List: Inventory Management | Curated by Samir Saci | Medium

Sep 17, 2022

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Inventory Management

In

Towards Data Science

by

Samir Saci

Product Segmentation for Retail with Python

A statistical methodology to segment your products based on turnover and demand variability

Sep 14, 2021

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

Sep 14, 2021

In

Towards Data Science

by

Samir Saci

Inventory Management for Retail — Deterministic Demand

Build a simple model to simulate the impact of several replenishment rules on inventory and ordering costs.

Oct 5, 2021

Inventory Management for Retail — Deterministic Demand

Oct 5, 2021

In

Towards Data Science

by

Samir Saci

Inventory Management for Retail — Stochastic Demand

Simulate the impact of safety stock level on inventory performance metrics with normal demand distribution.

Oct 14, 2021

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.

Oct 14, 2021

Samir Saci

Machine Learning for Retail Sales Forecasting — Features Engineering

Understand the impacts of additional features related to stock-out, store closing date or cannibalization on a ML model for sales…

Oct 21, 2021

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.

Oct 21, 2021

In

Towards Data Science

by

Samir Saci

Inventory Management for Retail — Periodic Review Policy

Implement inventory management rules based on a periodic review policy to reduce the number of stores replenishments

Jan 25, 2022

An infographic demonstrating a periodic inventory management policy. The first chart shows fluctuating demand over time with a normal distribution. The second chart illustrates replenishment at regular intervals (R, S order point), where inventory is refilled to a set level S after a fixed period R. The third chart displays the inventory level over time, with replenishment occurring when inventory falls to a reorder level, considering lead time (LD). The visual highlights inventory optimization.

Jan 25, 2022

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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