Traceability will be key in meeting consumer demands for sustainability.
The short version: if you can’t track the complete history of a product, including material and labor inputs, you can’t make an ironclad claim about its sustainability. There is no credible sustainability without comprehensive traceability. The latest twist: what’s always been an ethical issue is now a legal issue, too.
Happily, this means that greenwashing will soon be circling the drain. Based on the term “whitewashing,” greenwashing means “to make stakeholders believe that your company is doing more to protect the environment than it really is.” Greenwashing came about as unscrupulous businesses grappled with the advent of “conscious consumerism” or “consumers with a conscience.” In other words, greenwashing exists because green sells. A large body of research bears this out. For example, a Nielson poll found that 66% of adult consumers are willing to pay more for eco-friendly products and 50% of purchasing decisions are influenced by sustainability claims.
Faced with this rising demand for eco-friendly products and environmentally ethical business practices, some bad actors decided to greenwash because it’s less expensive than implementing the real thing. Sometimes greenwashing is subtle, sometimes it’s brazen, as we’ll see below.
According to Mark Burstein, executive vice president and industry principal at Logility, decades of conversation on sustainability has been 99 percent greenwashing. Burstein is wary of any claim that isn’t backed by data including visibility of the full chain of custody.
Greenwashing dates back to the ‘60s, when the nuclear power industry was trying to counter the claims made by the anti-nuclear movement. Greenwashing usually takes the form of one of the Seven Sins of Greenwashing, reproduced here:
- Hidden trade-off: Defining something as “green” by a narrow definition that ignores other environmental impacts. For example, a packaging innovation that reduces the use of black plastic at the expense of an increase in the rate of deforestation.
- No proof: Claims are not easily confirmed or are not verified by third-party certifications.
- Vagueness: Broad, insubstantial, or convoluted claims. These include statements like ‘new and improved’, ‘made with recycled materials’, ‘eco-friendly’, and ‘non-toxic’, with no further specificity.
- Irrelevance: Claims may be truthful but unrelated to the product or company.
How have they gotten away with it? Well, in a few of the more egregious cases, they haven’t. But let’s look at Ben & Jerry’s — by the way, a company that has done much admirable work in the areas of fair trade, sustainability and non-GMO standards — to illustrate a couple of points:
- This stuff is complicated; and
- Most consumers care up to the point where — you guessed it — the stuff gets complicated.
On the Ben & Jerry’s public website, we see the company acknowledging that the details surrounding non-GMO supply chain compliance are intricate, replete with arcane language, bewildering regulations, and exceptions that appear to be at cross purposes. Therefore, not everyone will want to forge ahead and truly understand the complexities.
For Ben & Jerry’s, claims related to non-GMO compliance require tracing animal feed sources and ingredients obtained from fermentation and processing aids or enzymes, potential comingling of GMO and non-GMO ingredients at any point in the supply chain, and any variances issued. And that’s a very abridged version of the company’s traceability requirements. Sustainability tracking is equally complex.
In the past, trust — even misplaced trust — was enough, but now we’re entering an era of “don’t trust and do verify.” Which brings us to the reckoning. To explore this, let’s shift our industry focus from dairy to denim.
Changing the Way Materials Are Sourced
On January 13, 2021 the United States Customs and Border Protection expanded the blanket Withhold Release Order (WRO) initially issued on December 2, 2020 and will detain all shipments containing cotton and cotton products originating from the Xinjiang Uyghur Autonomous Region (XUAR) and now includes tomato products. This potentially affects cotton products from countries other than China that use Chinese cotton inputs such as cotton fabric. Britain also announced they will tighten laws on imports linked to XUAR human rights abuses.
These announcements from both sides of the Atlantic highlight the urgency with which companies must act to ensure their products are not stopped at the border. According to the US Customs and Border Protection, the importer of record is responsible for proving its products do not contain any material, in whole or part, sourced from XUAR.
“My interpretation of it is any cotton product coming from any country has the potential to be detained unless you can show proof of admissibility that it wasn’t using Chinese cotton or inputs from that region,” Burstein said. As we’ve said before, this is a guilty until proven innocent stance with huge financial implications, a case where one bad apple does indeed spoil the whole bunch.
If suspected and unable to show verifiable proof, the importer has three options:
- Take the products out of the US market and export them somewhere else.
- Destroy the merchandise.
- Abandon the merchandise.
Meeting Consumer Expectations
The latest developments from the United Kingdom and United States show there is strong action being taken to change the way materials are sourced. Now, it is up to supply chain leaders to ensure they can prove through a digital thread they are in compliance across every link in their complex, global supply chain.
In the wake of these growing concerns, Logility launched a digital supplychain traceability solution giving brand owners and retailers the tools to document the chain of custody from component origin to importer of record. With this solution, users can trace the chain of custody through all tiers in the supply chain in one digital thread while storing and managing all supporting documents related to every transaction between supply chain trading partners.
The digital thread compiles and organizes a chronological and verifiable account of importer of record back through every tier of the supply chain to the original raw material source. Transactions are validated at every tier using POs, invoices and packing lists. All these documents are rolled up to a certificate of compliance with complete chain of custody. This comprehensive genealogy is sent electronically for all shipments arriving in the US. This is what United States Customs and Border Protection will examine to determine compliance with relevant rules and regulations.
Outside of the potential legal issues, traceability will be key in meeting consumer demands for sustainability across all industries. Coming full circle, brands now have compliance tools that also help them prove to jaded customers that environmental and social responsibility programs are in place and supported by tangible evidence.
Furthermore, consumers will come to understand that these tools exist. While we’ll always have lazy consumers, and while the Ben & Jerry’s example proves that modern, global supply chains are inherently complex, we won’t always have companies hiding behind the lament that “full supply chain traceability is too hard…there are no technology solutions for it.”
Finally, it’s refreshing to note that some global brands are joining the call for comprehensive traceability solutions to validate their sustainability claims. Rather than bemoan the loss of greenwashing as a tactic (as some surely will), these companies are proactively assessing the market for advanced supply chain traceability solutions that support overarching CSR strategies. They have essentially aligned themselves with consumers and put technology providers on notice.
These solutions must measure and manage the full environmental impact of internal and external facilities, rate material producers and garment manufacturers’ environmental performance, extend the lifespan of products and even provide insights into how to improve recyclability of textile waste and unsold products.
Burstein sums it up nicely: “We’re going to have something good come out of this situation in Xinjiang. The runway where we get to true traceability. Because without traceability at each one of these nodes, no one can accurately tell you the environmental footprint of their supply chain.”
Logility is working with a leader in the distribution and logistics space that provides delivery to auto dealers for the dealers’ parts and services business.
This specialization allows them to be more efficient and knowledgeable about their customers and the specific parts they are delivering. Because these parts cannot be sold and services cannot be scheduled and performed until the parts are delivered, it is exceptionally important that they deliver on time and in full.
The problem this organization faced was knowing which routes were responsible for delayed deliveries and what was causing the delays. The company felt their routes and stopping points caused inefficiency, but they also wanted to forecast the impact of extraneous factors such as weather, road construction, and traffic patterns. With this information, they could adjust schedules to proactively service their customers, adjust delivery times, and gain greater efficiencies from their fleet. In addition, they wanted to incorporate the inventories from their parts suppliers and their geographic locations so they could minimize empty return-trip trucks and duplicate runs, as well as minimize the inventory that they had to keep on hand in their distribution centers while knowing which suppliers had what parts in stock.
With 10 distribution centers, each with over 100 routes, and each route with over 15 stops, this posed a significant logistics problem. Add to that over 2,500 parts from over 200 manufacturers and this became a significant supply chain opportunity. In addition, each truck itself – and there are over 1,000 of them – is a constant big data generator, capturing location, efficiency, speed, right turns, left turns, stop times and the length of each stop. How do you coordinate all this data against the business issue of on-time deliveries with the greatest efficiency?
The Logility Difference
Using Logility, they first aggregated their enterprise inventory and logistics data with two external data feeds. Then they created a series of operating metrics to be used by managers as well as KPIs for reporting to executives. An important feature of these reports was predictive metrics. These metrics, using Logility’s predictive modeling capabilities, identify routes with risk factors and suggest the likelihood of one or multiple delays in the near future (weeks). Lastly, they distribute these efficiency reports and interactive dashboards using the Logility® Digital Supply Chain Platform. An important feature of this approach was deploying mobile versions of each report for traveling executives.
Today, everyone from the CEO down to the route dispatcher at each distribution center knows where any issues are and what needs to be done to correct them. It also allows our customer to be much more proactive in dealing with both the delivery points and their suppliers. As orders and inventory stocks can be readily shared, increasing parts production efficiency at the manufacturer and delayed delivery points can be more readily notified so they can more effectively reschedule their work loads. First-year savings from reduced inventory and greater fleet efficiencies are estimated at over $3 million.
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The science and practice of predictive analytics is well established and rapidly gaining ground in the public and private sectors. It’s not magic anymore because we now have advanced analytics systems that harness and organize massive amounts of disparate data and model that data in ways that allow humans to be proactive and make informed decisions. Take a moment to read our extremely popular post on selecting the right descriptive, predictive, and prescriptive analytics here. To review:
|Type of analytics||What does it do?|
|Descriptive Analytics||Answers the question, “What happened?” Uses data aggregation and data mining techniques.|
|Predictive Analytics||Answers the question, “What could happen?” Uses statistical models and forecasting techniques to understand the future.|
|Prescriptive Analytics||Answers the question, “What should we do?” Uses optimization and simulation algorithms to suggest the best course of action.|
The Science of Predictive Analytics
Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.
Predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. These models capture relationships amongst many factors to allow assessment of risk or potential associated with a set of conditions, guiding decision-making with better accuracy and significant cost savings.
Who Knew Supply Chain Optimization and Crime Fighting Were Related?
Let’s examine two popular applications: supply chain optimization and crime fighting. How can predictive analytics effectively address these seemingly unrelated topics? Because at a macro level the issues are identical. Consider this abbreviated chronology of our quest to make better, faster, data-driven decisions regardless of the setting and the objectives:
- We had no data. We used unstructured observations and gut feel
- We got some data, but it was incomplete and resided in silos
- We got more (and more comprehensive) data, eliminated silos, filled in the gaps, but lacked modeling tools. This was the era of data-rich but information-poor.
Today, predictive analytics tools allow us to compare possible outcomes of events using scenario analysis and foresee challenges and potential disruptions before they happen.
Supply Chain Optimization – Use Case for a Domestic Brewery
Our supply chain optimization use case comes from a top-10 domestic brewery that used Logility to gain better insight into production. Before Logility, this brewery had plenty of data, but was unable to make sense of it and “make it tell us something useful about the future”. Sound familiar?
The data needed to be more easily translated into actionable information for managers and executives. The company had a variety of tools in-house, but the fragmented technical environment was too difficult to manage for quick scalability. They needed a powerful, analytics-driven solution to integrate and transform the data from their disparate systems, along with a front end for visual analytics, designed for the specific challenges of the beverage industry.
A key point of differentiation for Logility was the ability to link multiple data sources to a single supply chain planning platform with reporting and analytics capabilities built into the functionality. Logility’s rapid integration framework enables a one-time setup of the platform, followed by easy report creation and access to analytics by business users. An early win included creating a daily shipments and depletions report for the CFO. Using a mobile-ready interface, the CFO can quickly scan variances each morning and immediately drill down to SKU and account-level data to see what’s driving exceptions.
Based on these early victories, the brewery believes that the early detection of production efficiencies will yield $550,000 to $800,000 savings within 18 months. In addition, the company points to a two full-time staffing equivalent reduction (about $300,000 annually), and the value of faster decision-making by business managers.
The Crime Fighting Analogy
Now let’s consider the case of crime fighting and a diagnostic technique called Risk Terrain Management (RTM). The premise of RTM is that location matters. However, it’s no secret that location matters. The question is: how do you utilize data that you currently have to assess spatial risks and prevent undesired outcomes? RTM helps in this process. With a diagnosis of how the environment correlates with certain behaviors or outcomes, you can make very accurate forecasts.
The reason RTM is used by practitioners across many disciplines, not just law enforcement, is because it was originally developed to solve a problem faced by many: how to leverage data and insights from various sources, using readily accessible methods. RTM gained fame as a crime prevention tool, but today it’s being used in urban planning, injury prevention, public health, traffic safety, pollution, and stopping maritime piracy. (Note that this is at its core the same problem the brewery faced, only the vocabulary and the objectives are different.)
In the context of crime prevention, the RTM process begins by selecting and weighting factors that are geographically related to crime incidents. Then a final model is produced that basically ‘paints a picture’ of places where criminal behavior is statistically most likely to occur.
With knowledge of spatial risk factors, intervention activities can be designed to suppress crime in the short term and mitigate the risk factors at these areas so they are less attractive to criminals for the long term. For instance, in one National Institute of Justice (NIJ) study, a 42% (statistically significant) reduction in robberies was achieved by focusing on environmental features of high-risk places, not merely the people located there. With RTM, you can prioritize risk factors and prescribe actions to mitigate these factors, even within the confines of limited resources.
As you can see, predictive analytics and the underlying tools that support the discipline can be applied in many settings. People like to solve problems, but they need the right information. As business leaders we need to make sure they have it and then set them free.
Interested to know where your ROI from a supply chain analytics platform will come from?
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