Managing the unseen - enhancing and improving buried asset location data

2014 ◽  
Author(s):  
J.M. Parker
Keyword(s):  
Location Data ◽  
Asset Location

scholarly journals Analysis of asset location data to support decisions in production management and control

Procedia CIRP ◽  
2020 ◽  
Vol 88 ◽  
pp. 197-202 ◽  
Author(s):  
Dávid Gyulai ◽  
András Pfeiffer ◽  
Júlia Bergmann
Keyword(s):  
Production Management ◽  
Location Data ◽  
Asset Location ◽  
And Control

Retail Mix Dan Kepuasan Konsumen Minimarket

Liquidity ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 53-62
Author(s):  
Siti Maryama ◽  
Yayat Sujatna
Keyword(s):  
Consumer Satisfaction ◽  
Quantitative Method ◽  
Descriptive Analysis ◽  
T Test ◽  
Primary Data ◽  
Retail Industry ◽  
Location Data ◽  
Store Design ◽  
Customer Services ◽  
The Difference

The purpose of this study is to (1) analyzing the level of retail mix consumer satisfaction; (2) analyze the dominant variable in retail mix consumer satisfaction; (3) analyze the difference of retail mix consumer satisfaction performed. The observed of the retail industry is Alfamidi and Indomaret. The study was designed into a descriptive-quantitative method. The source of primary data obtained from the questionnaire of 100 respondents. The formulating variable of retail mix includes: merchandise assortments, pricing, customer services Store design and display, communication mix, and location. Data analyze by using descriptive, analysis of factors, and t-test. The result confirmed that the level of retail mix consumer satisfaction in both industry is relatively similar. However, it can be stated that the respondents were more satisfied to Indomaret compared with Alfamart.

Asset Location: Case Study of a Critical Variable

2001 ◽  
Vol 2001 (4) ◽  
pp. 18-29
Author(s):  
Jean L.P. Brunel
Keyword(s):  
Critical Variable ◽  
Asset Location

scholarly journals Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dalton J. Hance ◽  
Katie M. Moriarty ◽  
Bruce A. Hollen ◽  
Russell W. Perry
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Gaussian State ◽  
Parameter Estimates ◽  
Accelerometer Data ◽  
Two Stage ◽  
Location Data ◽  
Gps Measurement ◽  
Activity Measurements ◽  
Behavioral States

Abstract Background Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data. Methods We developed a two-stage modelling approach to estimate when and where GPS-collared fishers were resting for 21 separate collar deployments on 9 individuals in southern Oregon. For each deployment, we first fit independent hidden Markov models (HMMs) to the time series of accelerometer-derived activity measurements and apparent step lengths to identify periods of movement and resting. Treating the state assignments as given, we next fit a set of linear Gaussian state space models (SSMs) to estimate the location of each resting event. Results Parameter estimates were similar across collar deployments. The HMMs successfully identified periods of resting and movement with posterior state assignment probabilities greater than 0.95 for 97% of all observations. On average, fishers were in the resting state 63% of the time. Rest events averaged 5 h (4.3 SD) and occurred most often at night. The SSMs allowed us to estimate the 95% credible ellipses with a median area of 0.12 ha for 3772 unique rest events. We identified 1176 geographically distinct rest locations; 13% of locations were used on > 1 occasion and 5% were used by > 1 fisher. Females and males traveled an average of 6.7 (3.5 SD) and 7.7 (6.8 SD) km/day, respectively. Conclusions We demonstrated that if auxiliary data are available (e.g., accelerometer data), a two-stage approach can successfully resolve both problems of latent behavioral states and GPS measurement error. Our relatively simple two-stage method is repeatable, computationally efficient, and yields directly interpretable estimates of resting site locations that can be used to guide conservation decisions.

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