Merchandise planning used to mean spreadsheets and a good gut for seasonality. In 2026 it means validating what an AI model already forecasted, and most job descriptions are still written for the version of the job that existed five years ago.
For most of retail history, one person owned the buy: decide what to stock, how much, and when to mark it down if it did not sell. That job started splitting years ago into buyer and planner, and in 2026 it is splitting again, this time around who validates the forecast an AI model just generated versus who used to build one by hand. Hiring managers who post a generic "Merchandise Planner" requisition without knowing which version of the job they actually need are often the ones still interviewing six weeks later with no offer out.
That gap between the job as written and the job as it actually works now is exactly why merchandising and planning searches are taking longer than most hiring managers expect. Excel fluency and a feel for seasonality used to be what separated a strong planner from an average one. Those skills are table stakes now. The skills that actually separate candidates today are newer, and most job descriptions have not caught up.
The talent pool for this practice area has been shrinking relative to demand for years, not months. McKinsey's Global Supply Chain Leader Survey found that only 8 percent of companies report having enough in-house talent to support their supply chain digitization goals, a number that has barely moved since 2020 despite heavy investment in workforce development. Demand for supply chain professionals outpaces supply by roughly 6 to 1 overall, and in specialized forecasting roles that gap widens to closer to 9 to 1, according to reporting in Supply & Demand Chain Executive. Add in that an estimated 25 to 33 percent of the current workforce is at or near retirement age, and the pipeline problem compounds rather than levels off.
The cost of getting this hire wrong is not hypothetical. IHL Group's research puts the global cost of inventory distortion, meaning combined out-of-stocks and overstocks, at roughly 1.73 trillion dollars annually, with supply chain disruption as the single largest contributor. IHL also attributes 248 billion dollars of that figure to personnel issues alone, a number the firm compares to Costco's total annual revenue. A planner who cannot build or validate an accurate forecast is not a minor gap, it shows up directly on the P&L.
Before writing the job description, it helps to know which layer of the merchandising function you are actually hiring for. The titles get used loosely across the industry, but the responsibilities underneath them are genuinely different.
| Role | What They Own | Core Skills |
|---|---|---|
| Buyer | Vendor selection, assortment curation, negotiated cost and terms | Vendor negotiation, trend and market research, margin management |
| Merchandise Planner | Sales and inventory forecasting, open-to-buy budgets, category-level financial plans | Demand forecasting, financial modeling, AI forecast validation |
| Allocation / Replenishment Analyst | Getting the right inventory to the right store or channel at the right time | Store-level data analysis, allocation systems, omnichannel inventory logic |
| Director of Merchandising & Planning | Category strategy and P&L accountability across the merchandising function | Cross-functional leadership, vendor strategy, forecasting oversight |
A few of these responsibilities brush up against practice areas we place for separately. Category-level assortment strategy at the portfolio level sits closer to Category Management, and the warehouse-side execution of allocation sits closer to Supply Chain & Operations. The four roles above are the ones that live squarely inside a Merchandising & Buying hire.
AI-powered forecasting tools now generate the baseline statistical forecast that planners used to build by hand in a spreadsheet. That has not eliminated the role, it has shifted what the role is. Planners now spend more of their time validating model outputs, managing the exceptions an algorithm misses, and injecting market context that historical data alone will not surface, like a competitor promotion or a product going viral on social. Reporting on the demand planning function has described this as the job changing faster than the talent pool has caught up.
McKinsey research has found that AI-based forecasting can cut errors by 20 to 50 percent compared with manual methods, which is a real gain. But that gain only shows up when a person with judgment is checking the model's work. A planner who accepts every AI-generated number without question is not actually doing the job anymore, they are just relaying it.
If your job description for a merchandise planner reads like it was written in 2019, mostly Excel and ERP bullet points, you are screening for a version of the job that no longer exists. Rewrite it around forecast validation and exception management, and you will attract a different, stronger pool of candidates.
Years of tenure and platform familiarity are the easiest things to check on a resume, and they are not what separates a strong planner from a weak one anymore. Ask candidates to walk through a forecast they built or corrected, and listen for whether they explain it as a narrative tied to real drivers, a promotion, a launch, a weather pattern, a competitor move, rather than as a spreadsheet number they cannot defend.
It also helps to test whether a candidate is comfortable overriding a model's output when they have good reason to, and whether they can articulate that reason clearly to a buyer or a finance partner who will push back. As omnichannel assortments get more complex, understanding how a return processed in one channel affects inventory position in another is now a baseline expectation, not a specialty. Cross-functional communication matters more than it used to as well, since a planner increasingly translates between buying, supply chain, and finance rather than sitting in a single lane.
If your current planner is still building forecasts from scratch in a spreadsheet with no AI tooling behind them, that is often a signal the role or the tech stack has fallen behind, not that the person is underperforming. And if one person is doing buying, planning, and allocation for a growing omnichannel assortment, that is usually the clearest sign the function needs to split into distinct roles rather than stretch further across one person's calendar.
We place across all four tiers of the Merchandising & Buying practice, from buyers and planners to allocation analysts and the directors who own the category strategy above them.
A buyer selects the assortment, negotiates cost and terms, and manages vendor relationships, deciding what goes into the store or catalog. A merchandise planner builds the sales and inventory forecast behind that assortment, sets the open-to-buy budget, and decides how much of each category to buy and when to reorder or mark it down. In smaller organizations one person often does both early on, but as an assortment grows across channels, the roles typically split so buying decisions and financial planning are not competing for the same person's attention.
According to McKinsey's Global Supply Chain Leader Survey, only 8 percent of companies report having enough in-house talent to support their supply chain digitization goals, a figure that has barely moved since 2020. Demand for supply chain professionals outpaces supply by roughly 6 to 1 overall, and that gap widens to roughly 9 to 1 in specialized forecasting roles, with an estimated 25 to 33 percent of the current workforce at or near retirement age.
AI-powered forecasting tools now generate the baseline statistical forecast that planners used to build by hand. That has not eliminated the role, it has shifted it. Planners spend more time validating model outputs, managing the exceptions an algorithm misses, and injecting market context, like a competitor promotion or a viral trend, that historical data alone will not surface. Hiring managers who screen only for spreadsheet and ERP proficiency risk missing candidates who can actually do this newer, judgment-heavy version of the job.
McKinsey research has found that AI-based forecasting can cut errors by 20 to 50 percent, but only when someone is checking it. Screen for candidates who can explain a forecast as a narrative tied to real drivers, such as a promotion, a launch, or a seasonal shift, rather than just a spreadsheet number, and who are comfortable overriding a model's output when they have good reason to. Cross-functional communication also matters more than it used to, since a planner increasingly has to translate between buying, supply chain, and finance rather than sitting in one lane.