Supply chain dynamics, control and disruption management

This special issue has been inspired by recent tracks and sessions on Supply Network Dynamics, Control and Disruption Management organised at a number of conferences:

•	14thIFAC Symposium on Information Control Problems in Manufacturing INCOM, Bucharest (Romania), May 23–25, 2012.
•	IFAC Conference on Manufacturing Modelling, Management, and Control MIM 2013, St. Petersburg (Russia), June 19–21, 2013.
•	EURO-INFORMS Joint International Meeting, Rome (Italy), July 1–4, 2013.

This issue has been motivated by the fact that companies develop strategies to adapt to supply chain dynamics and mitigate disruptions. However, current research has been mainly concentrated on the supply chain optimisation stage; studies on supply chain dynamics, control and disruption management are quite rare. As a consequence of this clear gap between practice and theory, decisions in the domain of supply chain dynamics, control and disruption management are frequently isolated from the planning level and mainly based on expert knowledge with limited application of quantitative analysis tools and information technology.

This special issue aims to summarise recent developments in the field of supply chain dynamics, control and disruption management from a multidisciplinary operational perspective that includes different quantitative methods and information technology. The aim of this issue has been to attract high-quality papers detailing the most recent developments in the field of tackling uncertainties, dynamics and disruptions in the supply chain from different disciplines of operational research, control theory, system dynamics and artificial intelligence that provide new insights into the theory and practice of supply chain dynamics, control and disruption management.

Submitted papers (over 100) have comprised a broad range of topics such as:

•	Quantitative analysis of strategies to supply chain disruption management.
•	Supply chain design with resilience and business continuity considerations.
•	Trade-off ‘resilience vs. efficiency’ in supply chain optimisation.
•	Ripple effect in the supply chain.
•	Quantification of supply chain risk management.
•	Supply chain robustness and stability analysis.
•	Planning supply chain performance under uncertainty.
•	Creating flexibility and adaptability in the supply chain.
•	Mitigating risks and disruptions in supply chain design, planning and scheduling.
•	Disruption prediction and preparedness.
•	Post-disruption supply chain recovery.
•	Supply chain (re)scheduling with closed-loop properties.
•	Supply chain visibility and monitoring process execution in the supply chain.
•	Quantitative analysis techniques for supply chain control.
•	Supply chain stabilisation and recovery.
•	Supply chain control policies and algorithms.
•	Costs analysis and performance measurement for supply chain control and adaptation.
•	Collaboration in supply chain control and disruption management.

Such a high number of submissions and the broad topic spectrum have not been surprising since International Journal of Production Research (IJPR) has been offering a platform for experts in the field of dynamics and control of manufacturing, logistics and supply chains from the very beginning in 1961. The journal IJPR celebrates in 2016 its 55th anniversary and has contributed a lot to development of this research domain. The first studies in this area have been devoted to inventory control (e.g. Eilon 1961). Hwang et al. (1970) were among the first to apply the optimal programme control and the maximum principle to multi-level and multi-period master production scheduling that determined the production as an optimal control with a corresponding trajectory of the state variables (i.e. the inventory). Axsäter (1974) extended the inventory control models. Albright and Collins (1977) developed a Bayesian approach to the optimal control of continuous industrial processes. Bedini and Toni (1980) developed a dynamic model for the planning of a manufacturing system. The maximum principle has been used to formulate the problem and to obtain a solution.

A large research area of flexible manufacturing systems and their dynamics has been examined in numerous studies (e.g. Stecke and Solberg 1981). The stream of production scheduling has been continued by Kimemia and Gershwin (1985), Kogan and Khmelnitsky (1996) and Khmelnitsky, Kogan, and Maimom (1997), who applied the maximum principle in discrete form to planning continuous-time flows in flexible manufacturing systems and transited from the hierarchical approach to heuristic rules for optimal programme control calculation.

Introduction of uncertainties and disruptions into dynamic models of production and inventory can be related to demand fluctuations, capacity disruptions and time delays on planning and scheduling levels (Bedini and Tono 1980; Hopp, Pati, and Jones 1989; Adamides, Yamalidou and Bonvin 1996; Kouikoglou and Phillis 1997; Abumaizar and Svestka 1999).

In 2000–2015, supply chains became one of the key topics of investigation. Uncertainties and disruptions play an important role in this research (Wu, Blackhurst, and O’grady 2007; Acar, Kadipasaoglu, and Schipperijn 2010; Cakici, Mason, and Kurz 2012; Khakdaman et al. 2015). Disney and Towill (2002) investigated the effects of inventory control policies on order and inventory variability with the help of linear classical control theory. They applied a discrete linear control theory model to determine the dynamic stability of vendor managed inventory supply chains. Villegas and Smith (2006) apply system dynamics for analysis of inventory and order oscillations trade-offs. One of the advantages of systems dynamics approaches is dealing with the non-linear issues of supply chain dynamics. For further reading on application of classic control theory to logistics and supply chain management, we refer to the study by Ortega and Lin (2004).

Louly, Dolgui, and Hnaien (2008) addressed uncertainty in lead time and developed an approach to optimal supply planning in MRP environments for assembly systems with random component procurement times. Scholz-Reiter et al. (2010) use stability analysis with regard to autonomously controlled production networks. Nair and Vidal (2010) apply multi-agent approach to supply chain robustness analysis. Vahdani, Zandieh, and Roshanaei (2011) applied fuzzy programme evaluation and review technique to calculate the completion time of supply chain operations in the case of a severe disruption. Benyoucef, Xie, and Tanonkou (2013) consider supply chain design with unreliable suppliers. The study by Lin, Huang, and Yeh (2014) concentrates on the reliability assessment for a multi-state SC with multiple suppliers as the probability to satisfy the market demand within the budget and production capacity limitations. They develop an algorithm in terms of minimal paths to evaluate the network reliability along with a numerical example regarding auto glass.

In the study by Ivanov and Sokolov (2012), a structure dynamics control approach is presented to model supply chains as multi-structural dynamic systems. On the basis of this approach, a dual-problem formulation and its application to optimal re-design of an integrated production–distribution network with structure dynamics and ripple effect considerations has been developed (Ivanov, Sokolov, and Pavlov 2013). In this study as well as in further works (Ivanov, Sokolov, and Dolgui 2014; Ivanov et al. 2015, forthcoming), this approach has been applied to supply chain control with ripple effect considerations as well as to scheduling in Industry 4.0 smart manufacturing networks.

In robust stochastic programming models (Sawik 2014), facility disruptions and capacity expansion costs are also considered to be uncertain. For example, Sawik (2013) develops a stochastic programming model to integrated supplier selection, order quantity allocation and customer order scheduling in the presence of supply chain disruption risks. Sawik (2014) underlines the crucial role of disruption events and recovery policies in supply chain planning.

Wakolbinger and Cruz (2011) analyse supply chain disruption risk management through strategic information acquisition and sharing and through risk-sharing contracts. Gupta, He, and Sethi (2015) study from game-theoretical perspective the implications of the contingent sourcing strategy under competition and in the presence of a possible supply disruption. The results imply that supply disruption and procurement times jointly impact the firms’ buying decisions, optimal order quantities and their expected profits. The studies by Aqlan and Lam (2015), Das (2015), Ho et al. (2015), Munoz and Dunbar (2015) and Tukamuhabwa et al. (2015) belong to the most recent publications in IJPR on supply chain risk management and resilience and confirm the importance of these topics now and in the next years.

It can be observed from this exemplary literature review that there is an extensive body of literature on all decision-making levels (supply chain design, supply chain planning and supply chain scheduling) in regard to protecting the supply chains against disruptions (proactive stage) and supply chain recovery (re-active stage). The selected papers for this Special Issue contribute to the extension of the existing literature in a multifaceted way with the help of mathematical programming, control theory, system dynamics, graph theory, game theory, heuristics and empirical research.

Strategic level

Kasin Ransikarbum and Scott J. Mason devote their study Multiple-objective analysis of integrated relief supply and network restoration in humanitarian logistics operations to the response and recovery phases in post-disaster operations in humanitarian logistics. The approach is based on a multiple objective, integrated network optimisation model for making strategic decisions in the supply distribution and network restoration phases of humanitarian logistics operations. The authors conduct designed experiments for this NP-hard problem to analyse full vs. partial restoration and pooled vs. separate budgeting approach. Finally, they apply the model to a Hazus-generated regional case study based on an earthquake scenario and generate efficient Pareto frontiers to understand the trade-off between the multiple objectives.

Boris Sokolov, Dmitry Ivanov, Alexandre Dolgui and Alexander Pavlov analyse in the paper Structural quantification of the ripple effect in the supply chain the issues of integrating operability objectives as new key performance indicators into supply chain design decisions with the help of graph theory. The authors develop a multi-objective approach to analyse supply chain design structures with the regard to robustness, flexibility, stability and resilience. The modelling approach is based on a combined application of a static and a dynamic model. A multi-criteria approach relies on the analytic hierarchy process method. The results of this research can be used by managers as an additional quantitative analysis tool in order to select a supply chain design.

In the graph-theoretical study Evaluation mechanism for structural robustness of supply chain considering disruption propagation by Jihee Han and KwangSup Shin, the impact of risks propagation through the whole supply chain along the connected structure is analysed. A structural robustness evaluation mechanism is devised by integrating two quantitative metrics, average path length and in-degree–out-degree. Especially, if the re-design of a supply chain structure is impossible or difficult to implement, the proposed mechanism may be utilised to verify whether the planned supply chain is robust to risks or not.

George A. Zsidisin, Boyana N. Petkova and Lammertjan Dam analyse in their empirical study Examining the influence of supply chain glitches on shareholder wealth: does the reason matter?, how different reasons for supply chain glitches influence shareholder wealth. The authors re-assess the effect of supply chain glitches on shareholder wealth for a new time period (i.e. 2001–2012) whilst including the moderators from the original study (growth prospects, firm size, debt–equity ratio and timing) and adding the reason for the supply chain glitch as an important new moderator. The results show that on average supply chain glitches decrease shareholder wealth by 1.94%. Further, the results indicate that supply chain glitches that arise due to regulatory, catastrophic and infrastructural reasons trigger more significant negative reactions in financial markets as compared with glitches that occur from the supply side.

Tianjian Yang and Weiguo Fan compare in their study Information management strategies and supply chain performance under demand disruptions the disruption mitigation effects of three information management strategies with the help of control theory. From the aspect of stability, the existing stability boundaries are revised by a new method in a two-echelon case. From the aspect of disruption recovery time, an innovative two-echelon swiftest response problem under these information management strategies is formulated and solved. Results show that a collaborative planning, forecasting and replenishment (CPFR) with complete information performs in the best way. However, in a later operational risk mitigation test, information sharing with partial information has the smallest bullwhip effect. From the aspect of demand amplification and frequency response, an innovative frequency response plot of order amplification is proposed in a time-continuous supply chain with moving average forecasts. It implies the best frequency response for concurrently mitigating both operational and disruption risks coming from CPFR.

In the study The impact of contract parameters on the supply chain performance under different power constellations by Richard Lackes, Philipp Schlüter and Markus Siepermann, a game-theoretic model has been developed to analyse special contracts to enhance the value of forecast data accuracy and the cooperation between supply chain partners. This paper analyses how different contract parameters affect the supply chain performance, in particular when the bargaining power of customer and supplier is not equally distributed. Results show that the supply chain is better off if the supplier leaves the contractual cost parameters untouched but hides the true value of flexibility, especially when the customer is less powerful than the supplier.

Yong Luo, Shi-zhao Wang, Xiao-chen Sun and Oscar D. Crisalle develop in their study Analysis of retailers’ coalition stability for supply chain based on LCS and stable set an analytical approach to analyse retailers’ coalition stability in a two-stage supply chain consisting of one supplier and multiple retailers. A profit gain function was established via introducing market gain coefficient and coalition cost coefficient for different coalition structures. Based on the function, the profit of each retailer in all kinds of coalition structures was analysed, and the general feature of a stable coalition structure was attained by the largest consistent set method and the stable set method.

The study Examining supply contracts under cost and demand uncertainties from supplier’s perspective: a real options approach by Wenbo Shi and Tianke Feng extends the existing literature by examination of potential values and risks of supply contracts from supplier’s perspective. Using a real options approach, the authors investigate a supplier’s acceptance decision towards a supply contract with variable cost and supply demand uncertainties. Through analytical and numerical examinations, conditions under which, it is economically viable for the supplier to accept the supply contract are derived. To facilitate the understanding of this duration range, the corresponding shortest and longest contract duration is derived through numerical examples.

The study New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management by James J.H. Liou, Jolanta Tamošaitienė, Edmundas K. Zavadskas and Gwo-Hshiung Tzeng provides an empirical example using data from a Taiwanese electronics company in regard to green-based supplier selection in the setting of incomplete information and differences in knowledge between various departments. The authors apply a modified COmplex PRoportional ASsessment of alternatives with Grey relations to resolve the multi-objective uncertainty.

Tactical level

In the study On the risk-averse optimization of service level in a supply chain under disruption risks by Tadeusz Sawik, a stochastic optimisation problem is formulated as a joint selection of suppliers and stochastic scheduling of customer orders under random disruptions at suppliers. The problem is formulated as a mixed-integer programme with conditional service-at-risk as a worst-case service-level measure. The findings indicate that the worst-case order fulfilment rate shows a higher service performance than the worst-case demand fulfilment rate.

Virginia L.M. Spiegler, Andrew T. Potter, Mohamed M. Naim and Denis R. Towill develop a method to use non-linear control theory in the dynamic analysis of supply chain resilience in an empirical context of a grocery supply chain in the study The value of nonlinear control theory to investigate the underlying dynamics and resilience of a grocery supply chain. The developed approach utilises block diagram development, transfer function formulation, describing function representation of non-linearities and simulation. The resilience is determined by investigating the dynamic behaviour of stock and shipment responses. The developed method provides insights into the non-linear system control structures, including a better understanding of the influence of control parameters on dynamic behaviour and the impact of non-linearities on supply chain performance.

The study A constrained EPSAC approach to inventory control for a benchmark supply chain system by Dongfei Fu, Clara M. Ionescu, El-Houssaine Aghezzaf and Robin De Keyser applies model predictive control to inventory control policies in the supply chain. The authors develop a generic process model and incorporate the physical constraints arising from practical operations to form the general constrained optimizsation problems. The authors provide evidence of the customer satisfaction increase by 30% with the help of the developed approach.

The paper Single-period inventory model for one-level assembly system with stochastic lead times and demand by Faicel Hnaien, Alexandre Dolgui and Desheng Dash Wu takes into account uncertainty in lead time and demand. It considers replenishment planning of an assembly system with one type of finished product assembled from n different types of components. The authors assume that the component lead times and finished product demand are random discrete variables. Five heuristics based on the Newsvendor model for lead time and demand are proposed and compared with the Branch and Bound algorithm. The proposed exact algorithm provides optimal solutions for all discrete distributions of probabilities of lead times and demand.

The study Developing a resilient supply chain through supplier flexibility and reliability assessment by Masoud Kamalahmadi and Mahour Mellat-Parast examines the optimal allocation of demand across a set of suppliers in a supply chain that is exposed to supply risk and environmental risk. A two-stage mixed-integer programming model integrates supplier selection and demand allocation with transportation channel selection under disruptions and provides contingency plans to mitigate the negative impacts of disruptions and minimise total network costs. The managers can learn from the results that developing contingency plans with the help of flexibility in suppliers’ production capacity is an effective strategy for firms to mitigate severe disruptions. The authors also show that highly flexible suppliers receive less allocation, and their flexible capacity is reserved for disruptions.

The study How a competing environment influences newsvendor ordering decisions by Yingshuai Zhao and Xiaobo Zhao puts the competition perspective in the forefront of consideration when two newsvendors compete for a common market. The authors assume that if stock-out occurs at one newsvendor, the unsatisfied demand is reallocated to the competitor. For this framework, a game-theoretical model has been developed. The findings indicate that the participants in the high-profit group tend to ignore distractions from competitors, whilst the participants in the low-profit group are highly influenced by their competitors. Observations from this study suggest that managers should pay particular attention to different profit margin products in a competing environment.

In the paper Risk assessment for the supply chain of fast fashion apparel industry: a system dynamics framework, Marzieh Mehrjoo and Zbigniew J. Pasek investigate the impact of lead time and delivery delays on the supply chain performance (inventory, total cost and risk) in the fast fashion apparel industry. A system dynamics model is where the behaviour and relationships of a three-level supply chain are studied. Since the key to success for this industry is to satisfy customers’ needs in the shortest time, the conditional value at risk measure is applied to quantify and analyse the risks associated with the supply chain of these products and also to determine the expected value of the losses and their corresponding probabilities.

In the study Optimal production and inventory decisions under demand and production disruptions by Xiaoling Xu, Jennifer Shang, Haiyan Wang and Wen-Chyuan Chiang, the authors analyse different disruption scenarios and propose optimal production–inventory models for products facing demand and production disruptions. The authors optimise the production run time, purchasing times and order quantity for the manufacturer with the objective of cost minimisation. Numerical experiments are conducted to examine the influences of disruption time and magnitude on optimal production and purchasing decisions.

Michael Phelan and Seán McGarraghy apply in their study Grammatical evolution in developing optimal inventory policies for serial and distribution supply chains a relatively new biologically inspired algorithm to the field of supply chain optimisation. The application of the grammatical evolution method is shown on the example of developing optimal inventory policies for serial and distribution supply chains.

Operative level

The study A survey on dynamic and stochastic vehicle routing problems by Ulrike Ritzinger, Jakob Puchinger and Richard F. Hartl provides a state of the art on recent developments on uncertainty consideration in vehicle routing problems. It also considers a novel problem class, aiming at an appropriate handling of dynamic events combined with the incorporation of stochastic information about possible future events. The authors also build a bridge to production scheduling issues.

The study Dynamic co-ordinated scheduling in the supply chain considering flexible routes by Aijun Liu, John Fowler and Michele Pfund considers coordination issues in supply chain scheduling. The authors take into account disruptions at some elements in the supply chain. The authors suggest a rescheduling technique for solving coordinated scheduling problems with due date changes and machine breakdowns taking into account flexible routes.

Enjoy the issue!

Special Issue Editors
Dmitry Ivanov
Berlin School of Economics and Law, Germany
[email protected]
Scott J. Mason
Clemson University, USA
Richard Hartl
University of Vienna, Austria

Acknowledgements

This Special Issue is a result of an integration of many parties. We thank all the contributors and reviewers who have very patiently and carefully examined the papers, sometimes on numerous occasions, and made this special issue of IJPR possible. We thank Professor Alexandre Dolgui, Editor-in-Chief, IJPR, for inviting us to organise this issue, and for his support during the review and decision-making process. Contributions from the IFAC TC 5.2 ‘Manufacturing Modelling for Management and Control’ to this Special Issue are also acknowledged. Finally, we sincerely thank Mrs Tamara Bowler, Peer Review Coordinator, Taylor & Francis for her sustained assistance and guidance in developing this special issue.

We sincerely hope that this special issue stimulates new research on supply chain dynamics and control theory and practice using the lens of multidisciplinary domains of knowledge.