The document describes a project to reduce inventory discrepancies between an E-book system and SAP system at a factory. It involved defining key metrics, measuring current performance, analyzing causes of discrepancies, designing database functions to compare transactions, and verifying improvements. The project reduced defects per unit by 12.58%, tariff loss by 89.36% (US$47,252), and process cycle time to 3 days, meeting all project goals.
How to Install Hyperion Planning - Part 1b - SQL Server Setup for Hyperion Pl...guestffe3111
The second section in Part one of the Hyperion system 9.3.1 install. Please visit my Blog for the added information to this:
http://www.atmyplace.co.uk
I would like to hear your comments on what content you would like to see.
Many thanks
This is part 5 in the series of Hyperion system 9.3.1 installation. We have previously looked at installing and setting up SQL Server 2008 Express, Hyperion Shared Services, Hyperion Essbase and Provider Services. Keep your eyes open for the next tutorial on Installing Hyperion Reporting and Analysis Services. Please help support my efforts by visiting my site http://www.atmyplace.co.uk
Thanks
Accurate and Reliable What-If Analysis of Business Processes: Is it Achievable?Marlon Dumas
In this talk, I discuss the problem of how to discover simulation models that can be used to, accurately and reliably, predict the impact of a change on a business process, e.g. what-if we automate an activity? what-if 10% of our workers become unavailable? I focus on recent approaches that exploit the availability of data in enterprise systems to address this question.
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)Brian Brazil
Counters are one of the two core metric types in Prometheus, allowing for tracking of request rates, error ratios and other key measurements. Learn why are they designed the way they are, how client libraries implement them and how rate() works.
If you'd like more information about Prometheus, contact us at prometheus@robustperception.io
The Prediction Of Time Trending Techniques. Is It A Reasonable Estimate?Gan Chun Chet
Time prediction (modelling) techniques use to analyze machine setup (performance) time. These time series techniques are compared with probability and categorization method, found to be coherent.
Also with reference to Noise Prevention in Factories training dated 20 March 2012 at IEM Penang (noise calculation).
How to Install Hyperion Planning - Part 1b - SQL Server Setup for Hyperion Pl...guestffe3111
The second section in Part one of the Hyperion system 9.3.1 install. Please visit my Blog for the added information to this:
http://www.atmyplace.co.uk
I would like to hear your comments on what content you would like to see.
Many thanks
This is part 5 in the series of Hyperion system 9.3.1 installation. We have previously looked at installing and setting up SQL Server 2008 Express, Hyperion Shared Services, Hyperion Essbase and Provider Services. Keep your eyes open for the next tutorial on Installing Hyperion Reporting and Analysis Services. Please help support my efforts by visiting my site http://www.atmyplace.co.uk
Thanks
Accurate and Reliable What-If Analysis of Business Processes: Is it Achievable?Marlon Dumas
In this talk, I discuss the problem of how to discover simulation models that can be used to, accurately and reliably, predict the impact of a change on a business process, e.g. what-if we automate an activity? what-if 10% of our workers become unavailable? I focus on recent approaches that exploit the availability of data in enterprise systems to address this question.
Counting with Prometheus (CloudNativeCon+Kubecon Europe 2017)Brian Brazil
Counters are one of the two core metric types in Prometheus, allowing for tracking of request rates, error ratios and other key measurements. Learn why are they designed the way they are, how client libraries implement them and how rate() works.
If you'd like more information about Prometheus, contact us at prometheus@robustperception.io
The Prediction Of Time Trending Techniques. Is It A Reasonable Estimate?Gan Chun Chet
Time prediction (modelling) techniques use to analyze machine setup (performance) time. These time series techniques are compared with probability and categorization method, found to be coherent.
Also with reference to Noise Prevention in Factories training dated 20 March 2012 at IEM Penang (noise calculation).
One of the most important, yet often overlooked, aspects of predictive modeling is the transformation of data to create model inputs, better known as feature engineering (FE). This talk will go into the theoretical background behind FE, showing how it leverages existing data to produce better modeling results. It will then detail some important FE techniques that should be in every data scientist’s tool kit.
As part of our team's enrollment for Data Science Super Specialization course under UpX Academy, we submitted many projects for our final assessments, one of them was Telecom Churn Analysis Model.
The input data was provided by UpX academy and language we used is R. As part of the project, our main objective was :-
-> To predict Customer Churn.
-> To Highlight the main variables/factors influencing Customer Churn.
-> To Use various ML algorithms to build prediction models, evaluate the accuracy and performance of these models.
-> Finding out the best model for our business case & providing executive Summary.
To address the mentioned business problem, we tried to follow a thorough approach. We did a detailed level Exploratory Data Analysis which consists of various Box Plots, Bar Plots etc..
Further we tried our best to build as many Classification models possible which fits our business case (Logistic Regression/kNN/Decision Trees/Random Forest/SVM) and also tried to touch Cox Hazard Survival analysis Model. Later for every model we tried to boost their performances by applying various performance tuning techniques.
As we all are still into our learning mode w.r.t these concepts & starting new, please feel free to provide feedback on our work. Any suggestions are most welcome... :)
Thanks!!
2. Define - Voice of Customer Voice of the Customer Operation Requirement CTQ Characteristics 1st Priority CTQ Flow Down Factory Management Material Planning Team External Customer Reduce inbound & outbound process cycle time Shorten process cycle time, improve on-time delivery No inventory discrepancy between E-book and SAP system No tariff penalty occurred due to inventory discrepancy Transaction DPO (Targeted to reduce DPO by 70% over existing performance level) Process cycle time Targeted to reduce CT to 3 days (Benchmark) China Customs No discrepancy between 2 systems’ transaction
3. Define - Project Scheduling Project Planning in Define Phase Project Planning Updated in Design Phase Project Milestone: “Define” Start to Completion: Sep.22, 2008 – Sep.26, 2008 “Measurement” Start to Completion: Sep.29, 2008 – Oct.24, 2008 “Analysis” Start to Completion: Oct.27 , 2008 – Oct.31, 2008 “Design” Start to Completion: Nov.3, 2008 – Dec.28, 2008 “Verify” Start to Completion: Dec.1, 2008 – Jan.9, 2009
10. Analysis – Pareto Chart We deeply investigate discrepancy root causes for two periods, week24 to week33, week34 to week43.
11.
12. Analysis – Failure Mode & Effect Analysis 1. Establish FMEA Rating Scales Severity of The Effect Probability of Occurrence Dectection of Difficulity R isk P riority N umber X X = Calculate Risk:
13. Analysis – Failure Mode & Effect Analysis Identify failure mode occurred in which process Root causes from Pareto Chart
19. Design – Database Functions From this database function, we can either see discrepancy result, or transaction details. Transaction Comparison Functions Discrepancy Tracking Function and Resolution Matrix
25. Verify – Achievement Our conclusion based on the normal distribution, 95% CI for difference: (0.1004, 0.1512), exclude (0, 0); P-Value <.05; So reject Ho (Ho: μ 1 = μ 2). In other words, there has been a decrease in DPU from the before and after data. 1. DPU Reduction DPU: - 12.58% Two-Sample T-Test and CI: DPU(W19-43), DPU(W44-53) Two-sample T for DPU(W19-43) vs DPU(W44-53) N Mean StDev SE Mean DPU(W19-43) 25 0.1632 0.0197 0.0039 DPU(W44-53) 10 0.0374 0.0343 0.011 Difference = mu (DPU(W19-43)) - mu (DPU(W44-53)) Estimate for difference: 0.1258 95% CI for difference: (0.1004, 0.1512) T-Test of difference = 0 (vs not =): T-Value = 10.90 P-Value = 0.000 DF = 11 Outlier: before data Normal distribution
Today‘s competitive world: Technology alone doesn‘t sell In addition to our technological strength: Solutions and customer closeness: Because we want to grow profitably: This means: We want to win new customers and make more business with existing customers