The PROJECT GENERAL COMPANY manages AGILE projects in Indonesia with AI.

Contact us: pmo@project-general.com www.project-general.com

The PROJECT GENERAL COMPANY Jakarta is a proud Diamond sponsor of #SYMEX2020. More information, please contact us: PMO@PROJECT-GENERAL.COM

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The PROJECT GENERAL COMPANY is a proud diamond sponsor of #SYMEX2020, Semarang, Indonesia

www.project-general.com  pmo@project-general.com

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The PROJECT GENERAL COMPANY, Jakarta manages projects in Jakarta trains project managers in Indonesia. The PROJECT GENERAL COMPANY offices are located in Cideng, central Jakarta, and also in South Jakarta.

pmo@project-general.com www.project-general.com

The PROJECT GENERAL COMPANY is a proud diamond sponsor of #SYMEX2020 in Semarang, Indonesia March 11 and March 12, 2019.

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Numerous customers have asked us this question: How do we realize business value from artificial intelligence (AI) initiatives after a proof of concept (POC)?  Companies are excited at the potential of AI, and some even create a POC as a first step.

However, some are confused by lack of clarity on the business value or return on investment. As a result we have heard the same question from data science teams that have created machine learning (ML) models that are under-utilized by their organizations.

The PROJECT GENERAL COMPANY is committed to helping organizations of all sizes to transform themselves with AI.  We believe AI is a team activity that requires integration and collaboration between business analysts, data engineers, data scientists and machine learning engineers. The PROJECT GENERAL COMPANY recommends discussing the following three steps with your team to realize the most business value from your AI projects:

1. Step 1: Align AI projects with business priorities and find a good sponsor.
2. Step 2: Plan for explainable ML in models, dashboards and displays.
3. Step 3: Broaden expertise within the organization on data analytics and data engineering.

 Step 1: Align AI projects with business priorities and find a good sponsor
The first step to realizing value from AI is to identify the right business problem and a sponsor committed to using AI to solve that problem.

Teams often get excited by the prospect of applying AI to a problem without considering how that problem contributes to overall business value. For example, using AI to better classify objects might be less valuable to the bottom line, than, say, a great auto responder on chat. Unfortunately, many businesses don’t start with the critical step of aligning the AI project with the business challenges that matter most.  

Identify the right business problem. To ensure alignment, start with your organization’s business strategy and key priorities. Identify the business priorities that can gain the most from AI. The person doing this assessment needs to have a good understanding of the most common use cases for AI and ML.

Create a shortlist of business priorities that can truly benefit from AI or ML.  During implementation, work through this list starting with the most feasible. By taking this approach, you’re more likely to generate significant business value as you build a set of ML models that solve specific business priorities.

Conversely, if a data science or machine learning team builds great solutions for problems that are not aligned with business priorities, the models they build are unlikely to be used at scale.

Find a business sponsor. We’ve also found that AI projects are more likely to be successful when they have a senior executive sponsor that will champion them with other leaders in your organization. Don’t start an AI project without completing this critical step. Once you identify the right business priority, find the senior executive to own it.  Work with their team to get their buy-in and sponsorship. The more senior and committed, the better. If your CEO cares about AI, you can bet most of your employees will.

Step 2:  Plan for explainable ML in models, dashboards and displays
An important, yet often skipped requirement from many business users is to have explanations from ML models. In many cases, it is not enough for an ML model to provide an outcome; it’s also important to understand why.

Explanations help to build trust in the model’s predictions and offer useful factors with which business users can take action. In regulated industries such as financial services and healthcare, for example, there are regulations that require explanations of decisions. For example, in the United States the Equal Credit Opportunity Act (ECOA) enforced by the the Federal Trade Commission (FTC), gives consumers the right to know why their loan applications were rejected.  Lenders have to tell the consumer the specific reasons why they were rejected. Regulators have been seeking more transparency around how ML predictions are made.

Choose new techniques for building explainable ML models. Until recently, most leading ML models have offered little or no explanations for their predictions. However, recent advances are emerging to provide explanations even for the most complex ML algorithms such as deep learning.  These include Local Interpretable Model-Agnostic Explanations (LIME),  Anchor, Integrated Gradients, and Shapley. These techniques offer a unique opportunity to meet the needs of business users even in regulated industries with powerful ML models.

Use the right technique to meet your users’ needs for model explanation. When you build ML models, be prepared to provide explanations globally and locally. Global explanations provide the model’s key drivers, and are the strongest predictors in the overall model. For example, the global explanation from a credit default prediction model will likely show the top predictors of default may include variables such as number of previous defaults, number of missed payments, employment status, length of time with your bank, length of time at your address, etc. In contrast, local explanations provide the reasons why a specific customer is predicted to default, and the specific reason will vary from one customer to another.  As you develop your ML models, build time into your plan to provide global and local explanations. We also recommend gathering user needs to help you choose the right technique for model explanation. For example, many financial regulators do not allow the use of surrogate models for explanations, which rules out techniques like LIME. In this instance, the Integrated Gradients technique would be more suited to this use case.

Also, be prepared to share the model’s explanations wherever you show the model’s results — this can be on analytics dashboards, embedded apps or other displays. This will help to build confidence in your ML models. Business users are more likely to trust your ML model if it provides intuitive explanations for its predictions. Your business users are more likely to take action on the predictions if they trust the model. Similarly, with these explanations, your models are more likely to be accepted by regulators.

Step 3: Broaden expertise in data analytics and data engineering within your organization
In order to realize the full potential of AI, you need good people with the right skills. This is a big challenge for many organizations given the acute shortage of ML engineers — many organizations really struggle to hire them. You can address this skills shortage by upskilling your existing employees and taking advantage of a new generation of products that simplify AI model development.

Upskill your existing employees. You don’t always need PhD ML engineers to be successful with ML. PhD ML engineers are great if your applications need research and development, for example, if you were building driverless cars.  But most typical applications of AI or ML do not require PhD experts. What you need instead are people who can apply existing algorithms or even pre-trained ML models to solve real world problems. For example, there are powerful ML models for image recognition, such as ResNet50 or Inception V3, that are available for free in the open source community. You don’t need an expert in computer vision to use them. Instead of searching for unicorns, start by upgrading your existing data engineers and business analysts and be sure they understand the basics of data science and statistics to use powerful ML algorithms correctly.

The PROJECT GENERAL COMPANY sees a wealth of ML training — from Qwiklabs to Coursera courses (e.g. Machine Learning with TensorFlow on Google Cloud Platform Specialization or Machine Learning for Business Professionals). We also see immersive training such as instructor-led courses and a four-week intensive machine learning training program at the Advanced Solutions Lab. These courses offer great avenues to train your business analysts, data engineers and developers on machine learning.

Take advantage of products that simplify AI model development. Until recently, you needed sophisticated data scientists and machine learning engineers to build even the simplest of ML models. This workforce required deep knowledge in core ML algorithms in order to choose the right one for each problem. However, that is quickly changing. Powerful but simple ML products such as Cloud AutoML from Google Cloud make it possible for developers with limited knowledge of machine learning to train high-quality models specific to their business needs. Similarly, BigQuery ML enables data analysts  to build and operationalize  machine learning models in minutes in BigQuery using simple SQL queries. With these two products, business analysts, data analysts and data engineers can be trained to build powerful machine learning models with very little ML expertise.

Make AI a team sport. Machine learning teams should not exist in silos; they must be connected to analytics and data engineering teams. This will facilitate operationalization of models. Close collaboration between ML engineers and business analysts will help the ML team tie their models to important business priorities through the right KPIs. It also allows business analysts to run experiments to demonstrate the business value of each ML model. Close collaboration between ML and data engineering teams also helps speed up data preparation and model deployment in production. The results of ML models need to be displayed in applications or analytics and operational dashboards. Data engineers are critical in the development of data pipelines that are needed to operationalize models and integrate them into business workflows for the right end users.

It is very tempting to think that you have to hire a large team of ML engineers to be successful. In our experience, this is not always necessary or scale able. A more pragmatic approach to scale is to use the right combination of business analysts working closely with ML engineers and data engineers. A good recommendation is to have six business analysts and three data engineers for each ML engineer. More details on the recommended team structure is available in our Coursera course, Machine Learning for Business Professionals.

Conclusion  
As many organizations start to explore AI and machine learning, they are confronted with the question of how to realize the business potential of these powerful technologies. Based on our experience working with customers across industries, we recommend the three steps in this blog post to realize business value from AI.

The PROJECT GENERAL COMPANY manages projects in Indonesia with AI.  We predict project issues before they happen based on human behavior.  The PROJECT GENERAL COMPANY is a sponsor of #SYMEX2020 in Indonesia.

To learn more about AI and project management visit us at www.project-general.com or contact us: pmo@project-general.com

Portions of this document by:  Val Fontama June 14, 2019

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The PROJECT GENERAL COMPANY manages projects in Indonesia

www.project-general.com pmo@project-general.com

The PROJECT GENERAL COMPANY is a proud sponsor of #SYMEX2020

The post Project risk management in Indonesia appeared first on Project General Company.

Date: November 11 – 15, 2019 (5 Days, 35 hours, 35PDU)

Place: Central Jakarta

All skills needed to pass the PMP exam including:

• PMBOK and AGILE knowledge
• Study skills and study guides
• Exam preparation skills
• Sample test questions
• Certified PMP trainers

More information? pmo@project-general.com www.project-general.com

Interested in advanced Project Management Tools listed below?  Contact the PROJECT GENERAL COMPANY, Jakarta

pmo@project-general.com www.project-general.com

The PROJECT GENERAL COMPANY is a proud Diamond Sponsor of #SYMEX2020 in Semarang, Indonesia March 11 -12 ,2020.  Looking forward to seeing you at our booth.

More information? pmo@project-general.com www.project-general.com

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SYMEX 2020, a special event symposium for the project management community to broaden your NETWORK, share and learn KNOWLEDGE and ENHANCE skills.

The aim of this symposium is to provide a forum for industry professionals, business owners, educators, researchers, policy-makers and graduate students with an opportunity to explore and discuss issues in project management topics.

This year’s theme will be Transformation in The World of Tomorrow. The two days symposium provides insight of the upcoming border less economic in ASEAN and how to take benefit from the project management perspective, and as an applied tool for achieving business transformation. International and local speaker both from practitioners and academicians will share their knowledge, experience and state-of-the art of project management. The symposium is also an opportunity for companies to meet project managers seeking career transition and matching the interests, skills, and experience to their needs.

More info? Contact us: pmo@project-general.com www.project-general.com

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Ever want to see the WBS in MS Project 2016 or MS Project 2019? Now you can!!

There is an easy way to display the Work Breakdown Structure (WBS) in the Microsoft Project Resource Usage view. This view is a great way to see all of your project resources, along with the tasks assigned to each resource and where they fit in the WBS.

A bonus feature is the Resource Usage view displays a time-phased grid on the right side of the view which allows you to see items like Work and Cost phased over time.

The Resource Usage view is missing one element, however. It displays the tasks assigned to each resource, but it does not display the summary tasks for these tasks. This means you cannot see the WBS for the tasks assigned to each resource.

For example, please consider the Resource Usage view of the project shown in Figure 1 (below).

Notice that you can see all of the tasks assigned to Jonathan Kine PMP, but you cannot see the summary tasks to which each of her assigned tasks belongs. For example, consider the two tasks PMBOK6 Formally close projects and Confirm HW is revision 1 tasks at the bottom of his list of assigned resources.

Are the 2 tasks in the same summary section or are they in different summary sections?

You cannot determine where in this project these two tasks belong in the WBS using the default resource view in figure 1 below.

Figure 1 default resource usage 

Many years ago, Microsoft introduced a powerful resource Group named Assignments Keeping Outline Structure in a much earlier version of Microsoft Project. This Group allows you to display the associated summary task for every task shown in the Resource Usage view.

To apply this Group to the Resource Usage view, click the View tab to display the View ribbon. In the Data section of the View ribbon, click the Group by pick list button and select the Assignments Keeping Outline Structure item on the menu, as shown in Figure 2.

Figure 2: Apply the Assignments Keeping Outline Structure groupFigure 3 (below) shows the Resource Usage view with the Assignments Keeping Outline Structure group applied. Notice that you can now see the following in the Resource Usage view:
• Each resource in your project team (formatted with the yellow cell background color)
• Detailed tasks assigned to each resource
• Summary tasks for each detailed task (formatted with the light blue cell background color)
• Outline numbering for each summary task (displayed to the left of each summary task name)

Figure 3: Resource Usage view with the Assignments Keeping Outline Structure group applied 

With the Assignments Keeping Outline Structure group applied, the Resource Usage view now displays the full WBS for every task assignment.

There is one severe limitation when you have this Group applied: the Resource Usage view will not format the names of over allocated resources using the red font color. This is due to the default font formatting of the Group. If you need to analyze and level resource over allocations, The PROJECT GENERAL COMPANY recommends removing the Assignments Keeping Outline Structure group until the leveling process is completed, and then you can reapply the Group.

The PROJECT GENERAL COMPANY manages projects and services Indonesia in English, Indonesian, Chinese, and Japanese. Please click here for The PROJECT GENERAL COMPANY client portfolio.

The PROJECT GENERAL COMPANY is a proud sponsor of #SYMEX2020 in Semarang, Indonesia March 11 -12, 2020

www.project-general.com pmo@project-general.com

Consolidated from Display the WBS in the Resource Usage View, Dale Howard , Written on October 5, 2018

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The two biggest evils in project scope management (Scope Creep and gold plating)

Two of the most indisputable evils regarding the scope of the project: Uncontrolled increase in the scope (Scope Creep) and delivering more than planned (gold plated).

How to avoid scope creep and gold plating.

1. Use approved WBS with control accounts
2. Use a PMBOK approved Perform Integrated Change Control (PICC) process
3. Perform regular project audits
4. Have and use an audited anti-corruption policy
5. Monitor and control projects on a daily or faster basis
6. Enforce integrity

The PROJECT GENERAL COMPANY manages projects and services Indonesia in English, Indonesian, Chinese, and Japanese. Please click here for The PROJECT GENERAL COMPANY client portfolio.

The PROJECT GENERAL COMPANY is a proud sponsor of #SYMEX2020 in Semarang, Indonesia March 11 -12, 2020

www.project-general.com pmo@project-general.com

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