Author / Robb Bush

by Robb Bush

SAP IOT – Product-Centric-View (PCV)

The 1992 a Harvard Business Review article entitled “Staple Yourself to an Order” helped to illustrate the customer-centric impact of all processes from the time an order is placed, to fulfillment of the order. Every touch point of the order management process – touches the customer to some degree.
So, what does it mean when every touch point also has an impact on the near and/or long-term performance and profitability of your products and services? the following examples are intended to illustrate various “product-centric” activities that affect a product or service’s overall lifecycle cost, profitability, and ultimately – market success.
This presentation explores the benefits of incorporating transformational “product-centric” applications of cross-functional information and services into everyday activities across diverse business functions and roles – and not just in the context of a single application or module.

Primary Role: Sr. Director – Solution Management
Secondary Role(s):

Business Driver(s): Capture Market Opportunity
Results & Benefits: Improved Market Perception

Time Range: 2006-2008

Class: Project

by Robb Bush

SAP PLM – Contextual Analytics

Robb was the product “owner” for SAP’s first “embedded analytics” solution that presented contextual analytics related to the business object (eg Project, Order, Customer, Item, etc). This capability shipped with SAP Business Suite 5.
With SAP’s acquisition of Business Objects, additional tools became available. Robb led the specification and development of Dashboards for analyzing Product and Service performance.

Primary Role: Sr. Director – Solution Management
Secondary Role(s):

Business Driver(s): Capture Market Opportunity
Results & Benefits: Improved Customer Enthusiasm

Time Range: 2008-2009

Class: Product

by Robb Bush

One Network 3D WMS / Dock / Yard

ONE Network 3D WMS Game Development
NAVIGATION – GETTING AROUND
– Between Levels
– Between Sites
– Assets
– Within Charts

OBJECTS (3D Models)
– All are Data-Driven to change shape (dimensions), color, or location (time & space)
– Sites / Buildings
– Physical Assets (containers, assembly lines, material, vehicles)
– Charts / Analytics

LEVELS
– Network Level View
– Corporate Level View
– LoB Domain Level View (My Locations)
– Site / Inside View
– Warehouse

3D Warehouse Mgmt

Show “Virtual Buffers” in 3D Warehouse
 – Receiving
 – QA
 – Storage
 – Staging

Drag and Drop from Storage to Staging (creates a pick-list)

“Level 6 Data” Where is it, what asset, how many, what order

GENERAL NAVIGATION
Navigation from Corp Level

from Inbound Docks / Yard
to
Warehouse (move something from Storage to Staging)
to
Outbound Docks / Yard
 – Ability to show resources form multiple locations in a single view
Asset View

GAME MECHANICS
 – Purpose / Objective for Player
 – Gain Points, Health, Badges, Unlock New Features / Levels

MULTI-PLAYER COLLABORATION

OVERALL SCENE
 – Add Timeline to Lanes
 – Navigator Menu – Jump to Top View, Side View, Reset View
 – Zoom In Out (Stay in Isometric mode)
 – Drill Down from a Higher Level to this DC level

APPEARANCE OF OBJECTS

Data to Define the Material (Object)
 – Dimensions (Different representations from being a “Trailer” to how it looks in timeline)
 – – Container (H=Fixed, W=Fixed, Length=53, 40, 24)
 – – Container ON Timeline (H=Fixed, W Fixed, Length=Duration on Schedule)
 – Color
 – Labels / Graphics (eg JB Hunt as Text or Logo)

Data to Define the Capacity (Lane)
 – Dimensions, Color of the Lane
 – Segmenting (Schedule States, Occupied, Maintenance, Open)

INTEGRATION

Writing to and from sample Data?

Primary Role: VP Product Management
Secondary Role(s): Designer

Business Driver(s):
Results & Benefits:

Time Range: 2014-2014

Class: Product

by Robb Bush

AI and Interdisciplinary R&D

In my previous article, “AI: a paradigm shift for software?”, where I explored how science historian / philosopher Thomas Kuhn might assess whether or not the current trends in AI could lead to a paradigm-shift, the topic of Interdisciplinary R&D comes up as a recurring theme in his work.

Kuhn explored many examples where an interdisciplinary approach has yielded better quality and/or faster progress by actively exploring multiple angles and approaches to a problem by actively challenging the established assumptions and concepts of any one specific field.

MY TAKE: Many of the complex problems and challenges associated with AI are beyond the scope of any single discipline. Interdisciplinary R&D has the potential to overcome disciplinary boundaries to promote new approaches that are more inclusive and integrated – that better reflect the complex reality of AI in society today.

What better way to get an idea of the key challenges in AI that could benefit from Interdisciplinary R&D – than to converse with an AI!

Examples of Interdisciplinary R&D in AI:

The concepts below are not “straight outta GPT” but have been human-curated a bit. I have broken them into what I think are “obvious” and “non-obvious” challenges associated with AI.

It’s the “non-obvious” problems I am most interested in, but I am also including the “obvious” (and still important) ones too.

Non-obvious AI challenges 👀

Autonomous systems and human-robot interaction

  • Autonomous systems have the potential to revolutionize transportation and logistics, but they also raise significant safety and ethical concerns.
  • Engineers, psychologists, industrial designers and ethicists can help to identify and address these concerns, and develop new approaches to designing autonomous systems that are more responsive to the needs and preferences of different users.

Healthcare and medical research

  • AI has the potential to revolutionize healthcare and medical research by enabling more accurate and personalized diagnoses and treatments.
  • Healthcare professionals, data scientists, industrial designers, and machine learning experts can help to identify and address the challenges associated with using AI in healthcare, such as ensuring patient privacy, interpreting complex medical data, and developing ethical guidelines for the use of AI in medical decision-making.

Environmental monitoring and sustainability

  • AI can be used to monitor and analyze environmental data, such as air and water quality, and to develop new approaches to sustainable development.
  • Environmental scientists, computer scientists, systems engineers and policy experts can help to identify and address the challenges associated with using AI in environmental monitoring and sustainability, such as ensuring the accuracy and reliability of environmental data and developing policies that promote sustainable development.

Obvious AI challenges 🤖

Fairness and bias

  • Developing algorithms and models that are fair and unbiased.
  • Computer scientists, social scientists, and ethicists can help to identify and mitigate the biases that can be introduced by AI algorithms and ensure that the resulting systems are more equitable and inclusive.

Explainability and transparency

  • Systems that are transparent and explainable, so that users can understand how they work and how decisions are being made.
  • Computer scientists, cognitive scientists, and ethicists can help to develop new approaches to explainable AI that are more effective and accessible to a wider range of users.

Social and ethical implications

  • AI has the potential to impact society in many ways, from job displacement to the development of new forms of surveillance and control.
  • Sociologists, philosophers, and policymakers can help to identify and address the social and ethical implications of AI, and develop policies and regulations that are responsive to these concerns.

Interpretable and robust machine learning

  • As machine learning models become more complex, it becomes increasingly difficult to interpret how they arrive at their decisions. This can be a significant barrier to the adoption of these models in critical domains, such as healthcare and finance.
  • Computer scientists, statisticians, and mathematicians can help to develop new methods for interpreting and understanding machine learning models, as well as methods for making them more robust and less susceptible to attacks.

NLP and language understanding

  • Linguists, computer scientists, and cognitive scientists can help to develop more sophisticated Natural Language Processing (NLP) models that better reflect the complex and nuanced nature of human language, as well as methods for training these models with smaller amounts of data.

Human-computer interaction (HCI) and user experience (UX)

  • As AI systems become more ubiquitous, it is important to ensure that they are designed with the user in mind and are accessible to a wide range of users.
  • Computer scientists, psychologists, and designers can help to develop AI systems that are more intuitive and user-friendly, and that take into account the needs and preferences of different users

Data privacy and security

  • As AI systems become more reliant on data, ensuring the privacy and security of that data becomes increasingly important.
  • Computer scientists, legal scholars, and cybersecurity experts can help to identify and address the privacy and security risks associated with AI, and develop new approaches to data governance and regulation that are responsive to these concerns.

The disciplines mentioned above are fairly generic and could expand or change based on the contours and topology of a specific ‘AI Challenge’ – but this looks like a good ‘starter-pack’. There could be more. There will be more.

NET: Interdisciplinary R&D has the potential to develop new approaches and solutions using AI that are more effective, efficient, and ethical, and that better reflect the diverse needs and values of society as a whole.

What do you think?

by Robb Bush

AI: a paradigm-shift for software?

In his landmark book “The Structure of Scientific Revolutions”, Thomas S. Kuhn introduced the concept of the paradigm-shift, which is a fundamental change in the way that we view and approach a particular problem or field.

For there to be a shift, the new paradigm:

  • explains previously inexplicable phenomena
  • resolves inconsistencies in the old paradigm
  • fundamentally changes the way we approach the field

Today, we are witnessing another shift in the computing paradigm with AI making it easier and faster to create code, automate tasks, and more – and improving itself at an accelerating pace. Like other paradigm-shifts in computing, it is also enabled by the predictable increase in computing power (Moore’s Law). In addition, there is a ‘stacking’ or multiplier-effect of AI-oriented computing capabilities – that may prove to scale or multiply in a similar way.

Will AI-oriented computing be a true paradigm-shift, or simply a continuation of the current paradigm?

There is a striking fundamental difference from the current paradigm of humans writing code to explicitly define the steps a computer should take, to a potentially new paradigm of algorithms and models that learn and make decisions based on data and objectives.

The AI-oriented computing paradigm can explain things that were previously inexplicable and it can resolve many of the inconsistencies and inefficiencies of the current paradigm.

Perhaps most importantly, AI has the potential to enable new applications and solve problems that were previously impossible or impractical to address using traditional methods.

More broadly, a true Kuhnian paradigm-shift is NOT just ‘new technology or breakthrough’, but must include:

  • a change in the way we think about and approach computing as a whole
  • a shift in the underlying assumptions and concepts that guide our understanding

My Take

The current trajectory of AI-oriented computing is approaching a new paradigm.

AI-oriented computing could fundamentally change the way that we think-about and approach what we currently call the ‘software design / development / maintenance’ process – and towards new-thinking and approaches that are not centered on ‘the code’, and the multitude of tasks and human-efforts around it, that we see today.

I anticipate the new paradigm will be increasingly interdisciplinary – where the definition, purpose, and process of what we call ‘software’ today, will become more inclusive across disciplines and domains, with an increasingly broad span of capabilities; from the most ephemeral / esoteric personal use such as, ‘l need a one-time app composed for what I am doing today’ – to running the most sensitive and mission-critical business functions such as ‘execute according to required policies and business objectives’.

What do you think?

Will AI-oriented computing be a new paradigm, or is it simply a continuation of the current paradigm?