To get the most bang out of their sustainability investments, manufacturers should focus on data-driven initiatives and indicators.
The methodologies of Kepner Tregoe, Ford’s 8D, TQM (Total Quality Management), Kaizen, PDCA (Plan, Do, Check, Act), Lean Enterprise, ISO standards, and Six Sigma are the foundation of today’s best manufacturing practices. The management mindset of continuous improvement, mistake and accident reduction, risk mitigation, benchmarking and KPI’s, compliance, agility, and safety have been in place for several decades. These practices improve efficiency and effectiveness by eliminating waste activities, maximizing productivity, and increasing resiliency in response to the single line at the bottom of a financial report.
John Elkington’s definition of the “Triple Bottom Line” (1994) proposed a new accounting method. It expanded the single-line financial approach to include social and environmental impact, reporting people, planet, and profit. The vision of linking financials based on efforts to improve social and environmental policies has never been timelier. Enter ESG.
ESG (Environment, Social & Governance) has different meanings for different audiences. The term ESG came from a landmark study entitled “Who Cares Wins,” initiated by UN Secretary-General Kofi Annan and UN Global Compact in 2004 in collaboration with the Swiss government. “The goal was to influence, support and enable capital market stakeholders to better integrate environmental, social, and governance (ESG) factors into capital allocation and portfolio management processes. Seventeen years after this study, manufacturers issue ESG reports, and banks use ESG ratings to valuate businesses (including manufacturers) for resiliency.”
The overarching definition of sustainability includes ESG and lean manufacturing within its scope. Therefore, lean manufacturing methodologies, including human, environmental, and financial impacts, have led to sustainable manufacturing and, henceforth, the base of ESG reporting. Enterprise systems are updated accordingly and can identify how a company is a steward of the environment and its resilience under various risks and volatilities. The current generation of practitioners captures energy efficiencies, greenhouse gas emissions, deforestation, biodiversity changes, waste management, and water usage. The driver is to build reputation and stakeholder engagement, grow business, and improve ESG ratings.
Only recently has the subject of Environment, Social and Governance become a C-suite topic of interest.
Despite those drivers, only recently has ESG become a C-level topic of interest. This reality is a response to government-sponsored targets, compliance/regulatory demands, and shareholder activism. In 2021, 82% of the executives interviewed by Deloitte (2021 Climate Check: Business’ Views on Environmental Sustainability) said that their organizations are concerned or very concerned about sustainability. However, 65 % of those same executives said that their organizations would need to cut back on environmental sustainability initiatives due to the Covid-19 pandemic. The conclusion is that firms will prioritize revenue-generating activities such as marketing and sales when facing uncertainties. For the sustainability practitioner, the grim reality is that sustainability investments remain sensitive to market fluctuations.
Therefore, efficient sustainability spending is required, so external sustainability programs will not be scaled down or rolled back whenever market oscillations impact the ability to generate cash to be reinvested.
The Power of Data
Efficient sustainability spending starts and ends with data-driven initiatives and indicators. Manufacturers collect data from functional areas, and they carry value for sustainability affairs, including operations, finance, marketing, own ESG-focused departments, human resources, and research & development. This functional data plays an essential role in internal and external sustainability programs in at least three dimensions:
Program Justification: Data is key for the sustainability practitioner to get enough funding to maintain and expand sustainability programs. Compliance and regulations aside, most organizations evaluate investment in sustainability like any other program. It is an exercise driven by financial metrics related to the expected return on investment.
Program Impacts: Data supports measuring corporate targets accurately, including sustainability targets. “What gets measured gets managed,” as the saying goes, and any sustainability program requires a solid baseline and KPI that would be tracked against. Only by measuring impact with credible data can companies change the perception that ESG programs are a cost center when they are a profit center.
Program Improvements: Data is also an instrument to achieve sustainability targets. Manufacturers have been leveraging data analytics in different sides of the business to improve their operations and the financial bottom line. The same logic can be applied to sustainability where data can drive sustainability indicators from the baseline toward committed targets.
The Challenges with Sustainability Data
Before assessing technical challenges, data-driven sustainability programs can face foundational problems inherent to company culture and organization.
Lack of Transparency: Like financial information, sustainability data reside in nearly every aspect of internal and external operations. Yet, the data are not always available or complete due to a lack of transparency in processes or supply chains. As a result of limited data access, public trust diminishes, and potential benefits are reduced.
Third-Party Certifications: Manufacturers have been confronted with an urgency to get third-party certifications which can be costly, time-consuming, and redundant. The data held by certification organizations tend to be subjective, unavailable for public review, updated only annually, driven by industry sectors, and costly. They report information from the certificate holder at a specific time and act as a barrier to innovation.
Furthermore, audit fatigue impacts both auditors and auditees. Brands and retailers end up pressuring manufacturers to adopt third-party sustainability solutions and certifications to manage impacts. Siloed solutions can be subjective, irrelevantly defined, expensive, and may not present valuable data to help drive sustainable change within a facility.
“Data analytics help manufacturers use large volumes of historical and streaming data from cross-industry systems.”
Reporting, not Reducing: Without advanced data analysis and predictive capabilities, data reporting only catches a moment in time. Data need to be modeled to identify pain points and hot spots, offer predictive insights, and drive change beyond its current capabilities. Risk assessments, underlying climate threats, political changes, and human rights can be added to make the tools more practical for business decisions.
Non-Data-Driven Culture: The manufacturing sector has antiquated subsets which are slow to identify the value and adopt data analysis for objective and predictive decision-making and adhere to the “if it isn’t broken, why fix it” mentality. The lack of a data-driven culture or excessive gut-feeling culture hinders progress on sustainability.
Rigid Phased-Approach Mindset: One misconception is the fact that sustainability datasets must be complete before any value is generated out of them, from basic data transformation to advanced analytics. Companies cannot wait for data completeness. Value realization should grow as the sustainability data infrastructure grows.
Bolt-on Solutions: Firms are recognizing the costs of reporting, which is leading to adoption of several tools within the organization, spanning homegrown tools, newcomer solutions, and additional modules of a well-established platform within the organization. And herein lies the issue: many pockets of data not being analyzed for greatest results.
Anonymization: Brands and retailers, manufacturers, suppliers, advocacy groups, labor unions, and academics invest time and effort to collect data for multiple organizations across the supply chain, including an extensive manufacturing value chain. This data should be anonymized and analyzed collectively for proprietary ownership and protection to make a credible impact. Then, many interested parties could scrape, share, manage, and analyze data, and develop objective environmental and social impact reports.
Limited Institutional Knowledge: The internal knowledge of any industry sector is limited, and new subject matter expertise is highly valuable to understand current challenges in data modeling and water, energy, carbon, data protection, fraud, and human impacts. Sharing manufacturing analysis between consumer products, food, automobiles, and white goods can help drive change across industries. Lean manufacturers hold the data and knowledge to benefit each other and the brands and retailers who look for the best suppliers.
Data Analytics Drive Sustainability
Data analytics help manufacturers use large volumes of historical and streaming data from cross-industry systems. Data can help forecast future conditions, identify pressures, constraints, threats, and opportunities, and foresee trends that can provide timely insights. Advanced modeling solves significant problems in new ways, allowing for objective, fact-based insights, while predictive analysis allows for the best objective decision-making. Software and services help manufacturers quickly access and prepare relevant data for modeling, simulation, and insight generation. Environmental metrics can be analyzed and put to work to drive lower impact decisions through advanced data modeling. Above there is a non-exhaustive list of available use cases for continuous improvements of sustainability programs using data management and data analytics.
Comprehensive Dashboarding: B2B integration via an accessible dashboard offers transparency and drives growth. In the B2C scenario, a connected dashboard can tell the story of manufacturing in an impactful and interactive manner, increasing stakeholder engagement and driving buying and consumer behavior changes. Manufacturers will ultimately have immediate access to impactful reports for making agile decisions for sustainability, as well as for public transparency.
Supply Chain Optimization: The world’s supply chains remain mostly horizontal and volatile, leaving sustainability efforts in the value chain isolated and making it challenging to offer comprehensive sustainability reports about finished goods. Amongst the slew of troubles manufacturers, brands and retailers have in traceability and transparency, they continue to lag in innovation and adoption of technological products that can collect data throughout their supply chains. Manufacturers still benefit from ESG activity in terms of savings and new businesses. However, brands and retailers, since they reside downstream in the value chain, are not successfully capturing the sustainability data from manufacturers.
The world’s supply chains remain mostly horizontal and volatile, leaving sustainability efforts in the value chain isolated
Energy Efficiency: Energy use is a basic datapoint monitored in lean manufacturing management and ESG reporting. Improvements in energy intensity can quickly translate into savings. Manufacturers invest significantly to replace outdated assets (from electrical drives to heat exchangers) with energy-efficient assets to save on electricity, fuel, steam, and other energy sources. However, even the most efficient industrial asset by design can be operated in a sub-optimal fashion. Data modeling allows energy forecasting and real-time process optimization to make the same product spec and yield lower energy consumption, resulting in higher energy efficiency.
Sustainability Risk Assessment: Manufacturers use traditional techniques and systems, such as first principles (laws of physics) and CAM (computer-aided manufacturing) for process simulation. Data models are proven to be as good as or better than those techniques and systems in specific scenarios. Data models also fit nicely as a hybrid solution when working in tandem with traditional simulation. Similarly, sustainability risks and impacts can be modeled and leveraged to predict the sustainability risks based on external and internal factors. Simulations look at weather, international regulations, and policies, and predict implications for manufacturing. Real-time tracking and alerts help improve sourcing decisions. Advanced forecasting models use existing and live data to enhance supply chain decisions, assess commodities, and predict climate risk.
Capital Allocation: Following the Triple Bottom Line, capital allocation considers not only economic but also social and environmental benefits. In this scenario, financial allocation is not a one-dimensional problem, but a multivariate optimization scenario. Data models are the only way to determine or validate decisions avoiding antiquated guesses that will likely not bring the best return on assets and cash reinvested.
Raw-Material Efficiency: Recent trade wars, COVID-19, and subsequent deglobalization drive manufacturers to identify more resilient raw material resources. Adding to this problem, manufacturers are also pressured to find more sustainable materials that meet specifications. Data analytics is vital in this process to support the practical design of experiments and quicker time to patent and time to market.
Manufacturers should benefit by externalizing the excellent work they do both B2B and B2C to improve reputation and grow business.
Customer Experience: Consumer insight software uses artificial intelligence and machine learning to engage consumers and drive change towards more sustainable consumer behaviors by identifying effects of policy and development changes and improving awareness of socio-environmental impacts for buyers and consumers. New generations are more concerned about the social and environmental impact (such as carbon footprint) of manufactured product and see that as a reason to buy.
Leap of Innovation
The intersection of lean manufacturing, data management and analytics, and sustainability is the foundation of ESG in the manufacturing sector. While a gap seems to exist between manufacturers’ and activists’ ideas of sustainability, manufacturing has been championing sustainable activities for quite some time, even if disguised within other industry terminologies, such as lean manufacturing.
Manufacturers should benefit by externalizing the excellent work they do both B2B and B2C to improve reputation and grow business. They also need to be open to making an innovation jump to improve their sustainability scores and ESG reports by using improved data management, modeling, analytics, and IoT in the Triple Bottom Line — people, planet, and profit. M