This study aims to analyze lean manufacturing effect on financial, non-financial and sustainability performances.Causal maps are used for defining the inter-relation and intra-relation of lean factors.Bayesian Belief Networks are used for linking the lean factors.Effects of various combinations of lean factors on business performance are analyzed.Developed scenarios can be a lead for defining the business strategies based on lean thinking in any industry. The challenge of agility for adopting new business norms creates the need for measuring performance under changing conditions. This study aims to demonstrate the financial and non-financial consequences of implementing different combinations of lean techniques on the business performance. Bayesian Belief Network is used in studying the effects of factors under changing conditions. There are seven lean factors and four achievements studied to analyze the impact on three performance indicators. Bayesian Belief Network is constructed on the lean aspects that stimuli flexibility, reliability, quality and time of operations, which will have positive impacts on the financial, non-financial and sustainability performances of suppliers. A case study is carried out for suppliers in the automotive industry and scenarios with different combinations of lean factors are studied. This study gives a new vision in applying Bayesian network for business performance measures considering both the tangible and intangible results under changing business conditions.

Assessment of lean manufacturing effect on business performance using Bayesian Belief Networks

Distributed unattended ground sensor networks used in battlefield surveillance and monitoring missions, have proven to be valuable in providing a tactical information advantage required for command and control, intelligence, surveillance, and reconnaissance planning. Operational effectiveness for surveillance missions can be enhanced further through network centric capability, where distributed UGS networks have the ability to perform surveillance operations autonomously. NCC operation can be enhanced through UGSs having the ability to evaluate their awareness of the current joint surveillance environment, in order to provide the necessary adaptation to dynamic changes. NCC can also provide an advantage for UGS networks to self-manage their limited operational resources efficiently, according to mission objective priority. In this article, we present a crosslayer approach and highlight techniques that have potential to enable NCC operation within a mission-orientated UGS surveillance setting.

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Networking and application interface technology for wireless sensor network surveillance and monitoring

The problem of decision fusion has been studied for distributed sensor systems in the past two decades. Various techniques have been developed for either binary or multiple hypotheses decision fusion. However, most of them do not address the challenges that come with the changing quality of sensor data. In this paper we investigate adaptive decision fusion rules for multiple hypotheses within the framework of Dempster-Shafer theory. We provide a novel learning algorithm for determining the quality of sensor data in the fusion process. In our approach each sensor actively learns the quality of information from different sensors and updates their reliabilities using the weighted majority technique. Several examples are provided to show the effectiveness of our approach.

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Learning the Quality of Sensor Data in Distributed Decision Fusion

We present and demonstrate a particle filtering approach to data fusion and situation assessment for military operations in urban environments. Our approach views such an environment as a physical system whose state vector is composed of a large number of both discrete and continuous variables representing properties of tracked entities. Inference on such vector-based models exploits both causal dependencies among variables in the state vector via its dynamic Bayesian belief network representation and vector decomposition into weakly interacting subcomponents. To effectively leverage the decomposition, instead of straightforward particle filtering, the proposed algorithm maintains factored particles over clusters of state variables, thus resulting in smaller variance. The algorithm samples discrete modes and approximates the continuous variables by a multi-normal distribution updated at each time step by an unscented Kalman filter. The approach is demonstrated using a Marine Corps operational scenario involving a potential ambush on city streets.

Factored particle filtering for data fusion and situation assessment in urban environments

High-level information fusion is a research field in which methods for achieving an overall understanding of the current situation in an environment of interest are studied. The ultimate goal of th ...

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Evaluating credal set theory as a belief framework in high-level information fusion for automated decision-making

A probabilistic approach to truth maintenance is presented, specifically geared for automated problem solvers based on Bayesian belief network (BN) technology. Nodes and links in a BN capture semantic relationships among various domain related concepts. In the absence of firmer knowledge, default assumptions provide the beliefs for some nodes in the BN. Before posting incoming evidence into a BN node, a truth maintenance procedure is invoked to check for information consistency between the node's current expected state and the new observed state. In case of inconsistency, the truth maintenance procedure revises some default assumptions, by isolating those nodes causing inconsistency, via a sensitivity analysis procedure that exploits the strengths of BN causal dependency. The approach is specifically targeted for trustworthy situation assessment in the context of a military stability and support operation (SASO) scenario.

https://ieeexplore.ieee.org/iel5/7951/21965/01021201.pdf

Trustworthy situation assessment via belief networks

We describe a novel clustering approach for aggregating mobile (typically potentially hostile) units in cluttered urban environments. The approach consists of a suite of spatiotemporal clustering algorithms that leverage the wealth of military sensor data available to provide insight into "what is strange" about a given situation, without knowing beforehand what exactly we are looking for. The algorithms perform a space and time-series analysis of sensor messages independently of any contextual or semantic information. The algorithms can, for example, detect patterns and track for spatially correlated moving units over time within the environment. The patterns thus detected trigger follow-up assessment of the newly developed situations, resulting in invocations of various doctrine-based computational models to identify higher-level situations (e.g. attack, ambush, interdiction, insurgency). We provide some experimental results analyzing the performance of the clustering algorithms.

Spatiotemporal Clustering for Aggregating Hostile Units in Cluttered Environments

: We present here the Probabilistic Unit Life Status Estimation (PULSE) methodology and software architecture. The PULSE approach leverages Temporal Belief Networks (TBNs) to model the subject's fundamental physiological dynamics (e.g. shock response) over time, to perform life status estimation in a robust manner that accounts for sensor hardware malfunctions. We developed a limited-scope prototype using our in-house TBN engine to estimate clinical status, which served as a framework to demonstrate the PULSE approach for two separate but related clinical assessment tasks. In the first task, the PULSE prototype provides a life status assessment of a simulated unit, allowing for the principled introduction of noise to demonstrate the system's ability to detect sensor failure. In the second task, the PULSE prototype provides a clinical assessment of a unit's degree of acclimatization, using pre-recorded data from studies of soldiers at altitude.

Dave Lawless

Papers

Trustworthy situation assessment via belief networks

Spatiotemporal Clustering for Aggregating Hostile Units in Cluttered Environments

Probabilistic unit life status estimation (PULSE)