This document provides documentation for an SSAS cube project using the ALLWORKS database. It includes:
1) Details of the data source view created using 4 fact tables and 9 dimension tables from the ALLWORKS database.
2) Descriptions of the cube structure and partitions created for the cube. Two partitions were used for each fact table to optimize query performance.
3) Screenshots and explanations of 5 KPIs created using calculations and measures in the cube to analyze business metrics for clients, jobs, and overhead categories.
Performance Monitor shows database performance problems in a clear way, yet is also able to precisely indicate their causes. One of the main issues in effectively providing IT services is maintenance of adequate performance levels of the system. Frequently, the only solution that many companies can offer to these problems is investment in larger, more efficient servers. Unfortunately, this does not always bring the anticipated results despite high expenditures. Optimisation of business system performance on the level of existing databases can be an effective solution to these problems.
Instead of investing in hardware, performance problems can be more effectively solved by using the appropriate database optimisation at the level of the most overloaded SQL queries. To optimise a database, it is essential to locate bottlenecks and understand why they arise.
Key features: rapid analysis of performance trends thanks to collected history of database metrics; minimal DB engine load from monitoring tools; users do not have access to business data in monitored databases; intuitive interface and simple navigation for business systems administrators; sdystematic updates and adaptation to the client’s needs.
Reporting Summary Information of Spatial Datasets and Non-Compliance Issues U...Safe Software
An overview of two groups of FME workspaces implemented at the Mapping and Charting Establishment (MCE) that include the generation of reports in Excel format is presented here. The first group includes data validation and data compliance assessments. An example showing Self Validation of Spatial Data Input from DND Bases using FME Server is presented. The second group, implemented using FME Desktop, includes the creation of statistical reports for some key datasets distributed by MCE. Two examples of FME workspaces are presented here: the first one showing reports created for NRCan CanVec plus charts, and the second one showing reports created for Open Street Map (OSM) data delivered in FGDB format for custom AOIs.
This tutorial covers the topics of introduction to business intelligence with examples of BI scenarios and touches upon ETL(Extract, Transform and Load) operations using SSIS on SQL 2005 & 2008 and using DTS on SQL 2000. It contains introductions to crystal reports and SSRS. It compares Data warehouse and OLAP Cube. This tutorial concludes with topics on Data Mining and Dashboards.
Performance Monitor shows database performance problems in a clear way, yet is also able to precisely indicate their causes. One of the main issues in effectively providing IT services is maintenance of adequate performance levels of the system. Frequently, the only solution that many companies can offer to these problems is investment in larger, more efficient servers. Unfortunately, this does not always bring the anticipated results despite high expenditures. Optimisation of business system performance on the level of existing databases can be an effective solution to these problems.
Instead of investing in hardware, performance problems can be more effectively solved by using the appropriate database optimisation at the level of the most overloaded SQL queries. To optimise a database, it is essential to locate bottlenecks and understand why they arise.
Key features: rapid analysis of performance trends thanks to collected history of database metrics; minimal DB engine load from monitoring tools; users do not have access to business data in monitored databases; intuitive interface and simple navigation for business systems administrators; sdystematic updates and adaptation to the client’s needs.
Reporting Summary Information of Spatial Datasets and Non-Compliance Issues U...Safe Software
An overview of two groups of FME workspaces implemented at the Mapping and Charting Establishment (MCE) that include the generation of reports in Excel format is presented here. The first group includes data validation and data compliance assessments. An example showing Self Validation of Spatial Data Input from DND Bases using FME Server is presented. The second group, implemented using FME Desktop, includes the creation of statistical reports for some key datasets distributed by MCE. Two examples of FME workspaces are presented here: the first one showing reports created for NRCan CanVec plus charts, and the second one showing reports created for Open Street Map (OSM) data delivered in FGDB format for custom AOIs.
This tutorial covers the topics of introduction to business intelligence with examples of BI scenarios and touches upon ETL(Extract, Transform and Load) operations using SSIS on SQL 2005 & 2008 and using DTS on SQL 2000. It contains introductions to crystal reports and SSRS. It compares Data warehouse and OLAP Cube. This tutorial concludes with topics on Data Mining and Dashboards.
Say you have made a Data warehouse using dimensional modeling. Now the question will come how you are presenting to your stakeholder. SSRS is a great tools of Microsoft can be used. I am sharing the doc for who is going to start reporting using QUBE. Read this material with source code :
https://gallery.technet.microsoft.com/Step-by-Step-SSRS-Report-8de35ea8
Microsoft SSAS: Should I Use Tabular or Multidimensional?Senturus
Learn the right version Microsoft SQL Server Analysis services to use to easily migrate the work to the other version. View the webinar video recording and download this deck: http://www.senturus.com/resources/microsoft-ssas/.
During this webinar, Senturus discussed how to choose between the tabular and multi-dimensional versions of SSAS for your analytic needs and the various features and benefits that each version provides.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Microsoft SQL Server Analysis Services (SSAS) - A Practical Introduction Mark Ginnebaugh
Patrick Sheehan of Microsoft covers platform architecture, data warehousing methodology, and multi-dimensional cube development.
You will learn:
* How to develop and deploy data cubes using SQL Server Analysis Services (SSAS)
* Optimal data warehouse methodology for use with SSAS
* Tips/tricks for designing & building cubes over no warehouse/suboptimal source system (it happens)
* Cube processing types - How/why to use each
* Cube design practices + How to build and deploy cubes!
Best practices and tips on how to design and develop a Data Warehouse using Microsoft SQL Server BI products.
This presentation describes the inception and full lifecycle of the Carl Zeiss Vision corporate enterprise data warehouse.
Technologies covered include:
•Using SQL Server 2008 as your data warehouse DB
•SSIS as your ETL Tool
•SSAS as your data cube Tool
You will Learn:
•How to Architect a data warehouse system from End-to-End
•Components of the data warehouse and functionality
•How to Profile data and understand your source systems
•Whether to ODS or not to ODS (Determining if a operational Data Store is required)
•The staging area of the data warehouse
•How to Build the data warehouse – Designing Dimensions and Fact tables
•The Importance of using Conformed Dimensions
•ETL – Moving data through your data warehouse system
•Data Cubes - OLAP
•Lessons learned from Zeiss and other projects
Say you have made a Data warehouse using dimensional modeling. Now the question will come how you are presenting to your stakeholder. SSRS is a great tools of Microsoft can be used. I am sharing the doc for who is going to start reporting using QUBE. Read this material with source code :
https://gallery.technet.microsoft.com/Step-by-Step-SSRS-Report-8de35ea8
Microsoft SSAS: Should I Use Tabular or Multidimensional?Senturus
Learn the right version Microsoft SQL Server Analysis services to use to easily migrate the work to the other version. View the webinar video recording and download this deck: http://www.senturus.com/resources/microsoft-ssas/.
During this webinar, Senturus discussed how to choose between the tabular and multi-dimensional versions of SSAS for your analytic needs and the various features and benefits that each version provides.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Microsoft SQL Server Analysis Services (SSAS) - A Practical Introduction Mark Ginnebaugh
Patrick Sheehan of Microsoft covers platform architecture, data warehousing methodology, and multi-dimensional cube development.
You will learn:
* How to develop and deploy data cubes using SQL Server Analysis Services (SSAS)
* Optimal data warehouse methodology for use with SSAS
* Tips/tricks for designing & building cubes over no warehouse/suboptimal source system (it happens)
* Cube processing types - How/why to use each
* Cube design practices + How to build and deploy cubes!
Best practices and tips on how to design and develop a Data Warehouse using Microsoft SQL Server BI products.
This presentation describes the inception and full lifecycle of the Carl Zeiss Vision corporate enterprise data warehouse.
Technologies covered include:
•Using SQL Server 2008 as your data warehouse DB
•SSIS as your ETL Tool
•SSAS as your data cube Tool
You will Learn:
•How to Architect a data warehouse system from End-to-End
•Components of the data warehouse and functionality
•How to Profile data and understand your source systems
•Whether to ODS or not to ODS (Determining if a operational Data Store is required)
•The staging area of the data warehouse
•How to Build the data warehouse – Designing Dimensions and Fact tables
•The Importance of using Conformed Dimensions
•ETL – Moving data through your data warehouse system
•Data Cubes - OLAP
•Lessons learned from Zeiss and other projects
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
By Design, not by Accident - Agile Venture Bolzano 2024
Agnes's SSAS Project Documentation
1.
2. Employees Dimension table was the target of a relationship from JobLaborFacts Table based on EmployeePK attribute.
3. MaterialTypes Dimension table was the target of a relationship from JobMaterialFacts table through MaterialTypePK attribute.
4. Overhead Dimension Table was the target of a relationship from JobOverheadSummaryFacts though OverheadPK attribute.
5. JobMaster Dimension Table was the target of 4 relationships from JobOverheadSummaryFacts, JobMaterialFacts, JobLaborFacts, and JobSummaryFacts Table through JobMasterPK attribute
7. Clients Dimension Table was connected to County Dimension Table which was connected also with Division Dimension Table based on CountyPK and DivisionPK attributes respectively.
18. [Measures].[TotalProfit] We will discuss each of them and where it was used in the KPI. Figure 3.2 KPI 1 Screenshot Requirements for KPI1 are the following: KPI1: Project (“Job”) Master Open Receivables as a % of Invoice AmountOpen Receivables = Invoice Amount minus Amount ReceivedWhen Invoice Amount is 0, display -100%0 – 10% OKGreater than 10%, less than or equal to 20% , warningGreater than 20% – badUse Traffic LightRun for all Clients in alphabetical order The solution for KPI1 includes the calculations/measures that were used. KPI Name: KPIOpenReceivablesPct Measures Used: (Taken from the AllWorks Cube Calculation Tab) Calculation Measure Name: OpenReceivablesPct Calculation Measure Expression: IIF(([Measures].[Invoice Amount])=0, -1, ([Measures].[Invoice Amount]-[Measures].[Amount Received])/[Measures].[Invoice Amount]) Note: The IIF function will return -1 when the Invoice amount is equal to zero, which in this case will return -100% after converted to a percentage format. Calculation Format String: ‘PERCENT’ KPI Value Expression: [Measures].[OpenReceivablesPct] KPI Goal: 0.10 KPI Status Indicator: Traffic Light KPI Status Expression: CASE WHEN KPIVALUE("
KPIOpenReceivablesPct"
) <= KPIGOAL("
KPIOpenReceivablesPct"
)AND KPIVALUE("
KPIOpenReceivablesPct"
)>= 0 THEN 1 WHEN KPIVALUE("
KPIOpenReceivablesPct"
) > KPIGOAL("
KPIOpenReceivablesPct"
) AND KPIVALUE("
KPIOpenReceivablesPct"
) <= .20 THEN 0 WHEN KPIVALUE("
KPIOpenReceivablesPct"
) >.20 THEN -1 END Figure 3.3 KPI 1 Excel Screenshot This was tested and run in excel. The display screen is at figure 3.3, where the clients name is in the row is listed in alphabetical order. Green light refers to a good status for the KPIOpenReceivablesPct value which is from zero to 10% , yellow light refers to a warning status wherein the value of KPIOpenReceivablesPct is between greater than 10% and equal to 20%, and the red light refers to a bad status of the KPIOpenReceivablesPct value if its value is greater than 20%. The rows that have no KPI status are those values that were negative because negative values were not included in our KPI status expression. 152401270 Figure 3.4 KPI 2 Screenshot Requirements for KPI2 are the following: KPI2:Project (“Job”) Master Increase in number of Jobs from the previous quarter to the current quarter0 or more is good (meaning we’ve done at least one more Job for this quarter than for the last quarter….a zero means we’ve done at least the same # of Jobs)-1 is bad (we’ve done less Jobs for the client in the current qtr, versus the previous quarterUse Traffic LightRun for all Clients in alphabetical order for the 2nd Qtr of 2006 KPI Name: KPIDiffCurrentPrevJobNum Measures Used: (Taken from the AllWorks Cube Calculation Tab) Calculation Measure Name: DiffCurrentPrevJobNum, PreviousJobNum, CurrentJobNum Calculation Measure Expression: (PreviousJobNum) ([Measures].[Job Summary Facts Count],[All Works Calendar].[FY Calendar].prevmember) Calculation Format String: ‘Standard’ Calculation Measure Expression: (CurrentJobNum) ([Measures].[Job Summary Facts Count],[All Works Calendar].[FY Calendar].currentmember) Calculation Format String: ‘Standard’ Calculation Measure Expression: (DiffCurrentPrevJobNum) [Measures].[CurrentJobNum]-[Measures].[PreviousJobNum] Calculation Format String: ‘Standard’ KPI Value Expression: [Measures].[DiffCurrentPrevJobNum] KPI Goal: 0 KPI Status Indicator: Traffic Light KPI Status Expression: CASE WHEN KPIVALUE("
KPIDiffCurrentPrevJobNum"
) >= KPIGOAL("
KPIDiffCurrentPrevJobNum"
) THEN 1 ELSE -1 END Figure 3.5 KPI 2 Excel Screenshot KPI 2 was tested and run in excel. The display screen is at figure 3.5, where the Clients Name is in alphabetical order in the row and filtered the client’s job increase from 2nd quarter of 2005 to 2nd quarter of 2006. Green light refers to a good status of the KPIDiffCurrentPrevJobNum value which is from zero and above. The red light refers to a bad status of the KPIDiffCurrentPrevJobNum value that is less than zero. Figure 3.6 KPI3 Screenshot Requirements for KPI3 are the following: KPI3:Project (“Job”) Master Overhead as a % of Total Cost(where total cost = Total Overhead + Total material Cost + Total Labor Cost)When Total Overhead is 0, display 0%0 – 10% OKGreater than 10%, less than or equal to 15%, WarningGreater than 15% - badUse Traffic LightRun For all Jobs in alphabetical order KPI Name: KPITotalCostPctOverhead Measures Used: (Taken from the AllWorks Cube Calculation Tab) Calculation Measure Name: TotalCostPctOverhead Calculation Measure Expression: TotalCostPctOverhead IIF([Measures].[Total Overhead]=0, 0,([Measures].[Total Overhead]/[Measures].[TotalCost])) Calculation Format String: ‘PERCENT’ KPI Value Expression: [Measures].[TotalCostPctOverhead] KPI Goal: 0.10 KPI Status Indicator: Traffic Light KPI Status Expression: CASE WHEN KPIVALUE("
KPITotalCostPctOverhead"
) <= KPIGOAL("
KPITotalCostPctOverhead"
) then 1 WHEN KPIVALUE("
KPITotalCostPctOverhead"
) > KPIGOAL("
KPITotalCostPctOverhead"
) and KPIVALUE("
KPITotalCostPctOverhead"
)<= .15 then 0 WHEN KPIVALUE("
KPITotalCostPctOverhead"
) >.15 then -1 END Figure 3.7 KPI3 excel Screenshot KPI 3 was tested and run in excel. The display screen is in figure 3.7, where the rows are the List of Job Description in alphabetical order. Green light refers to a good status of the KPITotalCostPctOverhead value which is from zero to 10%, yellow light refers to warning which is an indication that the KPITotalCostPctOverhead values were greater than 10% or less than and equal to 15% more, and the red light refers to a bad status of the KPITotalCostPctOverhead value, this is the values greater than 15%. Figure 3.8 KPI4 Screenshot Requirements for KPI4 are the following: KPI4:Project (“Job”) Master Profit %Total Profit / (Total Costs + Total Profit)Total Profit = Total Labor Profit + Total Material Profit + Additional Labor ProfitTotal Costs = Total Labor Cost + Total Material Costs + Total Overhead (hint: you created this as a calculation for KPI3, so you can reuse it)When Total Costs is 0, display 100%Less than or equal to 5% is badGreater than 5%, Less than or equal to 15%, warningGreater than 15%, goodUse Traffic LightRun for all Clients in alphabetical order KPI Name: KPIProfitPct Measures Used: (Taken from the AllWorks Cube Calculation Tab) Calculation Measure Name: ProfitPct , TotalCost, TotalProfit Calculation Measure Expression: TotalCost IIF([Measures].[Total Overhead]=0, 0,([Measures].[Total Labor Cost]+[Measures].[Total Material Cost]+[Measures].[Total Overhead])) Calculation Format String: ‘Currency’ Calculation Measure Expression: TotalProfit [Measures].[Total Labor Profit]+[Measures].[Total Material Cost]+[Measures].[Additional Labor Profit] Calculation Format String: ‘Currency’ Calculation Measure Expression: ProfitPct IIF([Measures].[TotalCost]=0, 1,[Measures].[TotalProfit]/([Measures].[TotalCost]+ [Measures].[TotalProfit])) Calculation Format String: ‘PERCENT’ KPI Value Expression: [Measures].[TotalCostPctOverhead] KPI Goal: 0.15 KPI Status Indicator: Traffic Light KPI Status Expression: CASE WHEN KPIVALUE("
KPIProfitPct"
) > KPIGOAL("
KPIProfitPct"
) THEN 1 WHEN KPIVALUE("
KPIProfitPct"
) > .05 and KPIVALUE("
KPIProfitPct"
)<= KPIGOAL("
KPIProfitPct"
) THEN 0 WHEN KPIVALUE("
KPIProfitPct"
) <= .05 then -1 END 47180527305Figure 3.2.5 KPI5 ScreenshotFigure 3.9 KPI4 Excel ScreenshotKPI 4 was tested and run in excel. The display screen is in figure 3.9. The rows are the List of all clients name in alphabetical order. Green light refers to a good status of the KPIProfitPct value which is greater than 15%, yellow light refers to a warning status which an indication that the KPIProfitPct values were greater than 5% or less than and equal to 15% more, and the red light status refers to a bad status of the KPIProfitPct value, these were the values less than or equal to 5%. Figure 3.10 KPI5 Screenshot Requirements for KPI5 are the following: KPI5Project (“Job”) OverheadDetermine % increase in Overhead category from one quarter to anotherWhen previous quarter is 0, display 100%Less than 10% increase is goodBetween 10% and 15%, warningGreater than 15%, badUse Traffic LightRun for each Overhead Category in alphabetical order for the 2nd Qtr of 2006 KPI Name: KPIPctIncreaseOverhead Measures Used: (Taken from the AllWorks Cube Calculation Tab) Calculation Measure Name: ProfitPct , TotalCost, TotalProfit Calculation Measure Expression: TotalCost IIF([Measures].[Total Overhead]=0, 0,([Measures].[Total Labor Cost]+[Measures].[Total Material Cost]+[Measures].[Total Overhead])) Calculation Format String: ‘Currency’ Calculation Measure Expression: PreviousQuarter (Parallelperiod([All Works Calendar].[FY Calendar].[Qtr],1,[All Works Calendar].[FY Calendar].currentmember),[Measures].[Weekly Over Head]) Calculation Format String: ‘Standard’ Calculation Measure Expression: CurrentQuarter ([All Works Calendar].[FY Calendar].currentmember,[Measures].[Measures].[Weekly Over Head]) Calculation Format String: ‘Standard’ Calculation Measure Expression: PctIncreaseOverhead IIF([Measures].[PreviousQuarter]=0, 1,([Measures].[CurrentQuarter]-[Measures].[PreviousQuarter])/[Measures].[PreviousQuarter]) Calculation Format String: ‘Percent’ KPI Value Expression: [Measures].[PctIncreaseOverhead] KPI Goal: 0.10 KPI Status Indicator: Traffic Light KPI Status Expression: CASE WHEN KPIVALUE("
KPIPctIncreaseOverhead"
) < KPIGOAL("
KPIPctIncreaseOverhead"
)then 1 WHEN KPIVALUE("
KPIPctIncreaseOverhead"
) >.15 then -1 Else 0 End Figure 11 KPI5 Excel Screenshot KPI 5 was tested and run in excel. The display screen is in figure 3.11. The rows are the List of all overhead categories in alphabetical order. Green light refers to a good status of the KPIPctIncreaseOverhead value which is less than 15%, yellow light refers to a warning status which is an indication that the KPIPctIncreaseOverhead values were between 10% and 15% , and the red light status refers to a bad status of the KPIPctIncreaseOverhead value, these were the values greater than 15%.