Emerging Challenges and Opportunities in Pharmaceutical Manufacturing and Distribution

Sarkis, Miriam; Bernardi, Andrea; Shah, Nilay; Papathanasiou, Maria M.

doi:10.3390/pr9030457

Cited by 40 publications

(39 citation statements)

References 70 publications

(91 reference statements)

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“…Few studies focus on VSCs, despite the substantial published research on supply chain optimization. Most related studies are concerned with problems related to the pharmaceutical sector [2] , [3] , [4] , [5] , [6] , [7] , [8] . More recent contributions address key problems of the pharmaceutical supply chain, like vehicle routing decisions [9] , [10] , uncertainty [11] , product perishability [12] and integrated capacity planning and scheduling [13] , while few recent contributions focused on supply chains of CAR T-cell therapies [14] , [15] , [16] .…”

Section: Introductionmentioning

confidence: 99%

Optimal planning of the COVID-19 vaccine supply chain

Georgiadis

2021

Vaccine

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This work presents a novel framework to simultaneously address the optimal planning of COVID-19 vaccine supply chains and the optimal planning of daily vaccinations in the available vaccination centres. A new mixed integer linear programming (MILP) model is developed to generate optimal decisions regarding the transferred quantities between locations, the inventory profiles of central hubs and vaccination centres and the daily vaccination plans in the vaccination centres of the supply chain network. Specific COVID-19 characteristics, such as special cold storage technologies, limited shelf-life of mRNA vaccines in refrigerated conditions and demanding vaccination targets under extreme time pressure, are aptly modelled. The goal of the model is the minimization of total costs, including storage and transportation costs, costs related to fleet and staff requirements, as well as, indirect costs imposed by wasted doses. A two-step decomposition strategy based on a divide-and-conquer and an aggregation approach is proposed for the solution of large-scale problems. The applicability and efficiency of the proposed optimization-based framework is illustrated on a study case that simulates the Greek nationwide vaccination program. Finally, a rolling horizon technique is employed to reactively deal with possible disturbances in the vaccination plans. The proposed mathematical framework facilitates the decision-making process in COVID-19 vaccine supply chains into minimizing the underlying costs and the number of doses lost. As a result, the efficiency of the distribution network is improved, thus assisting the mass vaccination campaigns against COVID-19.

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Section: Introductionmentioning

confidence: 99%

Optimal planning of the COVID-19 vaccine supply chain

Georgiadis

2021

Vaccine

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“…[73][74][75][76] This is especially relevant for the design of emerging supply chains with distinct characteristics such as biomass 77,78 or (bio)pharmaceutical value chains. 79,80 These "social optima" are often obtained as the result of centralized optimization formulations, which can be decomposed for numerical tractability, or in our case to fit organizational considerations.…”

Section: Value Chainsmentioning

confidence: 99%

Data‐driven coordination of subproblems in enterprise‐wide optimization under organizational considerations

Berg¹,

Petsagkourakis²,

Shah³

et al. 2023

AIChE Journal

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While decomposition techniques in mathematical programming are usually designed for numerical efficiency, coordination problems within enterprise-wide optimization are often limited by organizational rather than numerical considerations. We propose a "data-driven" coordination framework which manages to recover the same optimum as the equivalent centralized formulation while allowing coordinating agents to retain autonomy, privacy, and flexibility over their own objectives, constraints, and variables. This approach updates the coordinated, or shared, variables based on derivative-free optimization (DFO) using only coordinated variables to agent-level optimal subproblem evaluation "data." We compare the performance of our framework using different DFO solvers (CUATRO, Py-BOBYQA, DIRECT-L, GPyOpt) against conventional distributed optimization (ADMM) on three case studies: collaborative learning, facility location, and multiobjective blending. We show that in low-dimensional and nonconvex subproblems, the exploration-exploitation trade-offs of DFO solvers can be leveraged to converge faster and to a better solution than in distributed optimization.

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“…Even as technology continues to rapidly change, they must build solutions not just within their own four walls but in collaboration with numerous external partners throughout the supply chain ( Sarkis et al, 2021a , Sarkis et al, 2021b ). Businesses will need to be extremely transparent and overcome deeply embedded ways of working, as well as resistance from organizations and employees who are unduly habituated to decades-old methods.…”

Section: The Digitization Process In Contract Development Manufacturing Organizations (Cdmo)mentioning

confidence: 99%

“…While process uniformity is required for digitization in the pharmaceutical business, CDMOs must nevertheless meet the unique needs of each customer. For example, various clients' production specifications frequently utilize various terminology and taxonomies, resulting in many process variants that must be accounted for during production ( Sarkis et al, 2021a , Sarkis et al, 2021b ).…”

Section: The Digitization Process In Contract Development Manufacturing Organizations (Cdmo)mentioning

confidence: 99%

Digitalization in pharmaceutical industry: What to focus on under the digital implementation process?

Hole

Hole²,

McFalone-Shaw

2021

International Journal of Pharmaceutics: X

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Digitalization of any manufacture industry is a key step in any progress of the production process. The process of digitalization includes both increased use of robotics, automatization solutions and computerization, thereby allowing to reduce costs, to improve efficiency and productivity, and to be flexible to changes. Pharmaceutical Industry (PI) has however been resistant to digitalization, mainly due to fair experience and complexity of the entailed development and manufacture processes. Nevertheless, there is a clear need to digitalize PI as the demand in both traditional and new drugs is constantly growing. Contract Development Manufacture Organizations (CDMOs) have a special digitalizing challenge. Digitalization of PI, and CDMO precisely, should be tightly related to the main aspects of Good Manufacture Practice (GMP), and, to succeed in PI digitalizing requires constant focus on GMP. Close collaboration with constantly changing stakeholders is another important factor which should be in focus during digitalization of CDMO. This paper represents an overview over the main aspects of CDMO digitalization and discusses both the opportunities and challenges of the process, focusing on the practical solutions for successive digital implementation.

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Emerging Challenges and Opportunities in Pharmaceutical Manufacturing and Distribution

Cited by 40 publications

References 70 publications

Optimal planning of the COVID-19 vaccine supply chain

Optimal planning of the COVID-19 vaccine supply chain

Data‐driven coordination of subproblems in enterprise‐wide optimization under organizational considerations

Digitalization in pharmaceutical industry: What to focus on under the digital implementation process?

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