A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

Use of Audit Data to Improve the Supply Chain Performance

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn the last decades, globalization and digitalization were two of the main reasons for the increase of complexity in supply chains, altering the industries due to the massive amount of information available. This complexity started to become harmful for the companies that do not understand how to use data and information as their competitive advantage, increasing the risk and costs associated with their processes, and decreasing effectiveness and efficiency. We look for the concept and area of internal auditing and process mining techniques as a solution to revert this situation. While research has focused on different and mostly narrow aspects in these areas and solution-oriented and more practical approaches can be found and applied to a broader environment, a practical solution that incorporates these areas into the supply chain are hard to find. Therefore, following a design science research methodology, this study proposes an iterative framework that consists of a guide for an organization that wants to incorporate new technologies into their processes in the supply chain while making the best out of the massive amount of information available using internal auditing and focus on process mining techniques. The framework provides a chain of steps that can be adapted by the company during the transformational process, guaranteeing a smooth transition away from the traditional systems to a more modern and flexible architecture

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success

Improvement of inventory control systems for raw material in a make-to-order company

In any manufacturing industry, inventory is always an important part to be controlled; companies have to design a system that is able to avoid stock outs while keeping the stock at minimum. Nonetheless, to some extents, managing inventory is utterly complicated. In many cases, companies turn into fiasco when they design an efficient inventory control system, especially for make-to-order companies which deal with extremely high demand uncertainty. In this paper, several inventory control systems such as continuous review (s, Q system) and periodic review (R, s, S system) are investigated and compared to the existing inventory control system in the company. The objective is to obtain a better inventory control system for every raw material category in terms of total cost and service level. At the very first phase, considering usage volume and the coefficient of variance, several raw material samples are taken. This research develops a Monte Carlo simulation for generating probabilistic demand and shipment lead time. In carrying out simulation for generating demand, author uses several replications to evade improper results, which could lead to wrong decisions. Each scenario for inventory control system is evaluated in terms of total cost and service level. Heuristic methods for both continuous and periodic inventory control systems are also used to test the sensitivity of the parameters. This paper brings an important recommendation to the company as well as insight for maketo- order companies in general. Since cost is the ultimate output for profit-based companies including Karya Makmur Baru, Ltd., the proposed inventory control system for each raw material category could be implemented by the company as a means to reduce the total inventory cost while maintaining the service level target. The inventory cost reduction ranges from 39.69% to 72.85%, with the minimum service level of 98.48%

Improvement Of Inventory Control Systems For Raw Material In A Make To Order Company

In any manufacturing industry, inventory is always an important part to be controlled; companies have to design a system that is able to avoid stock outs while keeping the stock at minimum. Nonetheless, to some extents, managing inventory is utterly complicated. In many cases, companies turn into fiasco when they design an efficient inventory control system, especially for make-to-order companies which deal with extremely high demand uncertainty. In this paper, several inventory control systems such as continuous review (s, Q system) and periodic review (R, s, S system) are investigated and compared to the existing inventory control system in the company. The objective is to obtain a better inventory control system for every raw material category in terms of total cost and service level. At the very first phase, considering usage volume and the coefficient of variance, several raw material samples are taken. This research develops a Monte Carlo simulation for generating probabilistic demand and shipment lead time. In carrying out simulation for generating demand, author uses several replications to evade improper results, which could lead to wrong decisions. Each scenario for inventory control system is evaluated in terms of total cost and service level. Heuristic methods for both continuous and periodic inventory control systems are also used to test the sensitivity of the parameters. This paper brings an important recommendation to the company as well as insight for make- to-order companies in general. Since cost is the ultimate output for profit-based companies including Karya Makmur Baru, Ltd., the proposed inventory control system for each raw material category could be implemented by the company as a means to reduce the total inventory cost while maintaining the service level target. The inventory cost reduction ranges from 39.69% to 72.85%, with the minimum service level of 98.48%

Supply chain management for the process industry

This thesis investigates some important problems in the supply chain management (SCM) for the process industry to fill the gap in the literature work, covering production planning and scheduling, production, distribution planning under uncertainty, multiobjective supply chain optimisation and water resources management in the water supply chain planning. To solve these problems, models and solution approaches are developed using mathematical programming, especially mixed-integer linear programming (MILP), techniques. First, the medium-term planning of continuous multiproduct plants with sequence-dependent changeovers is addressed. An MILP model is developed using Travelling Salesman Problem (TSP) classic formulation. A rolling horizon approach is also proposed for large instances. Compared with several literature models, the proposed models and approaches show significant computational advantage. Then, the short-term scheduling of batch multiproduct plants is considered. TSP-based formulation is adapted to model the sequence-dependent changeovers between product groups. An edible-oil deodoriser case study is investigated. Later, the proposed TSP-based formulation is incorporated into the supply chain planning with sequence-dependent changeovers and demand elasticity of price. Model predictive control (MPC) is applied to the production, distribution and inventory planning of supply chains under demand uncertainty. A multiobjective optimisation problem for the production, distribution and capacity planning of a global supply chain of agrochemicals is also addressed, considering cost, responsiveness and customer service level as objectives simultaneously. Both ε- constraint method and lexicographic minimax method are used to find the Pareto-optimal solutions Finally, the integrated water resources management in the water supply chain management is addressed, considering desalinated water, wastewater and reclaimed water, simultaneously. The optimal production, distribution and storage systems are determined by the proposed MILP model. Real cases of two Greek islands are studied