Value stream mapping to reduce the lead-time of a product development process

Product development (PD) is a broad field of endeavor dealing with the planning, design, creation, and marketing of a new product. This revolutionary research domain has become of paramount importance to beat the competition for multidisciplinary products which are larger in size and have a longer development time. The main focus of this paper is to exploit lean thinking concepts in order to manage, improve and develop the product faster while improving or at least maintaining the level of performance and quality. Lean thinking concepts encompass a board range of tools and methods intended to produce bottom line results however, value stream mapping (VSM) method is used to explore the wastes, inefficiencies, non-valued added steps in a single, definable process out of complete product development process (PDP). This single step is highly complex and occurs once while the PDP lasts for 3-5 years. A case study of gas turbine product has been discussed to illustrate and justify the use of proposed framework. In order to achieve this, the following have been performed: First of all a current state map is developed using the Gemba walk. Furthermore, Subject Matter Experts (SMEs) brainstormed to explore the wastes and their root causes found during the Gemba walk and current state mapping. A future state map is also developed with removing all the wastes/inefficiencies. Besides numerous intangible benefits, it is expected that the VSM framework will help the development teams to reduce the PD lead-time by 50%

Value stream mapping to reduce the lead-time of a product development process

Product development (PD) is a broad field of endeavor dealing with the planning, design, creation, and marketing of a new product. This revolutionary research domain has become of paramount importance to beat the competition for multidisciplinary products which are larger in size and have a longer development time. The main focus of this paper is to exploit lean thinking concepts in order to manage, improve and develop the product faster while improving or at least maintaining the level of performance and quality. Lean thinking concepts encompass a board range of tools and methods intended to produce bottom line results however, value stream mapping (VSM) method is used to explore the wastes, inefficiencies, non-valued added steps in a single, definable process out of complete product development process (PDP). This single step is highly complex and occurs once while the PDP lasts for 3-5 years. A case study of gas turbine product has been discussed to illustrate and justify the use of proposed framework. In order to achieve this, the following have been performed: First of all a current state map is developed using the Gemba walk. Furthermore, Subject Matter Experts (SMEs) brainstormed to explore the wastes and their root causes found during the Gemba walk and current state mapping. A future state map is also developed with removing all the wastes/inefficiencies. Besides numerous intangible benefits, it is expected that the VSM framework will help the development teams to reduce the PD lead-time by 50%

Inkjet-printed microstrip patch antennas realized on textile for wearable applications

This letter introduces a new technique of inkjet printing antennas on textiles. A screen-printed interface layer was used to reduce the surface roughness of the polyester/cotton material that facilitated the printing of a continuous conducting surface. Conducting ink was used to create three inkjet-printed microstrip patch antennas. An efficiency of 53% was achieved for a fully flexible antenna with two layers of ink. Measurements of the antennas bent around a polystyrene cylinder indicated that a second layer of ink improved the robustness to bending. © 2014 IEEE

Adaptive Process Monitoring Using Covariate Information

Statistical process control (SPC) charts provide a powerful tool for monitoring production lines in manufacturing industries. They are also used widely in other applications, such as sequential monitoring of internet traffic flows, disease incidences, health care systems, and more. In practice, quality/performance variables are often affected in a complex way by many covariates, such as material, labor, weather conditions, social/economic conditions, and so forth. Among all these covariates, some could be observed, some might be difficult to observe, and the others might even be difficult for us to notice their existence. Intuitively, an SPC chart could be improved by using helpful information in covariates. However, because of the complex relationship between the quality/performance variables and the covariates, shifts in the quality/performance variables could be due to certain covariates whose data cannot be collected. On the other hand, shifts in some observable covariates may not necessarily cause shifts in the quality/performance variables. Thus, it is challenging to properly use covariate information for process monitoring in a general setting. This article suggests a method to handle this problem. An effective exponentially weighted moving average chart is developed, in which its weighting parameter is chosen large if the related covariates included in the collected data tend to have a shift and small otherwise. Because the covariate information is used in the weighting parameter only, the chart is designed solely for detecting shifts in the quality/performance variables, but it can react to a future shift in the quality/performance variables quickly because the helpful covariate information has been used in its observation weighting mechanism. Extensive numerical studies show that this method is effective in many different cases.</p

Understanding Ammonia and Water Transport in Direct Contact Membrane Distillation toward Selective Ammonia Recovery

Recovering ammonia from wastewater by membrane distillation (MD) is a sustainable approach to alleviate environmental stress, as well as reduce energy consumption from the Haber–Bosch process. MD utilizes the low-grade heat and leverages the volatility of ammonia for ammonia transport; however, the concurrent transport of water and ammonia molecules and their mutual influence, remains unclear. In this study, we combine experiments and a mathematical modeling approach to investigate both individual and combined water transport and ammonia transport from ammonia-rich wastewater in MD. The water flux exhibits minimal variation in response to changes in feed solution composition and pH value, while the ammonia flux demonstrates significant sensitivity to the pH variation of the feed solution. Although both water transport and ammonia transport increase with the increasing feed solution temperature, our simulation reveals that the water mass transfer coefficient remains unchanged, while the ammonia mass transfer coefficient varies in tandem with temperature changes. We analyze the ammonia-to-water transport selectivity (ρ), noting that a lower temperature yields a higher ammonia-to-water selectivity (ρ = 25.9 at 30 °C to ρ = 6.8 at 60 °C), and the selectivity is more sensitive at lower temperatures. The selectivity decay analysis indicates that the ammonia-to-water mass transfer ratio is a key factor that tunes the selectivity with respect to feed temperature variation. This integrated experimental and simulation study provides valuable insights into ammonia and water transport toward selective ammonia recovery in MD

Pd-Catalyzed Regioselective Arylboration of Vinylarenes

A palladium-catalyzed 1,2-arylboration of vinylarenes with aryldiazonium tetrafluoroborates and bis­(pinacolato)­diboron has been disclosed. It is reported for the first time that styrene derivatives can be successfully employed as good substrates for 1,2-arylboration of alkenes. Mechanistic studies suggest that no Pd–H reinsertion occurred under our standard conditions, which is the key for the success of this transformation

Palladium-Catalyzed Arylboration of Bicyclic Alkenes

A palladium-catalyzed arylboration of norbornene or norbornadiene with aryl halides and bis­(pina­colato)­diboron has been disclosed. Mechanistic studies suggest that the reaction proceeds under a Catellani-type coupling to render versatile multifunctionalized alkylboranes in good yields. This reaction is complementary to the existing methods and is well tolerable with a variety of functional groups and readily scaled-up to a gram scale without deteriorating the yield

Spatio-temporal process monitoring using exponentially weighted spatial LASSO

Spatio-temporal process monitoring (STPM) has received a considerable attention recently due to its broad applications in environment monitoring, disease surveillance, streaming image processing, and more. Because spatio-temporal data often have complicated structure, including latent spatio-temporal data correlation, complex spatio-temporal mean structure, and nonparametric data distribution, STPM is a challenging research problem. In practice, if a spatio-temporal process has a distributional shift (e.g., mean shift) started at a specific time point, then the spatial locations with the shift are usually clustered in small regions. This kind of spatial feature of the shift has not been considered in the existing STPM literature yet. In this paper, we develop a new STPM method that takes into account the spatial feature of the shift in its construction. The new method combines the ideas of exponentially weighted moving average in the temporal domain for online process monitoring and spatial LASSO in the spatial domain for accommodating the spatial feature of a future shift. It can also accommodate the complicated spatio-temporal data structure well. Both simulation studies and a real-data application show that it can provide a reliable and effective tool for different STPM applications.</p

SpTe2M: An R package for nonparametric modeling and monitoring of spatiotemporal data

Spatiotemporal data are common in practice. Such data often have complicated structures that are difficult to describe by parametric statistical models. Thus, it is often challenging to analyze spatiotemporal data effectively since most existing statistical methods and software packages in the literature are based on parametric modeling and cannot handle certain applications properly. This paper introduces the new R package SpTe2M, which was developed for implementing some recent nonparametric methods for modeling and monitoring spatiotemporal data. This package provides analytic tools for modeling spatiotemporal data nonparametrically and for monitoring dynamic spatial processes sequentially over time. It can be used for different applications, including disease surveillance and environmental monitoring. The use of the package is demonstrated using the Florida influenza-like illness data observed during 2012–2014 and the PM2.5 concentration data in China collected during 2014–2016.</p

Contemporary sources and levels of heavy metal contamination in urban soil of Broken Hill, Australia after <i>ad hoc</i> land remediation

This study used GIS-based and multivariate statistical approach to identify sources of Cr, Mn, Ni, Pb and Zn in urban soil surrounding a large Pb-Zn-Ag orebody. The results show that Mn-Pb-Zn-rich dust originating from the orebody and associated mining works has acted as an important contaminating source of topsoil metals. In contrast, Cr and Ni appear to be sourced naturally from weathering of parent materials. The calculation of contamination indices reflects a high level of Pb and Zn contamination in topsoil near the orebody and a moderate level of Mn contamination, while topsoil contamination with Cr and Ni is negligible.</p