Maximizing Return on Investment (ROI) for Pharmaceutical Production (original) (raw)
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MODELING SIMULATION AND OPTIMISATION OF A PHARMACEUTICAL PRODUCTION LINE
IAEME Publication, 2014
Pharmaceutical companies are allowed to deal in generic or brand medications and medical devices. Thus it is essential that the product conforms to specified customer requirements. In order to analyse the performance of the process, the critical factors which influence the production are evaluated. The objective of the study is to simulate the pharmaceutical production line using ARENA software and optimize the line by Design of Experiments technique in MINITAB by considering the response factor ‘work in progress’. The critical factors identified are batch size, transfer time and the number of resources. A Taguchi approach is employed to gather experimental data. Then, based on signalto-noise (S/N) ratio, the best sets critical parameters have been determined. Using these parameters values, the ‘work in progress’ may be minimized.
Simulation of data for drug manufacturing using optimization principles
2005
This paper deals with formulation and solution of an optimization problem related to a drug industry wherein a drug manufacturing experiment is conducted with multiple reagent levels, a single incubation temperature, and a single incubation time. Since various experiments are conducted with multiple values of reagent levels, it is decided to have a common measurement factor for all the experiments for comparison purposes. This factor is taken as a scalar binding factor. This binding factor is expressed as a linear function of reagent levels, incubation temperature, and incubation time. All these three variables are treated as probabilistic and, hence, are supposed to have a certain distribution. It is also assumed that each drug manufacturing experiment is characterized by a single binding value or binding factor. The higher the binding, the experiment is considered better. Further, it is assumed in this study that the mean binding factor is a monotonically non-decreasing function of incubation time. Since the experiments are expensive to run and the cost is proportional to incubation time, and, also, since there is a cost of a reagent, this is a complex problem. It becomes even more complex because the stochastic aspects of the process are to be considered in the optimization problem formulation. This problem dealing with manufacturing of a drug [1, 2] is formulated as an optimization problem.
Brazilian Journal of Operations & Production Management, 2016
Purpose – Clinical trials are the most critical step in the process of drug development and evaluation to bring a new drug to market. The purpose of this article was to show an approach of the planning and production of a new innovative drug for a clinical study, based on the presentation of decision-making process in a national pharmaceutical industry.Design / methodology / approach - Through a case-study methodology, it was described the pilot batch production planning of a new drug for clinical trial, focusing on how the company evaluates the adequacy of the available systems at the manufacturing plant and how they use them in drug production planning process.Findings – A previous planning for clinical trial supplies production is determinant to decide the order, amount and timing of the products to be produced when the manufacturing plant is shared with the production of commercial products. Also, even in a small pilot batch production, there is a substantial waste of supplies d...
Discrete Event Simulation Modelling for Dynamic Decision Making in Biopharmaceutical Manufacturing
Procedia CIRP, 2016
With the increase in demand for biopharmaceutical products, industries have realised the need to scale up their manufacturing from laboratorybased processes to financially viable production processes. In this context, biopharmaceutical manufacturers are increasingly using simulationbased approaches to gain transparency of their current production system and to assist with designing improved systems. This paper discusses the application of Discrete Event Simulation (DES) and its ability to model the various scenarios for dynamic decision making in biopharmaceutical manufacturing sector. This paper further illustrates a methodology used to develop a simulation model for a biopharmaceutical company, which is considering several capital investments to improve its manufacturing processes. A simulation model for a subset of manufacturing activities was developed that facilitated 'what-if' scenario planning for a proposed process alternative. The simulation model of the proposed manufacturing process has shown significant improvement over the current process in terms of throughout time reduction, better resource utilisation, operating cost reduction, reduced bottlenecks etc. This visibility of the existing and proposed production system assisted the company in identifying the potential capital and efficiency gains from the investments therefore demonstrating that DES can be an effective tool for making more informed decisions. Furthermore, the paper also discusses the utilisation of DES models to develop a number of bespoke productivity improvement tools for the company.
Optimization of the Automated Production Process Using Software Simulation Tools
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The purpose of this article is to point out the need to use software simulation tools in industrial practice to optimize the production process and assess the economic effectiveness of investment, including risk. The goal of the research is to find an optimal investment variant to ensure an increase in the production volume of at least 50% and to achieve the maximum economic efficiency of the investment, even considering the risk. The article presents a comprehensive approach that enables the achievement of the set research goal. The selection of the optimal version of the investment is carried out in three steps. Firstly, the versions of the investment variants are assessed from the production point of view using the program Tecnomatix Plant Simulation. Subsequently, the versions of the investment variants are assessed from an economic point of view and from a risk point of view. Economic efficiency is assessed using the financial criteria net present value (NPV), profitability ind...
Research Journal of Pharmacy and Technology, 2022
The pharmaceutical industry currently has computer systems that seek to optimize processes and allow the maximum performance of its resources through the use of computer tools. The objective of the work was to determine the costs and benefits of two different software in management of raw materials for the pharmaceutical industry. Methods: observational, descriptive, cross-sectional, study that compared ONDANET and SAP software in positive and negative costs inferred in the implementation of the tools and the benefits in terms of the profitability of the management of raw materials for the elaboration of pharmaceutical products. Results: it has been observed by using direct costs and benefits that the benefit-cost ratio of the SAP software implementation yielded a result of 15.37, whereas the ONDANET software presented a cost-benefit ratio of 0.05, which could have been demonstrated Conclusion: A widely favorable cost-benefit ratio was found for SAP Software compared to the previously used ONDANET Software, indicating that the new software used is profitable. It is desirable to periodically carry out this type of analysis to guarantee not only that the Pharmaceutical Industry has a greater profitability, without also being able to provide its products with continuity and at affordable prices for the population.
A Heuristic-Embedded Scheduling System for a Pharmaceutical Intermediates Manufacturing Plant
Industrial & Engineering Chemistry Research, 2010
This paper proposes a new mathematical model for a real pharmaceutical intermediates manufacturing (PIM) plant. To consider the general characteristics of the scheduling problems experienced at PIM plants, the proposed model employed a strategy to address a mixed-integer linear programming (MILP) formulation. Two heuristic techniquesspreclassification of equipment and sequential two-stage optimizationswere then proposed, to relax the complexities of scheduling problems that are due to practical constraints. The objective function of the proposed model is to minimize the total operation time (makespan) that is subject to the mass balance constraints and process boundary conditions. On the basis of the proposed models and heuristics, new packaged software is developed for an application to real PIM plants. To show the features and capabilities of the proposed scheduling system, four real examples were examined. The results reveal that the techniques are helpful for obtaining both higher accuracy of optimized solutions and higher computational performance.
Industrial & Engineering Chemistry Research, 2018
This work presents a decision support method for the choice between batch and continuous technologies in solid drug product manufacturing based on the economic evaluation. The method consists of four steps: (I) modeling of operating costs, (II) evaluation, (III) sensitivity analysis, and (IV) interpretation, with iterations. For a given design situation, manufacturing processes are modeled and evaluated with consideration for the characteristics of the two technologies. The sensitivity of the input parameters is analyzed; after interpreting all results, the economically preferable technology is suggested. As a case study, the method was applied to a situation where a new product was in the late development stage, and one of the two technologies needs to be chosen. After executing the four steps, the comparison result of the net present cost was obtained as the decision support information.
An Economic Production Quantity Model for Atlantic Pharmaceuticals Laboratories
International Journal of Engineering and Technology, 2020
Inventory in an industry is considered like a blood in the body, and almost 50% of investment is made on inventory. Therefore, keeping the optimum level of inventory at low possible cost is the primary focus of researchers. Industries have to make good choices for inventory management in order to compete in market and meet the demands. Major issues regarding inventory in pharmaceutical industry are overstock, unjustified forecasting technique, long manufacturing lead times and lack of IT support. For these reasons, optimizing inventory is more difficult for pharmaceutical companies as compared to other manufacturing companies. Such issues are also faced by Atlantic Pharmaceuticals Laboratories (APL). In order to address these issue, inventory management is performed in APL. Inventory is categorized in three classes through ABC analysis. Class A items are further analyzed and their optimum quantity is determined by EPQ model. At the end sensitivity analyses are performed to determine the most and least critical parameters of the model.