Leveraging Data Analytics for Sustainable Business Transformation
Learn how to use analytics to overcome the challenges of scaling sustainable initiatives that prevent organizations from achieving environmental goals.
Financial regulations now push companies to commit to carbon reduction roadmaps by 2030.
How can data analytics help organizations overcome sustainable supply chain management obstacles?
However, scaling green initiatives and achieving sustainability goals can be challenging for organizations.
The main challenge is that Supply Chain Management is at the core of a complex system involving manufacturing and logistics teams.
Since these teams are only sometimes accustomed to working together towards a common goal, many companies are stuck at the beginning of their green transformation.
How do we unlock these situations using data analytics?
The Harvard Business Review article “How Sustainability Efforts Fall Apart?” delves into companies' common challenges when implementing sustainability initiatives.
This article will explore how data analytics can help overcome these challenges by focusing on the four “hidden enemies” of your supply chain sustainable transformation.
Summary
I. How Sustainability Efforts Fall Apart?
1. The "Four Hidden Enemies"
2. Support of Supply Chain Analytics
II. Leveraging Data Analytics
1. Hidden Enemy 1: Structure and Governance
Solution 1: Descriptive Analytics
2. Hidden Enemy 2: Processes and metrics
Solution 2: Adapted Optimization Models
3. Hidden Enemy 3: Culture and Leadership
Solution 3: Diagnostic Analytics to Address Cultural Barriers
4. Hidden Enemy 4: Methods and Skills
Solution 4: Workforce Training
III. Conclusion
1. Data is your best ally
2. Drive an ESG-led Business TransformationHow Sustainability Efforts Fall Apart?
The “Four Hidden Enemies” of the Green Transformation
Sustainability has become a critical aspect of business operations as companies face mounting pressure to address environmental and social issues for their ESG reporting.
However, implementing a roadmap for carbon footprint reduction and effective sustainability initiatives is often easier said than done.
The article “How Sustainability Efforts Fall Apart” sheds light on the key barriers that companies face in their pursuit of sustainability, focusing on four “hidden enemies”:
- Structure and Governance: Siloed sustainability limits influence.
- Processes and Metrics: Unsustainable metrics hinder progress.
- Culture and Leadership: Old mindsets challenge transformation.
- Methods and Skills: Traditional tools obstruct change.
Have you heard about Supply Chain Analytics?
Support of Supply Chain Analytics for Sustainable Initiatives
A Supply Chain can be defined as several parties exchanging flows of material and information to fulfil a customer request.
In a previous article, Supply chain Analytics was introduced as a set of tools that help companies use systems-generated data to gain insights and optimize their operations.
What are the different types of analytics?
It can also be a great support to address the obstacles listed above:
- Descriptive Analytics by providing visibility with a single source of truth across the supply chain
- Diagnostic Analytics by deploying automated incident root cause analysis processes
- Predictive Analytics by supporting the forecast of customer demand, supply capacity and CO2 emissions volumes
- Prescriptive Analytics by helping the decision-making process towards resource optimization
In the following sections, we’ll explore each ‘hidden enemy’ in detail and explain how data analytics can help overcome these challenges.
🏫 Discover 70+ case studies using data analytics for supply chain sustainability🌳and business optimization 🏪 in this: Cheat Sheet
Leveraging Data Analytics for a Green Transformation
Hidden Enemy 1: Structure and Governance
The siloed nature of organizational structure can prevent effective collaboration for sustainability.
Indeed, sustainability has often been relegated to a separate company department, leading to its isolation from key corporate functions.
This restricts sustainability from transforming the entire organization and limits its power and relevance within the company.
An operational manager will always focus on her scope of operations:
- Store managers keep low quantities per order (and increase the frequency) to minimize their inventory
- Supply planners push for more production batches (with low quantities per batch) to get enough flexibility
- Finance managers always encourage inventory reductions
- Commercial teams advocate for high inventory coverage to avoid lost sales due
- Warehouse operations have to deal with these constraints
Who is in charge of CO2 emissions reductions? Everybody should be, but in reality no one.
This lack of collaboration significantly impacts the efficiency of transportation and production planning, hindering the progress of sustainability efforts.
What if we optimize the collaboration between sales and supply chain?
For more details, you can check
- An actual case study of Green Inventory Management to show the impact of order frequency on CO2 emissions
- Production Planning Optimization to understand the impact of the number of batches on production costs
Therefore, sustainability is seen as a nice-to-have or a marketing tool that affects the performance of each team.
First, let’s measure the actual performance of the entire value chain.
Solution 1: Descriptive Analytics
An end-to-end approach is needed to be more efficient and find the right balance that will lead to a minimal environmental footprint.
Numbers don’t lie, people do.
— Ernie Lindsey
By connecting to the different systems (ERP, WMS, CRM, …), descriptive analytics solutions can build a central source of truth across the supply chain.
📊 Example 1: Life Cycle Assessment
Life cycle assessment (LCA) is a method of evaluating the environmental impacts of your products over their entire life cycle.
In our example, it can be used to estimate the footprint of your products considering end-to-end supply chain processes.
And identify hotspots to provide data-backed diagnostics across the supply chain to break silos and promote collaboration.
- Total CO2e emissions per unit become a common KPI for all teams.
- This KPI can be included in all managers' performance reviews.
Store Manager: If I reduce my order frequency, the transportation team can optimize truck loading.
This will encourage collaboration to support cross-functional initiatives led by sustainability teams.
If you can’t measure it, you can’t manage it.
— W. Edwards Deming
Because these metrics are built from a trusted data source, managers will be more proactive in reducing emissions.
We can set a common objective of emissions reductions for the whole supply chain department.
For example,
- We want to reduce the overall CO2 emissions per unit produced by 20%
- 45% of emissions are coming from transportation and production
- Store managers will cut their order frequency by two
- Supply planners will increase their replenishment order quantity and reduce the frequency
- Transportation teams must provide adapted truck sizes
- Manufacturing teams will reduce the number of production runs
If you need an example of the application of this methodology, 👇
Great! What’s next?
While descriptive analytics can help break down silos, traditional processes and metrics may still represent significant obstacles.
This leads us to the next hidden enemy.
Hidden Enemy 2: Processes and Metrics
Sustainability is rarely integrated into companies’ core business processes.
They were designed in an era where profit was the primary concern, and environmental and social factors were not considered.
Indicators used to assess business performance are usually linked with cost, profit, market share or earnings per share.
An Operation manager to the sustainability team: “How could I help you to reduce the CO2 footprint?! I am already struggling to minimize my transportation costs.”
Therefore, traditional metrics can neutralise sustainability initiatives by prioritising short-term financial gains over long-term environmental benefits.
What if we switch the objectives functions?
From minimising costs to minimising CO2eq emissions.
Solution 2: Adapted Optimization Models
By incorporating sustainability metrics into existing business processes, companies can develop balanced optimization models considering financial and non-financial objectives.
With the help of optimization tools, continuous improvement engineers can improve processes towards optimal solutions that balance profit with sustainability.
What is the optimal factory network to balance costs and sustainability?
The objective is to find the correct parameters to optimize a specific metric considering external and internal constraints.
📊 Example 2: Sustainable Supply Chain Network Optimization
Supply chain optimization uses data analytics to find an optimal combination of factories and distribution centres to meet customers' demands.
Should we produce in Brazil or Portugal to minimize water usage?
In this classic linear programming problem, your model will select the correct set of production facilities.
- Respect the demand constraints: factories' supply should meet the market’s demand.
- Minimize the total costs of producing and delivering products
This will usually select factories in remote areas where production costs are lower, considering the weight of transportation costs.
What if we want to minimize the total CO2 emissions?
On the right, we propose to use the same model with an adapted objective function that minimises total carbon emissions.
With this simple change, we have entirely transformed the network.
The low-carbon solution pushes for the localization of production by adding factories to the European market.
A balanced approach is possible to keep business competitiveness.
You can adapt your objective function or add constraints to keep costs under a certain threshold.
I have implemented this approach in a ready-to-use application, 👇
However, as discussed in the following hidden enemy, old mindsets and habits can still be significant barriers to change.
Hidden Enemy 3: Culture and Leadership
Old mindsets and habits can be significant barriers to change.
When the leadership and operational teams are not aligned with sustainability and green transformation goals, efforts can be met with resistance or indifference.
Across the organization, we can find misaligned values that can hinder the adoption of green supply chain practices.
For example, here is an example seen in a project with a FMCG company
- Factories are sent to the warehouse pallets with multiple references inside (heterogeneous pallets) because it’s easier for them.
- The warehouse receiving team has to remove the plastic film, sort the items, repalletize them, and wrap them again.
This creates additional work, increases film consumption and generates waste.
Therefore, fostering a supportive organizational culture and strong leadership committed to sustainability is crucial.
Solution 3: Diagnostic Analytics to Address Cultural Barriers
Diagnostic analytics focuses on identifying the causes of specific past events or trends.
It involves examining historical data to determine the factors contributing to a particular outcome.
The sustainability team to factory’s logistics manager: “According to our diagnostic tool: we have 2 tons of additional film consummed per year because you mix items in the same pallet.”
These tools can help your organization understand the reasons behind failures using an objective external assessment.
📊 Example 3: Supply Chain Control Tower
A supply chain control tower is traditionally defined as a set of dashboards connected to various systems using data to monitor critical events across the supply chain.
If you take the example of the monitoring of a distribution network for a fashion retail company,
- The performance metric is On-Time-In-Full, also called OTIF
- Diagnostic algorithms conduct root cause analysis to understand who is responsible for delays
The idea is to compare the actual lead time per process and the targets set by service level agreements.
For more details,
Can we implement a sustainability control tower?
This approach can be easily adapted to environmental footprint monitoring
- Choose the metric to follow: for instance, CO2 emissions
- Set a target of emissions per process: for example, 160 (g CO2e/unit) for warehouse replenishment from factories
- Compare the actual emissions versus the target using the LCA approach
Root Cause Analysis process to spot the deviations, but additional analyses will be required to find the root cause.
Coming back to our wrapping film example, we would have
- A deviation in the consumption of wrapping film in the warehouse
- Explanations of the operational teams: “It is due to the depalletization of heterogeneous pallets.”
- The final root cause is the palletization method of factories
Having addressed the cultural barriers, we can focus on the methods and skills needed to drive a green transformation.
Hidden Enemy 4: Methods and Skills
Traditional tools and skill sets need to be improved to manage the complexity of sustainability initiatives.
A lack of expertise in using analytics can hinder organizations from leveraging data to optimize supply chain processes and make data-driven decisions for sustainability and green transformation.
Solution 4: Workforce Training
It’s not directly tied to a specific type of analytics but indicates the need to equip employees with the necessary skills to leverage data analytics in their roles.
Companies can build a workforce prepared to drive sustainability initiatives by providing access to analytics tools with training programs.
For example, I share my experience learning Python and VBA for Supply Chain Analytics in this short tutorial. 👇
What’s next?
I hope this quick review of the hidden enemies and the solution against them convinced you about the power of data to drive green transformations.
Conclusion
Data is your best ally.
Data analytics can be a powerful ally in overcoming the “hidden enemies” that block sustainability initiatives.
For each enemy, we found solutions using data analytics.
These different types of supply chain analytics can help corporations to break down silos to ensure that all departments move towards footprint reductions.
For more case studies of data analytics used for supply chain sustainability, you can have a look at this article 👇
impact of your initiatives?
Drive an ESG-led Business Transformation
All these initiatives can positively impact your ESG score.
The environmental, social, and governance (ESG) reporting method discloses companies' governance structures, societal impacts, and ecological footprint.
These three dimensions provide an in-depth understanding of a company’s sustainability and ethical impacts that can be improved with data-driven initiatives.
Considering that these reports are strategic, convincing your top management to invest in green initiatives is an effective way.
How do we use analytics to generate this report?
I propose several tools and methodologies to extract and process data to generate this scoring in the article linked below.👇
Why do we do that? […]
Have you heard about Sustainable Development Goals?
The Sustainable Development Goals (SDGs) are a set of 17 objectives established by the United Nations to address global challenges.
As a data scientist, how can you help your company contribute to these goals?
Look at my insights about how Data Analytics can support the United Nations’ Sustainable Development Goals in this article,
About Me
Let’s connect on Linkedin and Twitter. I am a Supply Chain Engineer who uses data analytics to improve logistics operations and reduce costs.
For consulting or advice on analytics and sustainable supply chain transformation, feel free to contact me via Logigreen Consulting.
If you are interested in Data Analytics and Supply Chain, look at my website.
💡 Follow me on Medium for more articles related to 🏭 Supply Chain Analytics, 🌳 Sustainability and 🕜 Productivity.
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📘 Your complete guide for Supply Chain Analytics: Analytics Cheat Sheet
References
- “How Sustainability Efforts Fall Apart?”, Harvard Business Review, Elisa Farri, Paolo Cervini, and Gabriele Rosani
