United States-based enterprise supply-chain systems and technology specialist Tushar Manohar Mumbaikar is advancing an approach to digital logistics that connects structured operational control with explainable artificial intelligence for enterprise decision-making.

His broader work sits at the intersection of enterprise systems, supply-chain operations and applied artificial intelligence, with his current logistics frameworks representing one area of that wider practice.

His work addresses a practical challenge that can persist even within organisations operating sophisticated enterprise resource planning systems: warehouse and logistics activities may still depend heavily on emails, spreadsheets, attachments and individual follow-up before a business transaction is fully completed.

Mumbaikar approached the issue not simply as a communication problem, but as one involving process control, accountability, traceability and measurable execution.

That thinking led to the development of the Warehouse Movement Control Framework, WMCF, a methodology designed to transform fragmented warehouse-movement activity into structured business transactions with defined ownership, status, fulfilment and formal closure.

In its generalised form, the framework describes a five-stage operating lifecycle:

Draft → Submitted → Processed → Fulfilled → Closed

The model creates greater clarity around when a transaction is being prepared, when it formally enters the operating process, who is responsible for acting on it, what was ultimately fulfilled and when the transaction formally ended.

Instead of leaving that information distributed across multiple communications, the methodology brings those elements into a controlled transaction history.

Mumbaikar subsequently led the implementation of the framework as a custom enterprise application that progressed from concept into production use and continues to support recurring warehouse-movement operations.

During the early design stage, the project was explored under a working “Power App” concept. As the functional requirements matured, the implementation approach evolved to better accommodate the original business need for structured warehouse-request tracking, controlled processing, fulfilment visibility, audit history and enterprise integration.

The final production solution was therefore developed as a custom full-stack enterprise application with an Angular front end implemented on an in-house workflow engine.

The progression from initial business requirements to sustained production use is central to Mumbaikar’s work. It demonstrates how a structured operating concept can evolve technically while remaining focused on solving the same underlying logistics problem.

Preserving business intent and operational execution

One of WMCF’s key design characteristics is the preservation of the quantity originally requested alongside the quantity ultimately fulfilled.

If a warehouse is asked to provide 10 units but fulfils only eight, the transaction history can preserve both the original business requirement and the final operational outcome.

For example:

Requested quantity: 10

Fulfilled quantity: 8

Variance: 2

That distinction preserves two different dimensions of operational information: what the business intended to happen and what actually happened.

The resulting variance can support analysis of partial fulfilment, recurring shortages, inventory constraints, warehouse execution, service-performance differences and other operational exceptions.

Over time, repeated structured transactions can create historical information that enables organisations to identify patterns rather than treating each warehouse movement as an isolated activity.

This also establishes a foundation for analytics because the original business requirement remains available for comparison with the final operational result.

For Mumbaikar, the significance lies in converting warehouse activity into data that can be measured, interpreted and eventually used for more intelligent decision-making.

Independent evaluation reports measurable improvements

The WMCF methodology has also undergone an external professional evaluation.

In a controlled assessment using eight representative warehouse-movement requests, Germany-based SAP supply-chain and logistics specialist Akshay Kadekar compared the structured WMCF methodology with a conventional communication-driven process.

The evaluation examined transaction status, operational ownership, preservation of requested and fulfilled quantities, exception identification, manual status reconstruction, completeness of transaction history and evidence of formal closure.

Within the eight-request assessment, five of eight requests in the conventional process had immediately identifiable transaction status, compared with all eight under WMCF.

The same improvement was observed in operational ownership, where clearly identifiable responsibility increased from five of eight requests to all eight.

Preservation of both requested and fulfilled quantities increased from 25 per cent to 100 per cent, while requests requiring manual reconstruction of transaction status declined by approximately 83.3 per cent within the evaluation population.

The assessment further reported that complete end-to-end transaction history increased from 37.5 per cent to 100 per cent, immediate exception visibility improved from 50 per cent to 100 per cent, and explicit evidence of transaction closure increased from 37.5 per cent to 100 per cent.

The evaluation was designed to assess the underlying methodology rather than audit Mumbaikar’s employer or calculate production-level financial savings.

Its focus was instead on whether the framework could transform fragmented operating activity into information that was structured, measurable and traceable.

Kadekar characterised Mumbaikar’s work as an “original, measurable, practically relevant, and transferable contribution,” with potential significance extending beyond the organisation in which the initial implementation occurred.

From operational visibility to usable intelligence

The warehouse framework also provides the foundation for the next stage of Mumbaikar’s work.

Once logistics transactions are consistently structured, organisations can begin measuring indicators such as processing time, fulfilment time, transaction ageing, requested-versus-fulfilled variance, exception frequency, partial fulfilment and recurring material shortages.

The transition can therefore be understood simply as moving from:

activity that happened

to:

activity that can be measured.

For Mumbaikar, this distinction becomes increasingly important as organisations expand their use of artificial intelligence.

Rather than introducing AI before the underlying operational process has been standardised, his approach follows a progression from operational visibility to structured information, measurement, analytics and ultimately intelligent decision support.

That progression led to the development of a second methodology, the Explainable AI Logistics Decision Layer.

Why the cheapest route may not be the best route

Mumbaikar’s AI logistics approach is based on the premise that the cheapest or fastest transportation option does not necessarily represent the best overall business decision.

A low-cost route may carry greater disruption exposure, weaker reliability or poorer service performance. A faster alternative may fail to meet an operating, regulatory or capacity requirement.

The proposed architecture therefore treats logistics routing as a multi-objective enterprise decision problem.

Potential decision factors include transportation cost, transit time, service commitments, reliability, operational risk, disruption exposure, capacity and environmental impact.

A central feature of the framework is the distinction between mandatory constraints and optimisation preferences.

An option that violates a required delivery condition, regulatory requirement, security rule, capacity restriction or another non-negotiable operating requirement should not remain eligible simply because it is inexpensive.

Under the methodology, alternatives are first evaluated against mandatory conditions.

Infeasible options are removed before remaining alternatives are compared across factors such as cost, time, reliability, risk and carbon impact.

The process can broadly be represented as:

Generate alternatives → Check mandatory constraints → Remove infeasible options → Compare feasible options → Evaluate business trade-offs → Explain the recommendation → Human authorisation

The final stage is deliberate.

The framework is intended to support professional judgement rather than treat artificial intelligence as an unquestionable autonomous authority.

Keeping AI-assisted logistics decisions explainable

Explainability is another central component of Mumbaikar’s approach.

A logistics system that simply tells a planner to “Use Route B” provides limited insight into why that recommendation was produced.

An explainable system could instead show that Route B was preferred because a moderate transportation premium was offset by stronger reliability, lower modelled risk, improved environmental characteristics or compliance with mandatory service requirements.

This allows planners and managers to understand, question and ultimately approve or reject an AI-assisted recommendation.

Kadekar, who also reviewed Mumbaikar’s Explainable AI Logistics Decision Layer, considered its constraint-first architecture and emphasis on explainability relevant to enterprise supply-chain environments.

Importantly, the assessment distinguished demonstrated results from future expectations.

The AI decision layer has not yet been validated against a sufficiently large real-world logistics dataset. Its proposed benefits are therefore being treated as areas for future validation rather than established production outcomes.

Future testing can examine measurable factors including high-risk route selection, service-compliant recommendations, modelled operational risk, relative carbon performance and the proportion of recommendations accompanied by understandable decision rationales.

The approach therefore places measurement, explainability and human oversight alongside optimisation itself.

Professional recognition and a broader applied-AI foundation

Mumbaikar’s enterprise supply-chain work forms part of a broader professional trajectory combining enterprise technology, supply-chain systems and applied artificial intelligence.

He has attained the grade of IEEE Senior Member and has undertaken peer-review assignments involving scientific journals and technical research venues, contributing to the evaluation of research produced by other professionals.

His broader applied-AI record also includes intellectual-property and research activity. Mumbaikar is a named inventor on a published Indian patent application involving artificial intelligence-based diagnostic decision-support technology.

During 2026, he also presented applied-AI research at three international technology conferences.

Although these research activities involve different AI applications, they demonstrate a broader technical engagement with machine learning, intelligent systems and applied artificial intelligence that complements his enterprise technology background.

Mumbaikar’s current enterprise supply-chain work brings these areas together by applying intelligent-system concepts to enterprise supply-chain decision-making, where operational constraints, business rules, explainability and human judgement remain central.

While his current explainable-AI framework is designed specifically for enterprise logistics, several of its underlying principles, including structured data, constraint-aware decision-making, explainability, human oversight and measurable outcomes, are relevant to intelligent systems in other operational settings. Any such application, however, would require its own domain-specific data, rules and validation.

From workflow control to decision intelligence

The relationship between Mumbaikar’s two logistics frameworks remains central to his current work.

The Warehouse Movement Control Framework focuses on establishing visibility, accountability, structured execution and measurable historical information.

The Explainable AI Logistics Decision Layer addresses the next question: what can organisations do once sufficiently structured and reliable logistics information becomes available?

Together, the progression can be represented as:

Unstructured activity

↓

Controlled workflow

↓

Structured operational data

↓

Measurement

↓

Analytics

↓

Intelligent decision support

↓

Explainable recommendation

↓

Human authorisation

↓

Execution and feedback

Kadekar’s assessment similarly characterised the relationship between the two frameworks as a progression from visibility and accountability toward measurable operational data and more intelligent logistics decision-making.

For Mumbaikar, broader validation of the AI logistics framework represents the next stage of development.

Larger real-world or appropriately anonymised logistics datasets will ultimately be required to determine how effectively the methodology can balance transportation cost with service performance, reliability, operational risk and environmental considerations.

The broader principle behind the work is straightforward.

As organisations invest more heavily in artificial intelligence, effective deployment may depend not only on sophisticated algorithms, but also on whether the underlying operational processes produce information that is structured, reliable, measurable and understandable.

For industrial supply chains, the journey toward intelligent logistics may therefore begin with something more fundamental: knowing what was requested, who was responsible, what was actually fulfilled, where an exception occurred and whether the transaction formally ended.

For Mumbaikar, those fundamentals are not separate from artificial intelligence.

They are what make meaningful intelligence possible.