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Dr. Owns

January 10, 2025

Use data analytics to help companies design and implement strategic sustainability roadmaps to reduce their environmental footprint.

Sustainable Business Strategy with Analytics — (Image by Samir Saci)

Consensus means that everyone agrees to say collectively what no one believes individually.

This quote captures a critical issue many companies face during their strategic green transformation: aligning diverse objectives across teams and departments.

Sustainability Team: “We need to reduce emissions by 30%”.

Imagine a hypothetical manufacturing company with, at the centre of its business strategy, an ambitious target of reducing CO2 emissions by 30%.

Illustration of a supply chain flow showing five stages: Factory, Freight, Warehouse, Delivery, and Customers. The icons represent each stage: a factory with a worker, freight involving air and sea transport, a warehouse with stacked boxes, a delivery truck, and a retail store alongside a residential home labeled ‘Customers’.
Value chain of our example — (Image by Samir Saci)

The sustainability team’s challenge is to enforce process changes that may disrupt the activities of multiple departments along the value chain.

Illustration of stakeholders involved in sustainability. At the top, a globe with leaves represents sustainability. Below are icons for various roles: a retail store, a finance manager holding a money bag, a warehouse with stored goods, and a logistics manager with a checklist, symbolizing collaboration across departments.
Sustainability Project Steering Committee — (Image by Samir Saci)

How do you secure the approval of multiple stakeholders, that potentially have conflicting interests?

In this article, we will use this company as an example to illustrate how analytics models can support sustainable business strategy.

How to Build a Sustainability Roadmap?

You are a Data Science Manager in the Supply Chain department of this international manufacturing group.

Under pressure from shareholders and European regulations, your CEO set ambitious targets for reducing the environmental footprint by 2030.

Illustration of a sustainability-focused project governance structure. A CEO at the top directs a sustainability initiative represented by a globe with leaves. Below, four stakeholders are involved: retail (a store), finance (a manager with a money bag), warehousing (a warehouse), and logistics (a manager with a checklist), symbolizing collaborative efforts.
Stakeholders Involved in the Process — (Image by Samir Saci)

The sustainability department leads a cross-functional transformation program involving multiple departments working together to implement green initiatives.

Sustainable Supply Chain Network Optimization

To illustrate my point, I will focus on Supply Chain Network Optimization.

The objective is to redesign the network of factories to meet market demand while optimizing cost and environmental footprint.

Icons representing various factory setups: small retail-like factories and a larger industrial-looking facility, each labeled with varying capacity levels. The illustration highlights different manufacturing setups to compare production capabilities or scale.
Five Markets of our Manufacturing Company — (Image by Samir Saci)

The total demand is 48,950 units per month, spread across five markets: Japan, the USA, Germany, Brazil, and India.

Doughnut chart showing demand distribution by market location. The USA accounts for 57.2%, Japan for 34.7%, India for 3.27%, Brazil for 2.96%, and Germany for 1.84%. The chart visually compares market share for demand distribution.
Demand Distribution per Market — (Image by Samir Saci)

Markets can be categorized based on customer purchasing power:

  • High-price markets (USA, Japan and Germany) account for 93.8% of the demand but have elevated production costs.
  • Low-price markets (Brazil and India) only account for 6.2% of the demand, but production costs are more competitive.

What do we want to achieve?

Meet the demand at the lowest cost with a reasonable environmental footprint.

Map illustrating supply chain manufacturing sites across the globe. Two views are presented: one highlighting low-capacity sites and another for high-capacity sites. Additional information includes production costs (fixed and variable) and freight costs for delivering goods from factories to markets.
Market Demand vs. Supply Capacity — (Image by Samir Saci)

We must decide where to open factories to balance cost and environmental impacts (CO2 Emissions, waste, water and energy usage).

Manufacturing Capacity
In each location, we can open low or high-capacity plants.

Bar chart comparing production capacity by location, with low capacity at 5,000 units per month (blue bars) and high capacity at 15,000 units per month (red bars). Locations include the USA, Germany, Japan, Brazil, and India.
Production Capacity per Location — (Image by Samir Saci)

Fixed Production Costs
High-capacity plants have elevated fixed costs but can achieve economies of scale.

Bar chart displaying fixed costs by production location and site type. Costs are divided between low-capacity sites (blue bars) and high-capacity sites (red bars). Locations include the USA, Germany, Japan, Brazil, and India, with the USA and Japan showing the highest fixed costs.
Fixed Production Costs — (Image by Samir Saci)

A high-capacity plant in India has lower fixed costs than a low-capacity plant in the USA.

Fixed costs per unit are lower in an Indian high-capacity plant (used at full capacity) than in a US low-capacity factory.

Variable Costs
Variable costs are mainly driven by labour costs, which will impact the competitiveness of a location.

Bar chart illustrating variable costs per unit produced by location. Costs are shown in euros per unit, with the USA and Germany having the highest costs (120 and 130 €/unit, respectively), while Brazil and India have the lowest costs (80 and 50 €/unit)
Production Costs per Location — (Image by Samir Saci)

However, we need to add freight delivery rates from the factory to the markets in addition to production costs.

If you move the production (for the North American market) from the USA to India, you will reduce production costs but incur additional freight costs.

What about the environmental impacts?

Manufacturing teams collected indicators from each plant to calculate the impact per unit produced.

  • CO2 emissions of the freight are based on the distance between the plants and their markets.
  • Environmental indicators include CO2 emissions, waste generated, water consumed and energy usage.
Maps comparing the environmental footprints of logistics and manufacturing by country. The left map shows freight emissions routes from factories to markets. The right map highlights CO2 emissions, water usage, waste generation, and energy usage for production in various countries.
Environmental Footprint of Manufacturing & Logistics — (Image by Samir Saci)

We take the average output per unit produced to simplify the problem.

Four bar charts showing environmental impacts per production location: (1) Energy usage (MJ/unit) with India having the highest at 780, (2) Water usage (L/unit) with India leading at 3,500, (3) CO2 emissions (kgCO2eq/unit) with India highest at 41, and (4) Waste generation (kg/unit) with India at 0.78.
Environmental Impact per unit produced for each location — (Image by Samir Saci)

For instance, producing a single unit in India requires 3,500 litres of water.

To summarize these four graphs, high-cost manufacturing locations are “greener” than low-cost locations.

You can sense the conflicting interests of reducing costs and minimizing environmental footprint.

What is the optimal footprint of factories to minimize CO2 Emissions?

Data-driven Supply Chain Network Design

If we aim to reduce the environmental impact of our production network, the trivial answer is to produce only in high-end “green” facilities.

Unfortunately, this may raise additional questions:

Illustration of stakeholder concerns in sustainability projects: a retail store questioning costs of goods sold in low-price markets, a financial manager evaluating profitability, a warehouse manager assessing logistics flows, and a logistics worker addressing variable costs in green locations, all connected under a sustainability initiative symbolized by a globe with leaves.
Steering Committee questions — (Image by Samir Saci)
  • Logistics Department: What about the CO2 emissions of transportation for countries that don’t have green facilities?
  • Finance Team: How much will the overall profitability be impacted if we move to costly facilities?
  • Merchandising: If you move production to expensive “green” locations, what will happen to the cost of goods sold in India and Brazil?

These are questions that your steering committee may raise when the sustainability team pushes for a specific network design.

In the next section, we will simulate each initiative to measure the impact on these KPIs and give a complete picture to all stakeholders.

Data Analytics for Sustainable Business Strategy

In another article, I introduce the model we will use to illustrate the complexity of this exercise with two scenarios:

  • Scenario 1: your finance director wants to minimize the overall costs
  • Scenario 2: sustainability teams push to minimize CO2 emissions

Model outputs will include financial and operational indicators to illustrate scenarios’ impact on KPIs followed by each department.

Diagram illustrating cost and environmental impact distribution along the supply chain. Costs of goods sold link to retail, production costs link to manufacturing, and logistics costs link to freight and delivery markets. Environmental impacts include production and logistics footprints, managed by the sustainability department.
Multiple KPIs involving several departments — (Image by Samir Saci)
  • Manufacturing: CO2 emissions, resource usage and cost per unit
  • Logistics: freight costs and emissions
  • Retail / Merchandising: Cost of Goods Sold (COGS)

As we will see in the different scenarios, each scenario can be favourable for some departments and detrimental for others.

Do you imagine a logistic director, pressured to deliver on time at a minimal cost, accepting the disruption of her distribution chain for a random sustainable initiative?

Data (may) help us to find a consensus.

Scenario 1: Minimize Costs of Goods Sold

I propose to fix the baseline with a scenario that minimizes the Cost of Goods Sold (COGS).

The model found the optimal set of plants to minimize this metric by opening four factories.

Icons representing manufacturing plants of various sizes and capacities, ranging from small factories to large industrial facilities. Each icon highlights capacity differences and potential production output.
Manufacturing network for Scenario 1 — (Image by Samir Saci)
  • Two factories in India (low and high) will supply 100% of the local demand and use the remaining capacity for German, USA and Japanese markets.
  • A single high-capacity plant in Japan dedicated to meeting (partially) the local demand.
  • A high-capacity factory in Brazil for its market and export to the USA.
Sankey diagram showing supply chain flows from production locations to markets. Japan, India, and Brazil production supply units to markets in Japan, the USA, Germany, Brazil, and India, with flows varying in size to represent volume distribution per market.
Solution 1 to minimize costs — (Image by Samir Saci)
  • Local Production: 10,850 Units/Month
  • Export Production: 30,900 Units/Month

With this export-oriented footprint, we have a total cost of 5.68 M€/month, including production and transportation.

Stacked bar chart showing the costs of goods sold (COGS) analysis by production location. The chart includes fixed costs (blue) and variable costs (red). The total cost is broken down into Japan (2.07 M€/month), Brazil (1.42 M€/month), and India (1.52 M€/month), with the highest total at 5.68 M€/month
Total Costs Breakdown — (Image by Samir Saci)

The good news is that the model allocation is optimal; all factories are used at maximum capacity.

What about the Costs of Goods Sold (COGS)?

Stacked bar chart showing COGS breakdown by market, highlighting transportation (green), production (red), and fixed costs (blue). Japan has the highest COGS at 4.12 €/unit, followed by Germany and the USA, while Brazil and India have the lowest at 80 and 50 €/unit respectively.
COGS Breakdown for Scenario 1 — (Image by Samir Saci)

Except for the Brazilian market, the costs of goods sold are roughly in line with the local purchasing power.

A step further would be to increase India’s production capacity or reduce Brazil’s factory costs.

From a cost point of view, it seems perfect. But is it a good deal for the sustainability team?

The sustainability department is raising the alert as CO2 emissions are exploding.

We have 5,882 (Tons CO2eq) of emissions for 48,950 Units produced.

Bar chart displaying CO2 emissions by market location and source. The USA market has the highest total emissions (4,980 tons CO2eq), with transportation contributing 3,870 tons and production 1,110 tons. Emissions for Brazil, Germany, India, and Japan are significantly lower, with Brazil at 55 tons CO2eq
Emissions per Market — (Image by Samir Saci)

Most of these emissions are due to the transportation from factories to the US market.

The top management is pushing to propose a network transformation to reduce emissions by 30%.

What would be the impact on production, logistics and retail operations?

Scenario 2: Localization of Production

We switch the model’s objective function to minimize CO2 emissions.

Icons illustrating a variety of manufacturing site configurations, representing low-capacity and high-capacity factories. The image compares different plant types based on their environmental and operational characteristics.
Manufacturing network for Scenario 2 — (Image by Samir Saci)

As transportation is the major driver of CO2 emissions, the model proposes to open seven factories to maximize local fulfilment.

A Sankey diagram depicting production and market flows for different locations. The USA, Germany, Japan, Brazil, and India are shown as production points linked to their respective or export markets with varying unit volumes represented by flow widths.
Supply Chain Flows for Scenario 2 — (Image by Samir Saci)
  • Two low-capacity factories in India and Brazil fulfil their respective local markets only.
  • A single high-capacity factory in Germany is used for the local market and exports to the USA.
  • We have two pairs of low and high-capacity plants in Japan and the USA dedicated to local markets.

From the manufacturing department’s point of view, this setup is far from optimal.

We have four low-capacity plants in India and Brazil that are used way below their capacity.

A bar chart comparing variable and fixed costs by production location (USA, Germany, Japan, Brazil, and India). The total cost is prominently displayed, highlighting how fixed and variable costs contribute to overall production costs.
Costs Analysis — (Image by Samir Saci)

Therefore, fixed costs have more than doubled, resulting in a total budget of 8.7 M€/month (versus 5.68 M€/month for Scenario 1).

Have we reached our target of Emissions Reductions?

Emissions have dropped from 5,882 (Tons CO2eq) to 2,136 (Tons CO2eq), reaching the target fixed by the sustainability team.

A bar chart showing CO2 emissions in tons by market (Brazil, Germany, India, Japan, and the USA) with sources split into production and transportation emissions. The USA has the highest combined emissions, with transportation dominating.
Emissions per Market (Scenario 2) — (Image by Samir Saci)

However, your CFO and the merchandising team are worried about the increased cost of sold goods.

A stacked bar chart showing the breakdown of the cost of goods sold by market (USA, Germany, Japan, Brazil, and India) into production, transportation, and fixed costs. India and Brazil have the highest COGS due to high fixed and production costs.
New COGS for Scenario 2 — (Image by Samir Saci)

Because output volumes do not absorb the fixed costs of their factories, Brazil and India now have the highest COGS, going up to 290.47 €/unit.

However, they remain the markets with the lowest purchasing power.

Merchandising Team: “As we cannot increase prices there, we will not be profitable in Brazil and India.”

We are not yet done. We did not consider the other environmental indicators.

The sustainability team would like also to reduce water usage.

Scenario 3: Minimize Water Usage

With the previous setup, we reached an average consumption of 2,683 kL of Water per unit produced.

To meet the regulation in 2030, there is a push to reduce it below 2650 kL/Unit.

Two charts: on the left, a donut chart displaying water usage distribution by country, with Japan leading at 38.8% and the USA at 33.5%. On the right, a bar chart showing water usage per production location, with India using the highest at 3,500 liters per unit.
Water Usage for Scenario 2 vs. Unit Consumption — (Image by Samir Saci)

This can be done by shifting production to the USA, Germany and Japan while closing factories in Brazil and India.

Let us see what the model proposed.

Icons of three types of factories: a small factory, a medium-sized factory, and a large factory with chimneys, representing various production capacities.
Manufacturing network for Scenario 3 — (Image by Samir Saci)

It looks like the mirrored version of Scenario 1, with a majority of 35,950 units exported and only 13,000 units locally produced.

A Sankey diagram showing production flows from countries (e.g., Germany, USA, Japan, Brazil, and India) to respective markets, with unit quantities labeled for each flow, highlighting production-to-market supply chains.
Flow chart for the Scenario 3 — (Image by Samir Saci)

But now, production is pushed by five factories in “expensive” countries

  • Two factories in the USA deliver locally and in Japan.
  • We have two more plants in Germany only to supply the USA market.
  • A single high-capacity plant in Japan will be opened to meet the remaining local demand and deliver to small markets (India, Brazil, and Germany).

Finance Department: “It’s the least financially optimal setup you proposed.”

A stacked bar chart showing the costs of goods sold (COGS) analysis by production location. Includes variable costs in red and fixed costs in blue, with total costs highest in the USA at 2.4M€/month.
Costs Analysis for Scenario 3 — (Image by Samir Saci)

From a cost perspective, this is the worst-case scenario, as production and transportation costs are exploding.

This results in a budget of 8.89 M€/month (versus 5.68 M€/month for Scenario 1).

Merchandising Team: “Units sold in Brazil and India have now more reasonable COGS.”

A grouped bar chart illustrating COGS per unit across markets, broken into transportation, production, and fixed costs. Brazil and India show the highest COGS due to higher transportation and production expenses.
New COGS for Scenario 2 — (Image by Samir Saci)

From a retail point of view, things are better than in Scenario 2 as the Brazil and India markets now have COGS in line with the local purchasing power.

However, the logistics team is challenged as we have the majority of volumes for export markets.

Sustainability Team: “What about water usage and CO2 emissions?”

Water usage is now 2,632 kL/Unit, below our target of 2,650 kL.

However, CO2 emissions exploded.

A bar chart showing CO2 emissions by market and source, separating transportation (green) and production (blue). The USA leads in emissions at 2,500 tons, mainly from transportation.
Emissions per Market (Scenario 3) — (Image by Samir Saci)

We came back to the Scenario 1 situation with 4,742 (Tons CO2eq) of emissions (versus 2,136 (Tons CO2eq) for Scenario 2).

We can assume that this scenario is satisfying for no parties.

The difficulty of finding a consensus

As we observed in this simple example, we (as data analytics experts) cannot provide the perfect solution that meets every party’s needs.

Three world maps illustrating sustainability scenarios for supply chain networks. Each map represents different setups for factory locations, logistics routes, and corresponding environmental impacts.
Scenarios and impacts on teams — (Image by Samir Saci)

Each scenario improves a specific metric to the detriment of other indicators.

CEO: “Sustainability is not a choice, it’s our priority to become more sustainable.”

However, these data-driven insights will feed advanced discussions to find a final consensus and move to the implementation.

A diagram with a sustainability team, analytics models powered by Python, and three supply chain maps showing factory locations, logistics routes, and impacts, demonstrating an integrated decision-making process.
Data Driven Solution Design — (Image by Samir Saci)

In this spirit, I developed this tool to address the complexity of company management and conflicting interests between stakeholders.

Conclusion

This article used a simple example to explore the challenges of balancing profitability and sustainability when building a transformation roadmap.

This network design exercise demonstrated how optimizing for different objectives (costs, CO2 emissions, and water usage ) can lead to trade-offs that impact all stakeholders.

A visual representation of optimal outcomes for supply chain decisions: COGS adapted to markets’ purchasing power, low overall cost of production and delivery, local production to minimize logistics costs, and export from low-cost countries to optimize production costs. Includes icons of a store, money bag, warehouse, and worker.
Conflicting interest among stakeholders — (Image by Samir Saci)

These examples highlighted the complexity of achieving consensus in sustainability transitions.

As analytics experts, we can play a key role on providing all the metrics to animate discussions.

The visuals and analysis presented are based on the Supply Chain Optimization module of a web application I have designed to support companies in tackling these multi-dimensional challenges.

A webpage snapshot presenting a problem statement for sustainable supply chain optimization. It includes demand distribution across five markets (Japan, USA, Germany, Brazil, and India), a question about network optimization, and an explanation of production costs, plant capacities, and transport expenses with accompanying icons and visuals.
Demo of the User Interface : Test it here — (Image by Samir Saci)

The module is available for testing here: Test the App

How to reach a consensus among stakeholders?

To prove my point, I used extreme examples in which we set the objective function to minimize CO2 emissions or water usage.

Three world maps illustrating supply chain flows under different scenarios. The maps include color-coded lines representing logistics routes, icons for factories, warehouses, and markets, and environmental sustainability metrics.
Extreme examples used in this case study — (Image by Samir Saci)

Therefore, we get solutions that are not financially viable.

Using the app, you can do the exercise of keeping the objective of cost efficiency and add sustainability constraints like

  • CO2 emissions per unit produced should be below XX (kgCO2eq)
  • Water usage per unit produced

This (may) provide more reasonable solutions that could lead to a consensus.

Logistics Operations: We need support to implement this transformation.

What’s next?

Your contribution to the sustainability roadmap can be greater than providing insights for a network design study.

In this blog, I shared several case studies using analytics to design and implement sustainable initiatives across the value chain.

A diagram explaining a circular economy process for clothing rentals. The steps include rental initiation (Day 1) at a store, customer usage, and return after 14 days. Features icons of clothes, a store, and a person.
Example of initiative: Implement a Circular Economy — (Image by Samir Saci)

For instance, you can contribute to implementing a circular economy by estimating the impact of renting products in your stores.

A circular economy is an economic model that aims to minimize waste and maximize resource efficiency.

In a detailed case study, I present a model used to simulate the logistics flows covering a scope of 3,300 unique items rented in 10 stores.

A flowchart showing the parameters used to simulate the circular economy. Includes icons for stores, warehouses, logistics vehicles, rental processes, returned garments, quality checks, and re-distribution for rent or sale.
Simulation Parameters — (Image by Samir Saci)

Results show that you can reduce emissions by 90% for some references in the catalogue.

A bar chart comparing CO2 emissions of garments under circular and linear economic models. The circular model displays significantly lower emissions per garment compared to the linear system.
Example of CO2 emissions reductions — (Image by Samir Saci)

These insights can convince the management to invest in implementing the additional logistics processes required to support this model.

For more information, have a look at the complete article

Data Science for Sustainability —  Simulate a Circular Economy

About Me

Let’s connect on LinkedIn and Twitter. I am a Supply Chain Data Scientist who uses data analytics to improve logistics operations and reduce costs.

If you need consulting or advice for your supply chain transformation, contact me via Logigreen Consulting.

If you are interested in data analytics and supply chain, please visit my website.

Samir Saci | Data Science & Productivity

Sustainable Business Strategy with Data Analytics was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.

​Use data analytics to help companies design and implement strategic sustainability roadmaps to reduce their environmental footprint.Sustainable Business Strategy with Analytics — (Image by Samir Saci)Consensus means that everyone agrees to say collectively what no one believes individually.This quote captures a critical issue many companies face during their strategic green transformation: aligning diverse objectives across teams and departments.Sustainability Team: “We need to reduce emissions by 30%”.Imagine a hypothetical manufacturing company with, at the centre of its business strategy, an ambitious target of reducing CO2 emissions by 30%.Value chain of our example — (Image by Samir Saci)The sustainability team’s challenge is to enforce process changes that may disrupt the activities of multiple departments along the value chain.Sustainability Project Steering Committee — (Image by Samir Saci)How do you secure the approval of multiple stakeholders, that potentially have conflicting interests?In this article, we will use this company as an example to illustrate how analytics models can support sustainable business strategy.How to Build a Sustainability Roadmap?You are a Data Science Manager in the Supply Chain department of this international manufacturing group.Under pressure from shareholders and European regulations, your CEO set ambitious targets for reducing the environmental footprint by 2030.Stakeholders Involved in the Process — (Image by Samir Saci)The sustainability department leads a cross-functional transformation program involving multiple departments working together to implement green initiatives.Sustainable Supply Chain Network OptimizationTo illustrate my point, I will focus on Supply Chain Network Optimization.The objective is to redesign the network of factories to meet market demand while optimizing cost and environmental footprint.Five Markets of our Manufacturing Company — (Image by Samir Saci)The total demand is 48,950 units per month, spread across five markets: Japan, the USA, Germany, Brazil, and India.Demand Distribution per Market — (Image by Samir Saci)Markets can be categorized based on customer purchasing power:High-price markets (USA, Japan and Germany) account for 93.8% of the demand but have elevated production costs.Low-price markets (Brazil and India) only account for 6.2% of the demand, but production costs are more competitive.What do we want to achieve?Meet the demand at the lowest cost with a reasonable environmental footprint.Market Demand vs. Supply Capacity — (Image by Samir Saci)We must decide where to open factories to balance cost and environmental impacts (CO2 Emissions, waste, water and energy usage).Manufacturing CapacityIn each location, we can open low or high-capacity plants.Production Capacity per Location — (Image by Samir Saci)Fixed Production CostsHigh-capacity plants have elevated fixed costs but can achieve economies of scale.Fixed Production Costs — (Image by Samir Saci)A high-capacity plant in India has lower fixed costs than a low-capacity plant in the USA.Fixed costs per unit are lower in an Indian high-capacity plant (used at full capacity) than in a US low-capacity factory.Variable CostsVariable costs are mainly driven by labour costs, which will impact the competitiveness of a location.Production Costs per Location — (Image by Samir Saci)However, we need to add freight delivery rates from the factory to the markets in addition to production costs.If you move the production (for the North American market) from the USA to India, you will reduce production costs but incur additional freight costs.What about the environmental impacts?Manufacturing teams collected indicators from each plant to calculate the impact per unit produced.CO2 emissions of the freight are based on the distance between the plants and their markets.Environmental indicators include CO2 emissions, waste generated, water consumed and energy usage.Environmental Footprint of Manufacturing & Logistics — (Image by Samir Saci)We take the average output per unit produced to simplify the problem.Environmental Impact per unit produced for each location — (Image by Samir Saci)For instance, producing a single unit in India requires 3,500 litres of water.To summarize these four graphs, high-cost manufacturing locations are “greener” than low-cost locations.You can sense the conflicting interests of reducing costs and minimizing environmental footprint.What is the optimal footprint of factories to minimize CO2 Emissions?Data-driven Supply Chain Network DesignIf we aim to reduce the environmental impact of our production network, the trivial answer is to produce only in high-end “green” facilities.Unfortunately, this may raise additional questions:Steering Committee questions — (Image by Samir Saci)Logistics Department: What about the CO2 emissions of transportation for countries that don’t have green facilities?Finance Team: How much will the overall profitability be impacted if we move to costly facilities?Merchandising: If you move production to expensive “green” locations, what will happen to the cost of goods sold in India and Brazil?These are questions that your steering committee may raise when the sustainability team pushes for a specific network design.In the next section, we will simulate each initiative to measure the impact on these KPIs and give a complete picture to all stakeholders.Data Analytics for Sustainable Business StrategyIn another article, I introduce the model we will use to illustrate the complexity of this exercise with two scenarios:Scenario 1: your finance director wants to minimize the overall costsScenario 2: sustainability teams push to minimize CO2 emissionsModel outputs will include financial and operational indicators to illustrate scenarios’ impact on KPIs followed by each department.Multiple KPIs involving several departments — (Image by Samir Saci)Manufacturing: CO2 emissions, resource usage and cost per unitLogistics: freight costs and emissionsRetail / Merchandising: Cost of Goods Sold (COGS)As we will see in the different scenarios, each scenario can be favourable for some departments and detrimental for others.Do you imagine a logistic director, pressured to deliver on time at a minimal cost, accepting the disruption of her distribution chain for a random sustainable initiative?Data (may) help us to find a consensus.Scenario 1: Minimize Costs of Goods SoldI propose to fix the baseline with a scenario that minimizes the Cost of Goods Sold (COGS).The model found the optimal set of plants to minimize this metric by opening four factories.Manufacturing network for Scenario 1 — (Image by Samir Saci)Two factories in India (low and high) will supply 100% of the local demand and use the remaining capacity for German, USA and Japanese markets.A single high-capacity plant in Japan dedicated to meeting (partially) the local demand.A high-capacity factory in Brazil for its market and export to the USA.Solution 1 to minimize costs — (Image by Samir Saci)Local Production: 10,850 Units/MonthExport Production: 30,900 Units/MonthWith this export-oriented footprint, we have a total cost of 5.68 M€/month, including production and transportation.Total Costs Breakdown — (Image by Samir Saci)The good news is that the model allocation is optimal; all factories are used at maximum capacity.What about the Costs of Goods Sold (COGS)?COGS Breakdown for Scenario 1 — (Image by Samir Saci)Except for the Brazilian market, the costs of goods sold are roughly in line with the local purchasing power.A step further would be to increase India’s production capacity or reduce Brazil’s factory costs.From a cost point of view, it seems perfect. But is it a good deal for the sustainability team?The sustainability department is raising the alert as CO2 emissions are exploding.We have 5,882 (Tons CO2eq) of emissions for 48,950 Units produced.Emissions per Market — (Image by Samir Saci)Most of these emissions are due to the transportation from factories to the US market.The top management is pushing to propose a network transformation to reduce emissions by 30%.What would be the impact on production, logistics and retail operations?Scenario 2: Localization of ProductionWe switch the model’s objective function to minimize CO2 emissions.Manufacturing network for Scenario 2 — (Image by Samir Saci)As transportation is the major driver of CO2 emissions, the model proposes to open seven factories to maximize local fulfilment.Supply Chain Flows for Scenario 2 — (Image by Samir Saci)Two low-capacity factories in India and Brazil fulfil their respective local markets only.A single high-capacity factory in Germany is used for the local market and exports to the USA.We have two pairs of low and high-capacity plants in Japan and the USA dedicated to local markets.From the manufacturing department’s point of view, this setup is far from optimal.We have four low-capacity plants in India and Brazil that are used way below their capacity.Costs Analysis — (Image by Samir Saci)Therefore, fixed costs have more than doubled, resulting in a total budget of 8.7 M€/month (versus 5.68 M€/month for Scenario 1).Have we reached our target of Emissions Reductions?Emissions have dropped from 5,882 (Tons CO2eq) to 2,136 (Tons CO2eq), reaching the target fixed by the sustainability team.Emissions per Market (Scenario 2) — (Image by Samir Saci)However, your CFO and the merchandising team are worried about the increased cost of sold goods.New COGS for Scenario 2 — (Image by Samir Saci)Because output volumes do not absorb the fixed costs of their factories, Brazil and India now have the highest COGS, going up to 290.47 €/unit.However, they remain the markets with the lowest purchasing power.Merchandising Team: “As we cannot increase prices there, we will not be profitable in Brazil and India.”We are not yet done. We did not consider the other environmental indicators.The sustainability team would like also to reduce water usage.Scenario 3: Minimize Water UsageWith the previous setup, we reached an average consumption of 2,683 kL of Water per unit produced.To meet the regulation in 2030, there is a push to reduce it below 2650 kL/Unit.Water Usage for Scenario 2 vs. Unit Consumption — (Image by Samir Saci)This can be done by shifting production to the USA, Germany and Japan while closing factories in Brazil and India.Let us see what the model proposed.Manufacturing network for Scenario 3 — (Image by Samir Saci)It looks like the mirrored version of Scenario 1, with a majority of 35,950 units exported and only 13,000 units locally produced.Flow chart for the Scenario 3 — (Image by Samir Saci)But now, production is pushed by five factories in “expensive” countriesTwo factories in the USA deliver locally and in Japan.We have two more plants in Germany only to supply the USA market.A single high-capacity plant in Japan will be opened to meet the remaining local demand and deliver to small markets (India, Brazil, and Germany).Finance Department: “It’s the least financially optimal setup you proposed.”Costs Analysis for Scenario 3 — (Image by Samir Saci)From a cost perspective, this is the worst-case scenario, as production and transportation costs are exploding.This results in a budget of 8.89 M€/month (versus 5.68 M€/month for Scenario 1).Merchandising Team: “Units sold in Brazil and India have now more reasonable COGS.”New COGS for Scenario 2 — (Image by Samir Saci)From a retail point of view, things are better than in Scenario 2 as the Brazil and India markets now have COGS in line with the local purchasing power.However, the logistics team is challenged as we have the majority of volumes for export markets.Sustainability Team: “What about water usage and CO2 emissions?”Water usage is now 2,632 kL/Unit, below our target of 2,650 kL.However, CO2 emissions exploded.Emissions per Market (Scenario 3) — (Image by Samir Saci)We came back to the Scenario 1 situation with 4,742 (Tons CO2eq) of emissions (versus 2,136 (Tons CO2eq) for Scenario 2).We can assume that this scenario is satisfying for no parties.The difficulty of finding a consensusAs we observed in this simple example, we (as data analytics experts) cannot provide the perfect solution that meets every party’s needs.Scenarios and impacts on teams — (Image by Samir Saci)Each scenario improves a specific metric to the detriment of other indicators.CEO: “Sustainability is not a choice, it’s our priority to become more sustainable.”However, these data-driven insights will feed advanced discussions to find a final consensus and move to the implementation.Data Driven Solution Design — (Image by Samir Saci)In this spirit, I developed this tool to address the complexity of company management and conflicting interests between stakeholders.ConclusionThis article used a simple example to explore the challenges of balancing profitability and sustainability when building a transformation roadmap.This network design exercise demonstrated how optimizing for different objectives (costs, CO2 emissions, and water usage ) can lead to trade-offs that impact all stakeholders.Conflicting interest among stakeholders — (Image by Samir Saci)These examples highlighted the complexity of achieving consensus in sustainability transitions.As analytics experts, we can play a key role on providing all the metrics to animate discussions.The visuals and analysis presented are based on the Supply Chain Optimization module of a web application I have designed to support companies in tackling these multi-dimensional challenges.Demo of the User Interface : Test it here — (Image by Samir Saci)The module is available for testing here: Test the AppHow to reach a consensus among stakeholders?To prove my point, I used extreme examples in which we set the objective function to minimize CO2 emissions or water usage.Extreme examples used in this case study — (Image by Samir Saci)Therefore, we get solutions that are not financially viable.Using the app, you can do the exercise of keeping the objective of cost efficiency and add sustainability constraints likeCO2 emissions per unit produced should be below XX (kgCO2eq)Water usage per unit producedThis (may) provide more reasonable solutions that could lead to a consensus.Logistics Operations: We need support to implement this transformation.What’s next?Your contribution to the sustainability roadmap can be greater than providing insights for a network design study.In this blog, I shared several case studies using analytics to design and implement sustainable initiatives across the value chain.Example of initiative: Implement a Circular Economy — (Image by Samir Saci)For instance, you can contribute to implementing a circular economy by estimating the impact of renting products in your stores.A circular economy is an economic model that aims to minimize waste and maximize resource efficiency.In a detailed case study, I present a model used to simulate the logistics flows covering a scope of 3,300 unique items rented in 10 stores.Simulation Parameters — (Image by Samir Saci)Results show that you can reduce emissions by 90% for some references in the catalogue.Example of CO2 emissions reductions — (Image by Samir Saci)These insights can convince the management to invest in implementing the additional logistics processes required to support this model.For more information, have a look at the complete articleData Science for Sustainability —  Simulate a Circular EconomyAbout MeLet’s connect on LinkedIn and Twitter. I am a Supply Chain Data Scientist who uses data analytics to improve logistics operations and reduce costs.If you need consulting or advice for your supply chain transformation, contact me via Logigreen Consulting.If you are interested in data analytics and supply chain, please visit my website.Samir Saci | Data Science & ProductivitySustainable Business Strategy with Data Analytics was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.  logistics, hands-on-tutorials, sustainability, supply-chain, data-science Towards Data Science – MediumRead More

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Dr. Owns

January 10, 2025

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