AI can help planners detect demand shifts, manage inventory, prioritize exceptions, and assess scenarios.
Metrics tell you what happened; leadership decides what happens next.
Consistency beats one strong quarter.
Check first: Confirm audience composition, more AI and planning examples, and commercial terms.
Main ideas
AI should make planners better, not replace them · Metrics tell you what happened; leadership decides what happens next · Consistency beats one strong quarter
Natalia Dubnyak writes about leadership in manufacturing, operations, and supply chain.
In the available LinkedIn posts, a recurring theme is that strong operational results depend on people, clear execution, and improving the whole system—not just tracking metrics or optimizing individual parts.
A post about AI in demand and supply planning extends that view to technology: AI can help planners detect shifts and risks, manage inventory, prioritize exceptions, and assess scenarios, rather than replace planning systems or planners.
The posts also include a first-person account of resisting pressure to fit a male-dominated workplace.
A reflection names Eliyahu Goldratt’s The Goal as an influence.
Ask for details of any book or other signature project if that matters to your decision.
Dubnyak connects leadership principles to operational concerns.
One post links frontline leadership to trust, engagement, accountability, and performance, then names safety, quality, service, cost, inventory, productivity, and OEE as measures that matter.
Another turns the question of where AI fits alongside existing planning software into a practical list of planner tasks.
Together, these posts show how management choices can affect day-to-day work.
The writing is mostly short, direct posts that often end with a question to practitioners.
The available material also includes a brief prompt about quarterly challenges, offering little evidence of a developed argument.
Ask for more examples if a fuller account of their career or signature work matters to your decision.
Natalia Dubnyak writes about running manufacturing, operations and supply chain organizations.
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A recurring argument is that hard numbers — safety, quality, service, cost, inventory, productivity, OEE — describe performance but do not create it.
The causes lie upstream, in frontline leadership, trust, clarity and consistent team development.
A second theme is the function's changing role: operations and supply chain are moving from cost centres to strategic drivers, and effective leaders can translate operational detail into business strategy.
On technology, Dubnyak argues for augmentation rather than replacement: AI makes existing planning platforms smarter while leaving the decision with the planner.
The posts often close with a direct question to readers.
AI should make planners better, not replace them
Dubnyak asks what AI adds when a company already runs sophisticated forecasting systems such as SAP IBP.
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The answer: AI does not replace the planning platform; it makes it smarter, while the planner keeps the decision.
The post lists five uses: detecting demand shifts earlier, predicting supply risks, optimizing inventory, prioritizing exceptions and evaluating scenarios faster.
Its conclusion is that AI will help supply chain planners decide better and faster, not replace them.
Dubnyak asks readers whether they have measured business benefits or are still testing.
Dubnyak cites research into three CPG companies — Kraft Heinz, PepsiCo and Coca-Cola Europacific Partners — as the basis for the pattern described. These are examples from that research, not accounts of work inside those companies.
Metrics tell you what happened; leadership decides what happens next
Dubnyak lists the metrics managed over a career — safety, quality, service, cost, inventory, productivity and OEE — and argues that sustainable results rarely begin with a metric.
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The reasoning is a chain: strong frontline leadership builds trust, trust drives engagement, engagement drives accountability, and accountability drives performance. Investing time in listening, coaching and supporting teams moves the operational numbers, not the reverse.
Dubnyak says service recovered, quality improved and safety performance reached new levels when leaders invested time in listening, coaching and supporting their teams. The closing contrast is clear: metrics tell us what happened; leadership determines what happens next.
Dubnyak compares manufacturing operations to long-running television series.
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A series survives not on one great episode but on strong characters, a clear direction and disciplined execution season after season.
Likewise, a single strong quarter does not define manufacturing performance; what counts is how consistently a team executes, develops people and improves processes over time.
Sustainable results require clarity, stability and leaders who invest in their teams for the long run.
The series comparison carries the argument: judge operations over the long term by repeated execution and team development, not by a single period's result.
Dubnyak also argues that strong leaders challenge the status quo. The two positions are compatible: challenge the status quo while staying grounded in execution, holding stability and change together.
Dubnyak writes about spending too much time adapting to a male-dominated environment — speaking differently, acting tougher, blending in — and says their impact grew once they stopped.
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Professional knowledge and experience matter, but so does perspective: operations and supply chain benefit from different angles on the same facts and different ways of connecting them. Inclusion is framed as an operational advantage, not a nice-to-have.
The post ends with three directives for people starting out or stepping into a bigger role: do not shrink, do not copy, do not wait to belong. The advice comes from Dubnyak's own account, and the post's hashtags include #WomenInLeadership and #DiversityOfThought.
Dubnyak frames the position as a correction learned late — the realisation came 'later than I should have' — presenting the advice as a change in their own practice.
Operations should help shape strategy, not only execute it
Dubnyak argues that supply chain and operations have moved from cost centres focused on efficiency to strategic drivers of growth, resilience and competitive advantage, amid talent shortages, digital transformation and sustainability expectations.
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From experience, three capabilities matter most: end-to-end visibility across the value chain, investment in people and leadership capability, and translating operational insight into business strategy. Successful organizations, they argue, are those where operations does not just execute strategy but helps shape it.
Dubnyak presents the three capabilities, drawing on an article about the evolving supply chain skill set that they reference in the post. The post closes by asking the field which capabilities are most critical today.
Reflecting on a period of stepping back, Dubnyak states four beliefs: high-performing teams are built on trust, clarity and accountability; real transformation takes both discipline and empathy; sustainable results come from a relentless focus on safety, quality and people; and strong leaders challenge the status quo while staying grounded in execution.
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The systems point draws on a named source: Eliyahu Goldratt's The Goal, cited as a continuing reference and a reminder that real impact comes from improving the system rather than optimizing individual parts.
Dubnyak presents the Goldratt reference as their takeaway from the book, applied to operations leadership. The post looks ahead to the next phase of their career rather than ending with a call to action.
Answers a question planners actually ask: if a company already runs forecasting software such as SAP IBP, what does AI add?
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A useful starting point for seeing how Natalia Dubnyak frames the technology question before a vendor weighs in.
Natalia Dubnyak opens with a genuine puzzle, not a product claim: after years of working with planning platforms, where does AI fit when sophisticated forecasting already exists?
The post cites research into Kraft Heinz, PepsiCo and Coca-Cola Europacific Partners, and argues that AI is making planning platforms smarter, not replacing them.
It lists five specific planner tasks: detecting demand shifts earlier, predicting supply risks, optimizing inventory, prioritizing exceptions and evaluating scenarios faster.
The close is clear: the future is AI helping planners decide better and faster, not replacing them.
An open question asks readers to weigh measurable benefits against exploration.
For brands planning a campaign
This is the closest match to an AI-enabled demand and supply planning brief.
A collaboration could expand the five listed tasks into a planner-facing walkthrough, test the Kraft Heinz, PepsiCo and Coca-Cola Europacific Partners examples against public disclosures, and define measurable benefits at each stage.
The unanswered question at the end offers a ready-made hook for a follow-up piece or co-hosted discussion.
Shows how Natalia Dubnyak defines the leadership skills that matter now—a quick orientation to their view of the field before the more personal posts.
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Supply chain and operations have shifted from cost centres focused on efficiency to strategic drivers of growth, resilience and competitive advantage.
Natalia Dubnyak references an article on the evolving skill set, then identifies three capabilities that matter most in their experience: end-to-end visibility across the value chain, investment in people and leadership capability, and translating operational insight into business strategy.
The argument centres on a clear point of view: successful organizations let operations help shape strategy, not just execute it.
The post closes by asking which capabilities matter most to the field.
For brands planning a campaign
A strong anchor for a leadership capability campaign.
Each of the three capabilities could become an assessment question, a maturity ladder or a short sponsored diagnostic.
The post ends with a question to practitioners, making a follow-up piece or event that collects and publishes their answers a natural next step.
Shows how Natalia Dubnyak treats inclusion as an operational question rather than a side topic, with a first-person account that explains why their leadership writing goes beyond metrics.
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Natalia Dubnyak writes about spending too much time adapting to a male-dominated environment—speaking differently, acting tougher and blending in—and says their impact grew when they stopped.
The post argues that professional knowledge and experience matter, but so does perspective: operations and supply chain need different angles on the same facts and different ways to connect the dots.
Inclusion is framed as an operational advantage, not a nice-to-have.
It ends with three directives for people starting out or stepping into a bigger role: do not shrink, do not copy, do not wait to belong.
The author's hashtags include #WomenInLeadership and #DiversityOfThought.
For brands planning a campaign
Best suited to a people-first operations, leadership development or inclusion campaign aimed at operations managers, not only HR.
The first-person register calls for treating the post as lived experience, not repackaging it as a statistic.
A collaboration could pair their account with published evidence on diverse teams and decision quality while preserving the personal voice.
Shows how Natalia Dubnyak connects people practices to hard operational results—a test of their credibility with both frontline managers and executives.
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The post lists the metrics Natalia Dubnyak has managed—safety, quality, service, cost, inventory, productivity and OEE—and acknowledges their importance, then argues that sustainable results rarely start with a metric.
The chain is clear: strong frontline leadership builds trust, trust drives engagement, engagement drives accountability, and accountability drives performance.
The post reports that service recovered, quality improved and safety performance reached new levels when leaders invested time in listening, coaching and supporting their teams.
It closes with a crisp contrast: metrics tell us what happened; leadership determines what happens next.
For brands planning a campaign
A strong fit for a people-first operations or frontline leadership campaign, especially when the buyer is a plant or operations executive sceptical of soft topics.
A collaboration could turn the trust-to-performance chain into a short self-assessment or pair it with plant-level case evidence.
The named metrics offer a clear structure for a joint piece on which leading indicators move first when frontline leadership improves.
Shows what Natalia Dubnyak values in operations leadership and which book shaped their systems thinking, without a sales pitch.
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Useful for assessing their voice and long-term commitments.
A reflective post on stepping back to reconsider what drives impact in operations and leadership.
It sets out four beliefs: high-performing teams are built on trust, clarity and accountability; real transformation takes both discipline and empathy; sustainable results come from a relentless focus on safety, quality and people; and strong leaders challenge the status quo while staying grounded in execution.
Natalia Dubnyak names The Goal by Eliyahu Goldratt as a perspective that continues to resonate, drawing the lesson that real impact comes from improving the system rather than optimizing individual parts.
The post ends with a forward-looking note on the next phase of their leadership journey, not a call to action.
For brands planning a campaign
Useful for a vendor selling end-to-end transformation or systems thinking.
The four beliefs map neatly onto a diagnostic or workshop, while the reference to The Goal offers an entry point for a theory-of-constraints or connected-planning conversation.
The post also shows the tone Natalia Dubnyak uses for leadership content, which should guide any ghostwritten collaboration.
Shows what Natalia Dubnyak believes separates durable operations from one-off wins, in a short read that is easy to compare with their longer posts.
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The post compares operations to long-running television series: success depends not on a single great episode but on strong characters, clear direction and disciplined execution season after season.
Manufacturing operations work the same way.
One strong quarter does not define performance; what matters is how consistently a team executes, develops people and improves processes over time.
The post closes on the conditions for sustainable results: clarity, stability and leaders who invest in their teams for the long run.
For brands planning a campaign
A natural fit for a continuous improvement, reliability or operational excellence campaign. The series analogy could support a co-created series, with each instalment returning to one condition—clarity, stability or long-term team investment—and showing what changes in practice.
Dubnyak examines how AI fits into demand and supply planning for companies that already use forecasting platforms such as SAP IBP.
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Drawing on examples from Kraft Heinz, PepsiCo, and Coca-Cola Europacific Partners, they describe AI as an enhancement to planners, not a replacement.
It can help teams detect demand shifts earlier, predict supply risks, optimize inventory, prioritize exceptions, and evaluate scenarios faster.
The central claim: AI makes existing planning systems smarter while planners still make the decisions.
Research-led synthesis with named company examples and a closing audience question.
For brands planning a campaign
A supply chain technology company can build a collaboration around practical AI augmentation for CPG operations leaders: show how AI improves planner decisions in existing workflows, take a planner-first approach, and invite readers to share measurable benefits or lessons from early exploration.
Drawing on experience managing KPIs across safety, quality, service, cost, inventory, productivity, and OEE, Dubnyak argues that sustainable results rarely start with a metric.
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They trace a path from strong frontline leadership to trust, engagement, accountability, and performance. Service, quality, and safety improve when leaders invest time in listening, coaching, and supporting teams.
Experience-based argument with a causal chain.
For brands planning a campaign
This theme suits campaigns on frontline leadership, operational excellence, or manufacturing culture. A brand can ask Dubnyak to connect people-first practices to specific operational outcomes, focusing on leadership behaviors rather than abstract claims about culture.
Dubnyak describes supply chain and operations as shifting from cost centers focused on efficiency to strategic drivers of growth, resilience, and competitive advantage.
Read more
They highlight three capabilities: end-to-end visibility across the value chain, investment in people and leadership capability, and the ability to translate operational insight into business strategy. They ask which capabilities others consider most critical.
Trend framing, a three-part capability framework, and an audience question.
For brands planning a campaign
A collaboration could focus on the changing supply chain leadership skill set, cross-functional leadership, or operations shaping strategy rather than simply executing it. The post's capability list gives the brand concrete discussion points.
Dubnyak advises their younger self not to adjust their leadership style to fit a male-dominated environment.
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They spent too much time trying to speak differently, act tougher, and blend in. They argue that professional knowledge and experience matter, but so does perspective, and call diversity of thought an operational advantage: it brings different ways to see facts, connect dots, and find human solutions.
Personal reflection with direct advice and audience prompts.
For brands planning a campaign
Brands assessing inclusive or authentic leadership content can explore this theme. A campaign could connect diverse perspectives to operational problem-solving, avoiding generic diversity messaging and focusing on how different viewpoints change decisions.
Using the metaphor of a long-running series, Dubnyak argues that manufacturing operations do not succeed on one great episode or one strong quarter.
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Sustainable results come from clarity, stability, disciplined execution, developing people, and leaders who invest in teams for the long run.
An analogy between episodic series and manufacturing performance.
For brands planning a campaign
This theme suits campaigns on continuous improvement, reliability, or long-term operational excellence. A brand can use the consistency angle to discuss disciplined execution and team development rather than short-term performance spikes.
Dubnyak reflects on a year of stepping back and what they believe more strongly now: high-performing teams are built on trust, clarity, and accountability; real transformation requires discipline and empathy; sustainable results come from a relentless focus on safety, quality, and people; and strong leaders challenge the status quo while staying grounded in execution.
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They cite Eliyahu Goldratt's The Goal as a reminder that impact comes from improving the system, not optimizing individual parts.
Reflective list of leadership beliefs with a book reference.
For brands planning a campaign
This theme suits transformation, operations leadership, or systems-thinking campaigns. The reference to The Goal can support a systems-improvement angle.
One post frames operations and supply chain leaders as central to competitive advantage amid talent shortages, digital transformation, and sustainability expectations, then asks readers to share one challenge their team is facing this quarter.
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It prompts discussion rather than developing an argument.
Brief trend framing followed by an open question.
A format observation with less standalone subject depth than the other posts.
For brands planning a campaign
This format can support community engagement or LinkedIn conversation campaigns. Pair it with a substantive topic to give the conversation a clear point of view.
The posts do not document a sponsorship, employment, client, affiliate, or ownership relationship with any brand or business.
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Natalia Dubnyak mentions SAP IBP as a planning system they say they have worked with, and Kraft Heinz, PepsiCo, and Coca-Cola Europacific Partners as examples from research into AI in demand and supply planning.
They appear as context and comparison in an argument about how AI supports planners, not as commercial relationships.
Ask Dubnyak to confirm whether any such relationships exist.
Natalia Dubnyak's posts cover two main areas: how manufacturing and supply chain organizations improve, and how operations leaders earn a place in shaping strategy.
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For technology, start with their AI planning post and its question about whether readers have measured benefits.
For leadership, their posts on frontline leadership and consistency carry the argument.
The writing is short, list-driven and usually ends with a question to readers, making it a starting point for discussion rather than a long-form reference.
For adjacent reading, Imran Choudhery goes deeper into planning mechanics, Jennifer White examines why transformation programs stall, and Nicholas Parton documents execution tooling and cross-functional alignment inside a manufacturing program.
Choudhery writes for the same supply chain planning audience and uses the same teaching habit of breaking a fuzzy area into clear, checklisted parts: demand planning versus supply planning, and what min-max levels should account for in utility operations.
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Dubnyak frames planning as a leadership and strategy question; Choudhery sticks to the mechanics planners work with daily.
Choudhery's material is almost entirely operational detail—lead times, safety stock, reorder points and stockout risk. Dubnyak's planning post steps back to ask what AI adds to systems a company already runs, while their other posts focus mostly on leadership rather than planning technique.
White also writes for operations and supply chain leaders about how improvement efforts succeed or fail, using a framing-list style: naming the warning signs, then the conditions for progress.
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Both tie results to leadership behavior rather than tools alone.
White's posts diagnose why transformation programs keep restarting and address CIOs, COOs and VPs from the position of an outside adviser. Dubnyak writes from inside an operating role, returning to people-first leadership and consistency rather than program failure.
Parton covers the same ground of manufacturing and operations improvement and reaches a similar conclusion about technology: it creates visibility, but people and leadership turn that into results.
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Both write about cross-functional alignment as a practical requirement.
Parton's posts document a specific program—a SAP-fed production tracker across fabrication—and a digital transformation role in aerospace. They read as project accounts, while Dubnyak's posts focus on leadership principles, broad function trends and one research-based look at AI in planning.
Natalia Dubnyak’s LinkedIn posts address manufacturing, operations and supply chain leadership, including AI in demand and supply planning.
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In a post on AI and planning, they frame AI as a way to improve existing planning systems: helping teams spot demand shifts and supply risks, manage inventory and assess scenarios.
Their content suits a campaign on AI-enabled planning.
Ask for audience data to confirm whether their followers work in CPG, supply chain or planning.
The seven posts do not say that Natalia Dubnyak offers a partnership program, sponsorship offer, or paid-collaboration format.
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They cover manufacturing, operations, and supply chain leadership: reflections on team performance, a post on AI in demand and supply planning that names Kraft Heinz, PepsiCo, and Coca-Cola Europacific Partners as researched examples, a cited external article, and a question prompt to peers.
None carries a sponsored-content label, a disclosed commercial partner, an affiliate link, or a call to collaborate.
Buyers have no published format, deliverables, or rates to plan against, and no official partnership inquiry route is listed.
The posts can still serve as a sample of subject focus and argument style for a campaign about AI-enabled planning; buyers would need to ask directly about collaboration terms.
A campaign aimed at a supply chain technology company's CPG operations leaders: the company is assessing Natalia Dubnyak for a content collaboration on AI-enabled demand and supply planning.
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Fit decision: a reasonable pilot, not a proven high-confidence match.
The strongest post examines where AI fits when a company already runs planning software such as SAP IBP.
It names Kraft Heinz, PepsiCo and Coca-Cola Europacific Partners as examples researched, and lists five planner tasks — detecting demand shifts earlier, predicting supply risks, optimizing inventory, prioritizing exceptions and evaluating scenarios faster — arguing that AI helps planners make better decisions rather than replacing them.
That aligns directly with a CPG operations campaign and offers a natural planner-first angle.
The limits are material: only one of the seven posts covers AI and planning; the rest focus on leadership, people and consistency.
The posts are commentary, not original research or documented CPG deployments, so the named companies should not be presented as case studies without details the brand can verify.
Before contracting, ask for audience composition data, additional AI and planning examples, and commercial terms, including sponsorship format, rate and performance history from comparable posts.
When to choose a specialist
A consumer goods or enterprise vendor wants a paid, conversion-tracked campaign with affiliate links, gated downloadable templates and a short-form video series on factory-floor safety and OEE performance.
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Fit decision: poor fit based on the posts here.
None of the seven posts shows a sponsorship disclosure, affiliate link, rate card or call to collaborate, leaving no published format, deliverable or measured conversion path to plan against.
The content is text commentary; there is no video, audio or transcript, and one post mentions safety and OEE only in a list of metrics, not as a developed subject.
The posts do not say whether the audience includes plant-level buyers, and no campaign performance data is published.
Before considering this collaboration, ask for audience composition and campaign performance data, and confirm whether the creator can produce the proposed video, downloads and commerce features.
This would be a new commercial format and production capability, not a documented fit.
Do not present unverified commerce, download or inquiry features as functioning.
A worked campaign example
For a campaign aimed at CPG operations leaders on AI planning, run a three-part planner-first pilot.
Start with the question in the AI and demand planning post: if a company already has sophisticated forecasting software, where does AI fit?
Part one is a short text post that states the planner-first position and walks through two or three of the five planner tasks with a concrete before-and-after workflow for an operations team.
Part two is a co-hosted live session or Q&A asking practitioners whether they have seen measurable business benefits or are still exploring; that unanswered question at the end of the original post is the hook.
Part three is a follow-up post that publishes what practitioners said and turns it into a practical checklist for CPG operations leaders, co-authored with the brand.
The brand provides verified product capabilities, one or two planner workflow examples it can stand behind, and the disclosure line.
Natalia Dubnyak brings the framing, practitioner voice and questions to peers.
Since the post names Kraft Heinz, PepsiCo and Coca-Cola Europacific Partners only as examples researched, avoid treating them as case studies or verify any details before publication.
Do not present video, downloads or tracked commerce as available unless the brand and creator agree and build them.
Before contracting, ask for audience composition data, any documented past collaboration terms, and results from comparable AI or planning posts.
A two-post pilot plus one live session, with a small budget and clear disclosure, is a proportionate first test.
If audience data is unavailable, treat the fit as topic-level only and keep spend limited.
Choose Imran Choudhery over Natalia Dubnyak when the collaboration needs to distinguish demand planning from supply planning and spell out the operational consequences.
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A post on the two functions lists what each owns: forecast material requirements, consumption trends, forecast accuracy and demand risk on one side; inventory balance, replenishment and safety stock, lead time and capacity constraints, working capital and material availability on the other.
It then identifies the failure pattern when roles are combined: more stockouts, more expediting, lower forecast accuracy, excess and obsolete inventory, reduced service levels and higher operating costs.
A second post argues that min-max levels should be reviewed against seasonal peaks, storm demand, supplier lead times and storage constraints rather than set and forgotten.
For a technology vendor selling planning or inventory software, this gives a useful structure for explaining where a tool adds value and where a process decision comes first.
The min-max post draws on utility operations, with ready-made reliability framing around outages, field crews and critical spare parts for energy and utility buyers.
Choudhery’s posts do not cover AI in planning or frame their examples around CPG or consumer goods.
The focus is utility materials management, so a CPG operations audience would need the examples translated; a vendor would be introducing a new sector angle, not building on an existing one.
Natalia Dubnyak takes a planner-first position on AI and names CPG companies they say they researched, topics Choudhery does not address.
Both profiles offer text posts only, with no published sponsorship format, audience-composition information or delivery history.
Choose Choudhery for demand-versus-supply planning education in an asset-heavy or utility setting, and Dubnyak for a campaign on AI in CPG planning.
Choose Jennifer White over Natalia Dubnyak when the campaign needs to explain why transformation programs stall and how to make change stick, rather than promote planning technology.
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One post dissects the recurring cycle in supply chain, manufacturing and business operations—process optimization, inventory accuracy, S&OP stabilization, ERP and WMS migrations, continuous improvement programs and PMO resets—and attributes it to checkbox execution, seeking results without changing behaviours or decision rights, and incentives that protect the status quo.
It closes with four questions a leader can ask before relaunching an initiative.
A second post frames transformation as running two modes at once: maintaining today’s service, safety, throughput and quality while building tomorrow’s digital and AI capability.
It lists warning signs of an unclear execution system: competing priorities, local optimization, stretched leaders as the bottleneck, and activity without measurable outcomes.
For a vendor whose product is bought alongside a process change, this is a candid, diagnostic voice suited to a campaign about adoption rather than features.
The posts reference The MJW Group, and the work is written from a consultancy position.
The posts do not address AI in demand and supply planning, and their examples focus on patterns and warning signs rather than named systems or workflows.
A CPG operations audience evaluating planning technology would get change-management framing but little planning-specific substance—the core of a planning-technology campaign.
Natalia Dubnyak offers a planner-first AI position and a planner task list; White explains the organizational reasons such projects fail.
They are complementary, not interchangeable: choosing White means shifting the campaign from technology and workflows to change readiness.
Both profiles offer text posts only, with no published sponsorship format, audience data or past delivery results.
Choose White for a campaign about adoption, accountability and sustained execution, and Dubnyak when the deliverable is about demand and supply planning itself.
Choose Nicholas Parton over Natalia Dubnyak when the collaboration needs a first-hand manufacturing-execution implementation story rather than a planner’s overview of AI.
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One post describes building and rolling out a SAP-fed production execution tracker—described as a “womb to tomb” tool—that connects planning, NC programming, manufacturing engineering, order release, material issuance, machining, outside processing, quality inspection and final inventory in one shared operational picture.
It reports improved cross-functional alignment, better work readiness and a larger executable machine backlog.
The account specifies which functions were joined, what the shared view showed and how the team’s feedback shaped the tool.
A second post describes joining a digital transformation team as a program subject matter expert while continuing on an existing program, and names the functions being aligned: supply chain, design engineering, manufacturing engineering, stress engineering and change management.
For a vendor selling execution, visibility or integration software, the named project, system and practitioner account of what the collaboration unlocked make this closer to an inspectable case study than commentary.
The setting is aerospace manufacturing at Spirit Defense, a Boeing company—not CPG planning—and the work concerns fabrication execution and program alignment rather than forecasting, demand signals or AI-assisted planning decisions.
A consumer-goods audience would have to bridge both a different industry and a different problem.
Natalia Dubnyak speaks directly to demand and supply planning and names CPG companies, so choosing Parton trades topical fit for a more concrete but less relevant implementation story.
Both profiles offer two text posts, with no published sponsorship format, audience-composition data or campaign performance history.
Do not treat the Parton posts as independently verified project outcomes.
Choose Parton for a manufacturing-execution or systems-integration campaign where buyers want a practitioner’s account of a rollout, and Dubnyak for an AI-enabled demand and supply planning brief aimed at CPG operations leaders.