Critical Capabilities for Supply Chain Planning Solutions: Process Industries

18 March 2026 - ID G00840106 - 44 min read

By Julia von Massow, Eva Dawkins, and 4 more

The SCP market is mature but evolving, with enterprises needing different capabilities based on their planning maturity. Process manufacturers can use this research on 16 vendors to find the right SCP solution for their specific needs and maturity levels.

Overview

Key Findings

The critical capabilities that most distinguish supply chain planning (SCP) vendors for process industries from each other in 2026 are AI-driven planning and decision automation, scenario management and financial impact modeling, user experience (UX), and unified data integration. These are important for enterprises at various levels of planning maturity.
There is more parity among SCP vendors in terms of demand planning functionality than supply planning functionality.
The user experience and workflow orchestration critical capability has the lowest average score among the vendors included in this research, closely followed by AI-driven planning and decision automation.
The decision-centric planning use case shows the least variation in vendor scores, while the collaborative demand planning and constraint-based multienterprise supply planning use cases have the highest scores in general.

Recommendations

Supply chain technology leaders responsible for developing selection criteria and evaluating SCP solutions to support digital transformation must:

Anchor evaluations on decision-centric use cases aligned to planning maturity. Clearly define current (“as is”) and future (“to be”) planning maturity to ensure solutions are assessed based on the decisions they must support, not just functional completeness.
Elevate user experience and workflow orchestration as core selection criteria by prioritizing solutions that enable exception-driven planning, improve explainability, and reduce planner effort across demand and supply planning processes.
Validate scenario management and decision automation in operational contexts. Focus on how effectively solutions support repeatable trade-off analysis, cross-functional alignment, and execution-ready outcomes rather than one-off analytical scenarios.
Use customizable capability weightings to align vendor evaluation outcomes with business priorities. The interactive version of this research enables leaders to adjust critical capability importance, ensuring vendor scores reflect enterprise-specific constraints, readiness, and transformation objectives.

What You Need to Know

The 2026 Gartner Autonomous Supply Chain Planning Survey revealed that 86% of organizations with higher levels of decision automation are using SCP solutions in at least one planning capability.¹ SCP platforms can help drive decision speed, quality and automation, but supply chain technology leaders are facing a growing and complex market.

This Critical Capabilities research enables supply chain technology leaders to interactively compare 16 SCP solutions as part of their evaluation, so they can determine the solutions that align with their organization’s needs. Gartner clients indicate that strength in relevant functional capabilities, such as demand planning, supply planning and scenario planning, is the most important factor boosted by AI-enabled features when choosing an SCP technology solution.

SCP platform vendors continue to excel at distinct planning use cases and cater to different industry needs, and it is not always readily apparent if an SCP solution aligns with an organization’s requirements. A vendor’s product marketing — especially messaging about AI-enablement, multienterprise collaboration, integration and event-driven/real-time disruption management — can create further confusion about what its product can actually deliver. As a result, many supply chain technology leaders end up investing in an SCP solution that either lacks the capabilities their teams need or, alternatively, overbuy capabilities that they don’t need or are not ready to use.

Evaluations are based on what Gartner considers to be the eight key capabilities across four SCP use cases that relate to the various levels of Gartner’s supply chain maturity model (see Supply Chain Score for Planning). The companion Magic Quadrant for Supply Chain Planning Solutions assesses the vendors of the SCP solutions evaluated in this research. Together, the Critical Capabilities and Magic Quadrant research will help supply chain technology leaders make choices that balance solutions’ capabilities and vendors’ overall performance.

Gartner recommends that you use the interactive version of this Critical Capabilities research to customize the weightings of the critical capabilities per use case to reflect your needs. The ratings used in this Critical Capabilities research are the same as those used for the product or service criterion in the Magic Quadrant for Supply Chain Planning Solutions. They reflect the current, product-specific Ability to Execute of the vendors featured. The ratings are a mix of request for proposal (RFP) requirement scores and user feedback, combined with analyst assessment (see the Evidence section for more details).

Analysis

Critical Capabilities Use-Case Graphics

Figure 1: Vendor Product Scores for the Demand Planning Use Case

Sixteen providers are ranked on a 1 to 5 scale according to how well their offerings meet the needs of demand planning in Supply Chain Planning Solutions: Process Industries as of March 2026. This allows comparison across a set of critical differentiators.

Figure 2: Vendor Product Scores for the Supply Planning Use Case

Sixteen providers are ranked on a 1 to 5 scale according to how well their offerings meet the needs of supply planning in Supply Chain Planning Solutions: Process Industries as of March 2026. This allows comparison across a set of critical differentiators.

Figure 3: Vendor Product Scores for the End-to-End Multienterprise Planning Use Case

Sixteen providers are ranked on a 1 to 5 scale according to how well their offerings meet the needs of end-to-end multienterprise planning in Supply Chain Planning Solutions: Process Industries as of March 2026. This allows comparison across a set of critical differentiators.

Figure 4: Vendor Product Scores for the Decision-Centric Planning Use Case

Sixteen providers are ranked on a 1 to 5 scale according to how well their offerings meet the needs of decision-centric planning in Supply Chain Planning Solutions: Process Industries as of March 2026. This allows comparison across a set of critical differentiators.

Vendors

AIMMS

AIMMS delivers supply chain planning (SCP) capabilities through two offerings: Its low-/no-code platform (Optimization Tooling) and its packaged SC Navigator offering. Optimization Tooling enables users to build and deploy custom SC planning apps across all layers of demand and supply planning, including sales and operations planning (S&OP) and detailed scheduling. SC Navigator is an off-the-shelf SaaS application hosted on Microsoft Azure that covers a broad range of strategic and tactical planning use cases. The AIMMS Optimization Tooling supports more bespoke use cases and runs on AIMMS’ or customers’ clouds. Functionally, AIMMS focuses on delivering an intuitive UX, among other initiatives, through the continued development of an AI assistant (SENSAI).

AIMMS’ Optimization Tooling allows organizations to define their own statistical methods, algorithms, or mixed‑model logic as well as user interface. Its multiechelon capabilities, such as stochastic safety‑stock approaches and time‑phased target setting, address variability. The platform’s hierarchical flexibility, attribute‑based segmentation, and open APIs give planners room to tailor specific planning characteristics even when out‑of‑the‑box functional coverage is comparatively light. Scenario planning capabilities allow users to run multiple parallel cases while simultaneously adjusting structural assumptions and comparing financial or operational outcomes.

AIMMS achieved low scores across all four evaluated use cases, partly due to its strategic emphasis on platform flexibility and extensive customization options over a broader set of standard, out-of-the box features. AIMMS’ strategy of focusing on an off-the-shelf solution within a limited area of planning (network design, tactical planning and transport optimization) while covering the other planning areas with a generic build approach pushes its scores down. Not surprisingly, AIMMS’ highest score among critical capabilities is in its flexible and extensible architecture, complemented by a solid rating in supply planning.

Anaplan

Anaplan’s SCP capabilities come from its cloud-native Connected Planning platform, deployed on Anaplan’s own cloud (leveraging its data centers in the U.S. and Europe) via Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure. Anaplan supports requirements ranging from strategic planning to production planning via its prebuilt applications, which provide flexible and configurable solutions. Recent product evolution includes Anaplan Forecaster’s ongoing rollout and rearchitected forecasting engine, alongside the planned integration of Syrup’s advanced neural network-based forecasting and store-level execution capabilities, acquired in September 2025.

With Anaplan’s enterprisewide capability coverage, it is recognized for its capabilities in tactical planning, such as sales and operations planning (S&OP), where volume-based plans are translated into financial outcomes, enabling thorough evaluation of potential scenarios. Anaplan leverages its proprietary Polaris engine to meet the growing demand for faster decision making and enhancing overall performance. Scenario management is a core platform capability, enabling comparison of alternatives, though scenario creation is primarily planner‑initiated rather than event‑driven or agent‑generated. AI capabilities focus on explainable analytics through natural language interaction, with basic autonomous execution.

Anaplan achieved upper midrange to high scores for all use cases. Its highest use-case score is for demand planning, reflecting its extensive statistical depth and multihierarchy forecasting capabilities. Its upper midrange scores in supply planning, end-to-end multienterprise planning, and decision‑centric planning are consistent with the midrange expectations, reflecting a configuration‑powered planning model rather than one oriented toward deeply constraint‑based optimization. The vendor’s lowest score is in supply planning, highlighting the intense competition in this area and indicating Anaplan’s opportunities for enhancement, especially in its operational and detailed supply planning capabilities.

Aptean (Logility)

Aptean (Logility) delivers its supply chain planning capabilities through the Logility Decision Intelligence Platform, covering integrated business planning (IBP), S&OP, network design and optimization, demand and supply planning, and detailed scheduling. Following Aptean’s acquisition of Germanedge in 2025, Logility has begun integrating Germanedge’s production scheduling technology into its platform to deepen its existing scheduling and manufacturing planning capabilities. The Logility Platform can be deployed on any public or private cloud environment, usually as single-tenant, supporting a range of enterprise deployment and hosting arrangements.

The platform supports end‑to‑end supply chain planning with an emphasis on scenario‑driven planning and analytics across demand, supply, inventory and network design. Scenario management and decision automation remain largely planner‑led today, with capabilities shaped through configuration and structured setup rather than fully guided or automated execution. Logility is continuing its migration to the AppCentral platform, Aptean’s connected workspace. As a result, solutions like Intelligent Order Response and DemandAI+ are being refreshed to match the AppCentral user interface and standard. With this ongoing replatforming and prior additions like production scheduling, customers may observe varying depth and maturity across modules as the suite evolves toward a more unified experience.

Logility achieved midrange scores for all use cases. Its highest use-case score is in demand planning, supported by solid forecasting, demand sensing and scenario evaluation. In supply planning and end‑to‑end multienterprise planning, the vendor demonstrates reliable functional coverage but limited automated optimization and cross‑horizon synchronization. In the decision‑centric planning use case, Logility’s decision support remains predominantly planner‑driven and configuration‑dependent rather than continuously automated or simulation‑led.

Arkieva

Arkieva delivers its SCP capabilities through the Arkieva Enterprise platform, where it supports customers across process industries. Arkieva offers a broad range of capabilities, from network design through S&OP and sales and operations execution (S&OE), to detailed scheduling. Arkieva can deliver its platform through various hyperscaler cloud options and has a wide variety of deployment options with on-premises, hosted, and private clouds leveraging Azure, AWS, and GCP.

With a long history supporting manufacturers, especially in the chemicals vertical, Arkieva has developed strong supply planning capabilities and the ability to model different types of constraints and interdependencies in process industry verticals. Arkieva’s evolved scenario management capabilities allow its platform to autonomously run scenarios in the background and notify users when a better solution becomes available. Additionally, customizable thresholds allow users to control when they receive notifications, bringing the vision of automated planning closer to reality. However, the unified user experience is still evolving, as Arkieva is in the process of migrating its scheduling UI to the web-based interface it uses for the rest of its planning functions.

Arkieva’s highest use-case score is in supply planning, demonstrating its capabilities in constraint-based multienterprise supply planning, along with its specialized capabilities for planning in process manufacturing industries. Arkieva also achieved a high score in the end-to-end multienterprise planning and decision-centric planning use cases, reflecting its capabilities in scenario management, financial impact analysis, AI-driven planning, and decision automation. Arkieva’s lowest use-case score is in demand planning, primarily due to moderate ratings in collaborative demand planning, user experience, and workflow capabilities.

Blue Yonder

Blue Yonder delivers its SCP capabilities mainly through its Cognitive Planning solutions, which support deployment flexibility across public cloud, private cloud, on‑premises environments, and customer‑specific data centers. SaaS deployments are hosted on the Blue Yonder-managed Azure tenant across regions. The platform provides end‑to‑end planning coverage across demand planning, S&OP, inventory management, replenishment, production planning and scheduling, combining long‑standing modules with more recently introduced components to cover a wide set of SCP processes.

A key differentiator for Blue Yonder is the breadth and depth across core SCP domains, supported by data and modeling structures that enable granular representation of constraints and attributes within complex supply chain networks. The platform also incorporates GenAI‑enabled assistants embedded throughout the user interface. Blue Yonder’s expanding portfolio integrates mature modules with recently enhanced components. This results in strong functional breadth, even as certain experience‑layer capabilities, such as fully consistent user experience across all modules, automatic UI adaptability and more autonomous scenario orchestration, are still maturing.

Blue Yonder achieved high scores across all use cases. Its top use-case score is in end‑to‑end multienterprise planning, reflecting its unified data structures, wide functional footprint and maturing multienterprise planning capabilities with the continued integration of the multienterprise network acquired from One Network in 2024. Its score in supply planning is driven by its constraint‑based optimization and detailed scheduling capabilities, which was strengthened with the recent acquisition of flexis AG in 2024. Its score in decision‑centric planning was slightly lower despite its strong scenario comparison, execution visibility and wide range of analytics, as its decision/plan alignment shows more variability due to differences across newer and older applications. In demand planning, Blue Yonder’s score reflects demand sensing, uplift modeling and segmentation capabilities.

Coupa

Coupa’s SCP capabilities are embedded into its Supply Chain Design & Planning suite. It offers a range of capabilities including strategic network design, S&OP, and procurement. Because of its strength in supply network modeling and design, customers tend to use the solution as a single global solution for those use cases. The solution can be deployed to a private cloud or hosted environment via Google Cloud and Microsoft Azure. The solution is available on AWS as a SaaS offering.

Coupa supports product‑mix and time‑based decomposition, giving planners clear visibility into how product sets and periods are structured for analysis. Coupa’s solution is centered around parameter‑driven logic and user‑guided adjustments, enabling planners to define rules, manage exceptions, compare scenarios, and evaluate outcomes through dashboards. The solution can incorporate supplier constraints and purchasing considerations, and supports demand and supply projections and basic scenario evaluation. Support for complex, multistep manufacturing environments, detailed scheduling, and fully integrated constraint propagation remains limited.

Coupa achieved scores in the lower midrange for all of the use cases due to its more limited scope and depth in supply chain planning capabilities. Its highest use-case score is for end-to-end multienterprise planning, which is based on its solid performance on unified data integration, especially around managing and incorporating various types of external data sources. Its score for the decision-centric planning use case is supported by its ratings for scalable high-performance planning. Opportunities for improvement include demand and supply planning, as well as in AI-driven planning and decision automation, which are reflected in the vendor’s lower use-case scores.

Dassault Systèmes

Dassault Systèmes’ SCP capabilities are delivered mainly through its DELMIA Quintiq platform, powered by the 3DEXPERIENCE platform. Its planning capabilities range from network design and modeling through S&OP to detailed scheduling and execution-focused capabilities such as workforce and transportation planning. Dassault provides depth of engineering, design and modeling capabilities as part of a portfolio of software solutions that can be deployed on hosted, private and public cloud options. However, many customers still retain on-premises versions.

Dassault Systèmes demand‑side capabilities include statistical modeling and configurable aggregation rules, enabling planners to adjust demand assumptions across product and customer hierarchies. Supply planning emphasizes parameter‑based replenishment logic, exception‑driven oversight and scenario comparison tools that help planners explore alternatives across supply, capacity, and inventory dimensions. The platform supports alignment between demand forecasts and supply‑oriented considerations, offering optimization capabilities and visibility into how supply responses are shaped by operational constraints.

Dassault’s highest use-case score is in supply planning, where it reflects strong capabilities in constraint-based multienterprise supply planning combined with solid scores in AI-driven planning, scenario management, and financial modeling capabilities. The vendor’s scores in the demand planning, end‑to‑end planning, and decision‑centric planning use cases are in the lower mid to midrange reflecting inconsistent user experience across the suite of applications required to support the broad scope of supply chain planning.

Infor

Infor’s SCP solution offers a range of capabilities around demand and supply planning, production scheduling, and financial modeling. It’s natively deployed as SaaS on Amazon Web Services (AWS) and comes with a multitenant cloud architecture. Infor invests in its SCP solution as part of a broader ecosystem that covers ERP, product life cycle management, warehousing and transportation, and more.

Infor’s demand planning supports probabilistic ranges through standard deviation and upper/lower bounds that feed inventory simulations. Event engines and the trade promotion management (TPM) module enable uplift calculations for promotions, and its AI engines can derive impacts. The platform’s Gantt‑based scheduling provides clear warnings and visibility, while Infor’s GenAI Assistant helps retrieve data, surface status information, and use templated skills within the environment to provide accessible support for planners. Together, these elements give Infor a stable, well‑structured foundation across demand, supply, and scheduling for process‑industry environments.

Infor achieved lower-tier scores across all four use cases, especially due to its lower ratings for scenario management and financial modeling, AI-driven planning, and extensible solution architecture. The vendor’s highest use-case score is a three-way tie between demand planning, supply planning, and end‑to‑end multienterprise planning. Infor’s unified data integration performance improves its decision-centric planning, its lowest-scoring use case, and provides acceptable scores in collaborative demand and constrained-based supply planning. Automation in the Infor planning solution is also generally less mature.

John Galt Solutions

John Galt Solutions delivers its SCP capabilities through the Atlas Planning Platform, offering a range of functionality covering areas such as S&OP, inventory, demand and supply planning. The vendor continues to invest in AI, decision intelligence and sustainability-driven planning. The solution can be deployed as SaaS hosted on Microsoft Azure or as a private cloud on most hyperscale clouds using VMware.

The vendor’s strengths center on event-driven responsiveness and workflow-oriented planning, enabling organizations to focus planning efforts on material changes rather than full-cycle planning. It has evolved its capabilities to include additional simultaneous considerations for risk factors by incorporating cost, sustainability and risk into its MEIO calculation. These capabilities are complemented by a risk register, where user-defined tolerance scores guide decision making. Enabled by scenario planning and postgame analysis, this allows planners to review how assumptions performed.

John Galt Solutions’ use-case scores span from midrange to upper midrange. Its highest use-case score is in demand planning, reflecting its particularly strong performance in the area of collaborative demand planning. Its score in supply planning is based on limited capabilities in network design and detailed scheduling, where the vendor is dependent on partner solutions. Its score in the decision-centric planning use case reflects effective scenario creation and KPI-driven analysis, while more advanced simulation and automated decision execution remain emerging capabilities.

Kinaxis

Kinaxis delivers its SCP capabilities through its Maestro platform, deployable across multiple public clouds, the Kinaxis private cloud, and on‑premises. The platform spans end‑to‑end planning, covering S&OP, demand, supply, inventory, production planning and scheduling. Kinaxis continues to broaden its ecosystem, including the integration of Databricks as its strategic data management and AI partner, based on its depth in data engineering and machine‑learning workloads.

Across functional domains, Kinaxis keeps its focus on rapid, dependency‑aware recalculation and full‑level pegging to propagate the impact of change quickly and transparently. Scenario management remains a signature strength, offering integrated scenario creation, visual side‑by‑side comparisons and structured collaboration. AI‑assisted planning capabilities help planners focus attention on the most impactful changes through alerts, prioritization, and decision-making support. Probabilistic forecasting and simulation are lighter weight, focused on confidence range outputs or external Python generation rather than full native probabilistic or simulation engines. Demand planning depth is solid but lacks diversity in modeling techniques.

Kinaxis achieved high scores in all use cases. Its highest use-case score, in supply planning, is based on fast recalculation, pegging‑aware change propagation, and mature exception governance. Its score in decision‑centric planning reflects rapid scenario evaluation and strong cross‑functional decision alignment, though its simulation capabilities, through techniques such as Monte Carlo and discrete event simulation, are less comprehensive. The vendor’s score in end‑to‑end multienterprise planning is based on its integrated visibility and synchronized demand‑supply planning logic. Its performance in the demand planning use case reflects the vendor’s ability to cover hierarchical and aggregated planning needs while offering fewer advanced statistical or probabilistic methods.

o9 Solutions

o9 Solutions delivers its SCP capabilities through the o9 Digital Brain platform, which brings together demand planning, supply planning, cross‑enterprise scenario planning, and workflow management within a unified environment. The platform is powered by its Graph-Cube in‑memory engine and intends to support large‑scale, multidimensional planning models. The solution can be deployed in single‑tenant and multitenant configurations, including private and public cloud options.

The platform supports a wide range of forecasting techniques, segmentation, attribute‑based modeling and configurable aggregation structures, enabling planners to tailor demand logic to product complexity and customer behavior. Supply‑side capabilities include rule‑driven planning parameters, scenario evaluation, and alignment between demand, replenishment strategies, and operational constraints. Scenario generation and evaluation are solid, though typically initiated by planners, while autonomous scenario generation remains limited. In recent years, native scheduling capabilities have become available but remain limited in scope and adoption relative to other core planning areas of the o9 platform.

o9 achieved high scores for all use cases. Its highest use-case score is in demand planning, supported by unified modeling, cross‑functional decision support, and an extensible architecture that allows users and partners to extend planning logic through externally built analytic applications. This reflects strong performance across critical capabilities with particular strength in flexible and extensible architecture, user experience and workflow orchestration, and scalable high-performance planning. The vendor’s score in the supply planning use case reflects performance in situations where production scheduling or fully system‑driven scenario orchestration are primary requirements.

OMP

OMP offers a broad range of capabilities through its Unison Planning solution, ranging from network design, S&OP and demand planning to detailed scheduling. The solution is deployable on-premises or on several hyperscale clouds, with most customers using Microsoft Azure. OMP continues to focus on expanding its capabilities, including further enhancements to its newest AI orchestrator, UnisonIQ, and its included natural-language chatbot Unison Companion.

OMP’s strengths reflect its deep expertise in process manufacturing, where highly complex industrial environments have historically shaped its planning and scheduling capabilities. The solution covers all horizons and organizes planning around linked scenario flows. Planners can set up multiple promotion simulations, test cannibalization dynamics, and maintain scenarios in a clear tree structure that preserves lineage and enables comparison. The Unison Companion is embedded throughout the solution’s workflows. It acts as a front‑end explainability guide for navigation and scenario exploration. OMP’s agentic AI orientation signals the direction of its future support for more automated decision making.

OMP achieved high scores across all four use cases, particularly in the supply planning and end‑to‑end multienterprise planning use cases, supported by its high ratings in constraint-based supply planning and unified data integration. Supply planning is the vendor’s highest-scoring use case, reflecting its expertise in optimization and scheduling for process manufacturing companies. OMP’s lowest use-case score is in decision-centric planning, indicating that the vendor has room for improvement in flexible and extensible solution architecture.

Oracle

Oracle provides its SCP capabilities through its cloud‑native Oracle Fusion Cloud Supply Chain Planning suite, offering end-to-end capabilities across IBP, S&OP, demand planning, supply network planning and detailed scheduling. The applications can be deployed on Oracle public cloud (Oracle Cloud Infrastructure [OCI]) or Oracle private cloud. The platform reflects a consistent architectural direction centered on the Redwood user experience, with planning workflows and scenario creation and evaluation supported across the suite.

Across functional areas, the platform offers constraint‑based planning, multilevel order promising, and alignment with adjacent execution processes. Its breadth enables Oracle to address complex, enterprise‑scale supply chain models and support planning across functions. Data quality management and enrichment rely primarily on rule‑based approaches, with opportunities to further increase automation over time. Scenario creation is well established, although scenario impact assessment requires more planner‑led exploration. AI‑enabled capabilities, including explainability and decision assistance, are expanding across the suite, reinforcing Oracle’s focus on embedding intelligence directly into core planning workflows.

Oracle scored in the midrange for all use cases. It achieved its highest scores in the end‑to‑end multienterprise planning and demand planning use cases as the unified cloud suite delivers solid capabilities across demand and supply. However, simulation‑intensive planning is less prominent and advanced probabilistic techniques are less differentiated. In supply planning, Oracle provides solid constraint modeling, but automated scenario exploration and insight generation are limited. In the decision‑centric planning use case, Oracle supports scenario initiation and offers emerging intelligent assistance, but the vendor’s score in this use case reflects the lower level of currently available automation and workflow adaptability.

RELEX

RELEX embeds its SCP capabilities into its RELEX Retail & Supply Chain Planning platform. It supports functionality ranging from S&OP and demand planning to supply-focused capabilities, such as capacity planning and detailed scheduling, as well as promotion optimization capabilities. Its detailed scheduling capabilities come from RELEX’s late-2023 acquisition of Optimity, which is now fully integrated in the overall RELEX platform. RELEX offers a cloud-only solution deployable on a wide range of cloud providers, and its strategy continues to focus on AI improvements to its platform.

RELEX’s strengths lie in configuration‑led capabilities like attribute‑driven NPI logic, selective recalculation and clear scheduling feedback. In these areas, planners remain firmly in control while receiving support from the platform. The platform can also select NPI references using product attributes, giving planners a structured way to anchor new item behavior. Its ability to limit or expand recalculation scope allows users to adjust quickly without reprocessing full plans, and scheduling offers immediate visual feedback when planners adjust orders. Scenario grouping helps keep related scenarios organized, but a tree-structure overview of scenarios isn’t available. As a result, planners must manually maintain a good overview of their scenarios. While the user experience generally is modern and intuitive, RELEX’s workflow capabilities for collaborative planning are not as strong.

RELEX’s highest use-case score is in demand planning, complemented by its strong score in collaborative demand planning. It also earned a strong rating for unified data integration, driven by packaged, real‑time integration capabilities and the ease of incorporating ready‑made external signals that positively impact all use cases. RELEX has development opportunities for scenario management and financial modeling, which achieved lower scores due to limitations in scenario grouping and collaboration. The solution generally scores well in user experience, contributed by its modern look and feel along with recently added workflow features like basic action lists and tracking. All in all, the platform provides customers with a consistent and intuitive user experience across various planning capabilities.

SAP

SAP delivers its SCP capabilities primarily through SAP Integrated Business Planning (IBP), while also leveraging its broader application portfolio for additional areas, such as S/4 HANA (for ERP) for its detailed scheduling capabilities. SAP offers both private‑cloud and public‑cloud deployment models, with cloud infrastructure provisioned on SAP‑selected hyperscalers, including Alibaba Cloud, AWS, Google Cloud, and Microsoft Azure. The SAP IBP suite spans demand, S&OP, inventory and supply planning.

SAP is leveraging its extensive portfolio of applications to address the broad scope of supply chain planning, which led it to dedicate significant time and resources to the development of Planner Workspace. Planner Workspace integrates capabilities from multiple underlying applications, aiming to deliver a unified end-to-end user experience for planners. A notable recent advancement is the introduction of “harmonized planning areas,” which enable companies to view and manage both time-series (bucketed) and order-based planning within a single planning area. Planners can compare scenarios and collaborate seamlessly on the platform or through Microsoft Teams integration, while the AI-powered Joule assistant provides real-time support throughout the planning process. Additionally, SAP IBP provides an accessible environment to compare scenarios for reporting purposes through the common SAP visualization capabilities of Analytic Stories. However, scenario analysis and simulation typically remain planner‑driven, with more advanced automation, integrated risk evaluation, and autonomous decision execution still evolving.

SAP achieved lower midrange scores across all use cases. Its highest use-case score is in demand planning, where the correlation between external factors, like weather, and confidence‑based ranges provided is positive, but probabilistic depth is basic. In supply planning, at the S&OP (IBP) level, plans can be generated using predefined heuristics or an optimization algorithm, but areas like tank management, trim optimization, and shelf life planning remain less robust. However, some support for areas of tank management and shelf-life planning capabilities are provided at the operational level in the S/4HANA solution.

Sunstice

Sunstice (formerly known as FuturMaster) offers its capabilities via its Sunstice platform. It orchestrates demand and supply planning, S&OP and, as part of a newer edition, production scheduling through the acquisition of PlaniSense in February 2025. The company also provides capabilities for revenue growth management, including pricing, promotions, and portfolio decisions. Sunstice has a cloud-agnostic architecture and can be deployed as both single and multitenant as well as on-premises.

Sunstice’s strengths stem from its strong footprint in the food and beverage industry, supported by solid integration and API breadth. Its APIs are both data‑ and function‑oriented, covering internal and external needs that help customers connect to surrounding systems. While its limited customization options and reliance on predefined structures lower its extensibility scores, Sunstice compensates with the ability to incorporate third‑party analytics and to serve as an analytics layer for other applications. Its service architecture, while more macroservice‑like, remains coherent and operationally robust, reinforcing the platform’s dependable foundations in promotions, clustering, new product introduction (NPI) handling, and event‑driven planning for process‑industry environments.

Sunstice’s highest use-case score is in demand planning, bolstered by its robust capabilities in collaborative demand planning. The platform offers a variety of statistics and machine learning algorithms for demand planning with the ability to correlate with external data streams while delivering effective scenario analysis. Sunstice achieved midtier scores in the remaining three use cases due to its strong user experience that features a flexible interface and intuitive visuals. This, combined with the absence of synthetic data support, lowers Sunstice’s score for unified data integration and affects performance across multiple use cases.

Context

This Critical Capabilities report examines 16 vendors’ solutions for process industries through the lens of eight critical capabilities and four use cases. It examines in detail the product or service criterion used to assess Ability to Execute in the companion Magic Quadrant for Supply Chain Planning Solutions: Process Industries. This Critical Capabilities research evaluates the solutions’ capabilities at the time of evaluation against the market’s expectations for SCP solutions.

Market Definition

Gartner defines supply chain planning (SCP) solutions as platforms that provide technological support to help companies manage, link, align and share planning data across an extended supply chain. SCP solutions support a wide range of planning activities, from demand planning and detailed supply planning, to strategic and execution-level planning. They establish a single version of the truth for planning data and decisions, regardless of the underlying execution technology environment.

Organizations use SCP solutions to enhance the quality and efficiency of their supply chain planning decisions and to achieve higher levels of maturity. These solutions enable and streamline planning decision making by providing access to planning data, applying advanced analytics, business rules and prioritization logic, and enforcing process governance across the planning cycle. When utilized optimally, the result is improved end-to-end supply chain planning decisions, including strategic priorities aligned with resource allocations to drive improved business outcomes.

Some of the common business problems that SCP solutions are designed to address include:

Aligning plans end to end — This involves creating plans that are aligned and feasible across all tiers of the supply chain, from suppliers to customers and further tiers out. It also focuses on connecting plans across the different planning layers, from strategic-level planning to execution-level planning.
Improving visibility — This involves providing insights into the status of the supply chain, partners and key metrics used for decision making. By having information readily available, organizations are better equipped to assess situations and deliver actionable insights based on data. Users can then identify areas where improvements can be made to increase efficiency and drive value creation for the organization.
Fostering decision-making speed and quality — This involves meeting the need for faster and higher-quality decision making in supply chain operations. Strong technology support and process automation enable greater efficiency and reduce human bias in decision making. The collaboration capabilities of SCP solutions allow planners and partners to work together directly on the platform, increasing confidence in both the inputs and outputs used to make decisions.

Mandatory Features

Demand planning, such as demand forecasting and consensus demand planning.
Supply chain planning, such as inventory planning, replenishment planning, order promising, production planning and production scheduling.
Support for the alignment of planning decisions across the enterprise and multiple planning decision layers.
Support for financial impact analysis and planning.

Optional Features

The optional features for this market include:

Advanced analytics and AI, such as machine learning and predictive/prescriptive analytics to enhance forecast accuracy, detect anomalies and optimize planning decisions.
Digital supply chain twin — creating a real-time, virtual representation of the end-to-end supply chain to simulate scenarios and assess impacts before execution.
Supply chain design, modeling and segmentation to support network optimization, segmentation strategies and network modeling to align supply chain structure with business goals.
Continuous planning to enable dynamic, always-on planning cycles that adapt to real-time changes in demand, supply and business constraints.
Multienterprise planning to facilitate synchronized planning across internal teams and external partners, ensuring visibility and collaboration across the extended supply network.

Product/Service Trends

The Supply Chain Planning Solutions Market is part of a long history of Gartner coverage. Over the past decade, the Supply Chain Planning Solutions market has shifted from traditional, on-premises software to advanced, cloud-based platforms with broad functional scopes using AI and analytics extensively. While established providers remain, new entrants and startups have pushed innovation, leading to more agile and user-friendly solutions. The market has consolidated somewhat, with major players acquiring smaller providers, but it remains competitive and dynamic. Today, customers prioritize SCP solutions that are characterized by their flexibility, user friendliness and ability to support complex, global and resilient supply chains.

Critical Capabilities Definition

Collaborative Demand Planning

The capability to generate reliable and accurate demand forecasts through collaborative processes involving internal and external stakeholders.

This includes support for consensus forecasting, demand segmentation, event-driven forecasting, and advanced forecasting algorithms across product hierarchies and time horizons. The solution should enable planners to align demand signals across departments and partners, facilitating agreement on a unified forecast that drives downstream planning.