Overcoming Roadblocks to Advance Sustainability Programs
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.
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.
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.
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.
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
