Sustainable Business Strategy with Data Analytics | by Samir Saci

Contents

Scenario 1: Minimize Costs of Goods Sold Scenario 2: Localization of Production Scenario 3: Minimize Water Usage The difficulty of finding a consensus

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.