Stochastic simulation under input uncertainty for contract-manufacturer selection in pharmaceutical industry

Simulation-based Optimization (SbO) assumes that the simulation model is valid, and that the probability distributions used therein are accurate. However, in practice, the input probability distributions (input models) are estimated by sampling data from the real system. The errors in such estimates can have a profound impact on the optimal solution obtained by SbO. The existing two-stage framework for SbO under computational budget constraint considers only the stochastic uncertainty. In our variant, we consider the input model parameter uncertainty as well. Our algorithmic procedure is based on the stochastic kriging metamodel-assisted bootstrapping with an efficient global optimization technique which sequentially searches the optimum and incorporates Optimal Computational Budget Allocation (OCBA). This framework is also used for determining tighter worst-case bounds of the SbO with input uncertainty. The proposed framework is illustrated with the M/M/1 queuing model.

Remanufacturing mode and strategic decision: A game-theoretic approach

Contract manufacturing is gaining momentum in the pharmaceutical industry. Many pharmaceutical companies that do not have enough time and financial resources to build a fullfledged drug development chain transfer some or all phases of development and production to outsourcing. Today, many even large pharmaceutical companies transfer production to developing countries on a contract basis in order to reduce costs. However, despite a number of advantages of outsourcing, it is worth considering the rather significant barriers to the organization of contract manufacturing of medicines in developing countries. The article discusses the main difficulties in organizing contract manufacturing, primarily on examples from other science-intensive and capital-intensive industries. The study shows the similarity of the difficulties that arise when transferring this practice to the pharmaceutical industry. It is also emphasized that in the world of pharmaceuticals there is a growing trend towards the use of generic drugs. One of the undeniable advantages of their production is their economic availability compared to the originals, however, it imposes increased requirements on the contract manufacturer due to the importance of not deviating too far from the original recipe. The research revealed that countries such as China, India and Japan occupy significant shares of the pharmaceutical contract manufacturing market, including generics - primarily due to various combinations of low labor costs, capital and overhead costs (compared to the United States and Europe), tax incentives and, as a result, underestimated production costs. This circumstance is extremely important for the current situation in the pharmaceutical industry in Russia, which must simultaneously make up for the dropped import volumes of finished drugs, and at the same time speed up the stalled import substitution of a number of the most critical products. As a result and purpose of the study, it is possible to identify the identified barriers to speeding up the organization of pharmaceutical contract manufacturing of drugs necessary for the domestic market, to take them into account in the process of developing appropriate government measures.

We describe the development over the past decade of a sourcing strategy within the Roche group for active pharmaceutical ingredients and the intermediates used in their manufacture. The roles of certain production sites have been modified in the light of this strategy. Before sourcing decisions are taken, criteria including life-cycle phase of the product, whether the step under review comes early or late in the synthesis, protection of proprietary know-how, quantities needed, speed, available capacity and full production costs are systematically evaluated on a case-by-case basis. For each sourcing decision, different scenarios are compared, in particular that of in-house vs. external production. In future, we envisage intensified competition both among Contract Manufacturing Organisations (CMOs) for business from large pharma concerns and among the large pharma concerns themselves for the capacity available from the CMOs. In consequence, the large pharma concerns will have to continually adapt their sourcing strategies to the changing environment and will have to have available flexible production plants and organisations if they wish to maintain a viable in-house alternative to contract manufacture.

Integrated Bayesian Multi-model approach to quantify input, parameter and conceptual model structure uncertainty in groundwater modeling

Manufacturers that work under contract for another firm may make their goods under their own brand or label. Using their own or their clients' designs, formulas, and needs as a guide, contract manufacturers perform this service. The pharmaceutical industry relies heavily on contract manufacturing. Due to a lack of capital, some companies are unable to obtain the required equipment for large-scale mass production of certain chemicals. In order to create the final product, they can work with a third-party chemical manufacturer to obtain the necessary chemicals and combine them with their own resources. An organization should carefully assess the benefits and drawbacks of contract manufacturing before committing to this strategy. There may be advantages to contract manufacturing, if the company works with the right service provider capable of delivering high-quality products. The contract manufacturing system proposed in this study is a blockchain-based solution for the pharmaceutical business. Initially, the customer uploads the order details that are preprocessed using normalization method. The preprocessed data is authenticated using the crypto smart contracts before being stored in the blockchain ledger. Then the stored data can be encrypted for security purpose by using the triple MD5 algorithm. Finally, the trust of the order data is evaluated using the proposed Imperative Ant loop optimization algorithm. In order to demonstrate the effectiveness of our system, we use the MATLAB simulation programme to compare it to other approaches.

We study a component inventory planning problem in an assemble‐to‐order environment faced by many contract manufacturers in which both quick delivery and efficient management of component inventory are crucial for the manufacturers to achieve profitability in a highly competitive market. Extending a recent study in a similar problem setting by the same authors, we analyze an optimization model for determining the optimal component stocking decision for a contract manufacturer facing an uncertain future demand, where product price depends on the delivery times. In contrast to our earlier work, this paper considers the situation where the contract manufacturer needs to deliver the full order quantity in one single shipment. This delivery requirement is appropriate for many industries, such as the garment and toy industries, where the economies of scale in transportation is essential. We develop efficient solution procedures for solving this optimization problem. We use our model results to illustrate how the different model parameters affect the optimal solution. We also compare the results under this full‐shipment model with those from our earlier work that allows for multiple partial shipments. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007

Efficient calibration of microscopic car-following models for large-scale stochastic network simulators

The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.

We dealt with a rather frequent and difficult situation while modelling extreme floods, namely, model output uncertainty in data sparse regions. A historical extreme flood event was chosen to illustrate the challenges involved. Our aim was to understand what the causes might have been and specifically to show how input and model parameter uncertainties affect the output. For this purpose, a conceptual model was calibrated and validated with recent data rich time period. Resulting model parameters were used to model the historical event which subsequently resulted in a rather poor hydrograph. Due to the bad model performance, a spatial simulation technique was used to invert the model for precipitation. Constraints, such as taking the precipitation values at historical observation locations in to account, with correct spatial structures and following the observed regional distributions were used to generate realistic precipitation fields. Results showed that the inverted precipitation improved the performance significantly even when using many different model parameters. We conclude that while modelling in data sparse conditions both model input and parameter uncertainties have to be dealt with simultaneously to obtain meaningful results.

Time-varying Analysis of Traffic Conflicts at the Upstream Approach of Toll Plaza

We study the effect of misclassification of a binary covariate on the parameters of a logistic regression model. In particular we consider 2 × 2 × 2 tables. We assume that a binary covariate is subject to misclassification that may depend on the observed outcome. This type of misclassification is known as (outcome dependent) differential misclassification. We examine the resulting asymptotic bias on the parameters of the model and derive formulas for the biases and their approximations as a function of the odds and misclassification probabilities. Conditions for unbiased estimation are also discussed. The implications are illustrated numerically using a case control study. For completeness we briefly examine the effect of covariate dependent misclassification of exposures and of outcomes.

Modelling food sourcing decisions under climate change: A data-driven approach

ملخص:- تحتوي نماذج الانحدار على مکونين أحدهما مکون عشوائي يمثل حد الخطأ (Error Term) والأخر مکون غير عشوائي يمثل متغير الاستجابة(Response Variable) وهذا المکون هو محل الاهتمام فيتم تقديره بإحدى الطريقتين هما: الطريقة المعلمية أو الطريقة اللامعلمية أو کلاهما. نموذج الانحدار المعلمي (Parametric Regression Model) هو النموذج الأکثر شيوعا حيث أنه يفترض أن متغير الاستجابة له صيغه دالية محدده مسبقا والتي تم الحصول عليها من معلومات سابقه حول هيکل دالة الانحدار من خلال ذلک يتم تقدير فئة من المعالم. ورغم أن النماذج المعلمية تعد أحد أهم أدوات تحليل البيانات؛ لأنها فعاله ويمکن تفسيرها بسهوله، إلا انها لم تستطع ان تکون کافية في بعض الحالات. أما نموذج الانحدار اللامعلمي (Nonparametric Regression Model) فانه يعتمد على تقدير دالة الانحدار مباشرة من البيانات کما أنه لا يفترض فروضا معينه لتقدير معالم النموذج مثل التي يفترضها النموذج المعلمي وبالتالي فهو يتمتع بمرونة أکثر لاکتشاف البيانات التي قد تکون مفقودة او في الحالات التي لا تتوافر فيها معلومات سابقة. يتم انشاء نموذج الانحدار شبه المعلمي (Semi parametric Regression Model) من خلال مزج الطريقتين السابقتين معا. فغالبا ما يتم استخدام هذا النموذج في المواقف التي تکون فيها الافتراضات المعلمية غير محددة وغير متسقة، أو أن النموذج اللامعلمي لا يعمل بشکل کامل. يشتمل هذا البحث على تطبيق نماذج الانحدار المعلمية وشبه المعلمية بالإضافة إلى النموذج اللامعلمى کحالة خاصة من نموذج الانحدار شبه المعلمى لتقدير الدالة الافتراضية وتحديد شکل العلاقة بين المتغير التابع والمتغيرات المستقلة بالاضافة الى شکل انتشار البيانات مع خط (أو منحني) التقدير وذلک على بيانات واقعية تم الحصول عليها من الإتحاد المصري لألعاب القوى والمعروفة بـبيانات لعبة العشاري . Regression models contain two components, one of which is a random component that represents the Error Term and the other is a non-random component that represents the response variable and this component is of interest and is estimated in one of two methods: the parametric method or the nonparametric method or both. Parametric regression model is the most common model since it assumes that the response variable has a predetermined functional formula which was obtained from previous information about the structure of the regression function by which a class of parameters is estimated. Although parameter models are one of the most important tools for analyzing data; Because it is effective and can be explained easily, but it could not be sufficient in some cases. As for the nonparametric regression model, it relies on estimating the regression function directly from the data, as it does not assume specific assumptions for estimating the model parameters, such as those assumed by the parametric model, and thus it has more flexibility to discover data that may be missing or in cases where previous information is not available. The semi-parametric regression model is constructed by blending the two previous methods together. This model is often used in situations where the parametric assumptions are indeterminate and inconsistent, or the nonparametric model is not fully functional. This research includes the application of parametric and semi-parametric regression models in addition to the nonparametric model as a special case of the semi-parametric regression model for estimating the hypothetical function and determining the form of the relationship between the dependent variable and the independent variables in addition to the data spread form with a line (or curve) estimation on realistic data On it from the Egyptian Athletics Federation, known as Decathlon data.

PurposeDespite its significance, research on how attribute framing affects ordering decisions in dual sourcing remains insufficient. Hence, this study investigated the effects of attribute framing in a sourcing task involving certain and uncertain qualities of two suppliers and analysed the role of attention with respect to suppliers' information in framing effects.Design/methodology/approachThe impacts of attribute framing on sourcing decisions were demonstrated in two online between-subject (2 × 2 factorial) experimental studies involving professional samples. Study 2 was an eye-tracking experiment.FindingsIn Study 1 (N = 251), participants presented with a “high-quality” rather than a “low-quality” frame made different sourcing decisions, opting for larger percentage of order(s) from a supplier under the “high-quality” frame. This pattern holds true for suppliers who differ in risk. This finding was replicated in Study 2 (N = 129). Attention asymmetry related to the information on supplier quality contributes to this effect. Attention directed towards information regarding the supplier's quality under a positive frame mediated the relationship between attribute framing and sourcing decisions.Practical implicationsHighlighting the positive attributes of a risky supplier is essential when ordering from the risky supplier is an optimal decision. It is advantageous for suppliers to highlight positive rather than negative attributes when describing the quality of their components against others.Originality/valueThis is the first study to examine the effect of attention on the relationship between attribute framing and dual sourcing. This presents a new behavioural perspective wherein managers' attention to information plays a vital role.