VMI versus information sharing: an analysis under static uncertainty strategy with fill rate constraints

A vendor-managed inventory (VMI) relationship between a downstream retailer and an upstream vendor consists of two distinct components: (i) information sharing (IS) and (ii) a shift in decision-making responsibility. This study compares these two components of VMI in a two-stage serial supply chain based on the ‘static uncertainty’ strategy under dynamic and random demand with fill rate constraints. Numerical experiments are conducted using analytical models to identify the conditions where the incremental value of VMI over IS is significant. The results provide guidelines relevant to academia and supply chain practitioners in taking VMI adoption decision above and beyond IS according to their specific business environment.     

Investigation of TiAlN coated roller burnishing on Al-(B4C)p MMC workpiece material

Burnishing avoids the need for super finishing operations after the conventional turning process, to enhance the surface quality. This paper deals with the surface modifications of Al(B₄C)_p Metal Matrix Composites (MMC) workpiece material after burnishing with a TiAlN coated WC roller. The burnishing speed, lubrication type, burnishing passes, and coating were the input parameters. Surface hardness and roughness after the burnishing were studied. It was found that the coating on the WC roller had enhanced the hardness in the workpiece after burnishing in the case of Al-5?wt.% (B₄C)_p, under all conditions. The effect of the coating on the work piece surface hardness was not significant with Al-10?wt.% (B₄C)_p. While burnishing Al-5?wt.% (B₄C)_p, the minimum surface roughness combined with maximum surface hardness was obtained, during the third pass under dry condition using uncoated rollers. The number of passes to achieve the desired surface conditions reduced, on using coated rollers with kerosene as the lubricant.     

Cutting force and hole quality analysis in micro-drilling of CFRP

Micro-drilling in carbon fiber reinforced plastic (CFRP) composite material is challenging because this material machining is difficult due to anisotropic, abrasive and non-homogeneous properties and also downscaling of cutting process parameters affect the cutting forces and micro-drilled hole quality extensively. In this work, experimental results based statistical analysis is applied to investigate feed and cutting speed effect on cutting force components and hole quality. Analysis of variance based regression equation is used to predict cutting forces and hole quality and their trend are described by response surface methodology. Results show that roundness error and delamination factor have similar trends to those of radial forces and thrust force, respectively. Non-linear trends of cutting forces and hole quality errors are observed during downscaling of the micro-drill feed value. Optimization results show that cutting forces and hole quality errors are minimum at a feed value which is almost equal to the tool edge radius rather than at the lowest feed value. Therefore, the presented results clearly show the influences of size effects on cutting forces and hole quality parameters in micro-drilling of CFRP composite material.     

Selection of industrial robots using compromise ranking and outranking methods

Selection of a robot for a specific industrial application is one of the most challenging problems in real time manufacturing environment. It has become more and more complicated due to increase in complexity, advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. At present, different types of industrial robots with diverse capabilities, features, facilities and specifications are available in the market. Manufacturing environment, product design, production system and cost involved are some of the most influencing factors that directly affect the robot selection decision. The decision maker needs to identify and select the best suited robot in order to achieve the desired output with minimum cost and specific application ability. This paper attempts to solve the robot selection problem using two most appropriate multi-criteria decision-making (MCDM) methods and compares their relative performance for a given industrial application. The first MCDM approach is ‘VIsekriterijumsko KOmpromisno Rangiranje’ (VIKOR), a compromise ranking method and the other one is ‘ELimination and Et Choice Translating REality’ (ELECTRE), an outranking method. Two real time examples are cited in order to demonstrate and validate the applicability and potentiality of both these MCDM methods. It is observed that the relative rankings of the alternative robots as obtained using these two MCDM methods match quite well with those as derived by the past researchers.     

Unknown input modeling and robust fault diagnosis using black box observers

A key issue that needs to be addressed while performing fault diagnosis using black box models is that of robustness against abrupt changes in unknown inputs. A fundamental difficulty with the robust FDI design approaches available in the literature is that they require some a priori knowledge of the model for unmeasured disturbances or modeling uncertainty. In this work, we propose a novel approach for modeling abrupt changes in unmeasured disturbances when innovation form of state space model (i.e. black box observer) is used for fault diagnosis. A disturbance coupling matrix is developed using singular value decomposition of the extended observability matrix and further used to formulate a robust fault diagnosis scheme based on generalized likelihood ratio test. The proposed modeling approach does not require any a priori knowledge of how these faults affect the system dynamics. To isolate sensor and actuator biases from step jumps in unmeasured disturbances, a statistically rigorous method is developed for distinguishing between faults modeled using different number of parameters. Simulation studies on a heavy oil fractionator example show that the proposed FDI methodology based on identified models can be used to effectively distinguish between sensor biases, actuator biases and other soft faults caused by changes in unmeasured disturbance variables. The fault tolerant control scheme, which makes use of the proposed robust FDI methodology, gives significantly better control performance than conventional controllers when soft faults occur. The experimental evaluation of the proposed FDI methodology on a laboratory scale stirred tank temperature control set-up corroborates these conclusions.     

Two-echelon fuzzy stochastic supply chain for the manufacturer–buyer integrated production–inventory system

This paper deals with two-echelon integrated procurement production model for the manufacturer and the buyer integrated inventory system. The manufacturer procures raw material from outside suppliers (not a part of supply chain) then proceed to convert it as finished product, and finally delivers to the buyer, who faces imprecise and uncertain, called fuzzy random demand of customers. The manufacturer and the buyer work under joint channel, in which a centralized decision maker makes all decisions to optimize the joint total relevant cost (JTRC) of entire supply chain. In this account, in one production cycle of the manufacturer we determine an optimal multi-ordering policy for the buyer. To be part of this, we first derive the JTRC in stochastic framework, and then extend it in fuzzy stochastic environment. In order to scalarize the fuzzy stochastic JTRC, we use an evaluation method wherein randomness is estimated by probabilistic expectation and fuzziness is estimated by possibilistic mean based on possibility evaluation measure. To derive the optimal policies for both parties, an algorithm is proposed. A numerical illustration addresses the situations of paddy procurement, conversion to rice and fulfillment of uncertain demand of rice. Furthermore, sensitivity of parameters is examined to illustrate the model and algorithm.     

Neuroproteomics and systems biology-based discovery of protein biomarkers for traumatic brain injury and clinical validation

The rapidly growing field of neuroproteomics has expanded to track global proteomic changes underlying various neurological conditions such as traumatic brain injury (TBI), stroke, and Alzheimer's disease. TBI remains a major health problem with approximately 2?million incidents occurring annually in the United States, yet no affective treatment is available despite several clinical trials. The absence of brain injury diagnostic biomarkers was identified as a significant road-block to therapeutic development for brain injury. Recently, the field of neuroproteomics has undertaken major advances in the area of neurotrauma research, where several candidate markers have been identified and are being evaluated for their efficacy as biological biomarkers in the field of TBI. One scope of this review is to evaluate the current status of TBI biomarker discovery using neuroproteomics techniques, and at what stage we are at in their clinical validation. In addition, we will discuss the need for strengthening the role of systems biology and its application to the field of neuroproteomics due to its integral role in establishing a comprehensive understanding of specific brain disorder and brain function in general. Finally, to achieve true clinical input of these neuroproteomic findings, these putative biomarkers should be validated using preclinical and clinical samples and linked to clinical diagnostic assays including ELISA or other high-throughput assays.     

Surface modes of a sessile water drop: an optical tweezer based study

A high-precision method to study the dynamics of two-fluid interfaces using an optical tweezer and a phase-sensitive detection technique are described. The disturbances set up at the interface are studied by analyzing the motion of an optically trapped particle in the bulk of the fluid, i.e., away from the interface. The usefulness of the technique is demonstrated for the well-known problem of a horizontally vibrated sessile liquid drop. The vibrational modes of the liquid drop excited by sinusoidally vibrating the support in a horizontal plane appear as resonances in the motion of the trapped particle. The nature of the resonance is studied in detail by measuring the real part, the imaginary part, and the phase response of the motion of the particle as a function of the "effective" size of the liquid drop. Excellent quantitative agreement with the theoretically predicted values of the eigenfrequencies and damping of the surface modes is obtained.     

Voltage sensitivity of the osteoclast calcium receptor

We demonstrated previously that osteoclasts possess a divalent cation-sensitive "receptor", the Ca2+ receptor. Activation of the Ca2+ receptor by the surrogate cation Ni2+ was shown to elicit an increase in cytosolic [Ca2+] to a peak value followed by an exponential decline. In the present study we examined the influence of surface membrane voltage on the kinetics of Ca2+ receptor inactivation. The K+ ionophore, valinomycin was applied to intercept the declining phase of the cytosolic [Ca2+] transient elicited by application of between 50 microM- and 5 mM-[Ni2+]. This resulted in a sustained elevation of cytosolic [Ca2+] or even a 'hump' followed by a gradual decline. Such a kinetic alteration persisted in a Ca(2+)-free solution, but was abolished in high extracellular [K+] (105 mM). Thus, we demonstrate for the first time to our knowledge, a modulatory effect of membrane potential on the function of the osteoclast Ca2+ receptor.