Leveraging Data Analytics for Sustainable Business Transformation | by Samir Saci | Mar, 2023
Overcome the challenges faced in scaling green initiatives that prevent organizations from achieving their sustainability goals
Financial regulations now push companies to commit to carbon reduction roadmaps by 2030.
As customer awareness grows, companies dedicate substantial resources and effort to design and implement cross-functional green initiatives.
The main challenge lies in the fact that Supply Chain Management is at the core of a complex system involving finance, commercial, manufacturing, and logistics teams.
Since these teams are not always accustomed to working together towards a common goal, many companies find themselves stuck at the beginning of their green transformation.
The Harvard Business Review article “How Sustainability Efforts Fall Apart?” delves into the common challenges that companies encounter when implementing sustainability initiatives.
In this article, we will explore how data analytics can help to overcome these challenges by focusing on the four “hidden enemies” of your Supply Chain green 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 #: Culture and Leadership
Solution 3: Diagnostic Analytics to Address Cultural Barriers
4. Hidden Enemy 4: Methods and Skills
Solution 4: Workforce Training
III. Conclusion
The “Four Hidden Enemies”
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.
Support of Supply Chain Analytics
A Supply Chain can be defined as several parties exchanging flows of material and information with the ultimate goal of fulfilling a customer request.
In a previous article, Supply chain Analytics was introduced as a set of tools helping companies to use data generated by systems to get insights and optimize their operations.
It can also be a great support to address the obstacles listed above:
In the following sections, we’ll explore each ‘hidden enemy’ in detail and explain how data analytics can help overcome these challenges to drive a successful 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 department within companies, 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
- And 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.
For more details, you can check
Therefore, sustainability is seen as a nice-to-have or a marketing tool that affects the performance of each team.
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 the performance review of all managers.
This will encourage collaboration to support cross-functional initiatives lead 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 source of data, managers will be more proactive in their approach to emissions reduction.
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
While descriptive analytics can help break down silos, traditional processes and metrics may still represent major obstacles, which leads us to the next hidden enemy.
Hidden Enemy 2: Processes and metrics
Sustainability is rarely integrated into companies’ core business processes.
It’s because 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 metrics such as 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, sustainability initiatives and green transformation efforts can be neutralized by traditional metrics that prioritize short-term financial gains over long-term environmental benefits.
Solution 2: Adapted Optimization Models
By incorporating sustainability metrics into existing business processes, companies can develop balanced optimization models that consider both 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.
The objective is to find the right set of parameters that will optimize a specific metric considering external and internal constraints.
📊 Example 2: Supply Chain Network Optimization
Supply chain optimization makes the best use of data analytics to find an optimal combination of factories and distribution centres to meet the demand of your customers.
Where should we locate our factories to optimize your Supply Chain Network?
In this classic linear programming problem, your model will select the right set of production facilities that
- 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 is minimizing total carbon emissions.
With this simple change, we have a complete transformation of the network.
The low-carbon solution is pushing for the localization of production by adding factories in 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.
As we’ve seen, adapted optimization models can help integrate sustainability into core business processes.
However, old mindsets and habits can still be significant barriers to change, as we’ll discuss in the next hidden enemy.
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,
- Factories send 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 and repalletize them, and wrap them again
This creates additional work, increases film consumption and generates waste.
For more details,
Therefore, it is crucial to foster a supportive organizational culture and strong leadership committed to sustainability.
Solution 3: Diagnostic Analytics to Address Cultural Barriers
Diagnostic analytics is focusing on identifying the causes of specific past events or trends.
It involves examining historical data to determine the factors that contributed 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 important 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,
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 instance 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 must now turn our attention to the methods and skills needed to drive a green transformation.
Hidden Enemy 4: Methods and Skills
Traditional tools and skill sets may not be sufficient for managing the complexity of sustainability initiatives.
A lack of expertise and capabilities in using advanced analytics can hinder organizations from fully leveraging the power of data to optimize supply chain processes, identify patterns, 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 rather indicates the need to equip employees with the necessary skills to leverage data analytics in their roles.
By providing access to advanced analytics tools and training programs, companies can build a workforce that is prepared to drive sustainability initiatives and make data-driven decisions.
For more details,
- Learn Analytics for Supply Chain (Excel VBA or Python?), Video Tutorial
Data analytics can be a powerful ally in overcoming the “hidden enemies” that hinder sustainability efforts.
These different types of supply chain analytics can help corporations to break down silos, integrate sustainability into their core business processes, and address cultural barriers to succeed in their green transformation journey.
By tackling these challenges head-on and embracing the potential of data analytics, companies can create a more sustainable future and secure a competitive advantage in an increasingly environmentally-conscious world.
Let’s connect on Linkedin and Twitter, I am a Supply Chain Engineer using data analytics to improve logistics operations and reduce costs.
If you are interested in Data Analytics and Supply Chain, have a look at my website
- “How Sustainability Efforts Fall Apart?”, Harvard Business Review, Elisa Farri, Paolo Cervini, and Gabriele Rosani
- Sustainable Logistics — Reduce Warehouse Consumables, Personal Blog, Samir Saci
- “What is a Supply Chain Control Tower?”, Towards Data Science, Samir Saci
- “What is Supply Chain Analytics?”, Towards Data Science, Samir Saci
- “What is Life Cycle Assessment? LCA”, Towards Data Science, Samir Saci
Overcome the challenges faced in scaling green initiatives that prevent organizations from achieving their sustainability goals
Financial regulations now push companies to commit to carbon reduction roadmaps by 2030.
As customer awareness grows, companies dedicate substantial resources and effort to design and implement cross-functional green initiatives.
The main challenge lies in the fact that Supply Chain Management is at the core of a complex system involving finance, commercial, manufacturing, and logistics teams.
Since these teams are not always accustomed to working together towards a common goal, many companies find themselves stuck at the beginning of their green transformation.
The Harvard Business Review article “How Sustainability Efforts Fall Apart?” delves into the common challenges that companies encounter when implementing sustainability initiatives.
In this article, we will explore how data analytics can help to overcome these challenges by focusing on the four “hidden enemies” of your Supply Chain green 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 #: Culture and Leadership
Solution 3: Diagnostic Analytics to Address Cultural Barriers
4. Hidden Enemy 4: Methods and Skills
Solution 4: Workforce Training
III. Conclusion
The “Four Hidden Enemies”
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.
Support of Supply Chain Analytics
A Supply Chain can be defined as several parties exchanging flows of material and information with the ultimate goal of fulfilling a customer request.
In a previous article, Supply chain Analytics was introduced as a set of tools helping companies to use data generated by systems to get insights and optimize their operations.
It can also be a great support to address the obstacles listed above:
In the following sections, we’ll explore each ‘hidden enemy’ in detail and explain how data analytics can help overcome these challenges to drive a successful 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 department within companies, 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
- And 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.
For more details, you can check
Therefore, sustainability is seen as a nice-to-have or a marketing tool that affects the performance of each team.
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 the performance review of all managers.
This will encourage collaboration to support cross-functional initiatives lead 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 source of data, managers will be more proactive in their approach to emissions reduction.
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
While descriptive analytics can help break down silos, traditional processes and metrics may still represent major obstacles, which leads us to the next hidden enemy.
Hidden Enemy 2: Processes and metrics
Sustainability is rarely integrated into companies’ core business processes.
It’s because 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 metrics such as 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, sustainability initiatives and green transformation efforts can be neutralized by traditional metrics that prioritize short-term financial gains over long-term environmental benefits.
Solution 2: Adapted Optimization Models
By incorporating sustainability metrics into existing business processes, companies can develop balanced optimization models that consider both 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.
The objective is to find the right set of parameters that will optimize a specific metric considering external and internal constraints.
📊 Example 2: Supply Chain Network Optimization
Supply chain optimization makes the best use of data analytics to find an optimal combination of factories and distribution centres to meet the demand of your customers.
Where should we locate our factories to optimize your Supply Chain Network?
In this classic linear programming problem, your model will select the right set of production facilities that
- 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 is minimizing total carbon emissions.
With this simple change, we have a complete transformation of the network.
The low-carbon solution is pushing for the localization of production by adding factories in 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.
As we’ve seen, adapted optimization models can help integrate sustainability into core business processes.
However, old mindsets and habits can still be significant barriers to change, as we’ll discuss in the next hidden enemy.
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,
- Factories send 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 and repalletize them, and wrap them again
This creates additional work, increases film consumption and generates waste.
For more details,
Therefore, it is crucial to foster a supportive organizational culture and strong leadership committed to sustainability.
Solution 3: Diagnostic Analytics to Address Cultural Barriers
Diagnostic analytics is focusing on identifying the causes of specific past events or trends.
It involves examining historical data to determine the factors that contributed 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 important 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,
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 instance 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 must now turn our attention to the methods and skills needed to drive a green transformation.
Hidden Enemy 4: Methods and Skills
Traditional tools and skill sets may not be sufficient for managing the complexity of sustainability initiatives.
A lack of expertise and capabilities in using advanced analytics can hinder organizations from fully leveraging the power of data to optimize supply chain processes, identify patterns, 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 rather indicates the need to equip employees with the necessary skills to leverage data analytics in their roles.
By providing access to advanced analytics tools and training programs, companies can build a workforce that is prepared to drive sustainability initiatives and make data-driven decisions.
For more details,
- Learn Analytics for Supply Chain (Excel VBA or Python?), Video Tutorial
Data analytics can be a powerful ally in overcoming the “hidden enemies” that hinder sustainability efforts.
These different types of supply chain analytics can help corporations to break down silos, integrate sustainability into their core business processes, and address cultural barriers to succeed in their green transformation journey.
By tackling these challenges head-on and embracing the potential of data analytics, companies can create a more sustainable future and secure a competitive advantage in an increasingly environmentally-conscious world.
Let’s connect on Linkedin and Twitter, I am a Supply Chain Engineer using data analytics to improve logistics operations and reduce costs.
If you are interested in Data Analytics and Supply Chain, have a look at my website
- “How Sustainability Efforts Fall Apart?”, Harvard Business Review, Elisa Farri, Paolo Cervini, and Gabriele Rosani
- Sustainable Logistics — Reduce Warehouse Consumables, Personal Blog, Samir Saci
- “What is a Supply Chain Control Tower?”, Towards Data Science, Samir Saci
- “What is Supply Chain Analytics?”, Towards Data Science, Samir Saci
- “What is Life Cycle Assessment? LCA”, Towards Data Science, Samir Saci
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