Supply chain design and optimization: Challenges and opportunities

Exploration and evaluation of modular concepts for the design of full-scale pharmaceutical manufacturing facilities

The design of biopharmaceutical processes is predominantly driven by the domain of experimental process design. This approach can be further improved by combining multiple domain information such as experiments, unit models, and flowsheet models. Approaches consisting of methods and flowsheet models provide the framework for exploring, analyzing, and ultimately evaluating the combinatorial space of all possible designs within the molecule-to-manufacturing value chain. In recent years, modular process designs are of interest in the pharmaceutical industry because of the shift toward multiproduct, mutiprocess processes. Therefore, a systematic approach for how to evaluate the utilization of the modular plug-n-play concept provides metrics that can propel modular design from a viable design alternative to the selected alternative for full-scale manufacturing. The objective of this paper is to present such an in silico approach for the evaluation of modular designs. The approach is presented as a systematic method and then, is exemplified through the manufacture of an active pharmaceutical ingredient (API). The application of the method shows how to transition from a typical design-for-purpose design alternative to a modular design through the utilization of data, modeling, simulation, and uncertainty/sensitivity analyses for quantification of various selection metrics such as process robustness and flexibility.

Lignocellulosic biorefineries: A multiscale approach for resource exploitation

Biomass can become the source for chemicals towards a sustainable production system. However, the challenges it presents such as the variety of species, their widespread and sparse availability, and the expensive transportation claims for an integrated approach to design the novel production system. Multiscale approaches have not been properly extended to biorefineryes design and deployment, due to the comprehensive experimental and modelling work they require. A systems perspective provides the systematic framework to analyze the availability and composition of raw materials across regions, how that affects process design, the portfolio of products that can be obtained by evaluating the strong link between the biomass features and the process design. The use of lignocellulosic materials requires for a multidisciplinary work, that must lead to new process engineers with technical competences in biology, biotechnology but also process engineering, mathematics, computer science and social sciences towards a sustainable process/chemical industry.

The role of bioprocess systems engineering in extracting chemicals and energy from microalgae

In this study, the role of process systems engineering in enhancing the algae economy is highlighted. First, basic characteristics of the various strains of microalgae are presented. In addition, the beneficial extracted bioproducts and their applications are reviewed. Then, an overview of the various technologies available in each step of biorefinery to produce added-value products and biofuels from microalgae is provided. These technologies are compared in terms of required energy and efficiency. Different perspectives of the algae industry, from molecule to enterprises scale where process systems engineering can have a role, are addressed. Subsequently, the roles of process systems engineering in process and product design, process control, and supply chain of the algae biorefinery are discussed. It is found that process systems engineering can play an important role in the biobased economy, especially by applying sustainability and economic concepts in the decision-making process for selecting the best feedstock, processing pathways, and desired products. Tools such as market analysis, techno-economic analysis, life cycle assessment (LCA), and supply chain (SC) analysis can be applied to design sustainable algae biorefinery. There are, however, several challenges such as the lack of data, the complexity of optimization, and validation that should be addressed before using these tools.

Local topological features of robust supply networks

The design of robust supply and distribution systems is one of the fundamental challenges at the interface of network science and logistics. Given the multitude of performance criteria, real-world constraints, and external influences acting upon such a system, even formulating an appropriate research question to address this topic is non-trivial. Here we present an abstraction of a supply and distribution system leading to a minimal model, which only retains stylized facts of the systemic function and, in this way, allows us to investigate the generic properties of robust supply networks. On this level of abstraction, a supply and distribution system is the strategic use of transportation to eliminate mismatches between production patterns (i.e., the amounts of goods produced at each production site of a company) and demand patterns (i.e., the amount of goods consumed at each location). When creating networks based on this paradigm and furthermore requiring the robustness of the system with respect to the loss of transportation routes (edge of the network) we see that robust networks are built from specific sets of subgraphs, while vulnerable networks display a markedly different subgraph composition. Our findings confirm a long-standing hypothesis in the field of network science, namely, that network motifs-statistically over-represented small subgraphs-are informative about the robust functioning of a network. Also, our findings offer a blueprint for enhancing the robustness of real-world supply and distribution systems.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-022-00470-2.

Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient

Supply chain management (SCM) is considered at the forefront of many organizations in the delivery of their products. Various optimization methods are applied in the SCM to improve the efficiency of the process. In this research, the projected stochastic gradient (PSG) method was proposed to increase the efficiency of the SCM analysis. The key objective of an efficient supply chain is to find the best flow patterns for the best products in order to select the suppliers to different customers. Hence, the focus of this research is on developing an efficient multi-echelon supply chain using factors such as cost, time, and risk. In the convex case, the proposed method has the advantage of a weakly convergent sequence of iterates to a point in the set of minimizers with probability one. The developed method achieves strong sequence convergence to the unique optimum, with probability one. The SCM dataset was utilized to assess the proposed method’s performance. The proposed PSG method has the advantage of considering the holding cost in the profit analysis of the company. The results of the developed PSG method are analyzed according to the product’s profit, stock, and demand. The proposed PSG method also provides the prediction of demand to increase profit.

Digitalization and Future Agro-Food Supply Chain Management: A Literature-Based Implications

Achieving transition towards sustainable and resilient food systems is a critical issue on the current societal agenda. This study examined the potential contribution of digitalization of the food system to such transition by reviewing 76 relevant journal articles, indexed on the Scopus database, using the integrative literature review approach and descriptive content analysis with MAXQDA 2020 software. ‘Blockchain’ was the top hit among keywords and main concepts applied to the food system. The UK as a country and Europe as a continent were found to lead the scientific research on food system digitalization. Use of digital technologies such as blockchain, the Internet of Things, big-data analytics, artificial intelligence, and related information and communications technologies were identified as enablers. Traceability, sustainability, resilience to crises such as the COVID-19 pandemic, and reducing food waste were among the key benefit areas associated with digitalization for different food commodities. Challenges to practical applications related to infrastructure and cost, knowledge and skill, law and regulations, the nature of the technologies, and the nature of the food system were identified. Developing policies and regulations, supporting infrastructure development, and educating and training people could facilitate fuller digitalization of the food system.

Developing Strategies for Onchocerciasis Elimination Mapping and Surveillance Through The Diagnostic Network Optimization Approach

Background

Onchocerciasis (river blindness) is a filarial disease targeted for elimination of transmission. However, challenges exist to the implementation of effective diagnostic and surveillance strategies at various stages of elimination programs. To address these challenges, we used a network data analytics approach to identify optimal diagnostic scenarios for onchocerciasis elimination mapping (OEM).

Methods

The diagnostic network optimization (DNO) method was used to model the implementation of the old Ov16 rapid diagnostic test (RDT) and of new RDTs in development for OEM under different testing strategy scenarios with varying testing locations, test performance and disease prevalence. Environmental suitability scores (ESS) based on machine learning algorithms were developed to identify areas at risk of transmission and used to select sites for OEM in Bandundu region in the Democratic Republic of Congo (DRC) and Uige province in Angola. Test sensitivity and specificity ranges were obtained from the literature for the existing RDT, and from characteristics defined in the target product profile for the new RDTs. Sourcing and transportation policies were defined, and costing information was obtained from onchocerciasis programs. Various scenarios were created to test various state configurations. The actual demand scenarios represented the disease prevalence at IUs according to the ESS, while the counterfactual scenarios (conducted only in the DRC) are based on adapted prevalence estimates to generate prevalence close to the statistical decision thresholds (5% and 2%), to account for variability in field observations. The number of correctly classified implementation units (IUs) per scenario were estimated and key cost drivers were identified.

Results

In both Bandundu and Uige, the sites selected based on ESS had high predicted onchocerciasis prevalence >10%. Thus, in the actual demand scenarios in both Bandundu and Uige, the old Ov16 RDT correctly classified all 13 and 11 IUs, respectively, as requiring CDTi. In the counterfactual scenarios in Bandundu, the new RDTs with higher specificity correctly classified IUs more cost effectively. The new RDT with highest specificity (99.8%) correctly classified all 13 IUs. However, very high specificity (e.g., 99.8%) when coupled with imperfect sensitivity, can result in many false negative results (missing decisions to start MDA) at the 5% statistical decision threshold (the decision rule to start MDA). This effect can be negated by reducing the statistical decision threshold to 2%. Across all scenarios, the need for second stage sampling significantly drove program costs upwards. The best performing testing strategies with new RDTs were more expensive than testing with existing tests due to need for second stage sampling, but this was offset by the cost of incorrect classification of IUs.

Conclusion

The new RDTs modelled added most value in areas with variable disease prevalence, with most benefit in IUs that are near the statistical decision thresholds. Based on the evaluations in this study, DNO could be used to guide the development of new RDTs based on defined sensitivities and specificities. While test sensitivity is a minor driver of whether an IU is identified as positive, higher specificities are essential. Further, these models could be used to explore the development and optimization of new tools for other neglected tropical diseases.

Mapping the environmental aspect of kernel product system in complex supply chains of the West Africa cashew industry

Global supply chains (GoSCs) have become common for industries. However, relatively little research has emphasized products' complex supply chain environmental assessments. Against this background, this paper maps key environmental issues in the GoSCs of the West Africa cashew industry. The initial-oriented green design and supply chain-wide assessment of the kernel product system was conducted through an eco-design approach based on the matrix method, life cycle design strategy wheel and analytical hierarchical process. The results reveal static/technical eco-design issues that can reduce negative environmental impacts. Additionally, it highlights the strategic opportunities for redesign related to the selection of low-impact materials and gaseous emission. The main issues that arise in mapping the complex supply chains such as the need to re-examine some environmental issues such as solid and liquid residues from production and processing stages as more strategic concerns are discussed. In conclusion, the study points out that it is important not to compromise supply chain-wide assessment of environmental impacts and to understand both technical and strategic values of environmental issues in complex supply chains of products even at the initial stage of interest in green supply chain design. Thus, the eco-design approach of the study has potential to enhance environmental sustainability.

Special Issue “Modeling of Supply Chain Systems”

Supply chain systems are complex networks of producers, service providers and consumers [...]

Life Cycle Assessment for the Design of Chemical Processes, Products, and Supply Chains

Design in the chemical industry increasingly aims not only at economic but also at environmental targets. Environmental targets are usually best quantified using the standardized, holistic method of life cycle assessment (LCA). The resulting life cycle perspective poses a major challenge to chemical engineering design because the design scope is expanded to include process, product, and supply chain. Here, we first provide a brief tutorial highlighting key elements of LCA. Methods to fill data gaps in LCA are discussed, as capturing the full life cycle is data intensive. On this basis, we review recent methods for integrating LCA into the design of chemical processes, products, and supply chains. Whereas adding LCA as a posteriori tool for decision support can be regarded as established, the integration of LCA into the design process is an active field of research. We present recent advances and derive future challenges for LCA-based design.

Supply chain lead time reduction in a pharmaceutical production warehouse – a case study

Purpose

The purpose of this study is to apply value stream mapping (VSM) in Malaysian pharmaceutical production warehouse. A current and future state value stream map from the raw material receiving end to the production unit was developed to find out waste and unwanted lead time. It was very much essential to cut down the supply chain lead time at the initial phase as the raw material unloading, sorting, temporary storage and dispatch to production were seen contributing to a huge lead time build-up.

Design/methodology/approach

The study was initiated with the selection of a product family, construction of the current state map, identification of various wastes and the development of future state map.

Findings

The expected outcomes of the study include the quantification of wastes, improvement in value-added percentage and lead time reduction.

Research limitations/implications

The study was carried out in a single pharmaceutical company. The results of the study are deployable and can be functional in similar production organizations. Contrary to common VSMs that capture core production processes, this study provides strong insights that shall help design lean supply chains, especially in the pharmaceutical domain. This paper has also addressed the viability of the lean in the pharmaceutical warehouse and the reduction in lead time to improve demand forecasting, marketing and sales.

Practical implications

The results of this study have indicated that a significant reduction in pharmaceutical warehouse supply chain lead time is possible as a result of the implementation of VSM from the supply chain’s perspective.

Social implications

The insights from this study help in understanding the pharmaceutical supply chain risks and their outcomes.

Originality/value

The paper reports a real-time study conducted in a warehouse of a pharmaceutical organization. Hence, the contributions are original.

Sustainable Liquefied Natural Gas Supply Chain Management: A Review of Quantitative Models

Natural gas is an essential fuel in the transitions towards a sustainable energy future as it is considered a cleaner source of fuel when compared to other hydrocarbon sources. To enable natural gas delivery from the producer to consumers, natural gas is liquified to enhance transportation efficiency and reliability. The main contribution of this paper is to develop sustainable LNG supply chain through a review of different sustainable supply chain management tools and assessing their applicability in managing LNG supply chains. Energy security has evolved to include the protection of the entire energy supply chain and infrastructure rather than a consideration for the availability of resources alone. There is a particular focus on coupling sustainability and resilience/risk as part of the need to develop integrated approaches to manage energy supply chains to deliver cargo at minimal cost and environmental impact, and to ensure that supply chains can overcome vulnerabilities withstanding potential disruptions to the supply chain. Outcomes of this review demonstrate the possibility to develop multi criteria models, which consider sustainability dimensions within the LNG supply chains and to integrate parameters that form part of the annual delivery plan ensuring day to day LNG supply chain planning consider multiple objectives.

Modeling and Simulation of Energy Systems: A Review

Energy is a key driver of the modern economy, therefore modeling and simulation of energy systems has received significant research attention. We review the major developments in this area and propose two ways to categorize the diverse contributions. The first categorization is according to the modeling approach, namely into computational, mathematical, and physical models. With this categorization, we highlight certain novel hybrid approaches that combine aspects of the different groups proposed. The second categorization is according to field namely Process Systems Engineering (PSE) and Energy Economics (EE). We use the following criteria to illustrate the differences: the nature of variables, theoretical underpinnings, level of technological aggregation, spatial and temporal scales, and model purposes. Traditionally, the Process Systems Engineering approach models the technological characteristics of the energy system endogenously. However, the energy system is situated in a broader economic context that includes several stakeholders both within the energy sector and in other economic sectors. Complex relationships and feedback effects exist between these stakeholders, which may have a significant impact on strategic, tactical, and operational decision-making. Leveraging the expertise built in the Energy Economics field on modeling these complexities may be valuable to process systems engineers. With this categorization, we present the interactions between the two fields, and make the case for combining the two approaches. We point out three application areas: (1) optimal design and operation of flexible processes using demand and price forecasts, (2) sustainability analysis and process design using hybrid methods, and (3) accounting for the feedback effects of breakthrough technologies. These three examples highlight the value of combining Process Systems Engineering and Energy Economics models to get a holistic picture of the energy system in a wider economic and policy context.

Game-based analysis of energy-water nexus for identifying environmental impacts during Shale gas operations under stochastic input

Environmental issues have become some of the greatest challenges encountered across the life cycle Shale gas operations, and mostly involve the management, disposal, and spill of flowback and produced (FP) waters during the process of hydraulic fracturing. This study evaluates Shale gas resources, addresses water resource management problems, and identifies the corresponding environmental implications of FP waters under uncertainty. Multiple tools, including structural optimization, process design, cost analysis, environmental assessment, and stochastic technology, are integrated into a general modeling framework based on game theory. This mathematic framework corresponds to a dominant-subordinate-interactive problem, where two major participants are identified as the downstream decision maker at the dominant level (e.g., power generation sector) and the upstream decision maker at the subordinate level (e.g., Shale gas producer). The Monte Carlo technique is used for simulating the estimated ultimate recovery (EUR) of a single well. Thereafter, the developed model is applied to a special case study of the Marcellus Shale play in Beaver County, Pennsylvania. Multiple decisions regarding gas production, processing, water management, as well as electricity generation would been examined under different probability levels. Results indicate that the changes in violation levels would lead to distinct environmental and economic performances of the supply chain. A lower probability level of the EUR value would correspond to an increased reliability on fulfilling the system demands, and then to higher economic benefits and freshwater supply; conversely, a higher probability level of the EUR value would result in lower economic benefits and lower freshwater supply, and the risk of violating the EUR value would also increase.