Tareak Dahini

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.

A food manufacturer’s costs rose 34%.

• Its direct supplier raised prices 8%, which looked like ordinary market movement.
• The cause lay a tier above: a Malaysian trading company sourcing from three Southeast Asian farms.
• One had weather damage, another switched to organic, and a third was price-fixing with competitors.
• Mapping the raw-material layer, dual sourcing, direct relationships and 48-hour change alerts brought costs to 19% below the original level within 90 days, according to the post.

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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.

A $12M food manufacturer spent 8 hours a week on production planning, while its owner worked 70-hour weeks.

• Three earlier AI tools had failed because none connected to the company’s systems.
• Automating production scheduling through the existing ERP cut weekly planning to 45 minutes, raised production efficiency 23%, brought delivery reliability to 99.2% and returned 15 hours a week to the team, the post states.

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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.

Dahini points to Ford’s 100+ days of unsold EVs as a contrast to its data-driven production system.

• A $50M manufacturer that copied the approach for $800K ended up with dead stock.
• After scrapping the forecasting and rebuilding demand planning around buyer input, it cleared $3.2M in dead inventory in 10 weeks.
• Dahini also reports that 7 of the 9 companies they advised spent $28M combined on write-offs and dead stock; the other 2 succeeded by fixing data quality first.

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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.

A $40M e-commerce client’s drone pilot failed: drones landed in neighbors’ pools, packages floated off and one delivery ended up on a roof.

• The post reports that 89% of the client’s customers lived in apartments, 67% of failed deliveries happened between 9 and 5 while customers were at work, and cart abandonment spiked 23% after one bad delivery.
• Partnering with 47 local coffee shops as pickup points raised customer satisfaction 34% and cut costs 60%.

Dahini opens with a joke about Taylor Swift’s engagement and rings before turning to last-mile logistics. The tone shifts with the topic, but the priority—designing around customer behavior—remains the same.

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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.

A Labor Day barbecue sets the scene: Dahini describes answering client emails, reviewing P&L reports and negotiating a supply contract by text with 23 apps open, then cites roughly 25% efficiency lost per task switch.

• A manufacturing owner manually tracked 847 SKUs across four warehouses and worked weekends on inventory spreadsheets.
• After automation, the post reports, stockouts fell 67%, carrying costs dropped $180K a year and the owner regained 15 hours a week.

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It helps readers judge whether their work is relevant to supplier visibility and resilience.

Dahini illustrates how a manufacturer’s costs can rise because problems sit several supplier layers beyond its direct vendor.

• They trace a food company’s price increase to issues at the farms supplying its supplier, then describe mapping deeper supply layers and setting alerts.
• The post makes supply-chain visibility concrete as a way to find hidden cost and risk.

For brands planning a campaign

A supply-chain visibility or risk platform could be evaluated against the specific job of tracing critical materials and surfacing changes early—not just displaying direct suppliers.

Dahini argues that forecasting tools cannot compensate for poor data or weak customer understanding.

• They contrast a manufacturer using corrupted historical data with another that first improved data quality and supplier relationships.
• The examples frame AI as an amplifier of planning practices, not a substitute for them.

For brands planning a campaign

A manufacturing software company could use this piece to open a discussion about data readiness and planner workflows before pitching AI forecasting features.

Using a food manufacturer’s production-planning bottleneck, Dahini describes ranking repetitive processes by impact and complexity, then automating one process through the existing ERP. They give before-and-after figures for planning time and production efficiency, and emphasize integration, measurement, and gradual rollout.

For brands planning a campaign

An automation or ERP partner could discuss a focused implementation around a measurable bottleneck, rather than promising broad transformation. The post also offers a concrete case for a manufacturing-focused talk.

It is useful for judging whether that conversational style suits a brand’s audience.

Dahini uses a personal story about juggling work during a holiday to introduce the cost of manual tasks and task switching.

• They then describe automating inventory tracking for a manufacturer managing 847 SKUs across four warehouses, with reported reductions in stockouts and carrying costs.
• The personal opening makes an operational problem relatable before turning to business results.

For brands planning a campaign

A workflow or inventory software brand could build a practical story around time returned to operators, supported by a specific process and outcome rather than a generic productivity claim.

It helps a newcomer assess whether their tone and last-mile expertise fit a campaign.

The post uses a deliberately playful celebrity hook to tell a last-mile delivery story: a drone pilot failed to account for customers living in apartments, while local pickup points are presented as a better fit. Its central lesson is to design around customer routines, not technology’s novelty.

For brands planning a campaign

A logistics or delivery brand could use the contrast between a flashy pilot and a customer-centered alternative to discuss practical service design. The humorous framing may suit an informal LinkedIn campaign better than a formal technical announcement.

In one food-manufacturer example, they trace a cost increase through a trading company to three farms, then describe mapping suppliers to raw-material sources, adding alternatives, and setting alerts. The post says costs fell 19% below their original level within 90 days.

Makes an abstract visibility problem tangible with a four-layer model, a client example, and a proposed set of actions.

Ask Dahini for independent verification of the client and results.

For brands planning a campaign

A supply-chain visibility or procurement brand could fit a story about finding upstream risks and responding sooner—not simply adding another dashboard.

Examples: Source 1

They argue that demand models cannot compensate for poor data or limited understanding of customers.

• In a manufacturing example, they say the team replaced AI forecasting with buyer-informed planning and cleared $3.2 million in dead inventory in 10 weeks.
• In another, they describe starting automation with production scheduling and integrating it with the company’s existing ERP.

Uses contrasting cases and practical steps to put data quality, customer knowledge, and system fit ahead of technological novelty.

Ask Dahini for independent substantiation of the company examples and outcomes.

For brands planning a campaign

A data, planning, or manufacturing-software brand could suit a campaign showing how its product supports better operational decisions, with a clear account of the data and workflows required.

Examples: Source 1 · Source 2

In a food-manufacturing example, they describe ranking processes by time, impact, and complexity before automating production scheduling.

• They say weekly planning fell from eight hours to 45 minutes.
• A separate post uses inventory work across four warehouses to show how automation can reduce manual effort and give owners time back.

Turns a broad technology topic into a sequence: find the pain point, work with existing systems, measure results, and scale gradually.

Ask Dahini for independent verification of the reported client outcomes and figures.

For brands planning a campaign

Operations software or automation tools are a stronger fit when they can be tied to a specific repetitive task and a credible measure of time, error, or service improvement.

Examples: Source 1 · Source 2

Dahini says most of the client’s customers lived in apartments, where drones could not solve the access problem.

• They describe a switch to local coffee-shop pickup points and report improved satisfaction and lower costs.
• The lesson: convenience and predictability can matter more than technical novelty.

Contrasts a vivid failed pilot with a simpler alternative, then draws out the customer-experience principle.

Ask Dahini for independent verification of the client story and results.

For brands planning a campaign

A logistics or retail-technology campaign could use this perspective to show how a service fits customers’ routines and constraints, not just how the technology works.

Examples: Source 1

Dahini describes unsold EV inventory and argues that forecasting failed because the company did not understand its customers.

• They also cite a manufacturer that copied Ford's predictive modeling and ended up with dead stock.
• Dahini analyzes Ford as an example, not as a representative or collaborator.
• Ask whether they have any commercial relationship with Ford.

View example

Like Dahini, Choudhery explains the trade-off between too little and too much inventory in operational rather than theoretical terms, for people who run the work.

Choudhery writes from inside a utility's planning function, using general planning principles, checklists, and industry hashtags.

• Dahini writes consultant-style, anonymized client stories with dollar amounts and before-and-after metrics across manufacturing and consumer packaged goods.
• For utility-specific planning practice, read Choudhery; for diagnoses of scale-up manufacturing operations, read Dahini.

Read original · Original 2

Nasserdeen's definitions piece distinguishes demand planning, forecast, inventory planning, supply planning, production planning, and S&OP—the same kind of planning clarity Dahini brings when they distinguish a forecasting problem from a data problem. Both write in the first person about their own client and career experience.

Nasserdeen's posts center on the fractional demand-planning market, including what a Director of Demand Planning earns compared with a fractional engagement priced per month.

• Dahini's posts focus on automation sequencing and multi-tier supply-chain visibility.
• Nasserdeen is the better follow for planning role design and fractional staffing; Dahini, for operational bottleneck diagnosis.

Read original · Original 2

Like Dahini, they argue that the useful starting point is a concrete operational problem—missed shift handovers, captured context, adoption without vendor spin—rather than forecasting buzzwords or digital twins. Both favor specific operational detail over abstract AI commentary.

Strahan is building tools: a voice-first shift-handoff concept called Shift Sync and a free AI supply-chain resource hub with curated tools, comparisons, and a readiness assessment.

• Dahini diagnoses client operations and argues for sequencing: data and supplier relationships come before analytics.
• Strahan is the follow for frontline shift communication and self-serve AI evaluation resources; Dahini, for consultant-led operational overhaul.

Read original · Original 2

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.

Dahini's posts focus on planning terminology and fractional advisory for CPG, rather than the wider operational overhaul, supply-chain visibility, or automation cases they discuss. Ask what sponsorship or speaking formats are available.

Examples: Source 1 · Source 2

His posts explain each function’s responsibilities and show how min-max settings balance stock availability against tied-up capital.

The examples are specific to utility materials and operational reliability, so they’re less directly suited to a manufacturing-software campaign aimed at CPG brands. Ask whether Choudhery has a commercial offer or campaign format.

Examples: Source 1 · Source 2

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.

The examples focus on frontline communication and broad AI discovery, not the demand-planning, inventory, or supplier-visibility problems central to Dahini’s strongest reviewed cases. Ask whether the handoff concept is deployed and what sponsorship or speaking formats are available.

Examples: Source 1 · Source 2