Documents

Once we sell eLearning courses or offer them as part of a paid subscription model, we will no longer use a Creative Commons license like CC BY-NC 4.0 for our commercial course content. Instead, we will use a standard copyright statement, such as:

© 2025 SCM Trainer. All rights reserved. This content is licensed for individual or organizational use by purchasers only. No part of this course may be reproduced, distributed, or shared without written permission.

Why Not Use Creative Commons for Commercial Products?

CC licenses are irrevocable and designed for open sharing.
Even the most restrictive CC license (like CC BY-NC-ND) allows broad distribution for non-commercial use.
They are not enforceable for gated, licensed, or subscription-based content where you charge access.

My Strategy

Use CC BY-NC 4.0 for free blog posts, infographics, and public-facing resources to support visibility and trust.
Apply “All Rights Reserved” or a custom license to your paid eLearning modules, SCORM files, and videos.
And, consider a terms of use agreement in your LMS or on your product pages that defines commercial use restrictions.

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Applying Behaviorism in eLearning vs. vILT

An overview of how I can apply Behaviorism principles in my course development of eLearning and Virtual Instructor-Led training:

Principle Source: InstrutionalDesign.org-Behaviorism

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Learning Models

Mini-Course Design Thoughts

Topic: Utilizing Artificial Intelligence to Optimize the Supply Chain

Issues in many Supply Chains: In supply chain operations, the lack of data-driven decision-making often results from a less-than-full grasp of understanding and using AI technologies effectively.
Even though we have a wealth of data and innovative AI tools at our fingertips, many supply chains continue to depend on traditional static rules, historical averages, and spreadsheet models for essential decisions like demand forecasting, inventory management, route planning, and evaluating supplier risk. This often results in inefficiencies, delayed responses, and missed opportunities for optimization.

Potential issues and Knowledge Gaps:

Limited understanding of AI concepts and terminology
Inability to identify AI use cases across supply chain functions
Lack of skills in interpreting AI-generated insights
Weak collaboration between supply chain and data/tech teams
Poor data literacy and low confidence in data-driven decision-making
Lack of experience applying AI to optimize end-to-end supply chain performance.

Introduction, History, Ethics, Accessibility, & Artifical Intelligence

Instagram Source: ChatGPT from OpenAI

Example of Specific Core Gaps:

Many supply chain professionals are unaware of the AI tools in their systems and how to utilize them for daily operations.

Awareness Gap: Many professionals are unaware of the AI tools already available to them.

Application Gap: Even when AI is available, people are unsure how to actually use it to improve forecasting, inventory management, logistics, or supplier decisions.

Confidence Gap: There is hesitation to trust AI insights over traditional methods, such as spreadsheets, experience, or gut feeling.

Skills Gap: Professionals frequently lack the practical skills necessary to interpret AI-driven dashboards and take action.

Identifying viable artificial intelligence applications throughout the supply chain. Today’s supply chains have endless opportunities to apply AI, but significant gaps are holding teams back:

Awareness Gap: Many professionals are not familiar with the practical applications of AI in various supply chain functions, including planning, sourcing, manufacturing, delivering, and managing returns.

Application Gap: Even when they know AI exists, it’s not always obvious how to link the technology to solving everyday supply chain problems.

Confidence Gap: There is uncertainty about identifying the right AI opportunities and a fear of suggesting the wrong fit.

Skills Gap: Many supply chain teams haven’t developed the capability to identify, assess, and prioritize AI use cases that provide tangible value.text here...

Additional Note on Audience Context

The learners come from diverse industries and belong to global supply chain organizations. Although their company sizes, regions, and products may differ, they share a common foundation in core supply chain processes — Plan, Source, Make, Deliver, and Enable — making this process-based focus highly relevant and universally applicable.

Process Focus

Plan Professionals:

Focus - Forecasting demand, aligning supply and demand, and analyzing supply chain data for planning.
Role - Demand Planners - Supply Chain Analysts - S&OP Coordinators - Forecasting demand - Aligning supply and demand - Analyzing supply chain data

Source Professionals:

Focus - Sourcing materials, evaluating supplier risk, and managing supplier partnerships with data-driven insights.
Role - Procurement Specialists - Supplier Relationship Managers - Category Managers

- Sourcing materials - Evaluating supplier risk - Managing supplier partnerships

Make Professionals:

Focus - Optimizing production scheduling, quality monitoring, and applying predictive maintenance through AI-driven tools.

Role - Production Planners - Manufacturing Supervisors - Quality Assurance Managers - Production scheduling - Quality monitoring - Predictive maintenance using AI

Delivery / Returns Professionals:

Focus - Route optimization, warehouse operations efficiency, inventory visibility, delivery and returns reliability using AI insights.
Role - Logistics Coordinators - Warehouse Managers - Transportation Managers - Returns Managers - Route optimization - Warehouse efficiency - Delivery reliability

Enable Professionals:

Focus - Driving Operational Excellence, Supporting digital transformation initiatives, implementing AI-enabled tools, ensuring compliance, and enhancing system adoption.
Role - Supply Chain Technology Analysts - Compliance Specialists - Process Improvement Leads - Cross-Functional and Enterprise Managers and Directors - Supply Chain Project Managers - Supply Chain Data Analysts - Site Managers

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ADDIE Design Model

Utilizing Artificial Intelligence for Data-Driven Decision-Making in Supply Chain Operations

Demographics

Age Range: 30–50 years old
Career Level: Mid-level supply chain professionals (e.g., Supply Chain Analysts, Demand Planners, Inventory Managers, Logistics Supervisors)
Education: Bachelor’s degree or higher, typically in Business, Engineering, Supply Chain Management, or a related field
Geography: Global audience, with learners primarily located in the Americas, Europe, the Middle East, and the Asia-Pacific regions
Language: Many are proficient in English and may speak English as a second language. Note: Arabic translation will be available within 90 days of course launch, followed by Latin American Spanish translation within 120 days.

Background and Prior Knowledge

Familiar with foundational supply chain concepts such as inventory management, forecasting, procurement, and logistics.
Has hands-on experience with ERP or supply chain software (e.g., SAP, Oracle, or JDA/Blue Yonder).
Limited to moderate exposure to AI tools, data visualization platforms (e.g., Power BI, Tableau), or advanced analytics.
Has heard of machine learning or AI applications but lacks confidence in interpreting or applying AI-driven insights.

ADDIE Design Model

Utilizing Artificial Intelligence for Data-Driven Decision-Making in Supply Chain Operations

Skills and Dispositions

Analytical mindset: Learners are used to working with data and spreadsheets, but want to upgrade from descriptive reporting to predictive and prescriptive insights

Tech-curious but not tech-savvy: Willing to adopt new tools, but may be intimidated by complex AI terminology or software interfaces

Action-oriented: Interested in applying what they learn directly to on-the-job challenges (e.g., improving forecast accuracy, reducing stockouts, optimizing transportation)

Problem-solvers: Motivated by efficiency, cost savings, and operational excellence

Skeptical of AI hype: Require clear, real-world applications to build trust in AI capabilities

Additional Considerations

Mid-level managers who lack sufficient experience or competence should consider an essential supply chain management skills course before attending this mini-course.

Likely juggling multiple responsibilities, so they need short, focused modules (35 minutes or less)

Value interactivity, practical examples, and scenario-based learning over theory

May need flexibility in accessing content across devices (desktop, tablet, mobile)

Methods to assess learner baseline data skills

When designing my mini-course, I take a thoughtful approach to understanding learners at three important stages: before, during, and after the course.

Before the course begins, I make it a point to evaluate their baseline skills and understanding using friendly self-assessments, engaging knowledge quizzes, or scenario-based diagnostics.

During the course, I love to use quick polls, interactive knowledge checks, and hands-on activities to keep track of students' progress and adjust their learning journey as needed.

After the course concludes, I gather valuable feedback and assess knowledge gains through enjoyable post-course quizzes or reflection activities.

These methods help create a learning experience that is both focused and effective. I'm excited to keep using and refining these approaches in the design of my mini-course!

Methods to build trust and reduce intimidation

WriteBuilding trust and confidence is a journey that starts even before the course begins! I love designing engaging interactions right from the get-go—like self-assessments, welcome surveys, and early discussion prompts—to help learners feel comfy and connected.

Throughout the course, I focus on serving up small, manageable bites of information so that learners can celebrate early victories in grasping and using AI.

The emphasis is on less theory and more hands-on application, with demonstrations, guided walkthroughs, and fun practice opportunities that help build skills and confidence step by step.

By fostering a warm and supportive atmosphere, and celebrating those practical wins, learners quickly recognize the incredible value of AI and gather the momentum they need for success!

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Dick and Carey Design Model

For my mini-course " Using AI to Optimize Your Supply Chain Operational Excellence, " I've created an engaging e-learning experience! This format offers flexible, self-paced learning for mid-level supply chain professionals navigating different global time zones. Given the fast-paced changes in AI tools and practices, eLearning makes it easier to update content promptly, ensuring you always have access to the most relevant information. With its asynchronous structure, you can revisit content whenever you need, which is super helpful for grasping the complex and ever-evolving topics like AI-driven analytics, forecasting, and optimization.

Alternatively, the mini-course can be offered as an engaging instructor-led session, either virtually or in person. This format is perfect for organizations looking for real-time interaction, lively Q&A sessions, or facilitated discussions. The instructor-led version beautifully complements the eLearning option by providing deeper insights into practical use cases or live demonstrations of AI-enabled supply chain planning and optimization tools.

Furthermore, creating a shorter executive version of the course could help offer a high-level overview of the topic. It would focus on the essential aspects like return on investment (ROI), implementation strategy, and organizational impact. Introducing this executive track can enhance awareness and garner support among senior leadership. This way, mid-level managers who complete the full mini-course will have the executive sponsorship and resources to confidently and swiftly apply their new knowledge and skills in their workplace. text here...

The chosen approach for this mini-course is asynchronous online learning (eLearning)! This format is perfect for our target audience—mid-level supply chain professionals—all around the globe, from the Americas to Europe, the Middle East, and Asia-Pacific. With asynchronous eLearning, everyone can dive into the material at a time that works best for them, no matter their time zone or busy schedules.

This course offers a flexible 3–4 hour learning experience with 3 to 5 engaging modules, each lasting around 35 minutes or less. This thoughtful design makes it easy for busy professionals to tackle a module in one sitting, letting them smoothly return to their daily supply chain tasks without interruptions. Plus, it allows learners to pause and pick up where they left off whenever they choose, making it especially useful in fast-paced and time-sensitive supply chain settings.

This approach encourages independence, caters to various learning speeds, and makes it easier to expand globally while also streamlining the process of maintaining and updating content—something that's particularly important as we see the fast-paced growth of AI in supply chain operations.

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Understanding by Design (UbD)

Mini-Course: “Using AI to Obtain Supply Chain Operational Excellence”

By the end of this course, learners will be able to:

Identify essential supply chain functions where artificial intelligence can enhance performance and efficiency.

Explain the differences between traditional and AI-driven methods in forecasting, inventory optimization, and logistics decision-making.

Examine operational data sets to uncover inefficiencies and opportunities for AI implementation throughout the supply chain.

Evaluate various AI technologies (e.g., machine learning, predictive analytics, robotic process automation) for their applicability in specific supply chain scenarios.

Create a strategic roadmap for integrating AI tools into supply chain processes (e.g., demand planning, warehouse management).

Develop a business case demonstrating the ROI potential of AI applications in supply chain optimization.

Interpret key performance indicators (KPIs) such as forecast accuracy, inventory turnover, and service level to assess the impact of AI interventions.

Effectively communicate AI-driven insights and recommendations to both technical and non-technical stakeholders.

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Rapid Instructional Design Approach

A list of six potential learning activities for my minicourse “Using AI to Achieve Supply Chain Operational Excellence,” aligned with the draft course learning outcomes (CLOs):

1. AI-Powered Decision Case Study

Overview: Learners will analyze a real-world supply chain scenario where AI was implemented to resolve a bottleneck or inefficiency (e.g., demand forecasting or inventory allocation). They will identify the problem, assess the AI intervention, and critique the outcome.

Aligned CLO(s): CLO1 – Evaluate how AI supports supply chain decision-making; CLO4 – Interpret AI-driven insights for supply chain optimization.

2. Interactive Tool Exploration

Overview: Learners will participate in an interactive walkthrough or sandbox simulation of a common AI tool (like a demand planning engine or routing algorithm). They will test basic inputs and observe how the AI model makes decisions based on real-time data.

Aligned CLO(s): CLO2 – Explore key AI tools and techniques relevant to supply chain applications.

3. Supply Chain AI Readiness Checklist

Overview: In this reflective activity, learners complete a guided AI readiness assessment of their own (or a sample) organization. They will evaluate factors such as data infrastructure, skills, and stakeholder alignment for AI adoption.

Aligned CLO(s): CLO3 – Assess the organizational factors necessary for successful AI integration.

Write 4. Micro Video Analysis + Poll

Overview: Learners watch a brief expert interview or explainer video about an AI technique (e.g., machine learning in inventory optimization), followed by an embedded poll with reflection prompts to relate the concept to their current role.

Aligned CLO(s): CLO2 – Explore key AI tools and techniques; CLO5 – Reflect on opportunities to apply AI in learners’ specific supply chain context.

5. Peer Discussion Forum: AI Success & Cautionary Tales

Overview: Learners will contribute to a discussion forum by providing one example each of a successful and a failed AI implementation in the supply chain. They will evaluate the critical factors that led to each outcome, drawing from readings and personal experience.

Aligned CLO(s): CLO1 – Evaluate AI’s impact; CLO3 – Assess readiness for AI integration; CLO5 – Reflect on opportunities for AI in practice.

6. End-of-Module AI Application Plan

Overview: Learners draft a concise AI Application Plan that summarizes how they would apply an AI technique to a specific supply chain challenge in their workplace. This plan synthesizes course insights and prepares learners to advocate for the use of AI.

Aligned CLO(s): CLO4 – Interpret AI-driven insights; CLO5 – Reflect on and plan for practical AI applications. text here. Reflect on and plan for practical AI applications.

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Successive Approximation Model (SAM)

In creating my minicourse, “Using AI to Drive Operational Excellence in the Supply Chain,” I’ve found that the Successive Approximation Model (SAM) is an excellent fit. This model’s iterative and collaborative nature and its focus on feedback work perfectly with our topic's lively and ever-changing landscape, making it just right for mid-level supply chain professionals.

Why SAM Is a Strong Fit:

Iterative prototyping allows us to swiftly craft and refine interactive eLearning modules, which is ideal for enhancing scenario-based activities that mirror real-world AI decision-making.
Involving stakeholders and SMEs from the start fosters alignment with technical accuracy and enhances the professional relevance throughout the various supply chain functions, such as Plan, Source, Make, and Deliver.
Ongoing feedback loops support quick replies to learner input, which is crucial when exploring new technologies like artificial intelligence in supply chain applications.

Alternative/Complementary Approaches Considered:

While SAM is the primary model, I also recognize the value in selectively integrating elements of:

Understanding by Design (UbD) is an excellent approach that helps us think backwards about learning, starting with the outcomes we truly want to achieve (for example, “Learners will be able to apply AI tools to real-world logistics problems”).
Rapid Instructional Design (RID) makes it possible to create smaller, focused learning objects quickly, perfectly aligning with the minicourse format.

My Final Recommendation:

The SAM model and backward design thinking (UbD) for clear assessments and outcomes create a flexible and purposeful framework. This blended approach ensures both efficiency and instructional quality, which are essential for crafting a 3–4 hour, modular eLearning course on exciting new areas like AI in supply chain operations.

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Learning Objectives, Bloom's Taxonomy, and SME

Course Learning Outcomes (CLOs)

Mini-course title: Using AI to Obtain Supply Chain Operational Excellence

Explain the key concepts of artificial intelligence (AI) and their significance to supply chain management.
Analyze current supply chain challenges and identify opportunities where AI can drive operational excellence.
Evaluate AI-enabled tools and technologies for their ability to enhance specific supply chain functions (e.g., forecasting, inventory, logistics, procurement).
Apply AI use cases to optimize key areas within the supply chain using real-world business scenarios.
Design a feasible improvement plan that incorporates AI into the current supply chain process to enhance efficiency, agility, or resilience.
Assess the risks, limitations, and ethical considerations of deploying AI in supply chain operations.

Note:

• Each CLO builds progressively from foundational knowledge to applied strategic thinking and design.

• These outcomes align with Bloom’s levels from Understand to Create.

• The outcomes provide a strong framework for measurable module-level learning objectives (MLOs) and project-based assessments.

Module Learning Objectives (MLOs)

By the end of this module, learners will be able to:

1. Identify common inventory-related challenges (e.g., stockouts, excess inventory, demand variability) that AI can help mitigate. Aligns with CLO 2: Analyze current supply chain challenges and identify opportunities where AI can drive operational excellence.

2. Describe how AI technologies such as machine learning and predictive analytics are applied to inventory forecasting and optimization. Aligns with CLO 1: Explain the core concepts of artificial intelligence (AI) and their relevance to supply chain management.

3. Compare traditional inventory management methods with AI-enabled approaches in terms of accuracy, responsiveness, and cost efficiency. Aligns with CLO 3: Evaluate AI-enabled tools and technologies for their ability to enhance specific supply chain functions.

4. Evaluate a sample AI inventory tool or dashboard and assess its capabilities for improving safety stock levels and reducing carrying costs. Aligns with CLO 3: Evaluate AI-enabled tools and technologies

5. Apply AI-driven inventory insights to a real-world supply chain scenario to improve stock replenishment planning. Aligns with CLO 4: Apply AI use cases to optimize key areas within the supply chain.

6. Summarize the risks and limitations associated with relying on AI in inventory decision-making (e.g., data bias, system dependency). Aligns with CLO 6: Assess the risks, limitations, and ethical considerations.

Mini-Course Subject Matter Experts

Jeffrey McDaniels

Role: Lead SME, Chief Instructor, and Instructional Designer

Credentials:

APICS Fellow and Master Instructor
Lean Six Sigma Master Black Belt (LSSMBB)
Certified in PMP and CPSM
CEO & Chief Learning Architect at SCM Trainer

David Thoma

Role: 2nd SME

Senior Instructor, SCM Trainer

Credentials:

SCM Practitioner, APICS Fellow, and Master Instructor

Resources to Review before developing my Mini-Course

Blockchain, IoT, and AI Technologies for Supply Chain Management: Apply Emerging Technologies to Address and Improve Supply Chain Management. (2024).
Townson, S. (2021, December). 3 Areas Where AI Will Boost Your Competitive Advantage. Harvard Business Review.
Beadle, R. (2025, April). AI: The Key to Navigating Supply Chain Challenges in an Uncertain World. Supply Chain Management Review.
Clowes, C. (2024, June). 6 Key Challenges in AI Implementation for the Supply Chain Industry. Supply Chain: Beyond the Hype.
ASCM. Supply Chain Technology Certificate.
ASCM. Certified in Transformation for Supply Chain (CTSC).

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My Top 10 Takeaways and Reflections

1. Prioritize comprehensive sourcing by gathering a balanced mix of books, peer-reviewed articles, credible blogs, podcasts, and templates to enhance research and practical application.

2. Instructional Design Models provide a Roadmap - Utilize frameworks such as ADDIE, SAM, Dick and Carey, and UbD to structure the development of goal-aligned, learner-centered courses.

3. Match Models to Project Needs - Different models excel in various scenarios—structured (ADDIE), iterative (SAM), systems-driven (Dick and Carey), or outcome-focused (UbD).

4. Backward Design Sharpens Focus - Starting with end goals ensures that all learning activities and assessments support real-world transfer and application.

5. Learning Objectives Anchor Design - Clear and measurable objectives aligned with Bloom’s Taxonomy guide content development, activity selection, and assessment creation.

6. Models Are Guides, Not Constraints - Models should guide structure without limiting creativity; blending models often results in better project outcomes.

7. Evaluation Is Continuous and Essential - Both formative and summative evaluations are crucial for ensuring course effectiveness and offering data for ongoing improvements.

8. Learner analysis is foundational - A deep understanding of learners' needs, motivations, and contexts leads to higher engagement and better learning outcomes.

9. Assessment Must Align Directly with Objectives - Effective assessment strategies measure precisely what the learning objectives specify, ensuring validity, reliability, and real skill transfer.

10. Reflection and Adaptation Build Expertise - Continual reflection and a willingness to adapt design models strengthen instructional design skills over time.

Sequencing, Assessments, & Alignment

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Digital Media, Digital Tools, and Technology

Digital Media

Completed checklist - Pathway to Supply Chain Excellence

Basic Information

Evaluator Name: Jeffrey McDaniels

Date of Evaluation: May 20, 2025

Title of Resource: Pathways to Supply Chain Excellence

Resource URL: https://directory.doabooks.org/handle/20.500.12854/65825

Media Format: ☐ Video ☐ Audio ☐ Interactive ☐ Infographic ☐ Document ☐ Simulation ☐ Other: ______

Open License Verified? ☐ Yes ☐ No

Instructional Relevance & Alignment

☐ Aligned with one or more learning objectives? YES

☐ Connects clearly to the course/module topic? YES

☐ Supports your instructional strategy (e.g., UbD, UDL, Andragogy)? YES

☐ Culturally inclusive and globally relevant? YES

☐ Is the language appropriate for the audience (plain language, jargon-free)? YES

☐ Bias-free and balanced in perspective? YES

Cognitive Learning Design (Mayer’s Principles)

☐ Does it apply coherence (excludes extraneous info)? YES

☐ Uses signaling to direct attention to key points? NO

☐ Combines audio and visuals effectively (modality)? NO

☐ Text and graphics aligned spatially and temporally? NO

☐ Is the content segmented or chunked for learner control? YES

Summary: Strong cognitive structure for reading; would benefit from instructional adaptation with Mayer-based visuals and interactive segments.

Accessibility & ADA Compliance

☐Closed captions or transcripts available for media? NO

☐ Text alternatives provided for visuals and charts? NO

☐ Compatible with screen readers? YES

☐ Available in multiple formats (HTML, PDF, mobile-ready)? NO

☐ Does it meet WCAG 2.1 or ADA standards? NO

Usability & Instructional Quality

☐ Interactivity encourages participation or feedback? NO

☐ Is the resource error-free (spelling, grammar, data)? YES

☐ Visually clear and logically organized? YES

☐ Does audio/video quality meet professional standards? YES

☐ Is navigation intuitive and user-friendly? NO

Reuse, Technical, & Sustainability Factors

☐ Can it be reused or adapted under its license terms? YES

☐ LMS-compatible (SCORM, HTML5, or link embedding)? NO

☐ Hosted on a reliable platform (not likely to disappear)? YES

☐ Likely to be updated or maintained regularly? YES

☐ No proprietary plug-ins or special software needed? YES

Privacy & Data Security (For Interactive Tools)

☐ No tracking or data collection without disclosure? YES

☐ Complies with FERPA/GDPR/privacy policies (if applicable)? YES

☐External links or embeds reviewed for content integrity? YES

Instructor Support & Pedagogical Value

☐ Includes instructor guides or support materials? YES

☐ Offers opportunities to differentiate learning (e.g., UDL)? NO

☐ Content reviewed or rated by other educators? YES

☐ Supports the development of core competencies or skills? YES

Evaluator Summary / Recommendation

This open-access article is a strong foundational resource for advanced topics in financial-physical integration in supply chain planning. It best reads with instructor-led discussion, interactive infographic add-ons, and scenario-based application questions. It is too general and strategic in focus; however, some use of segments may be acceptable with some modifications.

Final Decision

Use in Course? ☐ Yes – As Is ☐ Yes – With Modifications ☐ No – Not Recommended

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Visual Design

Digital Media Checklist - Updated

Digital Media Evaluation Checklist

Basic Information

Evaluator Name: Jeffrey McDaniels

Date of Evaluation: June 3, 2025 (updated from previous version)

Title of Resource: Using AI to Obtain Supply Chain Operational Excellence (SCM Trainer eLearning Mini-Course

Resource URL: https:

Media Format: ☐ Video ☐ Audio ☐ Interactive ☐ Infographic ☐ Document ☐ Simulation ☐ Other: ______

Open License Verified? ☐ Yes ☐ No

Instructional Relevance & Alignment

☐ Aligned with one or more learning objectives? YES

☐ Connects clearly to the course/module topic? YES

☐ Supports your instructional strategy (e.g., UbD, UDL, Andragogy)? YES

☐ Culturally inclusive and globally relevant? YES

☐ Is the language appropriate for the audience (plain language, jargon-free)? YES

☐ Bias-free and balanced in perspective? TBD

Cognitive Learning Design (Mayer’s Principles)

☐ Does it apply coherence (excludes extraneous info)? YES

Uses signaling to direct attention to key points? YES

Combines audio and visuals effectively (modality)? YES

Text and graphics aligned spatially and temporally? YES

Is the content segmented or chunked for learner control? YES

& ADA Compliance

☐ Closed captions or transcripts available for media? YES

☐ Text alternatives provided for visuals and charts? YES

☐ Compatible with screen readers? TBD

☐ Available in multiple formats (HTML, PDF, mobile-ready)? YES

☐ Does it meet WCAG 2.1 or ADA standards? YES

Usability & Instructional Quality

☐ Interactivity encourages participation or feedback?

☐ Is the resource error-free (spelling, grammar, data)?

☐ Visually clear and logically organized?

☐ Does audio/video quality meet professional standards?

☐ Is navigation intuitive and user-friendly?

Reuse, Technical, & Sustainability Factors

☐ Can it be reused or adapted under its license terms?

☐ LMS-compatible (SCORM, HTML5, or link embedding)?

☐ Hosted on a reliable platform (not likely to disappear)?

☐ Likely to be updated or maintained regularly?

☐ No proprietary plug-ins or special software needed?

Privacy & Data Security (For Interactive Tools)

☐ No tracking or data collection without disclosure? -

☐ Complies with FERPA/GDPR/privacy policies (if applicable)?

☐External links or embeds reviewed for content integrity?

Instructor Support & Pedagogical Value

☐ Includes instructor guides or support materials?

☐ Offers opportunities to differentiate learning (e.g., UDL)?

☐ Content reviewed or rated by other educators?

☐ Supports the development of core competencies or skills?

Evaluator Summary / Recommendation

The course is under development, so the checklist is only partially complete. As we progress through the course, I will review and update it to include TBD areas. More development and evaluation to come.

Final Decision

Use in Course? ☐ Yes – As Is ☐ Yes – With Modifications ☐ No – Not Recommended

Audio File

Audio File tools used: Speechify Studio (voice-overs), and, Iframely (embed code generation).

Audio introduction to my eLearning Mini-Course (mp3 file, 58sec)

Key Takeaways of0end of Module 1 Summary (mp3 file, 53sec)

Learning Objectives/Module it Supports

(Supports CO2, CO4, CO7, and CO8)

This week's digital document assignment supports multiple objectives from my micro-course, Using AI to Obtain Supply Chain Operational Excellence. I created three visuals:

An infographic titled "How AI Improves Forecasting, Inventory, and Logistics",
A job aid called "AI Tool Match Guide: What to Use for Each Supply Chain Problem", and
A quick reference sheet is titled "Top 5 KPIs for Evaluating AI Impact in Supply Chains."

These materials support CO2 by differentiating traditional vs. AI-enhanced methods, CO4 by aligning AI technologies to operational use cases, and CO7 by clarifying how to evaluate AI's impact through KPIs. I also recorded and embedded an audio description of the infographic to support accessibility, directly aligning with CO8 and reinforcing UDL principles.

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Digital Images

DropBox Link for copy: Digital Media Evaluation Checklist

https://www.dropbox.com/scl/fi/nbj9j7hauhaynsnsdrpq1/Digital-Media-Evaluation-Checklist.pdf?rlkey=br34168ff1wkfzsqj114y2qbv&st=ez85qdrt&dl=0

DropBox Link for copy: Image File

https://www.dropbox.com/scl/fi/iow0see46e0lx4jed4320/Wk3-Keynote-image.pdf?rlkey=ppl0id8ufpnynvzv335fepej6&st=gulmf00j&dl=0

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Title: Using AI to Achieve Operational Excellence

Version: 6

Date: July 3, 2025

YouTube Link: https://youtu.be/W1y52S5z4Wo

Tag: iMovie

Format: Video & Audio

Resolution: 1080P

Frame Rate: 60 FPS

Quality: High

Compress: Faster

Tools: Keynote - Speechify Studio - iMovie - YouTube

Digital Images - Animation

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Storyboards

Articulate 360 AI-Compatible Storyboard
Using AI to Obtain Operational Excellence – Introduction Module

This storyboard is designed to be compatible with Articulate 360 AI course authoring. Each slide features layout elements, narration, interactions, and media suggestions. Use this format to import into Rise or Storyline, or to copy and paste into Rise AI slide generators.

Slide 1: Welcome to the Course

Layout: Full-width introduction section with instructor photo or avatar

Narration: Welcome. I’m Jeffrey McDaniels—supply chain management instructor and practitioner. This course is designed to help you apply AI practically—right now—to solve real operational problems.

On-Screen Text: Welcome to Using AI to Drive Operational Excellence

Interaction: Button to start the module or open the instructor bio popup

Slide 2: Why Is It Urgent?

Layout: Two-column layout with an image (rocket launch) and accompanying text.

Narration: AI may seem futuristic, but it’s already being used—and making a real impact across supply chains.

On-Screen Text: Why this course, why now?

Interaction: Animated keyword reveal: Smarter, Faster, Resilient

Slide 3: Supply Chain Challenges We All Know

Layout: Split image with labeled icons and brief descriptions

Narration: Unreliable forecasts, supplier delays, and logistics issues… sound familiar?

On-Screen Text: What’s Holding You Back?

Interaction: Hotspot activity — Click icons to reveal problem descriptions.

Slide 4: Reflection Activity – Your Operational Gap

Layout: Question block with a single text entry

Narration:

On-Screen Text: What’s one area in your operation where making better decisions could improve performance?

Interaction: Text field for learner responses (optional voice reflection)

Slide 5: The AI Advantage

Layout: Icon grid with brief caption text

Narration: AI can analyze data more quickly, suggest next steps, and identify issues before they interrupt operations.

On-Screen Text: Smarter | Faster | More Resilient

Interaction: Click to reveal examples for each AI benefit

Slide 6: What This Course Will Cover

Layout: Four-square icon grid with labels and introductory text.

Narration: This course skips the theory and goes directly into practical AI applications that matter today.

On-Screen Text: Forecasting | Supplier Risk | Logistics | Performance

Interaction: Click each icon to view a brief preview of the module topic.

Slide 7: What You’ll Be Able to Do

Layout: Checklist format with animated check marks.

Narration: By the end, you’ll be able to evaluate AI tools, reduce forecast errors, optimize logistics, and communicate the impact.

On-Screen Text: Course Outcomes

Interaction: Display list with each outcome aligned to a course objective

Slide 8: Interactive Scenario – Where Would You Start?

Layout: Scenario block with decision branching

Narration:

On-Screen Text: Let’s Try It: Fix the Forecast

Interaction: Choose-your-path format; each option provides feedback on the selected AI solution.

Slide 9: Wrap-Up Reflection

Layout: Short-answer block with icon

Narration:

On-Screen Text: What’s the biggest opportunity for AI in your position?

Interaction: Text entry or optional voice input, plus download reflection.

Slide 10: Get Ready for Module 1

Layout: Tile preview block.

Narration: In the next module, you’ll learn how AI lowers forecast errors—and where to begin.

On-Screen Text: Up Next: AI Forecasting

Interaction: The Continue button links to Module 1

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Authoring Tools

IDD - Interactive Module

Interactive Module link:

https://scmtrainer.learnworlds.com/course/opportunities-for-ai-in-logistics

PDF Checklist:

https://www.dropbox.com/scl/fi/nbj9j7hauhaynsnsdrpq1/Digital-Media-Evaluation-Checklist.pdf?rlkey=br34168ff1wkfzsqj114y2qbv&st=cm60i6lt&dl=0

Signature Assignment Document

Course Title: Fixing Forecast Errors with Data

Course Overview

This interactive microlearning course provides mid-level supply chain professionals with the skills to identify, analyze, and correct forecast errors using real-world data. Through 5–6 brief, scenario-based modules, learners explore topics such as bias, variance, trend shifts, and error metrics like MAPE. The course integrates simulations, gamified challenges, and guided reflection to support learners in making better data-driven forecasting decisions.

Knowledge Gap Statement

Mid-level supply chain professionals often lack the practical, data-driven forecasting skills needed to identify and correct forecast errors. While many are familiar with basic forecasting tools (e.g., ERP or Excel), they do not have sufficient exposure to:

Forecast error metrics such as MAPE, MAD, and bias
Root cause analysis techniques for identifying over- or under-forecasting
Scenario-based decision-making that simulates real planning environments
Real-time data interpretation for adjusting forecasts in dynamic market conditions

This gap is not caused by a lack of access to tools but by training that doesn't connect theory with practice. Most existing programs focus too much on tools or ideas, providing few chances to apply forecasting concepts to real-world problems.

Why It Matters:

For the business, this gap leads to inaccurate forecasts, excess inventory, service failures, and misalignment in Sales and Operations Planning (S&OP).
For the learner: It hampers professionals from developing the judgment and confidence necessary to contribute meaningfully to planning discussions and ongoing improvement efforts.

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Target Audience & Learner Profile

This course is tailored for mid-level supply chain professionals, particularly those in charge of demand planning and forecast management within the broader supply chain planning function.

Persona Alignment: This audience aligns with the SCM Trainer’s Plan Persona, encompassing roles such as Demand Planner, Supply Chain Analyst, and S&OP Manager. These professionals are responsible for forecasting customer demand, analyzing historical and market data, balancing supply and demand, and supporting key planning processes, such as Sales and Operations Planning (S&OP).
Professional Level: Learners are categorized as mid-level professionals, generally with 3 to 10 years of relevant experience. They work independently in tactical planning roles and are increasingly involved in strategic decision-making. They are developing advanced skills in data analysis, system planning, and cross-functional collaboration.
Skills & Competencies: These learners are proficient in essential planning tools (e.g., Excel, ERP, forecasting modules) but have limited formal training in identifying, analyzing, and fixing forecast errors. They may understand the “what” of forecasting but need structured support to grasp the “why” and “how” behind error diagnostics and resolution.
Disposition: Goal-oriented, tech-savvy, and data-literate. They are highly motivated to quickly apply new skills to enhance accuracy, performance, and confidence in planning. They value concise, relevant learning that seamlessly fits into their daily work.
Global Learner Base: Over the past decade, I’ve taught thousands of supply chain professionals worldwide, including those in the Americas, the Middle East, Europe, and Central Asia, through instructor-led (ILT) and virtual instructor-led (VILT) delivery of globally recognized supply chain certifications and advanced microlearning programs.

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Course Type

This course is designed as a problem-based, scenario-driven microlearning experience because forecast error correction is a skill best learned through applied decision-making rather than passive content consumption. Real-world forecasting challenges are complex and contextual, requiring learners to interpret data, identify causes, and take corrective action.

A scenario-based format reflects these real-life conditions and encourages critical thinking, while problem-solving activities help strengthen retention and the transfer of learning. This method aligns with adult learning theory and supports the development of job-related forecasting skills.

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Course Modality

This course is offered as a self-paced, online microlearning experience designed to accommodate busy mid-level supply chain professionals seeking flexible and immediately applicable learning.

Why Self-Paced?

Learners can access content at their convenience—perfect for busy professionals managing daily tasks.
It supports personalized pacing, enabling learners to revisit complex concepts (e.g., MAPE calculation) or skip familiar material.
Self-pacing decreases cognitive overload by dividing content into manageable parts, consistent with cognitive load theory.

Why Online?

Forecasting relies on data and digital methods, making online delivery a perfect match for tools, simulations, and dashboards.
Learners are spread worldwide, with many located in North America, Europe, the Middle East, and Central Asia. Conducting in-person sessions would be cost-prohibitive and logistically challenging due to differences in time zones, travel expenses, and scheduling difficulties.
Online delivery ensures scalability and accessibility for global or hybrid teams, eliminating limitations imposed by live session constraints.
Integration with digital tools (e.g., Excel, BI dashboards, ERP simulations) enables learners to practice in environments that mirror their fundamental work tools.

Why Microlearning?

Each module (35–45 minutes) targets a specific skill or concept, such as identifying bias or analyzing trend shifts.
Microlearning aligns with behaviorist reinforcement: learners get quick feedback, complete brief tasks, and remain motivated.
It enhances just-in-time learning—planners can apply what they learn immediately to refine current forecasts.

Summary:

The chosen modality maximizes relevance, flexibility, and engagement—delivering measurable results without disrupting learners’ daily responsibilities. It’s a modality designed for how professionals learn and apply forecasting skills in the real world.

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Course Learning Outcomes

(C01) Identify common types of forecast errors (e.g., bias, variance, MAPE) and explain their operational impact on supply chain performance. (Understand)

(C02) Examine historical and real-time demand data to identify the root causes of forecast inaccuracies. (Analyze)

(C03) Apply error metrics such as MAPE, MAD, and Bias to evaluate forecast performance. (Apply)

(C04) Develop corrective forecasting strategies based on data patterns and market signals. (Create)

(C05) Evaluate multiple forecast scenarios to recommend data-driven adjustments that improve planning accuracy. (Evaluate)

(C06) Communicate forecasting insights and recommendations effectively within S&OP or cross-functional planning processes. (Apply/Communicate)

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Module/Week/Lesson/Unit Objectives, Learning Activities, Assessment Strategies

Module 3: Identifying Root Causes

Learning Objectives (Linked to CLOs)

Identify internal and external causes of forecast errors, such as promotional effects, weak baselines, and supply disruptions. (Analyze → CLO2)
Analyze demand history and forecast data to identify root causes of over- and under-forecasting. (Analyze → CLO2)
Differentiate between forecast variance due to planning assumptions versus external market signals. (Analyze → CLO5)

Possible Learning Activities

Interactive Dashboard Walkthrough: Learners examine 12 weeks of demand versus forecast data with tooltips highlighting promotions, constraints, and anomalies. LO2, LO3
Scenario-Based Quiz: “What Caused the Spike?”: A branching scenario where learners must identify whether a forecast error was due to internal planning issues or market volatility. LO1, LO2
Error Mapping Activity: Learners complete a drag-and-drop decision tree to trace forecast errors back to possible root causes. LO1, LO2
Reflection Prompt: “What Went Wrong?”: Learners examine a real or hypothetical forecast miss from their organization. LO1, LO3

Possible Assessment Strategies

Data Interpretation Quiz: Learners analyze line charts and variance tables, then respond to questions about possible root causes of errors. LO2, LO3. Auto-graded with feedback.
Root Cause Classification Quiz: Learners classify each error scenario as internal or external from a given set. LO1. Auto-graded, randomized
Decision Tree Completion: An interactive assessment where learners develop a root cause path for a forecast failure scenario. LO2. Interactive/formative.
Short Written Analysis: Learners submit a brief response analyzing the cause of a provided forecast error. LO2, LO3. Instructor-graded with rubric.

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Subject Matter Expert/Resources

Jeffrey McDaniels – APICS Master Instructor, Certified Supply Chain Professional, and Instructional Designer. Over a decade of experience delivering global supply chain planning and forecasting education through ILT, VILT, and eLearning.
David Thoma – Supply chain planning and forecasting expert with deep experience in demand planning, forecast diagnostics, and process improvement across multiple industries.
ASCM (APICS) CPIM Certification Course – A globally recognized credential that provides in-depth coverage of demand management, forecasting techniques, error measurement, and supply planning. A key foundational reference for terminology, metrics, and planning frameworks used in this course.
Institute of Business Forecasting & Planning (IBF) – Respected authority in demand planning and forecasting. Provides industry standards, certification programs, benchmarking, and case studies.

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Instructional Design Model

Instructional Design Approach: Blended ADDIE + SAM Model

To fix forecast errors with data, I used a blended instructional design model that combines the structure of ADDIE with the flexibility of the Successive Approximation Model (SAM). This hybrid approach creates a results-oriented, learner-focused course that is both scalable and adaptable.

ADDIE: Structured Design Foundation

The ADDIE model offered a strong basis for aligning instructional objectives with business results:

Analyze: Identified the main knowledge gap—professionals lack practical training in forecast error correction and root cause analysis.
Design: Created a modular framework with scenario-based learning, aligned with Bloom’s Taxonomy and real business KPIs.
Develop: Created course assets (simulations, dashboards, assessments) using Articulate Rise, ChatGPT, and Keynote.
Implement: Designed for online delivery via LearnWorlds, supporting asynchronous learning across global teams.
Evaluate: Embedded formative and summative assessments tied to forecast accuracy, with feedback loops for learning reinforcement.

SAM: Agile, Iterative Development

SAM added flexibility and speed to development:

Prototyping: Early versions of key simulations were tested with SMEs and refined based on feedback.
Iteration: Modules 3–6 were improved through design-test-refine loops to enhance realism and interactivity.
Collaboration: Regular SME input helped align scenarios with real planning challenges and learner context.

Why Combine ADDIE and SAM?

The combination of ADDIE’s framework with SAM’s flexibility created a structured yet adaptable design process. It maintained strong learning alignment while allowing quick, feedback-based improvements—perfect for delivering a data-driven, high-impact forecasting course.

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Learning Theory

The design of this minicourse is guided by three learning theories—each chosen to support real-world application, learner autonomy, and reinforcement necessary to improve forecast accuracy in supply chain roles.

Experiential Learning (Kolb)

This theory advocates for hands-on, scenario-based learning to mimic real-world planning decisions. Learners participate in forecasting simulations, work with demand data, and consider their choices to improve comprehension and develop judgment. Application: Realistic dashboards, error correction exercises, and guided reflection prompts enhance decision-making skills in context.

Behaviorism

Behaviorist principles, especially reinforcement through feedback and repetition, assist learners in practicing and retaining forecasting skills. Gamified quizzes, performance dashboards, and instant feedback motivate mastery of essential forecasting metrics. Application: Learners earn points, badges, and receive real-time feedback for completing challenges that reinforce the correct use of error metrics and correction strategies.

Adult Learning Theory (Andragogy)

This theory ensures the course respects the learner’s experience, need for autonomy, and preference for practical, job-relevant content. Adults are more motivated when learning is immediately applicable and connected to their current challenges. Application: Learners choose the order of module completion based on personal needs (e.g., “Bias Buster” or “Promo Fixer”) and relate course scenarios directly to their work.

Why a Combined Approach?

Each theory addresses a distinct aspect of the learner experience:

Experiential supports real-world skill development
Behaviorism provides motivation and structure through feedback and rewards
Adult Learning Theory ensures relevance, autonomy, and engagement

Together, they develop a course that is engaging, practical, and performance-driven, helping learners confidently and accurately apply forecasting skills in their roles.

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Digital Media Plan (using Keynote, ChatGPT, Speedhify, and Articulate 360)

Interactive Data Dashboards:

Tool: Articulate Storyline or Rise AI Blocks
Purpose: Let learners explore demand vs. forecast data with clickable insights (e.g., bias, promotions, external events).
Application: Modules 2–4
Supports: Experiential Learning – active pattern recognition

Branching Forecasting Scenarios

Tool: Articulate Storyline
Purpose: Learners make forecast decisions and see results based on their choices (e.g., “Fix the Forecast” or “Bias Buster”).
Application: Modules 3, 4, 6
Supports: Experiential + Adult Learning – realistic decision practice

Gamified Quizzes & Challenges

Tool: Articulate Rise (Quiz, Sorting, Fill-in-the-Blank Blocks)
Purpose: Reinforce key forecasting skills like calculating MAPE or identifying trends with points, badges, or feedback.
Application: Modules 2, 4, 5
Supports: Behaviorism – motivation through feedback/reinforcement

AI-Generated Scenario Text & Activities

Tool: ChatGPT
Purpose: Generate realistic demand planning scenarios, quiz distractors, reflection prompts, and error datasets.
Application: Across all modules
Supports: Customization, variation, rapid prototyping

Voice Narration & Audio Reflection Prompts

Tool: Speechify Studio
Purpose: Add dynamic, human-sounding narration to dashboards, introductions, and reflection questions.
Application: Modules 1, 3, 6, 7
Supports: Adult Learning Theory – supports auditory learners and flexible consumption

Learner Reflections (Voice or Text-Based)

Tool: Speechify Studio (Voice Prompt) + Rise AI Journal Block or Text Input
Purpose: Learners reflect on forecasting decisions, supported by voice-guided reflection questions.
Application: End of each core module
Supports: Experiential + Adult Learning – self-assessment and metacognition

Microlearning Video Walkthroughs

Tool: Speechify Studio (voice), Screen Recording + Rise Video Block
Purpose: Narrated walkthroughs of data analysis, forecast evaluation, or error correction examples.
Application: Modules 2–5
Supports: Adult Learning Theory – modeling expert thinking and tools

Downloadable Forecasting Tools

Tool: ChatGPT (template creation), Articulate Rise (Attachment Block)
Purpose: Provide downloadable Excel tools for error tracking, bias analysis, or forecast review templates.
Application: Modules 2, 4, 6
Supports: Transfer to job performance (Adult Learning)

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Instructional Design Documents

Learning Theories

Learning Theories and Copyrights

Learning Models

Instagram Source: ChatGPT from OpenAI

Additional Note on Audience Context

Digital Media, Digital Tools, and Technology

Digital Media

Visual Design

Audio File

Learning Objectives/Module it Supports

Digital Images

Digital Images - Animation

Storyboards

Authoring Tools

IDD - Interactive Module

Signature Assignment Document

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