Adil Salam

Optimising the design of a supply chain network with economies of scale using mixed integer programming

In this paper, we formulate and analyse the design of a supply chain network where the concept of economies of scale in transportations are considered. Having the demand of different products from various retailers, the model determines the numbers and locations of the consolidation and distribution centres. It also determines the routes and lot sizes of the shipments to fulfil the demand by the retailers. Piecewise linearisation technique is utilised to transform the non-linear concave-cost function of the transportation into a linear form. Using this linearisation technique, a mixed integer linear programming model is developed that can be efficiently solved using a branch-and-bound-based optimisation package. Numerical examples are presented to demonstrate the features of the proposed model and the impacts of certain input parameters on the design of the supply chain network.

A comparative study to estimate costs at Bombardier Aerospace using regression analysis

The purpose of this research is to develop a target cost model for the Main Landing Gear at Bombardier Aerospace. This will offer a cost understanding at the early conception stage. The study uses a comparative analysis between a linear and a non-linear parametric model in order to determine which estimation method will increase the credibility of the cost estimate. The reliability of the models will be validated by two different methodologies to determine which parameter(s) become significant and will therefore be used to determine the cost. These methodologies are the analysis of variance and path analysis. It was found that the non-linear regression analysis achieves a lower level of error when comparing it to the linear regression

A case study to estimate design effort for Pratt & Whitney canada

Abstract The design effort required to complete a project is an important aspect of a project. It impacts the final cost, as well as the lead-time of a project. In this paper, a case study, which is carried out at Pratt & Whitney Canada, a global leader in the design and manufacture of aircraft engines, is presented. A Parametric model is proposed to estimate the design effort required in for a particular department to complete their design phase of an integrated blade-rotor low-pressure compressor fan. In a sensitivity analysis, the model estimation is compared with the actual estimates and the comparison demonstrates that the parametric model results in a good estimation. The analysis further explores the impact of various factors used to develop the parametric model, as well as demonstrates the significance of the proposed modeling methodology.

Estimating design effort using parametric models: A case study at Pratt & Whitney Canada

Aerospace is very important to the Canadian economy, with over 80,000 employees and generating over

Estimating design effort in product development: A case study at Pratt & Whitney Canada

20 billion dollars in revenue. However, this industry like many others is facing many challenges. One of them is the difficulty in being able to estimate design effort required in a design project, which impacts not only resource requirements and lead-time but also the final cost. This article presents the findings of a case study conducted for Pratt & Whitney Canada, recognized as a global leader in the design and manufacturing of aircraft engines. The study models parametric cost estimation relationships to estimate the design effort of integrated blade-rotor low-pressure compressor fans. Several effort drivers are selected to model the relationship. Comparative analyses of three types of models are conducted. The model with the best accuracy and significance in design estimation is retained.

A Case Study on Target Cost Estimation Using Back-Propagation and Genetic Algorithm Trained Neural Networks

The design effort required in a project, not only impacts the final cost, but also the project lead-time. This paper presents a case study carried out with the collaboration of Pratt & Whitney Canada, a global leader in the design and manufacture of aircraft engines. The study uses a parametric model for the purpose of design effort estimation of an integrated blade-rotor low-pressure compressor (IBR LPC) fan. The model estimation is compared with the actual project performance, and results demonstrate good estimation of the design effort. The impact of various factors used for design effort estimation is also discussed. Finally, the usefulness of the model is demonstrated.

Product cost estimation using supper efficiency data envelopment analysis

Cost estimation of new products has always been difficult as only few attributes will be known. In these situations, parametric methods are commonly used using a priori determined cost function where parameters are evaluated from historical data. Neural networks, in contrast, are non-parametric, i.e., they attempt to fit curves without being provided a predetermined function. In this article, this property of neural networks is used to investigate their applicability for cost estimation of certain major aircraft subassemblies. The study is conducted in collaboration with an aerospace company located in Montreal, Canada. Two neural network models, one trained by the gradient descent algorithm and the other by genetic algorithm, are considered and compared with one another. The study, using historical data, shows an example for which the neural network model trained by genetic algorithm is robust and fits well both the training and validation data sets.

Estimating design effort for the compressor design department: a case study at Pratt & Whitney Canada

The various components of parametric and non-parametric cost estimation methods assume that the historic data used in cost analysis are true representation of the relation between the cost drivers and the corresponding costs of products. However, because of efficiency variations of the manufacturers and suppliers, changes in supplier selections, market fluctuations, and several other reasons, certain costs in the historic data may be too high whereas other costs may represent better deals for their corresponding cost drivers. Thus, it may be important to rank the historic data and identify benchmarks and estimate the target costs of the product based on these benchmarks. In this paper, a novel adaptation of cost drivers and cost data is introduced in order to use data envelopment analysis for the purpose of ranking cost data and identify benchmarks, and then estimate the target costs of a new product based on these benchmarks. An illustrative case study has been presented for the cost estimation of landing gears of an aircraft manufactured by an aerospace company located in Montreal, CANADA.