List: Warehouse Operations | Curated by Samir Saci | Medium

Sep 17, 2022

11 stories

Warehouse Operations

Samir Saci
in
Towards Data Science

Improve Warehouse Productivity using Order Batching with Python

Design a simulation model to estimate the impact of several Single Picker Routing Problem strategies in your Picking Productivity

Aug 5, 2020

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

Aug 5, 2020

Samir Saci
in
Towards Data Science

Improve Warehouse Productivity using Spatial Clustering with Python

Improve Warehouse Picking Productivity by Grouping Orders in Batches using Picking Location Spatial Clusters

Aug 11, 2020

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.

Aug 11, 2020

Samir Saci
in
Towards Data Science

Improve Warehouse Productivity using Pathfinding Algorithm with Python

Implement a Pathfinding Algorithm with Google AI Operation Research Python Libraries.

Aug 14, 2020

Improve Warehouse Productivity using Pathfinding Algorithm with Python

Aug 14, 2020

Samir Saci
in
Towards Data Science

Optimize Workforce Planning using Linear Programming with Python

What is the minimum number of temporary workers you need to hire to absorb your weekly workload while ensuring employees retention?

Apr 6, 2021

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.

Apr 6, 2021

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Samir Saci
in
Towards Data Science

Reduce Warehouse Space with the Pareto Principle using Python

How the 80/20 rule implemented using python can optimize your layout, reduce space utilization and improve the picking productivity

Apr 25, 2021

A warehouse storage system showing multiple racks filled with uniformly stacked pallets. The pallets are arranged in a vertical, three-level shelving unit, each containing boxes that are tightly packed. The shelving structure is made of metal frames, with green beams supporting the loads. This image represents an efficient use of warehouse space, visualizing the layout optimization process that could be improved using the Pareto Principle to focus on high-demand items.

Apr 25, 2021

Samir Saci
in
Towards Data Science

Deep Reinforcement Learning for AGV Routing

Increase Warehouse Productivity by using Reinforcement Learning for Automated Guided Vehicles Routing

Jun 9, 2021

This 3D rendering shows the layout of a warehouse with shelves arranged in multiple rows, where Automated Guided Vehicles (AGVs) are used to transport shelves directly to operators. The picking area is divided into sections with designated workstations where workers pack items. Operators are seen near the shelves and packing stations. The layout demonstrates an optimized warehouse design for minimizing walking time by using AGVs to enhance productivity.

Jun 9, 2021

Samir Saci
in
Towards Data Science

Lean Six Sigma with Python — Kruskal Wallis Test

Use the Kruskal Wallis Test in Lean Six Sigma with Python to evaluate the impact of training on warehouse productivity.

Aug 9, 2021

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.

Aug 9, 2021

Samir Saci
in
Towards Data Science

Central Limit Theorem for Process Improvement with Python

Estimate the workload for returns management assuming a normal distribution of the number of items per carton received from your stores.

Aug 24, 2021

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

Aug 24, 2021

Samir Saci
in
Towards Data Science

Lean Six Sigma with Python — Logistic Regression

Perform a Logistic Regression to estimate the minimum bonus needed to reach 75% of a productivity target.

Aug 31, 2021

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.

Aug 31, 2021

Samir Saci
in
Towards Data Science

Supply Chain Process Design using the Queueing Theory

Apply the Queueing Theory with Python to design a parcel packing process for an e-commerce fulfilment centre.

Sep 21, 2021

Diagram illustrating the “Parcel Packing Problem.” comparing two configurations for packing parcels in an e-commerce fulfillment center. The first case, “Single Line,” shows one conveyor belt with two parallel packing stations. The second case, “Multiple Lines,” shows two separate conveyor belts, each with its dedicated packing station. The objective is to determine the most efficient setup for parcel packing using the Queueing Theory with Python.

Sep 21, 2021

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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