Review of B2WISE, Supply Chain Planning Software Vendor

Last updated: November, 2025

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B2Wise is a mid-size, DDMRP-centric supply chain planning vendor founded around 2016 by Kevin and Trevor Boake, offering a cloud-based advanced planning system built on AWS that implements Demand Driven methodologies (DDMRP, DDOM, Drum-Buffer-Rope) for materials planning and shop-floor scheduling, embeds Forecast Pro as its primary forecasting engine, and increasingly wraps these capabilities with “AI agents” and a low/no-code platform called CommWise.AI for building workflows such as S&OP dashboards and factory boards on top of a central DataMart; the company operates through multiple regional entities (France, UK, South Africa, Iberia, LATAM, APAC, North America), positions itself as both software editor and training provider (notably via its DDBrix simulation game), claims several dozen recognizable manufacturing and distribution clients across Europe and South Africa, and appears to be a bootstrapped or lightly capitalized founder-led group with low-single-digit millions in recurring revenue and a niche but established position in the DDMRP ecosystem.

B2Wise overview

B2Wise (often styled “b2wise” or “B2Wise”) is a specialist supply chain planning vendor built around the Demand Driven methodology. Public corporate and database records converge on a founding date around 2016, with Kevin and Trevor Boake as co-founders and French and UK entities forming the core of the group.¹²³⁴⁵⁶ The company offers an end-to-end “flow-based” planning system whose central modules are Materials Planning (DDMRP), Control Point Scheduling / DDOM, and Demand Forecasting, all designed to run either in the cloud on AWS or on-premise.^7891011

Functionally, B2Wise acts as an APS (Advanced Planning System) implementing buffer-based replenishment (DDMRP), Drum-Buffer-Rope-style scheduling at bottlenecks, and S&OP workflows, with forecasting delegated to the commercial package Forecast Pro, which provides a mix of classical time-series methods and machine-learning models.^781213141516 Architecturally, B2Wise relies on an AWS-centric stack (Lambda, S3, EC2), MS SQL and Apache Spark, with microservices written in .NET Core, Node.js, Python and Go, plus Docker/Terraform for deployment.¹⁰¹⁷¹¹

More recently B2Wise has introduced CommWise.AI, a low/no-code environment where “AI Agents” assemble dashboards, S&OP workflows and scheduling boards on top of a central DataMart; marketing and award write-ups emphasize AI-assisted workflow composition, not automated black-box decision-making.^1819202122 The company is also tightly coupled to the Demand Driven Institute ecosystem, providing DDMRP/DDOM software, training and the widely used DDBrix simulation game.²³²⁴²⁵ Client references include manufacturing and distribution firms such as Etex, Goizper, LVMH Fragrance Brands, PPG, Takealot, Fruity Line, Fagor Ederlan and WeirdFish, with particularly strong presence in Europe and South Africa.^{7112627282930}

Financial and corporate registries (CB Insights, Tracxn, GetLatka) classify B2Wise as an unfunded or bootstrapped company with no disclosed venture rounds, no completed acquisitions, and estimated 2025 revenues around USD 7 million with a team size in the tens, making it a niche but commercially established vendor rather than an early-stage startup or a large suite provider.^12331321133

B2Wise vs Lokad

Although both B2Wise and Lokad operate in supply chain planning, their problem framing, technical mechanisms and product philosophies diverge sharply. B2Wise builds around Demand Driven Material Requirements Planning (DDMRP) and the Demand Driven Operating Model (DDOM): it places buffers at decoupling points, calculates min/target/max levels, uses real-demand netting to generate prioritized replenishment orders, and schedules bottleneck resources via Drum-Buffer-Rope-like rules and time buffers.⁷⁸¹⁰³⁴ Lokad instead organizes its platform around Quantitative Supply Chain, a probabilistic and financially driven paradigm where the primary artefacts are probability distributions for demand and lead time, plus numerical solvers that score discrete decisions (orders, allocations, production, pricing) based on expected economic return.^835343637

On the forecasting side, B2Wise delegates core algorithms to Forecast Pro, which offers automatic selection among exponential smoothing, ARIMA and intermittent-demand models, plus some gradient-boosted-tree-style ML models, wrapped in B2Wise’s “stability-driven forecasting” process and KPI layer.^1213141516 Lokad, by contrast, has built its own probabilistic forecasting engine that computes full demand distributions (quantile grids and Monte-Carlo-style scenarios) and has been publicly benchmarked in the M5 competition and subsequent publications as a core differentiator; probabilistic forecasting is not an add-on but the foundation of its optimization pipeline.^353839364041

For decision automation, B2Wise primarily uses heuristic rules encoded in Demand Driven concepts: buffer logic, priority indexes, and control-point scheduling guided by DDOM, with “AI agents” positioned as assistants for cleansing history, segmenting items and tuning parameters.^781210111819 Lokad explicitly formulates decisions as optimization problems over probabilistic futures, using custom numerical solvers (e.g., Stochastic Discrete Descent and later latent-space heuristics) to search the discrete decision space, and emphasizes that every recommendation is tied to explicit economic drivers (stock-out penalty, holding cost, etc.) expressed in its domain-specific language Envision.^3637424034

At the platform level, B2Wise is a traditional SaaS APS on AWS with a SQL-centric DataMart and connectors to >40 ERPs; data usually flows via sFTP and batch APIs, with CommWise.AI providing a low/no-code layer to assemble dashboards and workflows.^431017111819 Lokad is a programmable optimization environment on Azure with an event-sourced data store, a custom DSL (Envision) and a distributed virtual machine, deliberately minimizing external dependencies and exposing the entire forecasting-and-optimization logic as code that can be audited and modified.^3642834 B2Wise wraps pre-defined planning modules in configuration and some no-code AI assembly; Lokad exposes a language and expects solution design through supply chain scientists rather than purely configuration.

Methodologically, B2Wise strongly ties software adoption to Demand Driven training and change management: DDBrix workshops, DDMRP certifications and methodological coaching are central to its projects, and its software is presented as a DDAE (Demand Driven Adaptive Enterprise) enabler.²³²⁴²⁵ Lokad, by contrast, centers its services on supply chain scientists who co-develop Envision “apps” with the client, with less emphasis on a single external methodology and more on tailoring models to each business and measuring ROI via the quantitative framework.^3436374241 In short: B2Wise is best read as a DDMRP-first planning suite with some AI-assisted workflow tooling, whereas Lokad is a probabilistic, code-centric optimization engine whose planning behavior is not constrained by DDMRP and is aimed at economic optimization rather than buffer-driven execution.

Methodology and caveats

This review is based exclusively on publicly available information about B2Wise: official product and marketing pages, training and integration documentation, DDMRP/CommWise-related press, developer job profiles, GitHub challenges, third-party reviews, corporate registries and event listings.^{1233171024171118191315} No sources hosted on Lokad-controlled domains were used to characterize B2Wise; Lokad sources are confined to the comparison section above.

Key limitations:

• Many numerical claims (e.g., “over 250 implementations,” “$15B inventory managed,” “10,000+ clients”) originate from B2Wise marketing pages and partner collateral, with no independent public corroboration.^7241144
• There is no public source code for B2Wise’s core planning engines or AI agents, and no peer-reviewed technical papers describing algorithms or evaluation metrics.
• Some third-party profiles (Tracxn, GetLatka, LeadIQ) extrapolate revenues or headcount; these figures should be treated as estimates rather than audited facts.^3311133

The stance adopted here is deliberately skeptical: vendor claims are accepted only when backed by multiple independent sources or by detailed primary documentation; otherwise they are flagged as self-reported or left as hypotheses.

History, structure, and funding

Founding and global footprint

CB Insights and BounceWatch both describe B2Wise as founded in 2016 and headquartered in Lyon, France, with a focus on supply chain planning software.¹² Tracxn lists “B2Wise” as an “unfunded” company based in Birmingham, UK, with a founding year of 2017 and Kevin Boake as founder, which likely reflects the registration date and location of the UK holding entity rather than the original business inception.³⁶

Biographical and interview materials clarify that Kevin and Trevor Boake co-founded B2Wise; Trevor is listed as Co-Founder & EVP Development/CTO since July 2016, and an interview on The Lekker Network recounts Kevin founding B2Wise with his brother after frustrations with ERP-centric planning tools.⁴⁵⁴⁵ The company’s “Contact” page lists offices in the UK (Birmingham HQ), France (Lyon), North America (Cambridge, MA), Iberia (Spain), South Africa (Somerset West), LATAM (Querétaro) and APAC (Australia), confirming a multi-entity international footprint.⁴⁶

B2Wise is a Demand Driven Institute (DDI) affiliate for DDMRP solutions, with presence in Europe, UK, USA, Latin America, Australia and South Africa, corroborating its global focus on DDMRP projects.²³

Funding, ownership, and size

Tracxn and BounceWatch both categorize B2Wise as unfunded, with no venture or private-equity rounds recorded.²³ GetLatka lists a 2025 “M&A offer” but no closed transaction or round details, and the company does not appear in major M&A news sources, suggesting it remains independent.³² UK Companies House records show B2WISE HOLDINGS LTD with an individual significant shareholder holding 25–50% of shares and voting rights, consistent with founder control.⁶

GetLatka estimates 2025 revenue at about USD 7.3 million with a 66-person team, while OfferZen lists 15–50 employees for the South African development office, so global headcount is plausibly in the tens.³¹¹⁷¹¹ These numbers align with a mid-size, niche SaaS vendor: large enough to have dozens of clients, too small to be considered a tier-1 suite provider.

Acquisition activity

No credible public source indicates that B2Wise has:

• Been acquired by another vendor; or
• Acquired other companies.

The only hint is GetLatka’s “2025 M&A offer” tag, which is not accompanied by a closing announcement.³² For practical purposes, B2Wise should be considered a stand-alone, founder-led company without acquisitive history.

Product and functional scope

Core planning modules

B2Wise describes its platform as an end-to-end, flow-based APS whose core modules are:

• Materials Planning (DDMRP) – multi-echelon buffer-based planning at strategic decoupling points.
• Control Point Scheduling – DDOM-style scheduling and execution at bottlenecks.
• Demand Forecasting – Forecast Pro-based statistical/ML forecasting embedded into the planning process.⁷⁸⁹¹⁰

The Materials Planning module implements DDMRP concepts: inventory buffers (min/target/max), positioning at decoupling points, dynamic buffer adjustments and color-coded priority indicators to drive replenishment orders.^8103411 From an algorithmic standpoint, this points to deterministic netting and buffer-maintenance formulae rather than formal stochastic optimization.

The Scheduling & Execution (Control Point Scheduling) module uses Theory of Constraints and Drum-Buffer-Rope concepts; B2Wise’s blog explains that DDOM generalizes drums to “control points,” each protected by time buffers, and schedules them to maximize utilization and flow while limiting WIP.¹⁰¹¹ The scheduling logic appears to be heuristic dispatching and buffering at control points, not MILP-style global optimization.

Demand forecasting

B2Wise’s Demand Forecasting solution explicitly identifies Forecast Pro as the embedded forecasting engine.¹² Forecast Pro’s own documentation describes a library of methods (exponential smoothing, Box-Jenkins/ARIMA, intermittent-demand models) plus a machine-learning module (e.g., gradient boosting) and an “expert selection” mechanism that automatically picks the best model per series.^13141516 Thus, when B2Wise markets “machine learning forecasting,” it primarily refers to Forecast Pro’s engine.

B2Wise adds its own process framing:

• Data cleansing and transformation via AI agents that detect anomalies, spikes and noise in sales history.
• Focus on forecast stability over marginal accuracy, with threshold-based KPIs and forecast error evaluated over decoupled lead-time horizons (e.g., Threshold-based Forecast Accuracy Indicators and tournament techniques for model choice, according to partner descriptions).^12471315

There is no public evidence that B2Wise develops its own advanced forecasting algorithms beyond what Forecast Pro provides; the added value sits in data preparation, KPI design and integration with DDMRP/S&OP.

S&OP and Flow-Driven S&OP

The Sales & Operations Planning module (Flow-Driven S&OP) provides a structured process around:

• Product, demand and supply reviews.
• Pre-S&OP and executive S&OP meetings.
• Scenario modelling across flow groups, which aggregate products and resources by how they move through the value chain.⁹

B2Wise uses its central DataMart and CommWise.AI to:

• Aggregate demand and capacity into flow groups.
• Run what-if scenarios on constraints and demand assumptions.
• Use AI agents to propose flow group definitions and capacity defaults.
• Deploy an “AI Secretary” role that summarizes scenario changes and attaches these notes to financial and operational metrics.⁹¹⁸¹⁹

Again, the emphasis is on workflow and aggregation rather than mathematically rigorous optimization of S&OP trades; specific optimization algorithms are not disclosed.

CommWise.AI

CommWise.AI Studio is positioned as an AI-powered platform on top of the B2Wise DataMart:

• Users can design custom workflows (e.g., S&OP, shop-floor dashboards, scheduling boards) by specifying business rules and desired outputs.
• AI agents interpret these inputs to assemble dashboards, queries and potentially scheduling rules.
• A flagship demo shows CommWise taking a photo of a physical planning board and turning it into an interactive digital dashboard, implying OCR plus structure inference.¹⁸¹⁹²²

Supply Chain Event Award descriptions emphasize that CommWise agents are “autonomous and traceable,” and that AI is used to assemble solutions from client-defined rules rather than to make opaque decisions.¹⁹²⁰²¹ However, there are no technical papers, open APIs or architectural diagrams providing detail on model types, guardrails or evaluation.

Training and DDBrix

Training is a central pillar of B2Wise’s offer:

• The DDBrix workshop is a LEGO-based game widely used to teach DDMRP concepts; B2Wise claims >10 000 participants globally.²⁴²⁵
• Training materials highlight a 93% satisfaction rate and an 87% exam pass rate across courses.²⁴
• B2Wise provides DDMRP, DDOM and allied methodology training aligned with DDI, positioning itself as both software vendor and methods educator.²³²⁴²⁵

This tight coupling means that many B2Wise projects are as much methodology roll-outs as software deployments.

Architecture and technology stack

Cloud infrastructure and stack

B2Wise’s blog and third-party reviews state that the application is built on AWS serverless technology, with claims of “lightning-fast processing, massive scalability and robust data security.”¹⁰¹¹ The OfferZen tech-stack profile lists:

• Application/data: Node.js, .NET Core 2.0, C#, JavaScript/TypeScript, Python, Go, jQuery, Microsoft SQL Server, Apache Spark.
• Cloud services: AWS Lambda, S3, EC2.
• DevOps: Docker, Terraform, Prometheus, GitHub, npm, etc.¹⁷

This indicates a heterogeneous microservices environment mixing serverless and classic VM-based components, with SQL for transactional storage and Spark for heavier analytics. It is modern and conventional for 2020s SaaS, but not structurally unusual.

DataMart and integration

B2Wise’s Integrations page describes a straightforward batch pipeline:

1. Agree data specification (masters, transactions, forecasts, BOMs, routings).
2. Customer uploads data to a secure sFTP endpoint.
3. B2Wise maps files into internal tables.
4. The system automatically retrieves, validates and imports updated data.
5. Generated orders (PO/WO/DO) are exported back to sFTP and loaded into the ERP via API or file import.⁴³

B2Wise advertises connectors for SAP, Microsoft Dynamics 365 (F&O and BC), Oracle NetSuite and Sage X3, plus experience integrating with 40+ ERPs.⁴³¹¹⁴⁴ The architecture is primarily batch-oriented and DataMart-centric; there is no indication of event-driven or streaming architectures.

SDLC and deployment cadence

OfferZen describes B2Wise’s development process as:

• Fortnightly Scrum sprints with a Scrum Master and Product Owner.
• Deployment to a UAT environment, followed by monthly promotion to Live.
• Separate Live and UAT instances, consistent with multi-tenant SaaS practices.¹⁷

This suggests a reasonably mature SaaS SDLC, with predictable release cadence.

Developer expectations

A public GitHub repository showing a candidate’s solution to a B2Wise senior developer challenge requires:

• Importing sales and master data with pandas.
• Applying an ARIMA model to forecast 52 weeks.
• Simulating stock-on-hand and a binary stockout flag.⁴⁸

While not official product code, it indicates that B2Wise expects engineers to be comfortable with classical time-series modelling and Python-based analytics, reinforcing the interpretation that internal forecasting logic is conventional rather than cutting-edge.

AI, ML and optimization claims

Forecasting AI via Forecast Pro

B2Wise’s “AI” language in demand forecasting largely stems from its integration with Forecast Pro, which offers:

• Automatic model selection among exponential smoothing, Box-Jenkins/ARIMA and intermittent-demand models.
• A dedicated machine-learning module (e.g., gradient-boosted decision trees) for complex patterns.
• Probabilistic outputs (prediction intervals) in some configurations.^13141516

B2Wise adds value through:

• AI-assisted history cleansing, which aims to distinguish noise and promotions from genuine structural demand.
• Emphasis on stability-driven forecasting, where forecast volatility is managed to support DDMRP buffers rather than chasing minimal error at the SKU-period level.¹²⁴⁷¹³

From a technical standpoint, Forecast Pro is a mature commercial engine; B2Wise’s contribution lies in process design and integration, not novel ML research.

AI agents for cleansing, segmentation and S&OP

Across its site, B2Wise claims that AI agents:

• Cleanse sales histories and identify anomalies for forecasting.
• Segment products and customers.
• Suggest buffer settings and flow groups.
• Automate alerts, scenario building and dashboard assembly.^231291819

However:

• No details are provided on model families (e.g., clustering versus supervised learning), training data or evaluation metrics.
• There are no public case studies quantifying accuracy, data-quality improvements or decision impacts attributable specifically to these agents.

The conservative interpretation is that B2Wise uses a mix of rules and light ML components for data prep and classification, but the exact sophistication of these agents remains opaque.

Optimization depth

B2Wise’s published material consistently emphasizes DDMRP buffers, DBR scheduling and DDOM control points rather than generic OR terms like MILP, stochastic programming or constraint programming.⁷⁸¹⁰³⁴ There are no references to:

• Mixed-integer solvers or commercial optimizers.
• Explicit stochastic optimization formulations.
• Open benchmarks comparing B2Wise optimization to other engines.

The evidence therefore supports a view of B2Wise as a heuristic-driven planning system: it embodies codified Demand Driven logic rather than advanced numerical optimization. This does not mean the system is ineffective—DDMRP/DBR can work well in practice—but it is methodology-driven, not solver-driven.

Deployment and roll-out methodology

Methodology-centric projects

B2Wise’s implementation message revolves around four pillars: training and certifying planners, designing a flow-based model, agreeing processes, and fixing data—“Beyond go-live is where the real value is realized.”⁷²⁴²⁵ Training is heavily promoted, with DDBrix acting as both a teaching tool and a change-management device.²⁴²⁵

This indicates that B2Wise projects are:

• Methodology-led (DDMRP/DDOM first, tooling second).
• Designed to shift organizations toward Demand Driven thinking, not just swap out software.

Integration and rollout pattern

Typical deployment pattern (inferred from integration pages and case references):

1. Pilot & training – DDBrix sessions, planner certification.
2. Model design – identification of decoupling points, buffer locations and control points.
3. Integration – sFTP-based data flows, DataMart build, parameterization.
4. Go-live – DDMRP Material Planning, followed by DDOM scheduling and S&OP; integration gradually automated via APIs.^743242511

The reliance on batch file transfers and periodic imports highlights that B2Wise is oriented toward tactical planning horizons (daily to weekly), not minute-by-minute real-time control.

Clients, sectors and references

Named and corroborated clients

Across its marketing, videos and third-party references, B2Wise highlights clients including:

• Etex (building materials) – webinars on DDMRP transformation.⁷²⁸
• Goizper (industrial products) – case stories about sales growth and planning time reduction.⁷³⁰
• LVMH Fragrance Brands – testimonial on planning improvements.⁷
• PPG Industries – short video referencing better statistics via DDMRP.⁷²⁹
• WeirdFish (UK apparel) – testimonials describing store-replenishment improvements.⁷²⁷
• Takealot (South African e-commerce) – DDMRP in action video.²⁶
• Fruity Line and Fagor Ederlan – early adopters of CommWise.AI in S&OP and load-balancing respectively, cited in French supply chain press.¹⁹⁴⁹

Some of these names are corroborated by independent sources, such as personal profiles listing “implementing B2WISE (demand driven MRP)” at PPG, and Mondragon University events featuring B2Wise and Goizper jointly.²⁹⁵⁰³⁰

Additional claimed clients and sectors

Third-party directories (Promptloop, Nerdisa, LeadIQ) and portfolio lists mention further names such as bioMérieux, Air Liquide, SEB, Legrand, Afflelou, Piveteaubois and others, generally in manufacturing, chemicals, building products, luxury, distribution and MRO sectors.¹¹⁵¹³³ B2Wise’s own “Industries” page lists categories ranging from industrial products and food & chemicals to automotive and e-commerce, positioned under a unified Demand Driven Operating Model.³⁴

Some client names (e.g., NHS, DHL, Autoglass, Komatsu, BRP, Orica) appear in OfferZen’s company profile but lack independent corroboration; these should be treated as weak evidence of adoption.¹⁷

Scale claims

On pages such as the NetSuite integration marketing, B2Wise claims:

• $15 billion of inventory under management.
• 10 000+ clients.
• 8 000+ planning locations.⁴⁴

No external registry confirms these numbers, and they appear high relative to revenue estimates; they should be interpreted as self-reported marketing figures rather than audited metrics.

Commercial maturity and market position

Putting together age, scale and ecosystem signals:

• Age – operating since ~2016, with multiple geographic subsidiaries.^1245646
• Revenue & headcount – low-single-digit millions of USD in annual revenue and a team in the tens.³¹¹⁷¹¹
• Customer base – several dozen named industrial and distribution clients, plus a broader base of training participants through DDBrix.^{7242526272830}
• Ecosystem role – DDMRP/DDOM affiliate, training provider, and CommWise.AI finalist in supply chain innovation awards.^2318192021

B2Wise is therefore best characterized as an established niche player in the Demand Driven planning segment: neither experimental nor dominant, but with a meaningful footprint in DDMRP-oriented manufacturing and distribution.

Conclusion

Technically, B2Wise delivers a DDMRP-centric APS: it computes buffer-based replenishment recommendations, applies Drum-Buffer-Rope logic to bottleneck scheduling, embeds a professional forecasting engine (Forecast Pro), and wraps these components in S&OP workflows and an AI-assisted no-code layer (CommWise.AI) on top of a DataMart running on a modern AWS-based stack.^{78129431017111819131415} The architecture and implementation choices (serverless plus microservices, SQL + Spark, sFTP-based integration, Scrum delivery) are current-generation but conventional for SaaS APS offerings.

From a science and optimization perspective, B2Wise is methodology-driven rather than solver-driven: it operationalizes DDMRP and DDOM heuristics and leverages Forecast Pro’s well-established forecasting methods, but there is no public evidence of advanced stochastic optimization, custom numerical solvers, or deep technical transparency around its AI agents.^{78121011131415} Claims of AI-driven cleansing, segmentation and S&OP support are plausible but remain high-level, with no published metrics or algorithmic details.

Commercially, B2Wise appears to be a bootstrapped, founder-led vendor with a moderate but credible client base, notably in European manufacturing and South African distribution, closely tied to the Demand Driven Institute ecosystem and recognized in regional supply chain awards.^{123123242511181920212627283033} For organizations pursuing DDMRP/DDOM, B2Wise offers an integrated software-plus-training proposition that can institutionalize those methods. For buyers seeking mathematically transparent, probabilistic optimization with auditable algorithms and a broad modelling canvas, B2Wise’s current public footprint suggests a strong heuristic planning tool, but not a frontier optimization or AI research platform.

Sources