Integrating Kanban principles in a pharmaceutical campaign production system

Centralised planned campaign production is the predominant production system in process industries. In this study, we investigate whether a decentralised Kanban control system, which has proven to offer advantages in other industries, can be successfully integrated in a campaign production environment. The used research methodology combines model-based and case-study-based elements. Using the example of a pharmaceutical internal five-stage supply chain, we conceive a Kanban concept that integrates campaign formation, develop a discrete-event simulation model and conduct a range of explorative simulation experiments. We find that a Kanban campaign production system is not merely feasible but would also be favourable: throughput times can be reduced without increasing customer lead times. Sensitivity analysis shows that the system’s performance is relatively robust to changes in Kanban key configuration parameters such as number of Kanban cards or campaign size. We conclude by discussing our findings and formulating three propositions that might stimulate future research.     

Simulation analysis of cold chain performance based on time–temperature data

Perishable goods are a fundamental source of revenue for the retail sector; their management, however, constitutes a severe challenge for retailers and supply chain partners. A significant cost in particular is the fraction of products perished through the supply chain, which also constitutes an ethical and environmental concern. Supply chain organisation and operative characteristics have a significant influence on this matter, as in fact ensuring suitable temperature conditions for the stock-keeping units throughout the supply chain is mandatory for perishable products. Recent developments in sensing and communication technologies allow detailed monitoring and control of cold chain; however, depending on the characteristics of the supply chain, an inherent risk of perished products is often inevitable, even in the hypothesis of perfect control. This article proposes a methodology to evaluate the performance of a cold chain in terms of expected product quality at the retail store, and to estimate the expected fraction of perished products, according to the supply chain configuration. The approach is based on Monte Carlo simulation, and implements referenced shelf-life models. A real application is also presented, involving a preliminary analysis and mapping of the supply chain activities based on time–temperature data, in order to demonstrate the practicability of the approach proposed.     

Line balance analyses for system assembly lines in an electronic plant

This research analysed and optimised the assembly line balance of notebook computer system production lines in a major computer plant. The research developed a mathematical model for balancing assembly lines of a specific notebook computer system. Precedence relationship charts were created to enforce the required sequences and ILOG CPLEX software was used to obtain the optimal solution of the task assignments to the workstations. The new task assignments were simulated to verify the improvement recommendations and predict the potential benefits.The estimated improvements include: (1) 5.9% increase in average daily production; (2) 29% decrease in average daily WIP; (3) 67.9% reduction in cycle time; (4) 16.7% reduction in operator headcount over the whole system assembly area. The contributions of this research include: (1) Establishing line balancing mathematical model suitable for notebook computer factories; (2) Optimising the system assembly lines for the subject company; (3) Transferring the line balance modelling and optimisation techniques for company use.     

A simulation testing and analysis of aggregate production planning strategies

In this study, a hybrid discrete event simulation (DES) and system dynamics (SD) methodology is applied to model and simulate aggregate production planning (APP) problem for the first time. DES is used to simulate operational-level and shop-floor activities incorporated into APP and estimate critical time-based control parameters used in SD model of APP and SD is used to simulate APP as a collection of aggregate-level strategic decisions. The main objective of this study is to determine and analyse the effectiveness of APP strategies regarding the Total Profit criterion by developing a hybrid DES–SD simulation model for APP in a real-world manufacturing company. The simulation results demonstrated that the priority of APP strategies with regards to Total Profit criterion is: (1) the pure chase strategy, (2) the modified chase strategy, (3) the pure level strategy, (4) the modified level strategy, (5) the mixed strategy and (6) the demand management strategy, respectively. The APP system is first simulated under mixed strategy (basic scenario) conditions to include all APP capacity and demand options in constructed SD simulation model to show a comprehensive view of APP components and their interdependent interactions. Then, the obtained results will be used as Total Profit measure to compare with system's performance under some experimental scenarios applying different APP strategies.     

The use of performance measurement as a basis for project control of offshore modification oil and gas projects

The methods presented in this article are based on the qualitative and quantitative analysis of close-out data drawn from a portfolio of 38 modification projects. The primary premise is that modification projects are intrinsically subject to quantity and complexity growth during the course of detailed engineering as a consequence of the indeterminate interface with the existing facility. The normative project control routines need to be supplemented by material take-offs-based weight monitoring as a basis for re-estimation and re-calibration of the project baselines as detailed engineering proceeds in order to mitigate this uncertainty as early as possible. Similarly, estimating needs to be based on norms drawn from performance measurement of modification work rather than extrapolations from greenfield projects, particularly the work performed on the platforms. This article presents a portfolio of project control methods based on the performance measurement principle covering the short term of single projects as well as the longer term multi-project perspective.     

基于在线评分和网络效应的应用软件定价策略

在线评分可以为用户提供更多的产品信息,同时评分人数的多少也会影响用户效用。以 APP Store、Android Market 等平台上的应用软件为研究对象,以信息产品定价理论和博弈论为理论基础,通过建立两阶段的经济学模型,研究在线评分机制和信息产品网络效应的共同作用下垄断应用软件开发者的最优产品定价策略。研究结果表明,当软件质量低于用户事前期望、评分传递的质量信号较真实时,若网络效应强度较小,则软件定价应先高后低;若网络效应强度较大,则前期应该低价,甚至免费,以吸引更多用户,后期高价获利。当软件质量不低于用户事前期望、评分会限制高质量软件的信号传递时,用户只能通过评分推测软件质量等于其期望质量,因此最优质量策略等于用户期望质量。最后,探讨评分区间的局限性和弊端,软件开发者需要通过平台推广等策略提高用户的支付意愿,达到利润最大化。     

Unpacking Team Familiarity: The Effects of Geographic Location and Hierarchical Role

Examination of team productivity finds that team familiarity, i.e., individuals' prior shared work experience, can positively impact the efficiency and quality of team output. Despite the attention given to team familiarity and its contingencies, prior work has focused on whether team members have worked together, not on which team members have worked together, and under what conditions. In this paper, I parse overall team familiarity to consider effects of geographic location and the hierarchical roles of team members. Using data on all software‐development projects completed over 3 years at a large Indian firm in the global outsourced software services industry, I find that team familiarity gained when team members work together in the same location has a significantly more positive effect on team performance compared with team familiarity gained while members were collaborating in different locations. Additionally, I find that hierarchical team familiarity (a manager's experience with front‐line team members) and horizontal team familiarity (front‐line team members' experience gained with one another) have differential effects on project team performance. These findings provide insight into the relationship between team experience and team performance.     

An Evaluation of the NERJIT Priority Rule in a Kanban‐Controlled Flowshop

Significant progress in production and information technologies and innovations in management of operations during the last couple of decades have made the production of small lots and deployment of Just‐In‐Time (JIT) concepts in flowshops possible. As a result, some researchers and practitioners have been seeking to improve the performance of non‐repetitive systems using JIT concepts. In this process, the JIT concepts that were originally designed for mass production have been modified to adapt JIT to non‐repetitive systems. This article uses a priority rule that is based on real‐time demand and production information for sequencing jobs in a kanban‐controlled flowshop. The analysis of the effect of this priority rule; the number of kanbans; the length of the withdrawal cycle; First‐Come, First‐Served (FCFS); and Shortest Processing Time (SPT) on four performance measures—customer wait time, total inventory, input stock‐point inventory, and output stock‐point inventory, shows that the use of this priority rule results in a significant reduction of customer wait time and a slight decrease in inventory.     

A Universal Appointment Rule in the Presence of No‐Shows and Walk‐Ins

This study introduces a universal “Dome” appointment rule that can be parameterized through a planning constant for different clinics characterized by the environmental factors—no‐shows, walk‐ins, number of appointments per session, variability of service times, and cost of doctor's time to patients’ time. Simulation and nonlinear regression are used to derive an equation to predict the planning constant as a function of the environmental factors. We also introduce an adjustment procedure for appointment systems to explicitly minimize the disruptive effects of no‐shows and walk‐ins. The procedure adjusts the mean and standard deviation of service times based on the expected probabilities of no‐shows and walk‐ins for a given target number of patients to be served, and it is thus relevant for any appointment rule that uses the mean and standard deviation of service times to construct an appointment schedule. The results show that our Dome rule with the adjustment procedure performs better than the traditional rules in the literature, with a lower total system cost calculated as a weighted sum of patients’ waiting time, doctor's idle time, and doctor's overtime. An open‐source decision‐support tool is also provided so that healthcare managers can easily develop appointment schedules for their clinical environment.     

基于公理设计理论的仿真软件评价方法研究

针对仿真软件评价优选问题,提出一种基于公理设计理论的仿真软件评价方法。以公理设计理论为指导建立仿真软件评价模型,通过信息公理以信息量大小为测度来度量仿真软件产品的优劣。考虑到备选方案评价指标信息具有不完全性和模糊性特征,采用信息公理中的经典信息量计算方法存在局限性,对信息公理中的信息量计算方法进行拓展,提出基于单值点、区间数相离度、满意度和模糊信息的信息量计算方法。结合经典的和拓展的信息量计算方法,给出仿真软件评价流程。以某生产制造企业选择仿真软件产品为例,说明所提出方法的可行性和有效性,实验结果也揭示了在仿真软件商业领域竞争中软件质量起到至关重要的作用。