Faisal Aqlan

Supply chain risk modelling and mitigation

In today’s global competitive environment, supply chains are more susceptible to vulnerability due to the increasing occurrence of internal and external risk events. In addition, the trend associated with lean management, which involves reducing inventory, leads to more dependency of supply chain partners on each other which exacerbates risk exposure of companies in the supply chain. This creates the need for more effective management of supply chain risks. In this research, a methodology based on Bow-Tie analysis and optimisation techniques is proposed to quantify and mitigate supply chain risks. The proposed methodology takes into consideration risk interconnections, and it identifies the best combination of mitigation strategies under budget constraints. A real case study from a high-end server manufacturing environment is presented. Results from the case study showed that the proposed methodology for risk modelling and mitigation can effectively be used to quantify the risks and achieve the required risk reduction at minimum cost while considering risk correlations.

Supply chain optimization under risk and uncertainty

We propose an approach for supply chain optimization under risk and uncertainty.Simulation and optimization models are combined through an iterative procedure.A software application is developed based on the proposed approach.To validate the proposed approach, a case study is provided. This paper presents an approach and a software application for supply chain optimization under risk and uncertainty. The proposed approach combines simulation and optimization techniques for managing risks in supply chains. A multi-objective optimization model is developed which considers the deterministic features of the supply chain. A simulation model is used to represent the stochastic features of the supply chain. Both models communicate to achieve the best values for profit, lead time and risk reduction by selecting a combination of mitigation strategies and allocating orders and inventory. A case study from a high-end server manufacturing environment is used to demonstrate the validity of the proposed approach. The analytical results show clear trade-offs among the three objectives where changing the risk reduction goal value will affect the total profit and lead time. The proposed approach helps decision makers identify the best risk mitigation strategies and allocate inventory and customer orders effectively.

A software application for rapid risk assessment in integrated supply chains

We proposed a framework for rapid risk assessment in integrated supply chains.We used fuzzy logic to reduce the uncertainty inherent in the supply chain risks.We proposed risk priority matrix, a 2?×?2 matrix used for risk aggregation.We developed a software tool to implement the proposed framework.A case study from a real manufacturing was used to assess the proposed framework. Supply chain risk management (SCRM) has become a critical component of supply chain management with the movement to global supply chains and the increasing occurrence of internal and external risk events. Effective management of supply chain risks requires a comprehensive yet rapid assessment of all the risk factors in the supply chain and their potential impacts. This paper presents a software application framework for rapid risk assessment (RRA) in integrated supply chains. The proposed framework combines qualitative and quantitative methods to assess and prioritize the risks. Qualitative methods are based on surveys used to collect the risk probability and impact data for the main agents in the supply chain (i.e., supplier, customer, manufacturer, etc.). Quantitative methods are based on probability theory and fuzzy logic. Risks are calculated for each agent in the supply chain and are then aggregated per product type. The proposed RRA tool was tested in a manufacturing environment to assess the validity of the proposed framework. Results from the case study showed that the assessment obtained by the proposed framework agrees with what the risk management experts think about the risk levels and priorities in the company.

A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints

Abstract Supply chain network is very important to the development of industries. This paper integrates a location-inventory problem into a supply chain network and develops an optimization model for perishable products with fuzzy capacity and carbon emissions constraints. This model is formulated a mixed integer nonlinear programming model. In order to solve this model, hybrid genetic algorithm (HGA) and hybrid harmony search (HHS) are put forward to minimize the total costs. Instances under different situations are calculated using these two algorithms and Lindo (optimization solver). The impacts of some factors such as the number of facilities, intact rates, and demand on the total costs are investigated. The results of numerical experiments demonstrate that the proposed algorithms can effectively deal with problems under different conditions and these two algorithms have their own advantages. Specially, the quality of HHS’s solution is higher than that of HGA’s solution, whereas HGA is faster than HHS.

A decision support system for real-time order management in a heterogeneous production environment

We proposed a system for order management in heterogeneous production environments.The proposed system is supported by a case study form high-end server manufacturing.We presented an order prioritization tool to assess and prioritize customer orders.We used a risk mitigation approach to account for business risks.We developed a real-time dashboard to visualize order related information. In todays competitive market, many companies are morphing from the traditional new build, single brand, and silo environments to facilities accommodating diverse business missions. The later are called heterogeneous production environments in which the different business channels share their final production stage (shipping) to enable competitive advantages. In these production environments, at the operational level, the critical success factors are customer satisfaction, on-time delivery, product complexities, supply allocation, and resource utilization. At the strategic level, the success factors are revenue, customer urgency, and sales impact. This study proposes an End-to-End Customer Order Management System (E2E COMS) focusing on effective utilization of individual and shared resources to support real-time order management and mitigate risk of managing diverse missions. The proposed system consists of three integrated tools: Order Prioritization Tool (OPT) to assess and prioritize customer orders for each business channel, Order Fulfillment Progress Projection Tool (OFPPT) to predict the expected remaining order completion time considering inventory and resource capacity constraints, and risk mitigation tool to assess the risk of missing an order shipment due to shipping constraints. A real-time dashboard is developed to visualize the prioritized customer orders, expected time to arrive at the shipping area, shipping instructions, and two-dimensional risk assessment charts. The proposed system can effectively be used for shipping capacity management as well as prompt decision making.

A two-phase method for multi-echelon location-routing problems in supply chains

Abstract The multi-echelon location-routing problems (LRPs) arise from transportation applications such as distribution systems of supply chains in city logistics. The literature review shows that most of the previous studies on location-routing in supply chains involve two-echelon LRPs. The main objective of this study is to develop a two-phase method based on improved Clarke and Wright savings algorithm for three-echelon and four-echelon LRPs. Computational experiments show that compared with other methods, the proposed method can obtain the solution for two LRPs in a shorter time. Moreover, computational experiments show this method can solve three and four location-routing problems in reasonable time. The study also provides managerial insights on the proposed models and method. Finally, the limitations on models and method as well as future research directions are given.

Standard tables of truncated standard normal distribution using a new summarizing method

The single- and double-sided truncated normal distributions have been used in a wide range of engineering fields. However, most of the previous research works have focused primarily on the non-truncated population distributions. The authors present reference tables to estimate the values of density and cumulative density functions of truncated normal distribution for practitioners. Finally, the authors explain how to use the tables to estimate other properties, such as mean, median and variance. The purpose of this paper is to provide an efficient method to summarize tables, and furthermore, to provide readers with statistical tables on truncated standard normal distribution.,A new methodology is developed to summarize the tables with ordered values. The introduced method allows for the reduction of the number of pages required for such tables into a reasonable level by using linear interpolation. Moreover, it allows for the estimation of the required truncation values accurately with an error value less than 0.005.,The data in the tables can be summarized into a significantly reduced amount. The new summarized table can be designed for any number of pages and/or level of error wanted. However, with reducing the level of error, the number of pages increases and vice versa.,The value of this work is through two major points. First, all provided summarized tables in the literature are for single-sided and symmetry truncation cases. However, there is no attempt to summarize the tables of the asymmetry truncation normal distribution due to the requirement of huge number of pages. In this paper, the case of asymmetry truncation is included. Second, the methodology provided in this research can be used to summarize similar large tables.

A framework for selecting and evaluating process improvement projects using simulation and optimization techniques

Selection of process improvement initiatives can be a challenging task. Process improvement projects usually fall into the following categories: Lean, Six Sigma, Lean Six Sigma, Change Management, and Business Process Reengineering. The selection process of these projects is a multi-criteria decision making process which involves multiple conflicting objectives. In this study, we develop an optimization model to select process improvement projects taking into consideration resource availability, required skills, and budget constraints. In addition, discrete event simulation (DES) models are developed to evaluate some of the selected projects. The DES models account for the uncertainty in the system and allow for performing scenario analysis on the selected projects. To validate the proposed approach, we provide a case study from a high-end server manufacturing environment. Results can be used to enhance the decisions on selecting process improvement projects.

An approach for rush order acceptance decisions using simulation and multi-attribute utility theory

Rush orders are orders with shorter lead times and higher operating priorities compared to regular orders. A company may accept rush order, regardless of its capacity or raw material constraints, to maintain customer satisfaction and/or increase profit. On the other hand, rush orders can cause problems in managing production systems due to the unbalanced use of system resources. In this paper, discrete event simulation (DES) and multi-attribute utility theory (MAUT) are integrated to study the impact of rush orders on the performance of a hybrid push-pull production system. The proposed approach is used to identify the best acceptance levels of rush orders. Numerical results showed that prioritising customer orders based on their associated utilities can improve the performance of a production system. In addition, the best acceptance levels of rush orders can be determined by maximising the performance of the production system while considering production constraints. [Received 25 May 2015; Revised 1 August 2016; Revised 6 September 2016; Revised 3 March 2017; Accepted 5 June 2017]