Samir Saci on Medium curated some lists

Samir Saci

Samir Saci

Supply Chain Optimization

17 stories

A flowchart illustrating a supply chain control tower system. The chart shows three systems: Freight Forward, Warehouse Management, and Distribution Planning, all feeding data into SQL queries for data processing. The processed data is used for three separate analyses (Analysis 1, 2, and 3), each resulting in a report (Report 1, 2, and 3) sent via email.

Flowchart showing the different steps of a supply chain, from sourcing raw materials, manufacturing products, to distributing the final goods to stores. The image uses icons to represent each phase: a cotton plant for sourcing, a factory for manufacturing, a dress for the product, and a warehouse and store for distribution.

Illustration showing the relationship between inventory, working capital, and payment collection in cash flow management. The image depicts inventory in a warehouse connected to working capital, which is then linked to the collection of customer payments. It highlights the flow of money and goods, essential for managing inventory and liquidity in small businesses.

Samir Saci

Visualization & Business Intelligence

9 stories

A combination of supply chain analytics visualizations showcasing supply chain network optimization, production distribution, Pareto analysis, and capacity utilization. The top-left quadrant shows a network diagram with warehouses, routes, and truck connections. The top-right quadrant displays a heatmap of production schedules by country. The bottom-left quadrant features a Pareto chart illustrating warehouse output by volume.

Graphic illustrating six dimensions of data quality in supply chain management. ‘Accuracy’ is shown by a target and arrow, ‘Completeness’ by a 100% symbol, ‘Consistency’ by fitting puzzle pieces, ‘Timeliness’ by a clock and bar graph, ‘Validity’ by a database and magnifying glass, and ‘Uniqueness’ by people icons with a ‘≠’ sign, symbolizing no duplicates. Each dimension highlights a critical aspect of ensuring reliable, consistent, and complete data for efficient operations.

A geographic map displaying a road transportation network in China with nodes connected by lines representing Full Truck Load (FTL) routes. The visualization includes route paths with metrics like delivery count, cost per ton, and truck size generated with Python. The map helps assess performance across different routes and locations. This visualization is useful for optimizing transportation efficiency and cost.

Samir Saci

Automation (RPA)

13 stories

A diagram illustrating the automation of invoice processing using Robotic Process Automation (RPA). The process begins with multiple invoices in PDF format, which are then processed using Python. The data from the invoices is extracted and entered into an Excel file, followed by validation and uploading to a digital system. The diagram shows Python at the center, automating the data extraction, validation, and uploading of invoice data into a structured format, improving efficiency.

Flowchart showing the process of automating video editing with Python. The raw video is processed by muting the background music and cutting the video into shorter clips. Each short video is then paired with text-to-speech comments, creating three distinct clips, ‘Short 1’, ‘Short 2’, and ‘Short 3’, each with its corresponding comment.

SAP Automation for Retail

Samir Saci

Sustainability

14 stories

A flow diagram illustrating the rental process for clothing. The process begins with the customer picking up a garment from the store on day 1, wearing it for 14 days, and then returning it to the store. The returned item is inspected, cleaned, and made available for another rental cycle, emphasizing the reuse model of a circular economy to reduce waste and environmental impact with rental.

Three factories compared on three environmental factors: water usage, waste management, and CO2 emissions. Each factory is rated with stars on each factor, with one to five stars used for evaluation. The visual represents sustainable sourcing considerations for supply chain optimization, highlighting differences in supplier environmental performance used for sustainable sourcing with Python.

Samir Saci

Logistics Continuous Improvement

4 stories

A Gantt chart showing the sequence of production tasks for different products on three machines over 13 minutes. The chart displays three products: a dress, a handbag, and a belt. Each product is processed at different times on Machine 1, Machine 2, and Machine 3, illustrating the scheduling of tasks to optimize the production flow using Python.

A visual representation of supply chain process optimization, featuring two red forklifts unloading a large red shipping container marked ‘TEX’ on a warehouse floor. The title reads ‘Supply Chain Process Optimization,’ with a subtitle describing how linear programming can be a powerful tool for a supply chain continuous improvement engineer. The image emphasizes the importance of optimizing warehouse and logistics operations in the supply chain.

Samir Saci

Business Optimization

3 stories

Diagram showing the relationship between turnover, costs, and profit. The image highlights how increasing turnover, represented by a handshake and rising dollar signs, leads to higher profits. On the other side, reducing costs, represented by a box and gear with a downward arrow, also increases profitability. The diagram emphasizes balancing revenue optimization and controlling costs to maximize profit with Python.

Illustration showing the relationship between inventory, working capital, and payment collection in cash flow management. The image depicts inventory in a warehouse connected to working capital, which is then linked to the collection of customer payments. It highlights the flow of money and goods, essential for managing inventory and liquidity in small businesses.

Samir Saci

Generative AI

2 stories

A diagram showing the relationship between Excel file input, data analysis, and communication processes. It depicts a workflow automated with a custom GPT where an Excel file leads to the automation of decision-making processes for supply chain optimization. The output involves a GPT called Supply Chain nalyst performing classification tasks, with the final result displayed in an understandable format for communication.

This diagram depicts a supply chain control tower with five major elements boosted by LLMs using LangChain with Python. Starting from the top, we have factories, a warehouse, delivery trucks, and a store. Below that, a question flow appears, transitioning through machine learning systems and SQL databases before reaching an AI agent. This image represents the data pipeline and decision-making process from raw supply chain data to AI-driven analytics for optimized operations.

Samir Saci

Productivity

5 stories

Diagram illustrating how to connect to an API using Google Sheets without coding. On the left, an icon representing Google Sheets connects via a red line to an API icon in the center. Another red line connects the API icon to a JSON response icon on the right. This flow shows how Google Sheets can retrieve data from an API and return it in JSON format, demonstrating a no-code method for API integration.

Diagram showing the automation process for creating warehouse labels using Python Pillow. It starts with an Excel file containing location and product data. A Python script reads the data to create barcodes and adds a location number and stickers. The labels include different arrows and color-coded information, designed for warehouse signage of various storage locations.

A flowchart illustrating the automation of PowerPoint slide creation for supply chain operations using Python. The process starts with 5 weeks of operational activity data from a warehouse database. SQL queries extract prepared order lines, which are processed by Python scripts to calculate KPIs. The output is a PowerPoint report showing warehouse workload and order profiles per week.

Samir Saci

Warehouse Operations

11 stories

A warehouse floor layout is shown with aisles and a path marked for an order picker. The diagram illustrates a strategy for optimizing order batching by reducing the distance walked during the picking process. There are multiple aisles labeled A1 through A20, and the picker’s start point is highlighted along with a path that traverses various aisles to retrieve items. The image aims to show how different strategies can improve productivity by minimizing walking distance between picking locations

Improve Warehouse Productivity using Spatial Clustering with Python. A visual representation of a warehouse layout with aisles labeled A01 to A19. The image demonstrates the concept of spatial clustering for warehouse productivity. Red dotted clusters group picking locations, marked with coordinates (xi, yi), indicating proximity within a defined distance threshold. The goal is to reduce the walking distance of warehouse operators by clustering orders with nearby picking locations.

Improve Warehouse Productivity using Pathfinding Algorithm with Python

Samir Saci

Demand Forecasting

3 stories

A grocery store shelf filled with various dairy products, including milk, yogurt, and beverages.

This infographic illustrates the key features for improving retail unit sales forecasting using machine learning. In the center, “Retail Sales” is highlighted, connected by arrows to several features. To the left, a stock-out sign asks if the store faced stock-out recently. A sales quantity chart asks for the maximum sales in the last “n” days. A price tag icon prompts for recent pricing changes, and a closed sign queries if the store was closed. Lastly, a sales trend chart asks for the sales.

Samir Saci

Transportation Operations

7 stories

A geographic map displaying a road transportation network in China with nodes connected by lines representing Full Truck Load (FTL) routes. The visualization includes route paths with metrics like delivery count, cost per ton, and truck size generated with Python. The map helps assess performance across different routes and locations. This visualization is useful for optimizing transportation efficiency and cost.

Build a GPS Routing API with Python Flask

Samir Saci

Linear Programming

12 stories

A diagram showing different aspects of the workforce planning problem, including time constraints, tasks planning, schedule constraints, workload forecasting, and workforce planning. The diagram outlines how these factors interact to optimize workforce management with Python.

A graphic showing how to maximize bakery profitability by selecting the right items to sell. Three bakery items are listed: Cake (profit: 6€, preparation: 95 min), Croissant (profit: 0.9€, preparation: 55 min), and Sandwich (profit: 4.4€, preparation: 12 min). The graphic highlights the differences in profit margins and preparation times, helping business owners prioritize higher-margin, less time-intensive products to improve overall business profitability with Python.

A diagram illustrating the last-mile delivery process for e-commerce optimization using Python. A truck is shown delivering parcels to a warehouse, from which a delivery person on a scooter distributes packages to multiple locations. Dotted lines connect the scooter to several drop-off points marked with question marks, representing unknown delivery destinations. The graphic emphasizes the process of organizing delivery routes to minimize the number of drivers needed for parcel delivery.

Samir Saci

Logistic Performance Management

4 stories

A flowchart titled “Import Data, Select Objective, Visualize Results” shows a three-step process for supply chain optimization. The process begins with “Import Data” featuring a table icon, followed by “Select Objective” with a target icon. Below, three objectives are listed: minimize waste (factory icon), minimize cost (warehouse icon), and minimize CO2 emissions (truck icon). The final step is “Visualize Results” with icons representing a world map and data charts.

A comparison timeline showing both the actual and target timelines for logistic performance. The “Actual Timeline” is at the top, with white dots marking key events like picking, packing, shipping, and delivery. Below, the “Target Timeline” aligns these events with the expected timeline. This chart visually contrasts actual performance against target performance, highlighting gaps between expected and real operational timings.

Samir Saci

Lean Six Sigma

5 stories

A diagram showing the process to evaluate the impact of warehouse operator training on productivity using Kruskal Wallis Test with Python. Two groups of operators are randomly selected: one group consists of trained operators, and the other group consists of untrained operators. The productivity of both groups is measured using stopwatches, aiming to determine whether training has a significant impact on operator productivity.

An infographic explaining the application of the Central Limit Theorem for process improvement in returns management. The image has three sections: (1) Assumption of a normal distribution (N(σ, μ)) for the number of items per return carton, (2) Calculation of the probability of receiving between n1 and n2 items per carton, and (3) Estimation of the workforce needed for returns management. The graphic includes icons for data distribution, a work desk for processing returned items, and workers for

A diagram illustrating the “Minimum Bonus Problem.” The title asks, “What is the minimum bonus level needed to reach 75% of your productivity target?” Three rows show warehouse operators being randomly selected, each row with a worker icon holding a money bag. Arrows point from these workers toward stopwatch icons labeled “Productivity Target?”. Use of Kruskal Wallis Test with Python to evaluate how different bonus levels impact operator productivity, specifically aiming to achieve the target.

Samir Saci

Inventory Management

5 stories

A matrix for statistical product segmentation, which categorizes products based on demand variability (vertical axis) and economic value classification (horizontal axis). Products are classified into three groups: “High Importance” with high demand variability and high economic value (A category), “Stable Demand” with low demand variability (B category), and Low Importance with low economic value and low demand variability (C category). It highlights the need to focus on high-importance products

Inventory Management for Retail — Deterministic Demand

A three-panel chart showing inventory management with stochastic demand with Python. The top chart (red) displays demand fluctuations following a normal distribution over time. The middle chart (blue) depicts replenishment activity occurring when inventory falls below a certain threshold. The bottom chart (green) shows inventory levels decreasing and replenishing in cycles. The right side highlights key concepts: normal demand distribution, (s,Q) order policy, and replenishment with lead time.

Samir Saci

Samir Saci

Top Supply Chain Analytics Writer — Follow my journey using Data Science for Supply Chain Sustainability 🌳 and Productivity ⌛ https://samirsaci.com/about

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