Why Forecasting, Demand Planning, and Inventory Systems Fail at the SKU Level
A Story Unfolding Across Timelines.
Current Time.
And Why PlanToIt Exists
Inventory decisions in food are made at the SKU level. That is where orders are placed, shelf life is tracked, and substitutions are handled. That is also where problems are emerging.
I’m starting here on purpose. Not with definitions. Not with theory. This is where the work actually happens.
Where Food Planning Actually Happens
In practice, supply chain and operations teams in the food industry plan one item at a time. That is how teams place orders, track shelf life, and manage substitutions. This is not a modeling preference. It is how the work gets done.
Every purchase order is item-specific and not by group or as complementary products. Expiration dates are managed per product. When something runs out or goes bad, it is always one SKU at a time. Anyone who has worked in food knows this. It is the starting point, not a conclusion.
Furthermore, if suppliers change the packaging sizes, rebrand the same product, or procurement changes the quantities ordered, all such changes to the same product are reflected as different SKUs in the systems for that item. To be clear, these are not substitute products. These parallel names for the same item affect forecasting, and without grouping them and evaluating them as a group, they are treated as different items.
Yet most planning systems do not operate at that level, as providing this capability, for example, requires high investments in development and workforce.
What Teams Actually Deal with Day-to-Day
The gap does not exist because planners do not care about accuracy. It exists because SKU-level planning is hard to support in practice.
SKU-level data is scattered across systems. Some of it lives in ERP. Some of it sits in POS. Some comes from suppliers. Some is still handled in spreadsheets. Systems are rarely fully connected, and even when they are, the data does not line up cleanly.
To make sense of it, planners collect and consolidate all data in one place, analyze and process it, reconcile differences, and manually adjust numbers. This work takes a lot of time and people, and demands a high level of concentration to process it into practical action items to follow. It does not scale easily, and it is expensive to support with software.
Most teams know this solution is not stable or sustainable, since manually following up on all this is impossible, and mistakes are easy to make without software controls. They just hope nothing breaks before the next order cycle.
Promotions distort demand. New products break history without defining connections to similar or old items that are now purchased in a different form. Similar items exist under different SKUs for operational reasons or to make it easier for finance to control. Shelf life is short, so mistakes surface quickly and leave little room to react.
Why Volatility Makes Everything Worse
All of this chaos exists before volatility enters the picture. When conditions are stable, teams can often compensate and solve these problems. Manual work, spreadsheets, and experience help close the gaps. The system may not be perfect, but it holds together.
Then something changes. Whether it’s a small or big change, when it requires an immediate response, the same workarounds, bypasses, or patch solutions stop working, and teams pray for a magical solution to save them.
When supply is disrupted, ingredients become unavailable, price volatility spikes, or demand suddenly shifts, past data stops reflecting what customers are doing right now. So what is the value of forecasts that are built on rolled-up numbers? Processing past data without the ability to respond immediately is useless.
When people switch between products, looking at totals hides the real problem and makes teams think nothing is wrong. That way, teams look in the wrong direction and do not see clearly which products are actually failing. For example, looking at the total chicken sales or yogurt sales makes teams think that if the totals are stable, there are no shortages, issues, or urgent action needed. However, if you drill down into the group, one item is already empty, another item is selling much faster, and orders are wrong at the item level.
When trends or cultural shifts drive demand, sales data reflects the change only after customers have already moved on. The most recent examples happened during the pandemic, where consumers purchased more food than usual due to lockdowns. Due to these changes, retailers updated their procurement and stock plans; however, by then, lockdowns had been lifted, and people started eating outside, reducing their consumption compared to during the lockdowns.
This is why many planning platforms appear reliable during calm periods and fail exactly when teams need them most. They were built to keep forecasts stable, not to deal with a fast-changing reality.
At this point, someone usually says, “But the forecast still looks fine,” and the conversation starts going in circles. One person is talking about what is happening in stores, kitchens, or warehouses, while another is talking about what the system shows at an aggregated level. They end up repeating the same points, saying, “We are already seeing issues,” and “The forecast says everything is OK.” This is a never-ending loop that prevents teams from addressing the core problem.
What Most Systems Optimize for Instead
Most planning software is built to make life easier for the system, not for the people running inventory.
Instead of dealing with every item and every week, the system groups products together and looks far ahead. That way, there are fewer numbers to manage, the charts look clean, and reports are easier to present. It also costs less to build and maintain the software.
That choice looks good in presentations, but it doesn’t hold up in real stores. Shelves silently scream when they are empty. These forecasts are fine when teams talk about long-term capacity or big-picture plans. They fall apart when teams have to decide what to order, what is about to run out, or what will expire.
When the system gets an item wrong, there is no second chance. Food expires, shelves are empty, and sales are gone. The damage happens immediately. This is not because the calculation is wrong. The system was designed this way, and the risk ends up landing on the people running operations.
Orientation is the Real Choice
Orientation in demand planning is not a concept. It is a decision teams make. Orientation is a choice, not a technical limitation.
They decide what level to plan at and how far ahead to look. They decide whether to plan item by item or group products together. They decide whether to focus on the next few weeks or look far into the future and average things out. This is the definition of what “lazy choice” is, something that most software makes.
Once those decisions are made, everything else follows from them. This is a pure example of the waterfall effect, which is about inherited constraints. If you start with the wrong orientation, no amount of AI can save the output.
When planning starts at the wrong hierarchy, the Waterfall Effect unfolds step by step. Teams plan at the category level, so the system ignores SKU-specific details. The math averages demand, and the warehouse ends up sending the wrong item to stores.
Most systems fail because they make the wrong choices here. It is not because the math is bad. It is because the system does not align with how food operations actually run.
Planning at the SKU level, over short time frames, is not a nice-to-have. It is the basic requirement for managing food inventory without constant surprises. Proving that what others call a special extra softwear development is actually just the bare minimum needed to do the job right.
The External Narrative Baseline
While planning systems deal with data and rules, something else is happening simultaneously.
People change how they buy food before the system notices, since behavioral change happens weeks before a POS system records it. They react to prices, switch between products, some items stay important no matter what, and others stop selling very quickly. Often, this starts in one place and spreads elsewhere only later.
Understanding which items are “non-negotiable” in a culture vs. which are easily replaced is essential data to integrate into the system. Moreover, when social media is dominant in our lives and has a quick impact on many items we add to our cart, we identify more factors that reveal behaviour that could be relevant to a specific geographical location or country, but not to other locations.
By the time the system shows that change, teams on the ground have already felt it. The external narrative baseline is about paying attention to that gap. It looks at how people choose food, how they substitute items, and how demand begins to form before it shows up in reports.
This is not about tracking social media or measuring sentiment. It is about understanding real buying behavior and what drives it. Distinguishing between ‘what people say online’, which is the noise, from ‘how they actually spend their money at the shelf’, which is the reality, becomes critical for businesses.
Teams build this understanding over time by watching how food moves, how customers behave, and how habits change. Our reality requires building a proprietary knowledge baseline that acts as a human sensor. This kind of insight allows operations and supply chain teams to see what is changing before it shows up in the numbers.
Why this Context Matters
When products sell the same way week after week, looking at past data usually works fine. The problems start when the past stops being useful or when change moves faster than the planning cycle. This happens with new products, new branding, packaging resizing, supply disruptions, or sudden shifts in customer behavior.
In those moments, even fast-selling products stop behaving the way the system expects. The issue is no longer history versus the future. It is speed versus reaction time. Those are the moments teams remember later, because that is when things break, and the cost shows up fast. These moments define a company’s profit or loss for the entire year. Most systems assume the future is a mirror of the past. While software developers may argue that history repeats itself, the reality that operations and supply chain teams see is completely different.
Why PlanToIt Exists
PlanToIt exists because current systems do not solve this problem.
The external narrative baseline does not replace short-term forecasting. It adds context when data alone is not enough and strengthens it. Together, they help teams make decisions where food planning actually happens, at the speed the business needs.
PlanToIt was built to forecast at the SKU level, over short time periods, under real constraints that food teams deal with every day. This capability is not an extension of the platform. It is the PlanToIt foundation and how the system works from the start.
Ending Note
Forecasting failures in food are often framed as technical limitations. In practice, they are structural outcomes of choices made around level, horizon, and cost.
When forecasting in food fails, it is not necessarily because the technology is broken. It fails because of the choices made about what level to plan at, how far ahead to plan, and how much complexity the system can handle.
PlanToIt deals with those choices directly. It works where decisions are actually made and where mistakes show up right away. Most teams do not need better explanations or nicer reports. They need systems that operate where the real work happens.
That is the difference between forecasts that look good on paper and planning that actually works in day-to-day operations.
🧠 Q&A
Why do forecasting and demand planning systems fail in food operations even when data quality is high?
Because most systems plan at aggregated levels and long time horizons, while food decisions are made at the SKU level under short execution windows. This structural mismatch causes failures regardless of data quality.
Why is SKU-level planning critical in food inventory management?
Because ordering, shelf life, substitutions, and stockouts are all handled per item. When planning does not operate at the SKU level, teams are forced to translate abstract outputs into real decisions too late.
Why do reports often look fine while stores and warehouses are already under pressure?
Because reports smooth variation and confirm reality after it has already changed. Operational stress appears before aggregated data reflects it.
What is the Waterfall Effect in demand planning?
The Waterfall Effect describes how planning abstractions collapse as decisions move from corporate forecasts to regional plans and finally to daily SKU reality, where volatility is highest and time to act is shortest.
Why does timing matter more than forecast precision in food operations?
Because once inventory decisions are locked, higher accuracy no longer changes outcomes. Impact declines faster than accuracy improves.
What is the PlanToIt Window?
The PlanToIt Window is the short period where decision impact is still high, and data is sufficiently accurate to change outcomes at the SKU level.
Why do teams default to “same as last week” decisions?
Because alert overload and aggregation force humans to simplify decisions under pressure. This is a system design outcome, not a human failure.
Why are forecasting failures in food structural rather than accidental?
Because systems are designed for reporting stability and long-range visibility, not for short-horizon execution where food inventory decisions actually occur.
Why does PlanToIt exist?
PlanToIt exists to operate at the level and speed where food decisions are made, at the SKU level, under short horizons, before outcomes become irreversible.
What is the External Narrative Baseline in food planning systems?
The External Narrative Baseline is the layer that captures how demand, substitution behavior, and consumption patterns shift in the real world before they appear in transactional data. It explains why teams feel pressure on the ground long before systems confirm it, and why planning systems that rely only on internal data repeatedly react too late.
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I weave together episodes from my life with the richness of Israeli and American culture through music, food, the arts, architecture, wellness, entertainment, education, science, technology, entrepreneurship, cybersecurity, supply chain, and more, including the story of the AI era. I write on weekends and evenings and share each episode as it unfolds, almost like a live performance.
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This artilce is perfectly timed. Absolutely critical insight.