Smart Factory Workforce Planner
Agentski AI sistem za industrijske kompanije koji raspoređuje radnike na projekte prema veštinama, dostupnosti, opterećenju i rokovima.
Pritisak raspodele radnika
Menadžeri ručno balansiraju veštine, dostupnost, opterećenje, rokove, odsustva i hitne promene.
Asistent za planiranje operacija
Sistem predlaže planove raspodele prema podudarnosti radnika, potrebama projekta i promenljivim ograničenjima.
Jasnije odluke o kapacitetu
Timovi operacija dobijaju bolji način da uporede opcije pre nego što menadžer odobri promene.
Menadžeri operacija često ručno koordinišu ljude kroz projekte, uloge, odsustva, rokove i hitne promene.
Daje menadžerima operacija jasniji način da planiraju kapacitet i reaguju na promene u projektima.
- Uparuje radnike sa zahtevima projekta
- Uzima u obzir veštine, dostupnost, opterećenje i rokove
- Reaguje na odsustva i hitne promene
- Predlaže planove raspodele radne snage
- Pomaže menadžerima da smanje haos u raspoređivanju
Od ulaza do preglednog sledećeg koraka.
- 01
Prikupljanje potreba projekta
Pregledaju se rokovi, potrebni ljudi, veštine, dostupnost i ograničenja opterećenja.
- 02
Mapa veština i kapaciteta
Veštine radnika, trenutna zaduženja i dostupnost porede se sa zahtevima projekta.
- 03
Predlog raspodele
Generišu se mogući planovi raspodele koji uzimaju u obzir podudarnost i operativni pritisak.
- 04
Reakcija na promene
Plan se ažurira kada odsustvo, hitnost ili rok promene trenutni raspored.
- 05
Odobrenje menadžera
Konačna odluka o rasporedu ostaje kod menadžera operacija.
Case study notes
This project started from a planning problem common in industrial environments: the right people are not always available at the right time, and project schedules can change faster than manual planning can keep up.
Instead of designing a black-box autopilot, I framed the system as a reviewable workforce planning assistant. The AI helps organize worker skills, project requirements, workload, and deadlines, then prepares allocation suggestions that a human planner can inspect.
The goal of the concept is not to replace operations managers. The goal is to reduce planning friction, make constraints more visible, and support better staffing decisions when multiple projects compete for the same people.
Vidljiv dokaz, bez privatnih operativnih podataka.
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Tools, concepts, and architecture
The workflow is designed as a sequence of small planning steps rather than one large autonomous scheduling agent.
- 1Load workforce, skills, and project requirements.
- 2Match available people to required roles.
- 3Check workload, deadlines, and conflicts.
- 4Generate a draft allocation plan.
- 5Send the plan to a human planner for review.
Detailed case study report
For visitors who want to understand how the concept was shaped, how I framed the planning problem, and how I would develop it further.
Why I built it
I wanted to explore how agentic AI patterns could support real industrial planning problems, especially situations where people, skills, workload, and deadlines have to be balanced across multiple projects.
Problem framing
The core problem is not simply assigning workers to tasks. It is understanding which skills are required, who is available, where workload is already high, and how schedule changes affect the rest of the plan.
Workflow design
I shaped the concept around a planning loop: load workforce and project data, map skills to project needs, check constraints, suggest allocations, and keep the final plan reviewable by a human.
AI role
The AI role is limited and practical: organize constraints, identify possible conflicts, suggest allocation options, and explain why a plan may or may not work.
Testing approach
The concept can be tested with synthetic factory data by checking whether the system produces understandable allocations, surfaces conflicts, and keeps decisions easy to review.
Next improvements
The next layer would add richer scenario planning, drag-and-drop schedule adjustments, historical workload tracking, stronger constraint rules, and integration with ERP or project management tools.
What this proves
AI for planning support
AI can be useful in industrial planning when it helps structure constraints, compare options, and prepare decisions instead of acting as an uncontrolled scheduler.
Human-approved allocation
The strongest part of the design is the review loop: workforce suggestions remain visible and adjustable before any real schedule change is made.
What I would improve next
I would turn the concept into a richer interactive planner with scenario comparison, better workload visualization, and clearer explanations for each allocation recommendation.
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