Modelling of Financial Resource Allocation for Increasing the Supply Chain Resilience Using Markov Chains

Abstract

The concept of supply chain resilience has arisen in response to changing conditions in the global market environment. Although supply chain resilience building is gaining increasing interest among the professional public and business practice, supporting decision-making in supply chain resilience building is still in its infancy. This article aims to present a mathematical model of the supply chain based on Markov chains to assess the impact of funds allocated to strengthening the supply chain’s resilience to its overall performance and thus support decision-making in the field. Mathematical model assumptions are presented, then a mathematical model of a linear supply chain is developed and generalized, tested and methodological recommendations are presented. To support the use of the model, a set of managerial implications is presented, benefits and limitations are discussed, and further research direction is defined.

Causal viewpoint and ensemble interpretation: from physics to the social sciences

“…For the overall condition of the SC system it can then be described by specifying the condition of each individual link. For example, a SC system with two links can be found in any of the four states: 1≡O, O: (both operational);2≡O, D: (the first operational, the second disrupted);3≡D, O: (the first disrupted, the second operational);4≡D, D: (both disrupted). The stochastic dynamics of a SC system involves transitions between these states and can be modelled in terms of a finite state Markov chain [27].…”

“…The motivation of such a model lies in the pressing need of being able to monitor and manage the resilience of the SCs within the complex dynamics of the current global market environments. Specifically, it is becoming increasingly crucial to be able to identify the links of a SC system that may require the allocation of financial resources in order to increase the overall resilience and the economic performance of the system [27].…”

“…pij, i≠j, i,j = 1–4) are the probabilities that the system transits to a different state at a future time instant. These probabilities are generally expressed as simple frequencies that are the reciprocals of periods of two kinds: the mean time (in days) between the failures of a given link of the SC system (say, Tf) and the mean time (in days) to the recovery (say, Tr), see [27] for details. Following the authors Cech & Lenort [27] (see equations (6)–(9)), different transition probabilities for a given supply chain link can be given in the following manner.…”

“…These probabilities are generally expressed as simple frequencies that are the reciprocals of periods of two kinds: the mean time (in days) between the failures of a given link of the SC system (say, Tf) and the mean time (in days) to the recovery (say, Tr), see [27] for details. Following the authors Cech & Lenort [27] (see equations (6)–(9)), different transition probabilities for a given supply chain link can be given in the following manner. The probability of transition: from the ‘disrupted’ state to the ‘operational’ state ≡μ=1/Tr;from the ‘disrupted’ state to the ‘disrupted’ state (i.e.…”

Decision-Making under the Risk, Uncertainty and COVID-19 Pandemic Conditions Applying the PL9A Method of Logistics Planning—Case Study

“…Simultaneously, the ERP systems provide managers with integrated metadata representing real-time economic processes and events. This enhances the effectiveness of achieving business goals by providing organizations with the possibility of identifying barriers, targeting internal and external supply chain constraints [4]. The most common example of such constraint is the limited ability to respond to short-term changes [5] in supply and demand, and the ability to shape the size and suitability of the company's logistics assets concerning these changes.…”