Review of Infor, Supply Chain Management Software Vendor

Last updated: November, 2025

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Infor is a large US-based enterprise software vendor, founded in 2002 (originally as Agilisys) and grown almost entirely through acquisitions into a portfolio of ERP, financials, HCM, CRM and supply chain products now positioned as industry-specific “CloudSuites.”¹²³⁴ Since 2020 it has been a subsidiary of Koch Industries, which acquired all remaining equity from Golden Gate Capital, giving Infor access to substantial long-term capital while keeping it as a standalone software business.⁵⁶⁷⁸ Infor reports roughly 17,000 employees and more than 60,000 customers across 170+ countries, making it one of the larger players in enterprise applications despite relatively low public profile compared to SAP or Oracle.¹²⁴⁹ For supply chain, the relevant footprint spans the Infor Supply Chain Planning (SCP) suite (demand forecasting, demand planning, supply planning, S&OP, production scheduling), the Infor Nexus multi-enterprise network (global trade, visibility, predictive ETA), and warehouse/logistics products embedded in various CloudSuites; all of this sits on Infor OS, a cloud-native platform delivered as multi-tenant SaaS exclusively on AWS.^{10111213141516} Technically, Infor’s supply chain capabilities combine a library of classical forecasting algorithms, some machine-learning-driven demand sensing, constraint-based scheduling engines (optionally powered by ILOG solvers), and network-level predictive models for shipment ETAs.^{1112171819202192216} The end result is a broad, commercially mature suite aligned with mainstream Advanced Planning System (APS) patterns: time-phased plans, scenario-based S&OP, constraint-based scheduling, and event-driven supply chain visibility, rather than a single unified probabilistic optimization engine.

Infor overview

Infor is a multinational enterprise software company headquartered in New York City, primarily focused on industry-specific ERP and related applications that can be deployed on-premise or as cloud services.¹²² It was created in 2002 when private equity sponsors carved out a process manufacturing ERP unit from Systems & Computer Technology Corp (SCT) under the name Agilisys; in 2004, after acquiring German vendor Infor Business Solutions, the company adopted the Infor brand and subsequently relocated its headquarters to the US East Coast (first Atlanta, later New York).^12234241720 Through the mid-2000s and 2010s, Infor accumulated more than forty acquisitions across ERP, SCM, asset management, HR and analytics, including MAPICS, Geac, SSA Global (and thus Baan), Datastream, Lawson Software, and GT Nexus, among many others.¹¹⁸²⁰

Today Infor positions itself as a provider of “industry cloud” suites – vertically packaged combinations of ERP and surrounding modules (WMS, TMS, SCP, CRM, HR, etc.), all intended to run as multi-tenant SaaS on Amazon Web Services via the Infor OS platform.^2119141516 Its public material repeatedly cites >60,000 organizations and ~17,000 employees as of the early-2020s, with customers in manufacturing, distribution, healthcare, hospitality, public sector, fashion, and other sectors.¹²⁴²⁵⁹ Supply chain functionality is spread across several product lines:

• Infor Supply Chain Planning (SCP) – a portfolio for demand planning, demand forecasting, supply planning, inventory optimization, and production scheduling.^{10111219132616}
• Infor Nexus – a multi-enterprise supply chain network (originally GT Nexus) spanning global trade, supplier collaboration, logistics visibility and predictive ETA.¹¹⁸¹³⁵
• Logistics / warehouse capabilities – including warehouse management and transport management embedded in various CloudSuites, not the primary focus of this report but part of the broader stack.⁹¹⁴¹⁶

The rest of this review focuses on how these supply-chain-oriented components work technically, what is concretely implemented in terms of forecasting and optimization, and how this differs from more niche but deeply specialized optimization platforms such as Lokad.

Infor vs Lokad

Infor and Lokad both claim to help companies “optimize” supply chain decisions, but their starting points and technical architectures are almost opposite. Infor is a broad enterprise applications vendor: supply chain planning is one portfolio among many (ERP, financials, HR, CRM, asset management, industry-specific CloudSuites), and the planning logic is packaged as modules inside those suites. In practice, customers buy a CloudSuite (for example, for manufacturing or distribution) and SCP or Nexus is deployed as one element of an end-to-end transactional landscape. Lokad, by contrast, is not an ERP vendor at all; it is a single, focused SaaS platform whose only purpose is quantitative optimization of supply chain decisions (forecasting, replenishment, allocation, scheduling, pricing), sitting on top of whatever ERPs and WMSs a client already runs.

From a modeling standpoint, Infor’s supply chain stack revolves around classic APS concepts: time-phased demand plans, inventory targets, constrained supply plans, and finite-capacity production schedules.^111219132616 Forecasting in SCP is handled by a library of algorithmic techniques and “advanced time series methods” with some machine learning and demand-sensing capabilities layered on top, plus heuristic and optimization engines that generate feasible multi-echelon supply plans given constraints.^111217191316 Optimization in production scheduling is expressed as constraint-based scheduling; if licensed, the ILOG solver can search for optimal or near-optimal schedules given defined constraints and objectives.^1819202616 Infor Nexus applies machine learning at the network level to predict shipment arrival times based on a large history of tracking events and network data, exposing this via a Predictive ETA API and user-facing predictive dashboards.^219221327 In all cases, the “AI” is attached to specific modules (forecasting, scheduling, ETA prediction) and feeds into traditional plans and alerts.

Lokad, in contrast, centers everything on probabilistic modeling and decision optimization. Rather than separate forecasting and planning modules, it provides a domain-specific language (Envision) in which the entire model – data ingestion, probabilistic demand and lead-time modeling, and the optimization of replenishment, allocation, and scheduling decisions – is coded as one pipeline. Forecasts are not single point estimates but full demand distributions per SKU/time bucket, and decisions are derived via stochastic optimization (e.g., Stochastic Discrete Descent) and, more recently, differentiable programming that tunes forecasting and decision parameters jointly to minimize realized cost or maximize profit. The platform is not sold as a suite of configurable modules but as a programmable engine plus expert services: each client ends up with a bespoke “app” written in Envision, producing ROI-ranked decision lists (orders, transfers, maintenance actions, pricing moves) under a unified economic objective.

Architecturally, Infor’s cloud products run as multi-tenant SaaS on AWS using Infor OS as an integration and platform layer (with ION for message-based integration, API gateways, identity, and UX services).^101415166 This is a conventional microservices-style enterprise platform: each CloudSuite and SCP/Nexus module uses OS services for interoperability and identity but is otherwise a fairly self-contained application. Lokad, by contrast, was built as a single in-house stack on Azure with minimal external dependencies: an event-sourced back end, a content-addressable store, and the Envision compiler and “Thunks” execution engine forming a single codebase focused only on analytics and optimization. Lokad exposes no fixed “modules”; instead it exposes a programmable environment for supply chain scientists.

In terms of decision workflow, Infor’s planning modules are geared toward planners operating within S&OP cycles: users interact with configurable screens and workflows to review forecasts, align cross-functional plans, generate supply responses, run what-if scenarios, and, in some cases, auto-generate schedules or recommended orders.^101112132616 Automation is typically framed as system-generated plans that planners can adjust, rather than fully automated, ROI-ranked instruction lists. Nexus’ predictive ETA and control tower features add event-driven alerts and exceptions, helping planners intervene when shipments deviate from plan.^219221327 Lokad, instead, has been built expressly to output prioritized decision lists where each line is quantified in terms of expected financial impact; planners are expected to focus on exceptions and business judgment rather than on editing plans line-by-line.

Finally, commercial maturity differs: Infor is a very large, diversified vendor with tens of thousands of customers across many industries and a long on-premise heritage.¹²⁴²⁵⁹ Its supply chain products benefit from this breadth but are necessarily constrained by the need to integrate with a broad portfolio and long-lived installed base. Lokad is smaller but more specialized: its customer base is concentrated in supply-chain-intensive industries (retail, manufacturing, aerospace) and its entire stack exists only as SaaS; it does not carry legacy on-prem products. For a customer, the trade-off is essentially between a broad enterprise suite with embedded planning (Infor) and a narrowly focused but more radical quantitative optimization layer (Lokad).

Company history, ownership, and acquisitions

Infor’s history is unusually acquisition-driven even by enterprise software standards. The company traces back to June 2002, when a unit of SCT (Systems & Computer Technology Corporation) focused on process manufacturing ERP was spun out under the name Agilisys.¹⁴²⁰ Backed by Golden Gate Capital and later Summit Partners, Agilisys pursued an aggressive roll-up of mid-market ERP and related vendors; after acquiring German firm Infor Business Solutions in 2004, the company adopted the Infor Global Solutions name.^123241720

From 2004 through roughly 2016 Infor acquired more than forty companies, including MAPICS, Geac, SSA Global (and by extension Baan and Epiphany), Datastream (asset management), Workbrain (workforce management), Hansen (public sector), several hospitality vendors, and, importantly for this report, Lawson Software (ERP), Mercia (supply chain planning) and GT Nexus (multi-enterprise supply chain network).^1171820 The acquisition of Lawson in 2011 gave Infor two major ERP lines (M3 and S3) and a significant presence in manufacturing, distribution and healthcare.¹¹⁷ The 2015 acquisition of GT Nexus (later rebranded Infor Nexus) for about $675 million brought in a cloud-based global trade and supply chain network focused on logistics, supplier collaboration and visibility.^1171813 In 2016 Infor acquired Predictix, a retail forecasting/analytics startup, as part of a retail vertical push.¹¹⁷¹⁸ Subsequent deals included Birst (business intelligence), several consulting partners, and specialized vertical solutions.¹¹⁷¹⁸

Ownership changed materially in the late 2010s. Koch Equity Development, an affiliate of Koch Industries, invested in Infor as a minority shareholder and then, in early 2020, agreed to acquire the remaining stake from Golden Gate Capital; the acquisition closed in April 2020, at which point Infor became a wholly owned Koch subsidiary but remained as a standalone software business.^{311182215567828} Koch press materials highlight that its companies have invested tens of billions of dollars in technology and that Infor is a strategic element of that transformation.^3111822 Infor’s own news and hospitality growth releases emphasize that, as a Koch company, it can take a longer-term view and rely on stable capital.⁹

Commercially, Infor’s scale is well documented: Wikipedia, vendor collateral and independent analysts broadly agree on a headcount of ~17,000, a customer base of ~60,000 organizations, and presence in over 40–170 countries (depending on the exact measure) in the early-2020s.^12410259 Internal community content around Infor’s own hackathons and events also references 60,000+ customers, 2,000 partners, and 17,000 employees.²⁹ This indicates a mature, globally distributed organization.

Supply chain and planning product portfolio

Supply Chain Planning (SCP)

Infor Supply Chain Planning is a portfolio of applications designed to integrate demand, supply and inventory planning across the enterprise. Marketing and documentation describe SCP as using “big data and AI” to transform planning and decision-making, generating synchronized plans that cover demand planning, forecasting, inventory optimization, supply planning and production scheduling.^{10111219132616}

The SCP literature emphasizes:

• Demand planning & forecasting. Infor Demand Forecasting and Demand Planning aim to “simplify and expedite the forecasting process” using “advanced time series forecasting methods” and machine learning, supported by modeling wizards and live integration to reduce manual effort.¹²¹⁷¹⁹ The product pages describe built-in time-series modeling, data series analytics, demand sensing and near real-time data, plus collaboration workflows for consensus forecasting.^412171913
• Supply planning & inventory. Documentation for Infor Supply Planning states that, once enterprise demand plans and inventory targets are published, the system automatically computes time-phased supply plans, including purchase, production, inventory, and distribution plans, using “sophisticated planning engines based on heuristics and optimization.”¹¹¹⁹¹³ These engines consider constraints such as capacity, lead times, and business rules to propose feasible plans.
• S&OP / IBP. The SCP portfolio also includes Sales & Operations Planning (S&OP) and integrated business planning capabilities, facilitating cross-functional alignment around baseline and constrained plans, with scenario analysis and KPI dashboards.^10111330

Technically, SCP appears to follow a fairly standard APS pattern: a library of statistical models and ML routines for baseline forecast generation; heuristic and, in some cases, optimization-based engines for moving from demand to supply and inventory plans; and a collaborative planning UX for adjustment and consensus. The claims of AI/ML are concrete in the sense that demand forecasting and demand sensing do rely on algorithmic methods, but there is little public detail on specific architectures (for example, whether they use classical exponential smoothing families, gradient boosting, or deep learning). The available material stresses “advanced time series” and “machine learning” without exposing algorithm names, hyperparameters, or objective functions.^12171913

Production Scheduling

Infor Production Scheduling is SCP’s module for detailed, finite-capacity scheduling in process and discrete manufacturing. The product page describes it as a constraint-based scheduling tool that helps create accurate, efficient, and collaborative process manufacturing schedules while considering bottlenecks and interdependencies between production lines.²⁶¹⁶

Documentation explains that Production Scheduling uses a constraint-based logic that can schedule and synchronize operations across all production lines, accounting for inter-dependencies and constraints such as sequence-dependent changeovers or tank capacities.¹⁶ For more advanced use, the module can be licensed with an optional ILOG solver add-on. ILOG solver documentation notes that it enables creation and execution of solver-based scheduling “macros”, with a progress bar indicating solving progress; this is described explicitly as an optional, separately licensed component.^1171816 External implementation partners further clarify that Infor Production Scheduling uses “proven optimization engines (including ILOG solvers)” to consider constraints and optimization goals when generating schedules.²⁰

From a technical perspective, this suggests that the underlying optimization uses constraint programming or mixed-integer programming (standard for ILOG CP/CPLEX), with Infor providing an application shell and domain model around those solvers. There is no evidence of novel optimization algorithms developed by Infor specifically for scheduling; instead, Infor integrates standard commercial solvers into a domain-specific application. This is a perfectly reasonable design, but not state-of-the-art in the sense of proprietary algorithms – it leverages well-established OR libraries.

Infor Nexus

Infor Nexus (the evolution of GT Nexus plus TradeCard and subsequent additions) is Infor’s multi-enterprise supply chain network, aimed at global trade, logistics visibility, and partner collaboration.¹¹⁸¹³⁵ Historical material traces its roots to GT Nexus and TradeCard in the late 1990s/early 2000s, focused on ocean booking portals, letters of credit and financial services; later evolution added a broader control tower, live tracking and multi-mode visibility.¹³⁵

Recent product resources describe Nexus Predictive ETA as a machine-learning-based capability that uses the “dense shipping and supply chain data” flowing through the network to generate more accurate, dynamic predictions of shipment arrival times and near-future product availability.^2212213 A developer-facing Predictive ETA API allows clients to query predicted ETAs under hypothetical changes (e.g., switching carriers, changing vessel arrival dates), effectively exposing a learned ETA model as a service.²¹⁹

Infor and Databricks jointly promote Nexus as a data-intelligent platform where supply chain events are streamed into Databricks Lakehouse, enabling AI models for predictive ETAs, anomaly detection, and traceability, with data shared via Delta Sharing to other systems.²⁴²²²⁶ Conference material (e.g., Gartner sessions and Nexus Connect events) further mentions ongoing work on predictive analytics, process mining, generative and agentic AI, and digital assistants in Nexus.²⁵²⁷

In summary, Nexus appears to be one of the more “AI-heavy” parts of Infor’s supply chain portfolio, with concrete machine learning models for ETA prediction and anomaly detection over a large, multi-tenant event stream. The techniques are not fully disclosed but are consistent with mainstream supervised learning on event histories rather than novel probabilistic or causal modeling for inventory and pricing.

Platform architecture and technology stack

Infor’s cloud strategy is anchored by Infor OS (Infor Operating Service), described as the foundational application platform that connects Infor and non-Infor systems into a digital business platform. Infor OS runs exclusively as a multi-tenant SaaS platform on AWS.^1014156 Key OS components relevant to supply chain include:

• Infor ION – a message-based interoperability and business process management layer, used to integrate Infor and third-party applications on-premise and in the cloud, with pre-built connectors, workflows and alerts.¹⁰⁶ This is central for linking SCP, Nexus and ERP/WMS.
• API and microservices layer – exposing application services to other systems and, in some cases, to customers (e.g., Nexus Predictive ETA API).^2191415
• Identity, UX and data services – common authentication, single sign-on, data cataloging, and analytics building blocks across products.¹⁴¹⁵⁶

AWS material notes that Infor CloudSuite solutions are built on AWS infrastructure and Infor OS, enabling customers to stay current on software versions, scale services with demand, and integrate cloud and on-premise applications.^2991415 Implementation partners describe Infor’s multi-tenant SaaS as providing elasticity, device-agnostic access, pre-built APIs and lower total cost of ownership, with special Government SaaS offerings for regulated industries.¹⁴

From a technical standpoint, this is a conventional enterprise microservices architecture leveraging hyperscaler infrastructure and platform services. It supports multi-tenancy and rapid deployment but does not, by itself, imply any particular forecasting or optimization sophistication; those reside in application-level engines (SCP, Nexus, etc.). Unlike more specialized optimization platforms, there is no public evidence that Infor has built a domain-specific language for quantitative analytics or a custom execution engine; instead, it relies on conventional application stacks, database technologies and OR/ML libraries, combined into packaged apps.

AI, optimization, and decision automation

Demand forecasting and planning

Infor’s demand-related products make explicit AI/ML claims: Infor Demand Planning and Demand Forecasting are said to use “AI, machine learning, and near real-time data alongside collaboration and demand sensing to deliver more accurate forecasts.”¹²¹⁷¹⁹ Documentation and brochures talk about “state-of-the-art technology” and “advanced time series forecasting methods” used to capture patterns such as seasonality, intermittent demand and product lifecycle phases.^111217191330

The concrete implementation details exposed publicly are limited. SCP documentation references a “library of algorithmic techniques” used to generate best-fit demand pictures, implying a mix of time-series models selected by fit criteria.^11191330 Demand-sensing features likely ingest recent order and POS signals to adjust near-term forecasts, a common pattern in modern demand planning tools. However, there is no technical description of model classes (ARIMA variants, state space models, gradient-boosted trees, neural networks, etc.), loss functions, or how ML is combined with time series.

As such, while it is reasonable to accept that Infor uses ML in its forecasting stack (especially given the acquisition of Predictix and ongoing AI marketing), the implementation appears aligned with broader industry practice: a mixture of classical time-series forecasting plus some ML-based components, wrapped in a demand planning UI. There is no evidence that Infor has adopted radical approaches such as end-to-end probabilistic decision learning or differentiable programming in SCP analogous to research-level work.

Constraint-based scheduling and ILOG integration

In production scheduling, constraint-based optimization plays a more visible role. Infor Production Scheduling uses constraint-based logic to schedule and synchronize operations across lines with interdependencies; if activated, an optional ILOG solver module can search for optimal solutions under the expressed constraints.¹⁸²⁶¹⁶ Implementation partners describe IPS as “bundling optimisation, modelling and visual tools” and explicitly mention “advanced scheduling algorithms (ILOG/optimizer engines)” that respect manufacturing constraints and goals.²⁰

Given IBM/ILOG’s documented capabilities, it is reasonable to infer that Infor’s scheduling optimization is underpinned by standard constraint programming / mixed-integer solvers configured by scheduling models, rather than custom heuristics invented by Infor. This is strong from a practical standpoint – ILOG is a mature, high-quality solver – but technically mainstream: many APS vendors embed commercial solvers for detailed scheduling. The “AI” label here largely resolves into well-established optimization technology and graphical scheduling tools, not novel AI research.

Network analytics and predictive ETA

Infor Nexus’s Predictive ETA is one of the more concrete examples of machine learning in Infor’s supply chain offering. Product descriptions state that Nexus “leverages the latest AI technologies” and “machine learning algorithms” applied to network shipping data to produce better near-future availability predictions and ETAs with lower overhead than manual methods.^2212213 The Predictive ETA API allows users to query alternative scenarios (e.g., changing carrier or event dates) and receive updated predicted ETAs, implying that the model is conditional on journey attributes and route histories.²¹⁹

The Databricks collaboration outlines a more detailed architecture: Nexus data is streamed into Databricks Lakehouse, where AI models predict arrival times, detect anomalies, and provide multi-tier traceability; Delta Sharing is used to expose this high-quality data to other systems for analytics and “practical intelligence.”²⁴²²²⁶ This is consistent with contemporary “network AI” implementations: large volumes of time-stamped events feed into supervised models (likely gradient boosted trees, deep sequence models, or similar) to predict future event times and classify anomalies.

At the same time, messaging around Nexus now references generative and agentic AI, digital assistants and process mining, particularly in conference abstracts.²⁵²⁷ As of late 2025, there is limited technical detail available on these newer AI elements (e.g., which LLMs, how they are grounded, how they integrate with planning). They should therefore be treated as emerging features rather than proven, core planning engines.

Decision automation vs decision support

Across SCP, Production Scheduling and Nexus, Infor’s material emphasizes decision support: improving forecasts, generating feasible plans, scheduling production, and highlighting exceptions and risks.^101112132616 Automation exists – supply plans can be generated algorithmically and, in some cases, schedules can be optimized and released directly – but the dominant pattern is still that planners review and adjust outputs within S&OP, MPS or control tower processes.

There is no explicit evidence that Infor’s supply chain stack is organized around a single global economic objective (e.g., maximizing expected profit under uncertainty) across modules. Instead, optimization objectives appear to be module-specific: service-level and inventory targets in SCP, scheduling efficiency in IPS, on-time arrivals in Nexus. This is comparable to most APS vendors and contrasts with quantitative platforms such as Lokad that expressly encode a unified financial objective across forecasting and optimization.

In short, Infor’s AI and optimization components are substantive but mainstream: time-series plus ML for forecasting, commercial constraint/OR solvers for scheduling, supervised ML for network ETAs, and workflow-centric decision support for planners. They do not appear to push beyond current industry practice into radically new modeling paradigms.

Deployment model and commercial maturity

Infor’s supply chain solutions are typically deployed as part of an Infor CloudSuite, combining ERP and related apps on Infor OS and AWS infrastructure.^10299141516 Customers can run SCP and Nexus alongside Infor ERP or integrate them with third-party ERPs via ION and other integration patterns documented by Infor and AWS.^1027146 AWS field guides describe common integration scenarios and highlight that ION allows orchestration of workflows and alerts across Infor and non-Infor systems without heavy custom coding.¹⁰²⁷

The multi-tenant SaaS model allows Infor to keep customers on current versions and scale capacity up and down with usage peaks, while giving access from any device.^2991415 In regulated industries, Infor offers Government SaaS variants with FedRAMP-compliant stacks.¹⁴

Given the scale of the customer base (tens of thousands of organizations, many with complex on-prem legacies), Infor’s planning deployments tend to reflect enterprise realities: SCP may be rolled out gradually over specific business units, Nexus may coexist with existing TMS/WMS, and customization is often done via configuration and integrations rather than code-level extension. Public customer stories (across multiple industries) show a wide range of use cases but, unsurprisingly, emphasize business outcomes more than technical detail.²¹⁹

Commercially, there is no doubt that Infor is a mature vendor: large installed base, long history, stable backing from Koch, and broad partner ecosystem.¹²⁴²⁵⁹ The main technical caveat is not maturity but heterogeneity: decades of acquisitions and product evolution mean that the supply chain capabilities are distributed across multiple applications and codebases, rather than unified into one programmable optimization engine. This is typical of large enterprise vendors but important to keep in mind when comparing with younger, single-platform competitors.

Conclusion

In precise technical terms, Infor’s supply chain offering delivers:

• A planning portfolio (SCP) that generates statistical/ML demand forecasts, time-phased supply and inventory plans, and S&OP scenarios using a library of algorithmic techniques and heuristic/optimization engines under capacity and business constraints.^{10111219132616}
• A finite scheduling tool (Production Scheduling) that expresses detailed production constraints and, when licensed with ILOG, can use commercial OR solvers to search for optimal or near-optimal production schedules.^18202616
• A multi-enterprise network (Nexus) that applies machine learning to large volumes of shipment and event data to predict ETAs, detect anomalies, and provide end-to-end visibility via APIs and dashboards, with an increasingly modern data architecture via Databricks Lakehouse and Delta Sharing.^{22421922132627}
• A cloud platform (Infor OS on AWS) that provides integration, security, identity, and UX services, enabling multi-tenant SaaS deployments and gradual integration into heterogeneous enterprise landscapes.^102991415166

The architectural and algorithmic mechanisms are largely consistent with mainstream APS practice: time-series and ML in forecasting, constraint-based planning and commercial solvers in scheduling, supervised ML for ETA prediction, and workflow-centric decision support. The AI branding is backed by real implementations (particularly in Nexus and demand forecasting), but the public evidence does not indicate anything beyond current industry state of the practice. There is no sign of unified probabilistic decision frameworks or novel optimization algorithms analogous to those being explored by more specialized vendors; rather, Infor’s strength lies in breadth, integration and the ability to embed planning capabilities within large, industry-specific suites.

Commercially, Infor is an established, very large player with deep vertical reach and a long tail of on-prem and cloud customers. Organizations selecting Infor for supply chain planning are typically also buying into a wider CloudSuite and OS platform; the supply chain components benefit from this integrated context but are shaped by it. Companies seeking highly customized, end-to-end quantitative optimization may find that specialized platforms (such as Lokad) take a more radical approach; those seeking a broad, integrated enterprise suite with embedded planning, on the other hand, will find Infor’s offering technically solid and aligned with prevailing APS paradigms, albeit with the usual complexity of a multi-decade acquisition portfolio.

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