Judgement and supply chain dynamics

Forecasting demand at the individual stock-keeping-unit (SKU) level often necessitates the use of statistical methods, such as exponential smoothing. In some organizations, however, statistical forecasts will be subject to judgemental adjustments by managers. Although a number of empirical and ‘laboratory’ studies have been performed in this area, no formal OR modelling has been conducted to offer insights into the impact such adjustments may have on supply chain performance and the potential development of mitigation mechanisms. This is because of the associated dynamic complexity and the situation-specific nature of the problem at hand. In conjunction with appropriate stock control rules, demand forecasts help decide how much to order. It is a common practice that replenishment orders may also be subject to judgemental intervention, adding further to the dynamic system complexity and interdependence. The system dynamics (SD) modelling method can help advance knowledge in this area, where mathematical modelling cannot accommodate the associated complexity. This study, which constitutes part of a UK government funded (EPSRC) project, uses SD models to evaluate the effects of forecasting and ordering adjustments for a wide set of scenarios involving: three different inventory policies; seven different (combinations of) points of intervention; and four different (combinations of) types of judgmental intervention (optimistic and pessimistic). The results enable insights to be gained into the performance of the entire supply chain. An agenda for further research concludes the paper

Cooperation networks and innovation: A complex system perspective to the analysis and evaluation of a EU regional innovation policy programme

Recent developments in innovation theory and policy have led policymakers to assign particular importance to supporting networks of cooperation among heterogeneous economic actors, especially in production systems composed of small and medium enterprises. Such innovative policies call for parallel innovations in policy analysis, monitoring and assessment. Our analysis of a policy experiment aimed at supporting innovation networks in the Italian region of Tuscany intends to address some issues connected with the design, monitoring and evaluation of such interventions. Combining tools from ethnographic research and social networks analysis, we explore the structural elements of the policy programme, its macroscopic impact on the regional innovation system, and the success of individual networks in attaining their specific objectives. This innovative approach allows us to derive some general methodological suggestions for the design and evaluation of similar programmes.Innovation policy, cooperation networks, evaluation, regional development, SMEs production systems, complex systems

Systems thinking in the built environment: Seeing the bigger picture, understanding the detail

Over the last 20 years, environmental sustainability in buildings, with its links to climate change concerns, low embodied carbon and sustainably sourced materials and energy efficiency, has grown from being a niche enterprise to a major driver of new business – a trend that Indoor and Built Environment has followed with interest. However, beyond this and with the rise of the wellbeing agenda, clients are now beginning to expect buildings to also contribute to the health and wellbeing of the people who live, work and learn inside them. This has created a new focus around the issues of healthy environments, wellbeing and increased productivity in addition to the low-carbon agenda. Therefore, with this added and important emphasis, how do we ensure that this will be more than an ephemeral trend and that in the future ‘business as usual’ will be truly both sustainable and healthy? Previous research on the impacts of energy-efficient design on the indoor environment has shown that there is the potential for numerous unintended consequences when decarbonising the built environment.1,2 How can we be certain that processes to ensure wellbeing in buildings will be positive, or do we have to accept that as with energy efficiency measures, multiple trade-offs (for example between emissions reduction and public health) will occur? This editorial argues that to ensure health and wellbeing co-benefits two changes need to occur: First, we need to move away from a purely reductionist and siloed rationale towards integrative whole systems thinking and action; second, in order to achieve this, we cannot remain closed inside our disciplinary boundaries and we need to learn how to traverse them

Incorporating a new technology into agent-artifact space. The case of control systems automation.

We contribute to the debate on innovation theory and policy by exploring, through the interpretative framework provided by Lane and Maxfield’s theory of innovation (1997; 2005), a set of case studies concerning the implementation of a new technology for system automation and its incorporation into the structure of agent-artifact space (Lane and Maxfield, 1996). Our purposes are, on the one hand, to illustrate to what extent this theoretical approach can help us make sense of innovation processes, and, on the other, to derive some general implications for innovation theory.innovation, technological systems, complex systems, innovation networks, control systems automation

Future world cancer death rate prediction

Cancer is a worldwide illness that causes significant morbidity and death and imposes an immense cost on global public health. Modelling such a phenomenon is complex because of the non-stationarity and complexity of cancer waves. Apply modern novel statistical methods directly to raw clinical data. To estimate extreme cancer death rate likelihood at any period in any location of interest. Traditional statistical methodologies that deal with temporal observations of multi-regional processes cannot adequately deal with substantial regional dimensionality and cross-correlation of various regional variables. Setting: multicenter, population-based, medical survey data-based biostatistical approach. Due to the non-stationarity and complicated nature of cancer, it is challenging to model such a phenomenon. This paper offers a unique bio-system dependability technique suited for multi-regional environmental and health systems. When monitored over a significant period, it yields a reliable long-term projection of the chance of an exceptional cancer mortality rate. Traditional statistical approaches dealing with temporal observations of multi-regional processes cannot effectively deal with large regional dimensionality and cross-correlation between multiple regional data. The provided approach may be employed in numerous public health applications, depending on their clinical survey data.publishedVersio

A novel multi regional reliability method for Covid-19 death forecast

Novel coronavirus disease is spread worldwide with considerable morbidity and mortality and presents an enormous burden on worldwide public health. The present study describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period, resulting in a reliable long-term forecast of the novel coronavirus death rate. To determine extreme novel coronavirus death rate probability at any time in any region of interest. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between different regional observations. The present study presents a novel statistical method to analyse raw clinical data using a multicenter, population-based, medical survey data-based biostatistical approach. Due to the non-stationarity and complicated nature of the novel coronavirus, it is challenging to model such a phenomenon. The present study describes a novel bio-system reliability approach, particularly suitable for multi-region environmental and health systems, observed over a sufficient period, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. The method also indicates the accuracy by presenting the 95% confidence interval band. The suggested methodology can be used in various public health applications based on their clinical survey data.acceptedVersio

Novel reliability method validation for offshore structural dynamic response

The paper validates novel structural reliability Gaidai-Fu-Xing (GFX) method, particularly suitable for multi-dimensional structural responses, versus well established bivariate statistical method, that is known to accurately predict two-dimensional system extreme response levels. Classic reliability methods, dealing with time series do not have an advantage of dealing easily with system high dimensionality and cross-correlation between different dimensions. An operating Jacket located in the Bohai bay was taken as an example to demonstrate the proposed methodology. Novel state of art bimodal extrapolation technique was applied to predict system reliability with five years return period, which is of practical importance for design of fixed offshore structures. Unlike other reliability methods the new method does not require to re-start simulation each time system fails, in case of numerical simulation. In case of measured structural response, an accurate prediction of system failure probability is also possible as illustrated in this study. Jacket offshore platform subjected to large environmental wave loads, thus structural stresses in different structural critical locations were chosen as an example for this reliability study. The method proposed in this paper opens up the possibility to predict simply and efficiently failure probability for nonlinear multi-dimensional dynamic system as a whole.publishedVersio

Analysis of Chaotic Natural Convection in a Tall Rectangular Cavity with Non-isothermal Walls

A computational model is presented that extends prior work on unsteady natural convection in a tall rectangular cavity with aspect ratio 10 and applies Proper Orthogonal Decomposition to the results.The solution to the weakly compressible Navier-Stokes equation is computed for a range of Rayleigh numbers between 2x10^7 and 2.2x10^8 with Prandtl number 0.71.A detailed spectral analysis shows dynamic system behavior beyond the Hopf bifurcation that was not previously observed.The wider Rayleigh range reveals new dynamic system behavior for the rectangular geometry, specifically a return to a stable oscillatory behavior that was not predicted in prior work.Proper Orthogonal Decomposition (POD) has been used to analyze the computational results.Five eigenvalue modes were required to capture correctly the basic flow structure.The POD failed to capture subtle aspects of the flow structure at high Rayleigh numbers for the model, indicating that a POD and Galerkin projection for several Rayleigh numbers will be needed to reproduce the complex behavior of the system

A Hybrid Simulation Framework of Consumer-to-Consumer Ecommerce Space

In the past decade, ecommerce transformed the business models of many organizations. Information Technology leveled the playing field for new participants, who were capable of causing disruptive changes in every industry. Web 2.0 or Social Web further redefined ways users enlist for services. It is now easy to be influenced to make choices of services based on recommendations of friends and popularity amongst peers. This research proposes a simulation framework to investigate how actions of stakeholders at this level of complexity affect system performance as well as the dynamics that exist between different models using concepts from the fields of operations engineering, engineering management, and multi-model simulation. Viewing this complex model from a systems perspective calls for the integration of different levels of behaviors. Complex interactions exist among stakeholders, the environment and available technology. The presence of continuous and discrete behaviors coupled with stochastic and deterministic behaviors present challenges for using standalone simulation tools to simulate the business model. We propose a framework that takes into account dynamic system complexity and risk from a hybrid paradigm. The SCOR model is employed to map the business processes and it is implemented using agent based simulation and system dynamics. By combining system dynamics at the strategy level with agent based models of consumer behaviors, an accurate yet efficient representation of the business model that makes for sound basis of decision making can be achieved to maximize stakeholders\u27 utility