Dynamic analysis of a lean cell under uncertainty

One of the ultimate targets of lean manufacturing paradigm is to balance production and produce at takt time in production cells. This paper investigates the performance of a lean cell that implements the previous lean goals under uncertainty. The investigation is based on a system dynamics approach to model a dynamic lean cell. Backlog is used as a performance metric that reflects the cell’s responsiveness. The cell performance is compared under certain and uncertain external (demand) and internal (machine availability) conditions. Results showed that although lean cell is expected to be responsive to external demand with minimum waste, however, this was not the case under the considered uncertain conditions. The paper proposes an approach to mitigate this problem through employing dynamic capacity policy. Furthermore, the paper explores the effect of the delay associated with the proposed capacity policies and how they affect the lean cell performance. Finally, various recommendations are presented to better manage the dynamics of lean manufacturing systems

A system model for green manufacturing

Manufacturing systems evolution is afunction in multiple external and internal factors. With today’s global awareness of environmental risks as well as the pressing needs to compete through efficiency, manufacturing systems are evolving into a new paradigm. This paper presents a system model for the new green manufacturing paradigm. The model captures various planning activities to migrate from a less green into a greener and more eco-efficient manufacturing. The various planning stages are accompanied by the required control metrics as well as various green tools in an open mixed architecture. The system model is demonstrated by an industrial case study. The proposed model is a comprehensive qualitative answer to the question of how to design and/or improve green manufacturing systems as well as a roadmap for future quantitative research to better evaluate this new paradigm

Developing a Greenometer for green manufacturing assessment

In this paper a toolbox (Greenometer) to assess the greenness level of manufacturing companies is proposed. The assessment approach is based on capturing the relative greenness position of any company among other industries from different sectors as well as within the same sector. The assessment was based on selected greenness attributes and their composing indicators at each of the two levels of the developed Greenometer. Geometric Mean Method (GMM) was adopted to be the generic assessment technique for cross industries greenness evaluation, while Data Envelopment Analysis (DEA) was employed to assess the greenness level of intra-industries layer. Three different industrial applications were used to demonstrate the applicability of the developed Greenometer. Results highlighted how the proposed tool can be a useful for manufacturing managers not only in understanding their green performance position at various levels, but also aiding them in their green transformation/improvement efforts. Specifically, the Greenometer assessment scores will help in setting plans through highlighting prioritized areas of required improvement as well as offering quantitative targets and tracking metrics along the transformation journey

A dynamic approach to determine the product flow nature in apparel supply chain network

This paper presents a novel metric, product flow number, to determine the product flow nature across the supply chain of apparel industries. The metric is based on mapping the dynamics of fluid flow across a pipe to product flow across a supply chain. Numerical analysis is conducted to examine the impact of the different metric parameters on the product flow. Results showed that smooth dynamics can be achieved through scaling production and extending delivery times, while undesirable dynamics are tied to complex product designs and increasing number of suppliers. Finally, the paper demonstrated how the new metric can be used as a planning and control tool for supply chain management in the apparel industry

Assessing leanness level with demand dynamics in a multi-stage production system

Purpose – The purpose of this paper is to present a dynamic model to measure the degree of system’s leanness under dynamic demand conditions using a novel integrated metric. Design/methodology/approach – The multi-stage production system model is based on a system dynamics approach. The leanness level is measured using a new developed integrated metric that combines efficiency, WIP performance as well as service level. The analysis includes design of experiment technique at the initial analysis to examine the most significant parameters impacting the leanness score and then followed by examining different dynamic demand scenarios. Two scenarios were examined: one focussed low demand variation with various means (testing the impact of demand volumes) while the second focussed on high demand variation with constant means (testing the impact of demand variability). Findings – Results using the data from a real case study indicated that given the model parameters, demand rate has the highest impact on leanness score dynamics. The next phase of the analysis thus focussed on investigating the effect of demand dynamics on the leanness score. The analysis highlighted the different effects of demand variability and volumes on the leanness score and its different components leading to various demand and production management recommendations in this dynamic environment. Research limitations/implications – The presented lean management policies and recommendations are verified within the scope of similar systems to the considered company in terms of manufacturing settings and demand environment. Further research will be carried to extend the dynamic model to other dynamic manufacturing and service settings. Practical implications – The developed metric can be used not only to assess the leanness level of the systems which is very critical to lean practitioners but also can be used to track lean implementation progress. In addition, the presented analysis outlined various demand management as well as lean implementation policies that can improve the system leanness level and overall performance. Originality/value – The presented research develops a novel integrated metric and adds to the few literature on dynamic analysis of lean systems. Furthermore, the conducted analysis revealed some new aspects in understanding the relation between demand (variability and volume) and the leanness level of the systems. This will aid lean practitioners to set better demand and production management policies in today’s dynamic environment as well as take better decisions concerning lean technology investments

Investigating optimal capacity scalability scheduling in a reconfigurable manufacturing system

Responsiveness to dynamic market changes in a cost-effective manner is becoming a key success factor for any manufacturing system in today’s global economy. Reconfigurable manufacturing systems (RMSs) have been introduced to react quickly and effectively to such competitive market demands through modular and scalable design of the manufacturing system on the system level, as well as on the machine components’ level. This paper investigates how RMSs can manage their capacity scalability on the system level in a cost-effective manner. An approach for modeling capacity scalability is proposed, which, unlike earlier approaches, does not assume that the capacity scalability is simply a function of fixed increments of capacity units. Based on the model, a computer tool that utilizes a genetic algorithm optimization technique is developed. The tool aids the systems’ designers in deciding when to reconfigure the system in order to scale the capacity and by how much to scale it in order to meet the market demand in a cost-effective way. The results showed that, in terms of cost, the optimal capacity scalability schedules in an RMS are superior to both the exact demand capacity scalability approach and the approach of supplying all required capacity at the beginning of the planning period, which is adopted by flexible manufacturing systems (FMSs). The results also suggest that the cost-effective implementation of an RMS can be realized through decreasing the cost of reconfiguration of these new systems

Modelling and analysis of dynamic capacity complexity in multi-stage production

The uncertainty associated with managing dynamic capacity problem is the main source of its complexity. This article presents a system dynamics approach to model and analyse operational complexity of dynamic capacity in multi-stage production. The unique feature of this approach is that it captures the stochastic nature of three main sources of complexity associated with dynamic capacity. These are the demand, internal manufacturing delay and capacity scalability delay. The developed model was demonstrated by an industrial case study of multi-stage printed circuit board assembly line. The analysis of simulation experiments showed that ignoring complexity sources can lead to wrong decisions concerning both scaling levels and backlog management decisions. In addition, a general trade-off between the controllability and complexity of the dynamic capacity was illustrated. Finally, comparative analysis of the effect of each of these sources on the complexity level revealed that internal delay has the highest impact on dynamic capacity efficiency. Guidelines and recommendations for better capacity management and reduction of its complexity are presented

Variety and volume dynamic management for value creation in changeable manufacturing systems

In today’s uncertain market and continuously evolving technology, managing manufacturing systems are more complex than ever. This paper studies the dynamics of managing variety and volume to enhance value creation in manufacturers implementing system-level advanced and automated manufacturing technology (AAMT). The demand is composed of heterogeneous customers who make purchasing decisions depending on the variety levels and lead times of the firm’s product offerings. The cost structure adopted calculates profit as the difference between customer value creation rate (VCR) and costs associated with the process of creating this value. Reported results contribute to the variety and volume management literature by offering analytical clarity of factors affecting product platforms and capacity scalability management for systems with AAMT. In addition, insightful answers to the trade-offs between profit maximising market coverage and investments, smoothing demand policies and system stability for this type of environment are presented. Furthermore, the value of market information in deciding the industrial technology investment and also the impact of product life cycle on the same investment is captured

A multiple performance analysis of market-capacity integration policies

A model that uses simulation augmented with Design of Experiments (DOE) is presented to analyse the performance of a Make-to-Order (MTO) reconfigurable manufacturing system with scalable capacity. Unlike the classical capacity scaling policies, the proposed hybrid capacity scaling policy is determined using multiple performance measures that reflect cost, internal stability and responsiveness. The impact of both tactical capacity and marketing policies and their interaction on the overall performance was analysed using DOE techniques and real case data. In addition to the different insights about the trade-offs involved in capacity planning decisions, the presented results challenged the conventional capacity planning wisdoms in MTO about the negative role of the capacity scalability delay time. Finally the analysis demonstrated the importance of inter-functional integration between capacity and marketing policies

A Control Approach to Explore the Dynamics of Capacity Scalability in Reconfigurable Manufacturing Systems

This paper presents a dynamic model and analysis for one of the major characteristics of reconfigurable manufacturing systems (RMSs) capacity scalability. The dynamic model is analyzed using its transfer function. Dynamic characteristics associated with the delay in capacity scalability and how to minimize this delay are discussed using control approaches. The problem of how to supply exact capacity in response to market changes is also examined by solving the dynamic problem of the production offset phenomenon in RMSs. The effect of work in process as a damping factor for production disturbances during capacity scalability is addressed. Finally, a general capacity scalability controller design is proposed to improve the dynamic performance of RMSs in response to sudden demand changes. The proposed controller considers the different activities associated with the capacity scalability process. A numerical example is also presented to highlight the applicability of the approach