Review of B2WISE, Supply Chain Planning Software Vendor
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B2WISE positions itself as a cloud‐based, DDMRP‐compliant supply chain planning solution provider that pairs software with training and consulting services. Founded by two brothers around 2016–2017 and achieving rapid global expansion into regions including South Africa, the USA, and Europe, B2WISE actively aims to replace traditional, forecast‐driven MRP systems with a pull-based, demand-driven approach. Its platform integrates modules for real-time demand and supply planning, ERP connectivity, capacity planning, manufacturing scheduling, and KPI monitoring—all deployed on a modern tech stack that leverages AWS Serverless architecture (with options for on‑premise installation as well). Although the vendor promotes the use of AI, machine learning, and predictive analytics to refine forecasting and buffer sizing (via tournament techniques, hierarchical methods, and consensus algorithms), the technical disclosures suggest that these capabilities lean toward optimized heuristics rather than next‑generation deep learning innovations. Overall, B2WISE’s solution seeks to modernize supply chain planning by providing a comprehensive, easy-to-integrate tool for companies willing to embrace a demand-driven paradigm.
Company History and Evolution
B2WISE’s origins trace back to its founding by two brothers in 2017—with some materials hinting at an inception as early as September 2016—and it rapidly secured early client wins and accolades as part of its evolutionary journey12. The company has strategically expanded its presence across key global markets (South Africa, the USA, and Europe) without any major acquisitions or significant external investments, underscoring an organic growth strategy that emphasizes its commitment to modernizing supply chain planning.
Global Expansion and Investment
Through its focused rollout and evolving product suite, B2WISE has positioned itself as a notable player in supply chain planning. Its ability to capture diverse markets while remaining self-contained has fueled its reputation as a nimble and dedicated vendor in the DDMRP space.
Product Overview and Functionalities
B2WISE’s core offering is a supply chain planning application based on Demand Driven Material Requirements Planning (DDMRP). The product is engineered to sense changes in customer demand in real time and adjust production and inventory planning dynamically. Key functionalities include:
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Data Integration & ERP Connectivity:
The solution automates the integration of disparate data sources with seamless ERP connectivity, ensuring uniform data across operational processes3. -
Demand & Supply Planning:
It features dedicated portals for demand and supply planning, utilizing advanced forecasting methods such as tournament techniques and hierarchical approaches for seasonality and trend detection, complemented by consensus-based adjustments. -
Additional Capabilities:
The platform further includes modules for capacity planning, manufacturing scheduling, alert-based workflows, KPI monitoring, and role-based security to support comprehensive planning and execution456.
Technology and Deployment
B2WISE offers flexible deployment models, being available both as a cloud-based solution (leveraging a multi-regional, multi-language AWS Serverless architecture) and as an on-premise installation. The AWS Serverless upgrade is credited with delivering processing speeds up to 10× faster—a critical attribute for handling large volumes of real-time data7. Its technical stack is robust and modern, incorporating languages and frameworks such as JavaScript, Node.js, Python, Go, C#, .NET Core, and TypeScript, in tandem with cloud and DevOps tools like Docker, Git, Terraform, and Prometheus, ensuring scalability and performance across deployments8.
AI and ML Components – Evaluation
B2WISE markets its solution as incorporating AI, machine learning, and predictive analytics to enhance forecasting accuracy and optimize buffer parameters. Promotional materials emphasize features like “improve your forecast” and “optimize your parameters” that are designed to support rapid S&OP scenario planning. However, the technical details provided remain high level. The AI/ML components appear to be embedded within the forecasting and parameter optimization modules—relying on tournament forecasting techniques and consensus algorithms rather than on state‑of‑the‑art deep learning models. This suggests that while B2WISE leverages modern analytical methods, its AI may serve more as an enhanced heuristic component than as a breakthrough, autonomous intelligence platform.
Critical Assessment
B2WISE exhibits several strengths:
• Methodology & Architecture:
Grounded in the proven DDMRP methodology with a flexible, high-performance deployment model, the solution effectively bridges real-time decision-making with comprehensive planning needs.
• Comprehensive Feature Set:
Offering end-to-end functionalities—including ERP connectivity, demand/supply modules, and detailed KPI monitoring—B2WISE presents a one-stop solution for modern supply chain challenges.
Conversely, areas warranting skepticism include:
• Foundational AI/ML Claims:
Despite bold assertions regarding AI-enhanced forecasting and optimization, the lack of detailed disclosures on model architecture and performance metrics suggests that the AI components might be more marketing-oriented than truly disruptive.
• Transparency and Validation:
Potential adopters are advised to request empirical evidence or pilot studies to validate the claimed performance improvements, particularly in relation to the AI/ML modules.
B2WISE vs Lokad
When comparing B2WISE with Lokad, several key differences emerge:
• Methodological Focus:
B2WISE centers its solution on the DDMRP framework, emphasizing a pull-based, real-time approach that adapts production and inventory planning to immediate demand signals. In contrast, Lokad is known for its quantitative supply chain optimization platform that leverages predictive optimization via probabilistic forecasting and a custom domain-specific language (Envision).
• Deployment and Infrastructure:
B2WISE supports both cloud-based and on-premise deployments using an AWS Serverless architecture, which offers rapid processing capabilities and versatile integration options. Lokad, meanwhile, is delivered exclusively as a cloud-hosted SaaS solution on Microsoft Azure, with a heavy focus on internally developed components and event sourcing to drive decision automation.
• Approach to AI/ML:
B2WISE’s AI/ML claims are underpinned by advanced forecasting techniques (such as tournament forecasting) that are integrated into its system, though these seem to be less radical compared to Lokad’s implementation of deep learning, probabilistic forecasts, and differentiable programming. Lokad’s solution is designed to automate routine decisions by processing complex scenarios through a programmable, high-tech framework, whereas B2WISE leverages enhanced heuristics to optimize planning parameters.
• User Orientation:
B2WISE emphasizes ease of integration with conventional ERP systems and a straightforward, real-time planning interface. Lokad, in contrast, offers a highly technical, quant-focused platform that may appeal more to organizations willing to invest in a programmable, bespoke optimization environment.
Conclusion
B2WISE delivers a comprehensive, DDMRP-centric solution for modern supply chain planning via a flexible deployment infrastructure and a wide array of integrated functionalities—from real-time demand sensing to ERP connectivity and capacity planning. Its approach offers significant advantages for organizations seeking to transition from traditional forecast-driven systems to more responsive, pull-based models. However, while B2WISE promotes advanced AI and ML capabilities to enhance forecasting and optimization, available disclosures suggest that these features are likely built upon refined heuristic methods rather than on transformative deep learning innovations. As such, supply chain executives should critically evaluate the practical performance and transparency of its AI components before full-scale implementation.