# Tareak Dahini

Tareak Dahini advises manufacturing and consumer packaged goods companies on improving operations, supply chains, and automation. Dahini's posts focus on solving practical business problems before investing in new technology.

- Profile: https://b2binfluencer.marketing/creators/tareak-dahini/
- LinkedIn: https://www.linkedin.com/in/tareakdahini
- LinkedIn followers: 6,658
- Topics: artificial-intelligence, supply-chain
- Based on 14 dated posts, latest 2026-09-01

## Summary

Tareak Dahini is most useful for explaining operational problems before brands invest in new technology.

- Best for: Manufacturing and CPG software campaigns about demand planning, inventory, or supply-chain visibility.
- Not for: Drone delivery or technology novelty campaigns that present tools as the fix.
- Check first: Confirm audience job titles, platform analytics, and whether they offer sponsorship formats or rates.

### Takeaways

- Supply-chain risk can sit several layers past direct suppliers.
- AI forecasting can fail on unreconciled data and shallow customer understanding.
- A failed drone pilot showed customers in apartments needed local pickup points.

## Main ideas

- **Supply-chain risk sits several layers past your direct supplier**: Dahini argues that most companies know their direct supplier and almost nothing about the tiers above it, where cost increases and disruptions often begin. Visibility declines at each tier: buyers know their direct supplier, see roughly half of that supplier’s suppliers, about 17% of the actual source and roughly 3% of raw-material origin. Their argument: each layer a buyer cannot control creates an unpriced margin leak that surfaces as a price increase.
- **Automation starts with bottlenecks, not tools**: Dahini argues that automation fails when companies shop for software before identifying the process that consumes the most time. Their method is narrow: list every process taking more than two hours a week, rank them by impact and complexity, then fully automate one through the systems already in place before moving on. Integration with existing tools matters more than sophisticated features, and teams need to understand why a process is changing before they will use it.
- **AI forecasting fails on bad data and shallow customer understanding**: Dahini treats forecasting tools as amplifiers, not answers. When historical data is corrupted or a company does not understand its buyers, predictive modeling scales the error rather than correcting it. They put data cleanup and supplier relationships ahead of analytics, arguing that a planner with clean inputs and good tools beats an algorithm fed the wrong map.
- **Delivery design should follow customer routine, not technology novelty**: Dahini argues that convenience and predictability beat technically impressive logistics. The design question is what a customer’s day actually looks like: when they are home, how a package reaches them and how many attempts it takes. A failed delivery costs far more than a successful one because it can damage the next purchase decision. They treat customer behavior as a design constraint, not a variable to optimize around later.
- **Manual work is a strategic cost, not a badge of effort**: Dahini argues that owners who do repetitive operational work themselves are not being diligent; they are paying twice, in wasted hours and in strategic decisions left undone. They frame task switching as a measurable drag on operators and automation as a way to regain both time and accuracy. The argument is aimed at founders and small-company operators, not technology teams.

## Work to read

- [The $23 trillion supply-chain blind spot](https://www.linkedin.com/posts/tareakdahini_the-23-trillion-supply-chain-blind-spot-activity-7364986020720701441-3_46)
- [Ford’s AI predicted demand that doesn’t exist](https://www.linkedin.com/posts/tareakdahini_fords-ai-is-so-smart-it-predicted-demand-activity-7371517049895854080-y0nH)
- [The AI automation revolution](https://www.linkedin.com/posts/tareakdahini_the-ai-automation-revolution-68-of-activity-7363899355885367296-pyfF)
- [Most business owners think they’re productive heroes by juggling 63 tasks at once](https://www.linkedin.com/posts/tareakdahini_%F0%9D%97%A0%F0%9D%97%BC%F0%9D%98%80%F0%9D%98%81-%F0%9D%97%AF%F0%9D%98%82%F0%9D%98%80%F0%9D%97%B6%F0%9D%97%BB%F0%9D%97%B2%F0%9D%98%80%F0%9D%98%80-%F0%9D%97%BC%F0%9D%98%84%F0%9D%97%BB%F0%9D%97%B2%F0%9D%97%BF%F0%9D%98%80-%F0%9D%98%81-activity-7368247511037145090-QLZ7)
- [Taylor Swift just got engaged and Taylor Swift’s logistics team is already planning the most complex last-mile delivery in entertainment history](https://www.linkedin.com/posts/tareakdahini_%F0%9D%97%A7%F0%9D%97%AE%F0%9D%98%86%F0%9D%97%B9%F0%9D%97%BC%F0%9D%97%BF-%F0%9D%97%A6%F0%9D%98%84%F0%9D%97%B6%F0%9D%97%B3%F0%9D%98%81-%F0%9D%97%B7%F0%9D%98%82%F0%9D%98%80%F0%9D%97%B1-%F0%9D%97%B4%F0%9D%97%BC%F0%9D%98%81-%F0%9D%97%B2%F0%9D%97%BB%F0%9D%97%B4%F0%9D%97%AE%F0%9D%97%B4%F0%9D%97%B2%F0%9D%97%B1-activity-7366435588654166016-xTnR)

## Brand relationships

- **Ford** (mention)

## Partnerships

Tareak Dahini’s five posts are first-person operations case studies drawn from advisory work, not a partnership or sponsorship program. None includes a sponsorship disclosure, published format list or rate card. The only commercial call to action appears at the end of one post: they say they help manufacturing and CPG companies scale from startup to $50M+ through operational overhaul and strategic automation, and invite readers to connect. That reads as an offer of advisory services, not a defined sponsored-content product a brand can book. For a B2B manufacturing software company, there is no published sponsorship inventory to plan against: no disclosed formats, audience or placement specifications, pricing or production requirements. Ask what partnership arrangements, if any, are available, and frame the outreach around their stated focus on fixing operational and supply-chain problems before adding technology.

## Alternatives

- Walid N. Nasserdeen: Choose Nasserdeen when the campaign specifically targets growing food and beverage brands that need demand planning or S&OP. His posts define the difference between demand planning, sales forecasting, inventory planning, and supply planning, and describe a fractional planning service aimed at smaller CPG companies. That makes the audience and service angle more explicit than Dahini’s broader manufacturing and supply-chain examples.
- Imran Choudhery, M.S., CSCP: Choose Choudhery when a campaign needs a utility-operations perspective on the distinction between demand planning and supply planning, or on maintaining inventory for reliability and outage readiness. His posts explain each function’s responsibilities and show how min-max settings balance stock availability against tied-up capital.
- Wiley Strahan: Choose Strahan when the campaign is about practical AI adoption in supply-chain operations or clearer frontline shift handoffs. His posts describe a voice-first handoff concept and a free AI supply-chain resource hub, giving a software company a possible discussion about concrete operational use cases rather than forecasting and inventory diagnosis.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/