Last updated: September 2026
Supply chain planning (SCP) is the process of forecasting, and collaborating around, future demand, then lining up production, supply, distribution, and inventory decisions so a business has the right product in the right place at the least cost. It's a subset of supply chain management, not a parallel discipline: SCP decides what should happen, and the rest of SCM (procurement, manufacturing, warehousing, transportation) carries it out. Most organisations run SCP through three top-level disciplines: demand planning, supply planning (which itself covers production planning, procurement planning, distribution planning, and inventory optimisation), and production scheduling. Horizon organises its platform around these disciplines, tied together in a monthly or continuous S&OP/IBP cycle.
This guide covers the whole topic: definitions and frameworks, how to actually run SCP well, how to evaluate software, and how the major platforms (SAP IBP, Kinaxis, o9, Blue Yonder, Anaplan, Logility, RELEX, ToolsGroup, and Horizon) stack up against each other. It's organised into eight sections.
Supply chain planning is a subset of supply chain management, not a parallel or comparable discipline. Supply chain management covers the full scope of getting product to customers:
Planning happens first and feeds everything after it. It's the decision layer inside supply chain management, not a separate function running alongside it.
Demand forecasting spans different time horizons, not just one.
Long-term demand forecasts guide big, slow-to-change decisions: where to build a warehouse, which suppliers to use, how much capacity to add over the next few years.
Most of the day-to-day work happens in the middle: a rolling 6-to-18-month forecast, updated every month, is what most demand planners actually work with. It's the number that inventory, production, and procurement plans get built around.
Short-term demand forecasts are more detailed and change often. They look week by week, or even day by day, at specific products in specific locations, and they drive near-term supply and inventory decisions.
After planning comes execution. On the factory floor, a Manufacturing Execution System (MES) decides which machines run, when, and on what. Planning decides what should happen. MES makes it happen.
Supply chain planning software doesn't run in isolation. It sits alongside, and often exchanges data with, several other systems that come up constantly in this space:
FP&A and executive strategy sit at the company level, S&OP and IBP form the cross-functional process connecting finance to operations, and disciplines like demand planning and production planning sit inside that, feeding down into ERP, WMS, and MES at the execution layer.
SCP breaks into three top-level disciplines:
All of it gets reconciled through Sales & Operations Planning / Integrated Business Planning, the cross-functional process that ties the disciplines above together with financial and strategic goals on a recurring cycle.
Two of these disciplines get formalised into named, company-wide processes.
S&OP (Sales & Operations Planning) is a monthly, cross-functional cycle that reconciles demand and supply at the volume/category level, and it isn't itself a finance-driven process.
IBP (Integrated Business Planning) extends that same cycle to include financial planning and scenario modeling, tying the operational plan directly to revenue and margin targets.
In practice, the two terms get used almost interchangeably. The most common distinction: S&OP is operations and sales reconciling a plan; IBP is what you call it once finance is genuinely in the room shaping the plan, not just reviewing the output afterward. Both get described as supply chain processes, but they're really company-wide, cross-functional processes that supply chain often drives rather than owns outright.
A handful of other terms get grouped alongside S&OP and IBP in a lot of writing on this topic, but they're not the same kind of thing. They're specific techniques that live inside individual disciplines above, not company-wide processes in their own right:
This didn't used to be the priority. A decade ago, most planning teams optimised mainly for cost. After several years of tariff shocks, port congestion, and demand swings that history-based forecasts didn't catch, resilience has become the harder constraint to solve for. Forecast accuracy, safety stock strategy, and scenario planning have moved from a quarterly footnote to a board-level metric in a lot of organisations.
Explore the foundations in depth:
Advanced Planning and Scheduling (APS) is one of the oldest and broadest terms in this space, and it's still in active use, not a legacy label. The problem is that it doesn't mean one fixed thing: some vendors use it narrowly, for detailed production scheduling only; others use it broadly, for the full planning stack (demand, supply, master scheduling, and detailed scheduling combined). "Supply chain planning software" and "supply chain planning platform" get used alongside APS today, often for the exact same products, rather than as a replacement for it.
"Software" and "platform" aren't quite synonyms either. Historically, software meant something narrow and purpose-built, a platform implied something you configure or build on top of. Horizon calls itself a Decision Execution Platform, it's configurable, so you're not building your own system from scratch, but you still get a complete, out-of-the-box system rather than a single-purpose tool. In practice, most vendors compared later in this guide now blend both: a configurable platform that ships with real out-of-the-box capability.
Demand planning software forecasts future demand. Supply chain forecasting software is often that same underlying engine, just applied at a network level instead of a single node. Two more categories round out the list: distribution planning software, which allocates inventory across a network, and production and manufacturing optimisation software, which sequences the factory floor. Replenishment planning software is the narrowest of the group, it just automates reorder decisions once the inventory policy is already set.
Knowing which category actually solves your problem heads off a common buying mistake: teams buy a broad "supply chain planning platform" when the real gap was one narrow process, usually replenishment or detailed scheduling.
Understand each software category:
A supply chain planning process can look complete on paper and still not move the numbers. The gap is often a handful of specific, fixable habits: how forecast bias gets measured and corrected, how safety stock actually gets set, how S&OP meetings are run (or aren't), and where AI genuinely helps versus where it just adds another dashboard nobody checks.
Tactical guides:
Software selections often fail not because the shortlist lacked a feature, but because the team shopped a checklist instead of the actual decision the software needs to support (a forecast, a safety stock policy, a production sequence), and picked whichever vendor's demo looked cleanest. The guides in this section go through the evaluation criteria, the red flags, and the questions each stakeholder in the room should actually be asking.
For an outside perspective on how these decisions actually get made (and un-made) in practice, supply chain analyst Lora Cecere's conversation on our podcast, Outside-In Planning, the Orchestrator Role, and "Don't AI Stupid", is worth a listen before you build a shortlist.
The lists below are organised first by category (demand planning, IBP, production scheduling, and so on), then by industry where the buying criteria genuinely diverge: regulatory constraints in pharma, shelf life in food & beverage, bill-of-materials complexity in industrial manufacturing.
By category:
By industry:
These guides compare the platforms head-to-head on what actually matters: data model rigidity, time-to-value, total cost of ownership, and fit with your planning cadence.
Platform vs. platform:
Three-way comparisons:
Build vs. buy:
An "alternative" search is usually driven by a specific pain point: an implementation that ran long, licensing costs that scaled worse than expected, or a data model that doesn't fit how the business actually plans.
AI-driven forecasting is moving out of pilot programs and into actual production use. IBP is turning from a finance-adjacent process into the primary decision cadence at a lot of enterprises. And planning teams are increasingly judged on resilience metrics, time-to-detect and time-to-recover, not forecast accuracy alone.
None of this happened overnight. The category has consolidated through decades of vendor acquisitions. JDA absorbing multiple planning vendors before being rebranded as Blue Yonder is one well-known example.
Supply chain planning is deciding, ahead of time, how much of a product a business will need, when, and where. Then it's lining up demand forecasts, inventory, production capacity, and suppliers so that need gets met without over-stocking or under-stocking.
Three top-level types: demand planning, supply planning, and production scheduling. Supply planning is itself an umbrella covering production planning, procurement planning, distribution planning, and inventory optimisation. All of it gets coordinated through S&OP or IBP.
Supply chain planning is a subset of supply chain management, not a separate, parallel discipline. SCM covers the full scope of getting product to customers, including planning, procurement, manufacturing, warehousing, and transportation. SCP is specifically the planning piece: deciding what should happen before execution starts.
S&OP balances demand and supply at a volume/category level on a monthly cycle, and isn't itself a finance-driven process. IBP extends that same cycle to include financial planning and strategic scenario modeling, connecting operational plans directly to revenue and margin targets. In practice the two terms are often used interchangeably. See IBP vs. S&OP for the full breakdown.
Common categories include demand planning, supply planning, inventory optimisation, production scheduling, and integrated S&OP/IBP platforms. Many of these are also described under the broader, and often ambiguous, term APS (Advanced Planning and Scheduling), which different vendors use to mean different scopes. Leading vendors include SAP IBP, Kinaxis, o9, Blue Yonder, Anaplan, Logility, RELEX, and ToolsGroup, alongside newer AI-native platforms like Horizon. See our Best Supply Chain Planning Software (2026) guide for a full comparison.
Most commonly with MAPE (Mean Absolute Percentage Error) or WMAPE (weighted MAPE), sometimes paired with Forecast Value Add (FVA) to measure whether a planning process is actually improving on a naive forecast. See Forecast Accuracy and the FVA formula.
Excel works fine for simple, low-SKU, low-volatility businesses. It breaks down once a company needs multi-echelon visibility, collaborative forecasting across teams, or scenario planning at scale. See Excel vs. Supply Chain Planning Software for where that line typically falls.
Mostly in three places: demand sensing (near-real-time signals instead of only historical data), automated ensemble forecasting (blending multiple statistical and ML models per SKU), and scenario simulation (running "what-if" disruption scenarios in minutes instead of days). See How Does AI Improve Demand Planning?
