A. Zadeh, I have proposed the way to process the information allowing the partial belonging of an element to some sets that he called fuzzy sets. In set theory, an element belongs or not to a set, whereas in a diffuse set the boundary is not precisely defined, and the membership degree is subjective and dependent on the domain (DNegri and De Vito, 2006). This tool allows intermediate values to be able to define evaluations between true/false, hot/cold, and helps us for this case to determine how disruptive a process deviation is. Simulation The simulation, allows to estimate the behavior of the disruption in time scales predicting its impact, actions of correction or improvement without disturbing the operation of the real system, to create hypotheses on certain events to validate them and, to analyze in a holistic way the affectation in the SC. Being able to evaluate different scenarios with corrective actions provides a better decision for SC managers. This practice has been recognized and systematically considered in the simulation literature the decision-making process by hypothetical analysis and evaluation of quantitative benefits of SC as determined by Terzi and Cavalieri (2004). The research is also supported by Tako and Robinson's (2012) work on simulation in the context of logistics and SC, where emphasis is placed on the use of simulation paradigms according to the characteristics of the processes in question. Control In the control function the selected corrective actions are carried out as the best alternative offered by the simulation. During this stage it is important to validate that what was desired to obtain according to the simulation and planned again is the result obtained with the effectiveness of the actions taken. Otherwise, at this stage, the process parameters are recalibrated so as not to affect the performance of the SC. Measuring Standardizing actions is the objective of this function. One of the ways to do this is by changing or adjusting the indicators based on the information provided by the same disruption with respect to the initially planned objectives and the lessons learned from the control, which will serve for a new monitoring continuing with the SCEM methodology cycle, providing a proactive and holistic sense, adjusting KPIs or creating new ones to monitor future deviations and avoid disruptions.
3. ADAPTATION FROM SCEM METHODOLOGY TO STUDY CASE The Original Equipment Manufacturer (OEM) automakers are forerunners of cutting-edge operational methods, currently seeking to increase their competitive advantage by establishing partnerships and alliances with their SC partners, however, they are also the ones who can have the greatest losses in the event of a disruption due to working under manufacturing systems like Just in Time (JIT) and Just in Sequence (JIS) that requires a total control and rigorous logistics processes and the aforementioned alliance itself that often becomes a dependency. The case study deals with the SC of an OEM automaker with a production capacity of close to 300,000 units per year, focusing on downstream logistics processes, specifically the distribution process. The distribution process covers domestic and foreign destinations, for domestic distribution is used land transport and for foreign is multimodal transport. Supply Chain Dowstream, downstream processes begin in a logistics yard, where the shipment of finished units is planned and executed in 3 ways, via rail for destinations in the United States (west) and Canada; via multimodal for the rest of the world and eastern United States; and via land for domestic destinations. Only for national distribution is made the direct shipment to dealers, for the rest the shipments are made to reach the vehicles to logistics yards, that is where our case study will cover. According to data from the study Logistics Cost and Service of firma logistics expert Establish Inc. (The Establish Inc., 2010), 49% of logistics costs are attributed to SC's downstream transportation and/or distribution, which is why such disruption is of paramount importance. For the present case, a disruption occurs due to intense rain, which is not common in the area, which causes total and/or partial damage to some finished units, such vehicles were considered for distribution and not having optimal conditions to continue its shipping process would cause the delay of orders. Another consideration is that the loading process does not allow incomplete orders to be shipped, which means that the unavailability of one unit delays the shipment of an order of 10 units, consequently for the multimodal transportation, the ship cannot be delayed or incompletely loaded. For the three destination there are a penalty for delaying equipment that is not loaded on time. The application of the SCEM methodology is carried out by adapting it to the characteristics of the company, to the conditions of its processes, limitations of physical infrastructure and communications, but above all to the fact that this methodology gives the guideline to continue relying on other tools in each of the functions as we will see below. Process monitoring With the support of Information Technologies (IT), the monitoring of the logistic processes of the organization is carried out, it is so that digital information is available in real time, IT such as Radio Frequency Identification (RFID), where there is an object status information module to report any process deviation, that is, what escapes the tolerance limits of
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the process. By means of RFID tracks the flow of the vehicle into SC downstream are tracked. For the case of damage to the vehicles, these were reported at the time of the damage by a member of the SC (alert activator) who had the vehicles in safety. Disruption Notification This stage is supported by FL to transform the inputs obtained from the information available from the disruption (monitoring). Then, the KPIs position them on a critical level scale
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Priority. Matlab software is used to this Fuzzy Logic process, part of this process is illustrated in Fig. 2. It is worth mentioning that each organization or company will have the measurement indicators that suit it best according to their processes and can assign their rules in the same way. The same process is carried out with the severity of the disruption. In Fig. 3 we can see an illustrated summary of the application of FL to obtain the priority and severity of the disruption.
The critical level scale is based on the adaptation of event notification messages in Lonvick's computer networks (2001) to the definitions made by Zimmermann (2006) in the context of SC described in Table 1. Table 1. Index alert Critical Warning Alert Fail Advice
Actions must be taken immediately, a critical condition with high impact to the SC performance in the run. Critical condition of non-compliance with high impact Significant compliance issues with medium impact Slight compliance issues with low impact Normal compliance with no negative impact
Fig. 2. Fuzzyfy the disruption priority with the KIPs affected in the SC process.
The way to obtain the level of criticality of the disruption is based on the FL analysis of the input variables integrated by the priority and severity of the disruption. Priority in a SC means the classification of customers (e.g. customers A, B, C), compliance failures (e.g. delays, product quality problems, non-compliances), which in summary indicates that the greater the importance of the customer and the order, the more the SC will eventually be affected with a disruption with RE. Severity in the context of SC is a concept in terms of a penalty as a remedy for damage. For Zimmermann (2006) severity is the damage metrically measurable of the disruption, where costs will tend to increase in proportion to the disruption and its impact; this is also often used in the occurrence or frequency of the disruptive event. Input variables for both priority and severity are consistent with the performance indicators affected by the disruption. Frequency of disruption, transport time cycle and order fulfillment are some of the indicators used as input variables for priority; for severity, indicators like affected levels of SC, transport costs and remediation costs are considered for this item. Then, fuzzify of variables consists of taking the input variables and determining the membership degree of these in the diffuse sets associated with the diffuse rules. Subsequently, the defuzzify consists to obtain a linguistic value taking as input the fuzzy set previously obtained to give an output value; relying on the most widely used method of centroid defuzzify which lies in changing the membership values in the corresponding rule (For example, disruption frequency=1, transport time cycle=5, order fulfillment=2; then the software gives us the output value of 7 that corresponds to a High
Fig.3. Fuzzy Logic process to obtain priority and severity. Subsequently, supported by a set of rules that determines the criticality level of the disruption with respect to priority and severity, the criticality index of the alert to be sent is obtained. The scale to obtain this index is simple and pre-established by the company, an example of this can be seen in Fig. 4, where the level of criticality of the alert that must be issued to notify is "Critical" as an intersection of the items of High Priority and Very High Severity.
Fig. 4. Criticality scale for disruption priority and severity. Finally, a set of simple rules according to the criticality level will choose the recipients of the mailing list for the notification.
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we can adjust the time scale to which the model is executed to observe the dynamics closely, this is one of the advantages of simulation, we do not need to wait to see the real impact of a disruption because we can visualize it in advance when handling the time scales.
IF alert is <> THEN select recipients <> THEN <> END. The purpose of the notification is to inform decision-makers in a timely manner with a sense of urgency proportional to the level of criticality of the disruption for its correct treatment solution. Simulation model A simulation model is made under the paradigm of system dynamics supported by Tako and Robinson (2012), this includes the OEM's finished vehicle inspection processes, transportation and arrival at first-tier logistics yards, i.e., warehousing logistics yards for foreign destinations and concessionaires for domestic destinations. The model is made with the help of Anylogic software, which has the option to simulate under the three paradigms (agent-based, discrete events and system dynamics) and will allow us to visualize the flow to finished vehicles downstream into the SC, the impact of disruption and analyze recovery actions. The simulation process is carried out in 5 main steps: Step 1: The creation of a stock and flow diagram as a causal structure with system feedback links, stocks identification and the flows that affect them. Step 2: Stock formulas: the software automatically generates the formula of an action according to the user's stock-and-flow diagram and is not editable. The value of the stock is calculated according to the flows in and out of the stock. The flows increase according to the value of the stock that is added and the value of the outputs is subtracted from the current value of the stock. The flows are predefined and averaged until reaching the 3 loading points for transport to their destination. Step 3: Parameters and dependencies: the necessary parameters are added with the corresponding values, some of these are: total daily production=1100 units, nonconforming units=0.05, damaged units (disruption)=200 units, inspection of units=1000 units daily, units with route asigned=1,200±100 units daily, daily load=1200 units, units with destination rest of the world=60% units produced, units with destination USA and Canada=25% units produced, units with domestic destination=15% units produced. Step 4: Establish dependencies: links are used to graphically define the dependencies between the flow chart elements and stock, if necessary, add dependency formulas. For example, the damage of units and their unavailability for the flow towards the following logistic process. Step 5: Execute the model and inspect the dynamics of the variables: compile the model and observe flow through the distribution links, as soon as there is a stock-out or the available stock is exhausted to continue with the flow, it turns red, as can be seen in Fig. 5 (disruption impact). In this phase
Fig. 5. Disruption impact into the SC. Step 6: in addition to the problem raised that allows us to see the impact of the disruption, we can edit some parameters in order to see the disruption of other processes. Based on the critical points identified in the simulation model that in this case are the logistic costs, then alternatives of solution are proposed, opting for the least expensive alternative and with greater possibility of success when evaluated with the software. Disruption control For the implementation of corrective actions and their followup, an action plan is drawn up with responsible parties and dates of completion, which describes immediate contingency actions and long-term corrective actions to prevent the disruption from occurring again. Measurement The necessary changes and adjustments are made to the indicators affected by the disruption; if necessary, new indicators are also created to give it a proactive utility by continuing the monitoring cycle with the new information. 4. RESULTS In the first instance, SCEM's first two functions succeeded in transmitting a sense of urgency to the disruption management procedure, allocating from the outset the necessary resources for its solution, avoiding bureaucratic activities that would delay its solution. By opting to implement the most promising alternative (alternative 2) offered by the simulation, the overall SC performance was less affected, Fig. 6 compares the 3 solution alternatives evaluated.
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Fig. 6. Comparison of solution alternatives Another result of the experimentations which carried out was the identification of a limitation: to perform each of the functions with the support tools, one must contemplate a leader who dominates the use of these tools and have average/advanced knowledge in statistics, otherwise the results would be unsatisfactory. 5. CONCLUSIONS This paper presents an interesting adaptation of SCEM's methodology and the combination of different support tools favourable to SC management, the characteristics of the problem and the expected results. The management of disruptive events becomes a way of obtaining information on the dynamics of the SC itself and a tool for understanding and managing it efficiently in order to minimizing RE, in addition to the fact that when the methodology cycle is completed there is a constant supply of information to avoid future disruptions.
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Palma, F. (2018). Solución Colaborativa de Eventos disruptivos en la Cadena de Suministro. Universidad Autónoma de Nuevo León. México. Park, Y., Hong, P., Jungbae Roh, J. (2013). Supply Chain lessons from the catastrophic natural disaster in Japan. Business horizons. 56, pp 75-85. Popov I., Krylatov, A., Zakharov, V., and Ivanov, D. (2017). Competitive energy consumption under transmission constraints in a multi-supplier power grid system. International Journal of Systems Science. 48(5), pp 9941001. Riker R.R., and Fraser G.L. (2011). Altering intensive care sedation paradigms to improve patient outcomes. Anesthesiology Clin. 29:663–674. Stölzle, W. (2004). Supply Chain Event Management. Gabler Lexikon Logistik: Management logistischer Netzwerke und Füsse, 3, pp. 503–507. Tako, A., Robinson, S. (2012). The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems. 52(4), pp. 802–815. Terzi, S., Cavalieri, S. (2004). Simulation in the supply chain context: a survey. Computers in Industry. 53, 3–16. Zimmermann, R. (2006), Agent-based Supply Network Event Management, Birkhäuser Basel. First edition. Switzerland.
REFERENCES Chanques, G., Jaber, S., Barbotte, E. (2006). Impact of systematic evaluation of pain and agitation in an intensive care unit. Crit Care Med. 34(6):1691-1699. DNegri, C. E. and De Vito, E. L. (2006). Introducción al razonamiento aproximado: lógica difusa. Revista Americana de Medicina Respiratoria. 6(3), pp. 126–136. Dolgui A., Ivanov D., and Sokolov B. (2018). Ripple effect in the supply chain: an analysis and recent literature. International Journal of Production Research, 56:1-2, pp. 414-430. Giannakis, M. and Louis, M. (2011). A multi-agent-based framework for supply chain risk management. Journal of Purchasing and Supply Management. 17(1), pp. 23–31. Hendricks, K. B., and V. R. Singhal. (2005). Association between Supply Chain Glitches and Operating Performance. Management Science 51 (5): 695–711. Heusler, K., Stölzle, W., and Bachmann, H. (2006). Supply Chain Event Management. Grundlagen, Funktionen und potenzielle Akteure, WiSt Heft, pp. 19–24. Hugos, M. (2010). Essentials of Supply Chain Management. Second Edition. John Wiley and Sons. EUA. Ijioui, R., H. Emmerich, M. Ceyp, and Diercks, W. (2007). Supply Chain Event Management als strategisches Unternehmensführungskonzept. Physica Verlag. pp. 3– 13. Ivanov, D., Sokolov B. and Solovyeva, I. (2016). Dynamic recovery policies for time-critical supply chains under conditions of ripple effect. International Journal of Production Research. 54(23), pp. 7245–7258. Lonvick, C. (2001). The BSD syslog Protocol. The Internet Society. pp. 1–29.
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The supply chain event management application: a case study ScienceDirect
Fanny Palma*. Jania A. Saucedo*. J.A. Marmolejo-Saucedo** Roman Rodriguez-Aguilar*** IFAC PapersOnLine 52-13 (2019) 2698–2703 * Universidad Autónoma de Nuevo León, Monterrey, México, e-mail: [email protected]; The supply chain event management application: a case study [email protected]. **Universidad Panamericana, Facultad de Ingeniería, Augusto Rodin 498, México, Ciudad de México, 03920, México; eFanny Palma*. Jania A. Saucedo*. J.A. Marmolejo-Saucedo** Roman Rodriguez-Aguilar*** mail: [email protected]. *** Universidad Panamericana, Escuela de Ciencias Económicas y Empresariales, Augusto Rodin 498, México, Ciudad de México, 03920, México; e-mail: [email protected] * Universidad Autónoma de Nuevo León, Monterrey, México, e-mail: [email protected]; [email protected]. **Universidad Panamericana, Facultad de Ingeniería, Augusto Rodin 498, México, Ciudad de México, 03920, México; email: [email protected]. *** Universidad Panamericana, Escuela de Ciencias Económicas y Empresariales, Augusto Rodin 498, México, Ciudad de México, 03920, México; e-mail: Abstract: A deviation from planned processes reveals [email protected] consequences that a disruption can cause in any area, even more a disruption with Ripple Effect (RE), which implied for the global Supply Chain (SC) when depends essentially from the stakeholders in all levels and chains. The learnt lessons from these tragical events show that many companies could not assess the impact and its side effects, therefore they cannot respond adequately, prolonging the crisis and expanding the disruption. A disruption evaluation model would allow to know from the beginning the potential impact to check carefully into the critical Abstract: A deviation from planned processes reveals the consequences that a disruption can cause in any events to provide the necessary resources to control it. Hence, it is relevant that SC managers know and area, even more a disruption with Ripple Effect (RE), which implied for the global Supply Chain (SC) employ the right tools to focus on the control and the solution; as well to evaluate the impact and the when depends essentially from the stakeholders in all levels and chains. The learnt lessons from these disruption criticality level to mitigate and control the impact with total certainty that the appropriate actions tragical events show that many companies could not assess the impact and its side effects, therefore they are being taken according to the problematic. Copyright © 2019 IFAC cannot respond adequately, prolonging the crisis and expanding the disruption. A disruption evaluation model would allow to know from the beginning the potential impact to check carefully into the critical events to provide the necessary resources to control it. Hence, it is relevant that SC managers know and © 2019, the IFAC (International Federation Automatic Control) Hosting by Elsevier Ltd. the All impact rights reserved. employ right tools to focus on the of control and the solution; as well to evaluate and the disruption criticality level to mitigate and control the impact with total certainty that the appropriate actions are being taken according to the problematic. Copyright © 2019 IFAC © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Keywords: Supply chains andFederation networks;ofRisk Management; Modelling Supply Chain © 2019, IFAC (International Automatic Control) Hosting by Elsevier Ltd.Dynamics. All rights reserved. even higher for enterprises without planification. A disaster or a disruptive event do not notify, is not predictable. However, Catastrophes that have had numerous consequences in the SC the impact could be mitigated if they provide risk management chains and networks; Risk 2011, Management; Modelling Supply Chain and solution models; being anDynamics. opportunity for companies to we have asKeywords: an exampleSupply what happened in Japan in March an event known by many as the RE disrupted not only to build strategies to ease these disruptions. manufacturing companies in the country but many around the even higher for supposed enterprises to without A disaster or Digitalization be aplanification. radical change in the 1. INTRODUCTION world which they were suppliers, the automakers were one of information a disruptive processing event do not notify, is not predictable. However, and use, allowing a significant storage the many affected, the supply problems did not take long to be the impact mitigated if they provide risk management in acould shortbe space, nevertheless, the revolution relays in Catastrophes that have had numerous consequences in the SC as well present affecting the availability of some products in the and solution models; being an opportunity for companies to we have as an example what happened in Japan in March 2011, the potential strategies of the generated information in the market, as a final balance, the fall in shares of some affected build strategies chainstoofease thethese SC disruptions. and their accessibility. The SC an event known by many as the RE disrupted not only to different companies followed the crisis (Park et al. 2013). Nowadays, information digitalization implies new ways to obtain benefits, manufacturing companies in the country but many around the Digitalization supposed to be a radical change in the the geographical borders had disappeared for the SC and its complementing with tools or methodologies to support the world which they were suppliers, the automakers were one of information processing and use, allowing a significant storage chains, increasing its competitive advantage and the management. There are reactive strategies centered in the the many affected, the supply problems did not take long to be as well in a short space, nevertheless, the revolution relays in complexity and vulnerability to handle it as well, considering process design when a disruption is produced and how it is present affecting the availability of some products in the the potential strategies of the generated information in the it requires collaboration of third parties and intermediaries. managed. The Supply Chain Event Management (SCEM) is a market, as a final balance, the fall in shares of some affected different chains of the SC and their accessibility. The SC Besides, a domino effect disruption with an economic tool employed for the disruption. SCEM consists in companies followed the crisis (Park et al. 2013). Nowadays, managing information digitalization implies new ways to obtain benefits, performance impact is more latent for a global SC. In short, an timely identification of the disruptions happening along the the geographical borders had disappeared for the SC and its complementing with tools or methodologies to support the RE occurs when a disruption does not remain located or SC; providing alerts from the digital information to being chains, increasing its competitive advantage and the management. There are reactive strategies centered in the contained at one level of the SC, being expanded in into other aware of what kind of disruptions been occurred to notify complexity and vulnerability to handle it as well, considering process design when a disruptionhave is produced and how it is levels and impacting the SC performance (Dolgui et al. 2018). on time to the decision makers, assessing the criticality level, it requires collaboration of third parties and intermediaries. managed. The Supply Chain Event Management (SCEM) is a the scenario building with the potential contingency and Besides, a domino effect disruption with an economic Despite the fact the companies have an effective planification managing tool employed for the disruption. SCEM consists in activities, and finally, the elaboration of control performance impactstrategies, is more latent global SC. In short, with contingency they for area not exempted from corrective an timely identification of the disruptions happening along the activities to recover the SC operability (Heusler et al. 2006). RE occurs when a disruption does not remain unexpected disruptive events. The impact of theselocated events or is SC; providing alerts from the digital information to being contained at one level of the SC, being expanded in into other aware of what kind of disruptions have been occurred to notify levels and impacting the SC performance (Dolgui et al. 2018). on time to the decision makers, assessing the criticality level, Copyright © 2019 IFAC 2758 Despite the fact the companies have an effective planification the scenario building with the potential contingency and with contingency strategies, they are not exempted from corrective activities, and finally, the elaboration of control activities to recover the SC operability (Heusler et al. 2006). unexpected disruptive events. TheFederation impact ofof these events is Hosting 2405-8963 © 2019, IFAC (International Automatic Control) by Elsevier Ltd. All rights reserved. 1. INTRODUCTION
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2. LITERATURE REVIEW Nowadays, the management within the SC is not limited to a company; this is expanded to their stakeholders, giving the opportunity to create the concept Supply Chain Management (SCM). For Hugos (2010), is the processes coordination such as production, inventory and transportation through all the SC participants; achieving a better responsibility and efficiency composition for the targeted market. This management could turn complex due the to the natural implication of it based on the SC members relationship; being exposed to suffer unexpected events despite an adequate planification, impacting its performance and the customer satisfaction as well. Financial losses arise because of the damage fixing, and if the disruption is not controlled on time, the damage would be even higher to many levels in the SC; denominating this a Ripple Effect. If the RE is not mitigated opportunely it could spread to other levels, where the impact cost increases and even take to companies to bankruptcy when they cannot recover after a disruption (Hendricks & Singhal 2005). Dolgui et al. (2018) were the pioneers exploring the term RE in depth and defining it as: the result of the disruptive spread of an initial interruption to other stages of the SC, supplying, production and distribution networks. An exhaustive literature review about the disruption managing within the SC and the RE in the work of Palma (2018), conducted us to the SCEM, being the proper deviations identification to the SC processes; the alerts and detouring analysis of the potential failings or taken place, the control strategies building to recover the SC operability and the proactivity to avoid future disruptive events through a fivefunction cycle. The relevance of risk analysis to disruption events has a range of applications in different sectors with their respective specificities, such as the case of disruptions in supply of electricity that have a huge number of distinctive features. One of the ways to avoid disruptions in such kind of supply chains is multi-supplier power grids (Popov, Krylatov, Zakharov and Ivanov, 2017). 2.1 Supply Chain Event Management (SCEM) SCEM is a tool used as a link between the planification and the SC execution as a recovering strategy in the face of disruptions that may happen (Ivanov et al. 2016). It is integrated by five main functions: process monitoring, disruption notification, possible corrective actions simulation, selection, launching of a control action and measuring to eliminate a future deviation based on Key Performance Indicators (KPI) adjust (Ivanov et al. 2016). Ijioui et al., (2007) defines SCEM as the processes and systems that alert companies to unplanned changes in supply lines or other events so that they can respond with alternatives with support of a five set integrated functionalities.
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For SCEM, the disruption is a key part; being the start for the monitoring, built on the status information about logistical entities managed in SC processes by time and space. The disruption happens comparing the available digital information from the sequence state of the logistical process with the planned, considering a defined tolerance. If the real course is turned from the planned state beyond the tolerance level, there is a disruptive event; forcing to an action impulse (Heusler et al., 2006), being not only an insufficient point of this, but also an excess (Stölzle 2004) as shown in Fig. 1.
Fig. 1. SCEM approach, monitoring and notification (adopted from Heusler et al., 2006). Monitoring The monitoring function is a continuous activity, which involves detecting relevant disruptions in real time; to carry out this functionality, an observation and comparison must be carried out in the execution of planned processes against the real. The particularity of SCEM points to a timely identification of such disruptions, which for Zimmermann (2006) involves four categories of relevant situations: alert trigger: an alert that there is a disruption is issued by the SC member who detects it; status request: an explicit request is made for information on the status of an order. Hence, a process is observed and evaluated; if a disruption occurs as planned; random trigger: orders or processes are randomly selected and then monitored; probabilistic trigger: enough information is available to predict a high probability that the order will be affected by a disruption. Notification For the medical area it was frequent to observe that pain was not treated in time and effectively to certain types of patients, which if not treated properly could predispose to increased complications and infections that ended in surgical interventions (Riker and Fraser, 2011). Many researchers in the field have agreed on the use of protocols and algorithms that promote evidence-based behavior for the correct treatment of the patient, such as pain assessment scales (Chanques et al, 2006). Similarly, once a disruption has been detected in the SC, it is important to notify the members of the SC by means of a protocol to act through the previous evaluation of the disruption itself on a critical scale, in this way, the necessary resources and the way to treat the eventuality will be available in time. Once the disruption has been detected, immediate notification to those responsible is essential for the execution of immediate
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actions. This function seeks to facilitate the flow of information between SC members (Giannakis and Louis, 2011) and consists of the following steps: establish a level of criticality according to the disruption, identify the recipients and send the notification. Fuzzy Logic (FL) supported this phase, FL is a method that tries to reduce the difference between the natural mechanism of human thought and the classical logic for decision making with unclear decision rules. FL was drawn up by Lotfi A. Zadeh, I have proposed the way to process the information allowing the partial belonging of an element to some sets that he called fuzzy sets. In set theory, an element belongs or not to a set, whereas in a diffuse set the boundary is not precisely defined, and the membership degree is subjective and dependent on the domain (DNegri and De Vito, 2006). This tool allows intermediate values to be able to define evaluations between true/false, hot/cold, and helps us for this case to determine how disruptive a process deviation is. Simulation The simulation, allows to estimate the behavior of the disruption in time scales predicting its impact, actions of correction or improvement without disturbing the operation of the real system, to create hypotheses on certain events to validate them and, to analyze in a holistic way the affectation in the SC. Being able to evaluate different scenarios with corrective actions provides a better decision for SC managers. This practice has been recognized and systematically considered in the simulation literature the decision-making process by hypothetical analysis and evaluation of quantitative benefits of SC as determined by Terzi and Cavalieri (2004). The research is also supported by Tako and Robinson's (2012) work on simulation in the context of logistics and SC, where emphasis is placed on the use of simulation paradigms according to the characteristics of the processes in question. Control In the control function the selected corrective actions are carried out as the best alternative offered by the simulation. During this stage it is important to validate that what was desired to obtain according to the simulation and planned again is the result obtained with the effectiveness of the actions taken. Otherwise, at this stage, the process parameters are recalibrated so as not to affect the performance of the SC. Measuring Standardizing actions is the objective of this function. One of the ways to do this is by changing or adjusting the indicators based on the information provided by the same disruption with respect to the initially planned objectives and the lessons learned from the control, which will serve for a new monitoring continuing with the SCEM methodology cycle, providing a proactive and holistic sense, adjusting KPIs or creating new ones to monitor future deviations and avoid disruptions.
3. ADAPTATION FROM SCEM METHODOLOGY TO STUDY CASE The Original Equipment Manufacturer (OEM) automakers are forerunners of cutting-edge operational methods, currently seeking to increase their competitive advantage by establishing partnerships and alliances with their SC partners, however, they are also the ones who can have the greatest losses in the event of a disruption due to working under manufacturing systems like Just in Time (JIT) and Just in Sequence (JIS) that requires a total control and rigorous logistics processes and the aforementioned alliance itself that often becomes a dependency. The case study deals with the SC of an OEM automaker with a production capacity of close to 300,000 units per year, focusing on downstream logistics processes, specifically the distribution process. The distribution process covers domestic and foreign destinations, for domestic distribution is used land transport and for foreign is multimodal transport. Supply Chain Dowstream, downstream processes begin in a logistics yard, where the shipment of finished units is planned and executed in 3 ways, via rail for destinations in the United States (west) and Canada; via multimodal for the rest of the world and eastern United States; and via land for domestic destinations. Only for national distribution is made the direct shipment to dealers, for the rest the shipments are made to reach the vehicles to logistics yards, that is where our case study will cover. According to data from the study Logistics Cost and Service of firma logistics expert Establish Inc. (The Establish Inc., 2010), 49% of logistics costs are attributed to SC's downstream transportation and/or distribution, which is why such disruption is of paramount importance. For the present case, a disruption occurs due to intense rain, which is not common in the area, which causes total and/or partial damage to some finished units, such vehicles were considered for distribution and not having optimal conditions to continue its shipping process would cause the delay of orders. Another consideration is that the loading process does not allow incomplete orders to be shipped, which means that the unavailability of one unit delays the shipment of an order of 10 units, consequently for the multimodal transportation, the ship cannot be delayed or incompletely loaded. For the three destination there are a penalty for delaying equipment that is not loaded on time. The application of the SCEM methodology is carried out by adapting it to the characteristics of the company, to the conditions of its processes, limitations of physical infrastructure and communications, but above all to the fact that this methodology gives the guideline to continue relying on other tools in each of the functions as we will see below. Process monitoring With the support of Information Technologies (IT), the monitoring of the logistic processes of the organization is carried out, it is so that digital information is available in real time, IT such as Radio Frequency Identification (RFID), where there is an object status information module to report any process deviation, that is, what escapes the tolerance limits of
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the process. By means of RFID tracks the flow of the vehicle into SC downstream are tracked. For the case of damage to the vehicles, these were reported at the time of the damage by a member of the SC (alert activator) who had the vehicles in safety. Disruption Notification This stage is supported by FL to transform the inputs obtained from the information available from the disruption (monitoring). Then, the KPIs position them on a critical level scale
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Priority. Matlab software is used to this Fuzzy Logic process, part of this process is illustrated in Fig. 2. It is worth mentioning that each organization or company will have the measurement indicators that suit it best according to their processes and can assign their rules in the same way. The same process is carried out with the severity of the disruption. In Fig. 3 we can see an illustrated summary of the application of FL to obtain the priority and severity of the disruption.
The critical level scale is based on the adaptation of event notification messages in Lonvick's computer networks (2001) to the definitions made by Zimmermann (2006) in the context of SC described in Table 1. Table 1. Index alert Critical Warning Alert Fail Advice
Actions must be taken immediately, a critical condition with high impact to the SC performance in the run. Critical condition of non-compliance with high impact Significant compliance issues with medium impact Slight compliance issues with low impact Normal compliance with no negative impact
Fig. 2. Fuzzyfy the disruption priority with the KIPs affected in the SC process.
The way to obtain the level of criticality of the disruption is based on the FL analysis of the input variables integrated by the priority and severity of the disruption. Priority in a SC means the classification of customers (e.g. customers A, B, C), compliance failures (e.g. delays, product quality problems, non-compliances), which in summary indicates that the greater the importance of the customer and the order, the more the SC will eventually be affected with a disruption with RE. Severity in the context of SC is a concept in terms of a penalty as a remedy for damage. For Zimmermann (2006) severity is the damage metrically measurable of the disruption, where costs will tend to increase in proportion to the disruption and its impact; this is also often used in the occurrence or frequency of the disruptive event. Input variables for both priority and severity are consistent with the performance indicators affected by the disruption. Frequency of disruption, transport time cycle and order fulfillment are some of the indicators used as input variables for priority; for severity, indicators like affected levels of SC, transport costs and remediation costs are considered for this item. Then, fuzzify of variables consists of taking the input variables and determining the membership degree of these in the diffuse sets associated with the diffuse rules. Subsequently, the defuzzify consists to obtain a linguistic value taking as input the fuzzy set previously obtained to give an output value; relying on the most widely used method of centroid defuzzify which lies in changing the membership values in the corresponding rule (For example, disruption frequency=1, transport time cycle=5, order fulfillment=2; then the software gives us the output value of 7 that corresponds to a High
Fig.3. Fuzzy Logic process to obtain priority and severity. Subsequently, supported by a set of rules that determines the criticality level of the disruption with respect to priority and severity, the criticality index of the alert to be sent is obtained. The scale to obtain this index is simple and pre-established by the company, an example of this can be seen in Fig. 4, where the level of criticality of the alert that must be issued to notify is "Critical" as an intersection of the items of High Priority and Very High Severity.
Fig. 4. Criticality scale for disruption priority and severity. Finally, a set of simple rules according to the criticality level will choose the recipients of the mailing list for the notification.
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we can adjust the time scale to which the model is executed to observe the dynamics closely, this is one of the advantages of simulation, we do not need to wait to see the real impact of a disruption because we can visualize it in advance when handling the time scales.
IF alert is <
Fig. 5. Disruption impact into the SC. Step 6: in addition to the problem raised that allows us to see the impact of the disruption, we can edit some parameters in order to see the disruption of other processes. Based on the critical points identified in the simulation model that in this case are the logistic costs, then alternatives of solution are proposed, opting for the least expensive alternative and with greater possibility of success when evaluated with the software. Disruption control For the implementation of corrective actions and their followup, an action plan is drawn up with responsible parties and dates of completion, which describes immediate contingency actions and long-term corrective actions to prevent the disruption from occurring again. Measurement The necessary changes and adjustments are made to the indicators affected by the disruption; if necessary, new indicators are also created to give it a proactive utility by continuing the monitoring cycle with the new information. 4. RESULTS In the first instance, SCEM's first two functions succeeded in transmitting a sense of urgency to the disruption management procedure, allocating from the outset the necessary resources for its solution, avoiding bureaucratic activities that would delay its solution. By opting to implement the most promising alternative (alternative 2) offered by the simulation, the overall SC performance was less affected, Fig. 6 compares the 3 solution alternatives evaluated.
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Fig. 6. Comparison of solution alternatives Another result of the experimentations which carried out was the identification of a limitation: to perform each of the functions with the support tools, one must contemplate a leader who dominates the use of these tools and have average/advanced knowledge in statistics, otherwise the results would be unsatisfactory. 5. CONCLUSIONS This paper presents an interesting adaptation of SCEM's methodology and the combination of different support tools favourable to SC management, the characteristics of the problem and the expected results. The management of disruptive events becomes a way of obtaining information on the dynamics of the SC itself and a tool for understanding and managing it efficiently in order to minimizing RE, in addition to the fact that when the methodology cycle is completed there is a constant supply of information to avoid future disruptions.
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Palma, F. (2018). Solución Colaborativa de Eventos disruptivos en la Cadena de Suministro. Universidad Autónoma de Nuevo León. México. Park, Y., Hong, P., Jungbae Roh, J. (2013). Supply Chain lessons from the catastrophic natural disaster in Japan. Business horizons. 56, pp 75-85. Popov I., Krylatov, A., Zakharov, V., and Ivanov, D. (2017). Competitive energy consumption under transmission constraints in a multi-supplier power grid system. International Journal of Systems Science. 48(5), pp 9941001. Riker R.R., and Fraser G.L. (2011). Altering intensive care sedation paradigms to improve patient outcomes. Anesthesiology Clin. 29:663–674. Stölzle, W. (2004). Supply Chain Event Management. Gabler Lexikon Logistik: Management logistischer Netzwerke und Füsse, 3, pp. 503–507. Tako, A., Robinson, S. (2012). The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems. 52(4), pp. 802–815. Terzi, S., Cavalieri, S. (2004). Simulation in the supply chain context: a survey. Computers in Industry. 53, 3–16. Zimmermann, R. (2006), Agent-based Supply Network Event Management, Birkhäuser Basel. First edition. Switzerland.
REFERENCES Chanques, G., Jaber, S., Barbotte, E. (2006). Impact of systematic evaluation of pain and agitation in an intensive care unit. Crit Care Med. 34(6):1691-1699. DNegri, C. E. and De Vito, E. L. (2006). Introducción al razonamiento aproximado: lógica difusa. Revista Americana de Medicina Respiratoria. 6(3), pp. 126–136. Dolgui A., Ivanov D., and Sokolov B. (2018). Ripple effect in the supply chain: an analysis and recent literature. International Journal of Production Research, 56:1-2, pp. 414-430. Giannakis, M. and Louis, M. (2011). A multi-agent-based framework for supply chain risk management. Journal of Purchasing and Supply Management. 17(1), pp. 23–31. Hendricks, K. B., and V. R. Singhal. (2005). Association between Supply Chain Glitches and Operating Performance. Management Science 51 (5): 695–711. Heusler, K., Stölzle, W., and Bachmann, H. (2006). Supply Chain Event Management. Grundlagen, Funktionen und potenzielle Akteure, WiSt Heft, pp. 19–24. Hugos, M. (2010). Essentials of Supply Chain Management. Second Edition. John Wiley and Sons. EUA. Ijioui, R., H. Emmerich, M. Ceyp, and Diercks, W. (2007). Supply Chain Event Management als strategisches Unternehmensführungskonzept. Physica Verlag. pp. 3– 13. Ivanov, D., Sokolov B. and Solovyeva, I. (2016). Dynamic recovery policies for time-critical supply chains under conditions of ripple effect. International Journal of Production Research. 54(23), pp. 7245–7258. Lonvick, C. (2001). The BSD syslog Protocol. The Internet Society. pp. 1–29.
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