Industrial Systems, Manufacturing and Management - Final Projects 2016
Utilisation of Data Mining for Inventory Checking in Warehousing
Supervisors: Vaggelis Giannikas and Philip Woodall
Warehouse operations depend on accurate information in the Warehouse Management System (WMS). To decide if an item can be sold or if an item has to be reordered, the inventory record in the data system is used. Unfortunately, the information in the data system often does not match the actual hold-on quantities. Instead, it tends to increasingly deviate over time. To decrease inventory record inaccuracies, companies require countermeasures. Several different inventory checking approaches already exist, but the required time and money for their execution is high.
This project aims to develop an approach, which allows to decrease the time and money spent for the detection of inventory record inaccuracies. To achieve this goal, a data mining approach is used. It derives conclusions from historical data, which then shall be used for the design of an improved inventory checking approach.
Design for Resilient Supply Chain for Modular Housing Jade Robinson
Supervisor: Tariq Masood
In the past, the UK house building industry has lacked significant drivers towards innovation, however the modular off-site manufacturing industry is one such innovation that is challenging traditional methods of construction and on the way to addressing current housing shortages due to lower costs, improved functionality and flexibility and shorter construction times. The construction of a resilient modular building supply chain will involve the collaborative efforts of multiple stakeholders in a number of areas, most importantly customer demand and product design.
The aim of this research is to develop a framework for linking the two crucial areas of product design and supply chain, the interactions of which have not yet been extensively studied.
Concepts from supply chain design, and product architecture will be explored to determine the interdependencies of these two areas. These will subsequently be used to assess the effects of changes in customer demand on the supply chain and the resulting supply chain resilience. Case studies from the modular housing industry will be carried out to collect the required data. In conclusion, this study aims to address product design variation challenges and provide tools and approaches which support decision making early in the design stage for advanced manufacturing of modular homes, while concurrently considering the design for a resilient supply chain.
Investigating disruption predictions and mitigation/contingency options for an aircraft manufacturer
Supervisors: Phil Woodall, Pascal Wichmann, Alexandra Brintrup
Problem Statement: Aerospace manufacturers often face a constant stream of supply problems from their suppliers. Due to the complexity of aircraft manufacturing, many components are outsourced to tier 1 suppliers, each of which outsource to other suppliers, making up a highly complex supply network. This could lead to a supplier delivering a component late, at a low quality, or not delivering at all. Commonly, these issues are only known at a point where production would be significantly affected and, are usually, costly.
Research Objective: The general aim of this dissertation is to enhance supply chain operations by (i) exploring how characteristics of a disruption influence the choice of the most appropriate mitigation/contingency option, and (ii) understanding to what extent a given disruption prediction capability can lead to the avoidance of a disruption.
Optimal allocation of demand across a set of suppliers
Supervisor: Alena Puchkova
First part of this dissertation will focus on review of existing approaches for assessment of supplier reliability in production networks and techniques for resources reallocation in face of disruptions.
Existing mathematical model will be extended to incorporate suppliers of different tiers and their reliability levels. The model will then be used to quantify the impacts of disruptions and their propagation through the system. The aim is to develop an approach that will allow to identify the best choice of suppliers and optimal strategy for allocation of demand across all suppliers. Finally, the model will be tested on a simple case study, where solutions will be found for scenarios with different disruption profiles.
Impact of additive manufacturing on resilience of manufacturing networks
Supervisor: Alena Puchkova
The dissertation will investigate how additive manufacturing (AM) effects efficiency of production processes. Firstly, the dissertation will focus on literature review of existing approaches to the modelling of AM production systems. Mathematical model of a production network including 3D printing facilities will be developed.
The model will then be used to compare the performance and resilience of the production system in face of disruptions (e.g. rush orders) with and without additive manufacturing. The last part of the dissertation will aim to test the model on a particular example, where the conditions under which additive manufacturing is beneficial compared to conventional manufacturing will be identified.
Dynamic approach to aircraft schedule recovery during disrupted operations
Supervisor: Alena Puchkova, Alexandra Brintrup
This project aims to develop a dynamic approach for managing airline operational disruptions as they appear in real time. It will first help to understand what the main types of disruptions are, and how they are currently being handled by airline companies.
A further study will then be carried out to analyse how these disruptions propagate to other flights and to identify what the best recovery strategy is. The dissertation will compare a dynamic approach with an iterative approach, where existing static model is being run every time a disruption appears. The model will be extended to incorporate dynamically changing constraints (e.g. constraints on ground resources). Finally, the model will be tested on the airline data found in literature.