Burak Suyunu — Lumtify

Worked on model development for the Çimsa and MMK optimization projects.

Projects

Çimsa

Çimsa S&OP: from global network optimization to the fuel procurement decision

We brought Çimsa's production, logistics and sales network across 15 locations into a single mixed-integer model, then built a second model that turns the resulting clinker production plan into a fuel recipe and procurement decision.

10% ↓ Inventory cost
3% ↓ Manufacturing, warehousing, distribution cost

MMK

MMK: line balancing and order–raw material matching in flat steel production

An optimization system that matches open orders to the right raw material while respecting production line capacity and every stage of the bill of materials. Built across three phases.

4,000+ Open sales orders
~9 Raw material candidates per order

Kuveyt Türk

Kuveyt Türk: master–apprentice mentoring for two analytics teams — from digital marketing to retail customer analytics

It began in 2023 with training for Digital Marketing's analytics team: SQL, Power BI, Python, machine learning. The team then built its own projects under Lumtify's mentoring — digital score, target management, churn cause analysis, UI usage analysis, product recommendation and churn prediction models. From 2025, spending analytics, a luxury spend score and next-best-action prioritisation with the Retail Customer Analytics team. The team writes the code; Lumtify sets direction, brings method and checks the output.

Master – apprentice Model
8+ Digital projects

Kalekim

Kalekim: from MVP to a forecast management system — three phases in three years

We started with a machine-learning MVP for two product groups fed from Excel; in year two it became a forecasting process retrained every month for every product, and in year three the Kalekimforecast application that planners run themselves. The forecast is now an input to Kalekim's supply, production and logistics planning.

2 groups → all Scope
3 Encoding methods

Hayat Kimya

Hayat Kimya: weekly e-commerce order forecasting on Microsoft Fabric

A system that forecasts marketplace and e-commerce orders weekly, at the granularity the warehouse shift plan needs, and runs end to end as a single Fabric pipeline from data warehouse to report.

Weekly Forecast horizon
One pipeline Automation

Türkiye Finans Participation Bank

Türkiye Finans: training and mentoring for the HR analytics team — from recruitment assessments to employee attrition

One day a week in 2024 with the participation bank's HR Analytics team: Python and machine learning training, then mentoring on the team's own projects. The relationship between the recruitment assessment inventory and performance and retention was analysed by job family; a literature review and model design were done for employee attrition prediction. The method learned was applied to the institution's own data at once.

1 day a week Format
Python · ML Training

Kale Seramik

Kale Seramik: a demand forecasting PoC — and why we did not continue

An MVP that started on the same roadmap as Kalekim: two product functions, 34 methods, three encoding approaches. The result fell short of the target and the second phase was not started. This page explains what did not work, and why.

141 DFUs
200+ Product attributes

Banks Association of Türkiye (TBB)

TBB: an end-to-end data scientist programme for bankers

Within the Banks Association of Türkiye's training programme, a 62-hour, 21-day 'Data Analytics Journey' for bank employees who use data in their work: introduction to data science, SQL, Python, statistics and EDA, visualisation, machine learning, industry cases. Evening sessions, homework for every module, hands-on work in Colab; every module was scored out of 10 in TBB's participant survey — averaging above 9.

62 hours Duration
9+ / 10 Evaluation