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. 

Logistical Challenges 

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

Read this similar story: 

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? 

Read this great blog:  

This post has been prompted by conversations I’ve had recently with a client in the early stages of implementing a Sales and Operations Planning (S&OP) strategy. They had been relying on a phased approach and they knew that they needed an integrated set of business processes to go with their newly purchased technology. 

They understood that the focus needed to be on information, not just the volumes of data they had at hand. They knew that in order to implement a successful S&OP strategy, they needed clean, current, and accurate data. As with many organizations, time and effort was being wasted gathering data that had minimal importance to the overall project. But in this case, senior leadership was able to articulate the business problem they were trying to solve, and were able to help define, with some difficulty mind you, the minimum data necessary for the project. 

It all sounds wonderful on paper, and they were destined for success! But like other businesses, their attempts to implement S&OP were frustrated by internal tensions between departments. What followed was this seemingly innocent statement on my part: “Not everyone will be a convert immediately, so we watch for resistance and address it as part of our strategy. Push, but not too hard, or we will get resistance.” 

And that’s when the fireworks started. Or to be precise, my somewhat nonchalant mention of possible resistance sparked some great comments and questions. 

Dealing With the Resistance 

Classic best practice suggests that S&OP must ‘belong’ to the Chief Executive Officer. If that’s not possible then a strong, united coalition of department heads may be able to lead the process if they set clear ground rules and boundaries for working together. In this case, we had senior management buy-in and support, but what we really needed was their ‘ownership’ of the project. 

Some amount of resistance is inevitable, and it usually boils down to cultural or people issues, not in any way exclusive to S&OP implementations, but let’s go ahead and tackle them in the context of an S&OP project. Here are the two most prevalent issues: 

The internal obstructionist 
This is the presence of a few highly regarded and influential employees who either passively or actively undermine the changes in behavior that the new initiative requires. You know you have this problem if the water-cooler conversation sounds something like this: “That new analytics program won’t work for us…”; “We’ve always done it this way…”; or, “That new initiative will make us have to change.” (Basically, anything eluding to “change is bad”.) 

The dirty-data diversion 
This is the belief that there’s no use starting an analytics implementation until the company’s data is polished, scrubbed, cleaned, pressed and folded to perfection. It’s the same argument some use to avoid going to the gym: “I’ve got to get in shape first!” 

I’ll address the Internal Obstructionist in another post. Today, let’s grapple with #2. 

The evil genius of the dirty-data roadblock is its apparent logic and deceptive concern for company well-being. In an analytics context, artful proponents of this argument appear to have the best interests of the company in mind. “We don’t want the C-suite making decisions based on bad data; that’s bad for all of us. Let’s get it cleaned up first.” 

Of course what’s really at work here is what Seth Godin calls the assertiveness of the lizard brain, also known as ‘the resistance’, also known as fear. No one wants the bright light pointed at their bad data or poor processes. 

And since no data-scrubbing project has ever succeeded, there’s little risk in promising a thorough clean-up as a prelude to an S&OP kick-off. That’s the diversion. Confucius might say it like this: “If you think you need to finish before you can begin, you will never begin.” 

Here’s how to overcome this type of resistance in three steps: 

1. Inject some honesty. Everyone knows there are data quality issues. Yes, even management knows. In fact, they’ve known for a long time. That’s not news. The point is to work together to improve the speed and decision-making ability of the enterprise, not place blame. This gives everyone who needs it some ‘cover’ and puts the lizard back in its cage. 

2. As soon as possible, start reviewing some basic KPIs in the new S&OP system. 
Embrace the fact that the data isn’t where it needs to be. Make reliable data part of the project, not a prerequisite. Doing this will help everyone envision the desired future and will set the stage for some quick wins. And quick wins will unleash the dynamic duo of momentum and optimism. The system should be viewed as a catalyst for operational improvement, not a tyrant that demands operational perfection to function. 

3. Meet often, especially in the beginning. In fact, consider daily meetings. More importantly, set overall priorities in a way that gives the team the time and the freedom they need to make progress between meetings. This helps reinforce everyone’s commitment to a successful outcome. In every meeting talk about how awesome life will be when the system is trusted and providing accurate information. 

Nike got it right with its slogan, “Just do it”. Starting has its own virtues. 

For more information, check out this e-book: 

A Roadmap for Designing an Enterprise that Thrives During Supply Chain Disruptions 

Congratulations! You’ve made it to the final installment of our series on building a supply chain that can thrive, not merely survive, during crises and curve balls. That demonstrates a certain amount of resilience on your part! We know there’s a fierce, never-ending battle for attention out there, so thank you for giving Logility some of yours. 

In the first three posts I covered steps 1 through 9:  

  1. Assessment 
  1. Justification 
  1. Vision 
  1. Explore 
  1. Collaborate 
  1. Elevate 
  1. Business Continuity 
  1. Elevate IBP 
  1. Change Management 

Now let’s examine the final three steps.  

  1. Alignment. Alignment has been called a “high degree of difficulty move” under the umbrella of change management. Why? Because this journey will require a new shared vocabulary, updated process definitions, new clarity around roles and ongoing synchronization of metrics and compensation. And you need to be prepared for some to say I didn’t sign up for this.  
    An aligned, mature organization encourages team members to think like a CEO every day. Instead of sub-optimizing (AKA optimizing locally), you’ll be expected to evaluate potential actions in light of strategic goals like improved customer service, margin growth and time-to-market. (Independent study opportunity for those interested in avoiding the sub-optimization trap: How is Elon Musk able to launch the Falcon 9 at 20% of the cost of the Space Shuttle?)  
  1. Culture. Leadership sets the tone, as always. There are many ways to convey an “embrace change” message. Pick one and stick to it. View disruptions as growth opportunities. Run away from the status quo. Change is a tonic. But understand that if you don’t get alignment right, culture will stagnate or even deteriorate. 
  1. Celebrate. If you get the first 11 right, I promise you will have plenty to celebrate. Research shows that advanced use of IBP (see step 6) will put you in the upper echelon of your peers when grouped by key supply chain performance measures. Take the time in small and large groups, inside and outside the company, to reflect on success. This practice is a powerful momentum builder.  

When you’re done, you’ll have more than a Center of Excellence, which implies a preference for or superiority of a single physical location. In contrast, you’ll have created a Nexus of Excellence. A dynamic yet stateless collection of people, process and technology on a global scale.  

Guard against complacency by constantly checking your achievements against this list: 

  • Platform has transformed us from data rich to insight rich. 
  • Teams use guided workflows with contextual drill downs. 
  • Accountability is clear and includes actionable outcomes. 
  • Planning and execution are integrated to the point where they feel like a single function. 
  • The organization is at once empowered and hungry. Achievement has unleashed a sense of how much more can be done. 

That completes our series on the 12 steps necessary for building an agile, resilient enterprise that consistently leverages people, process and technology to translate actionable insights into marketplace success regardless of circumstances.  

Time to Adopt Next Genalytics

The pace of change in retail has never been faster than it is now. Couple that with disruptions, big and small, and it’s not surprising that consumers are now even more unpredictable than before. 

In a recent Logility blog post, Richie Proud, vice president of planning and inventory at Curaleaf, said this about the upheaval in the retail fashion sector: The digital transformation of the fashion industry has been immense over the past 6 months. Almost all companies that I speak with are ecstatic over their e-commerce sales, but readily admit that the growth in e-commerce does not offset the decline in brick & mortar stores. This divide will only increase as we enter the make-or-break time period of Q3 2020 and Q4 2020 – as we have never experienced a “COVID Black Friday.” Traditional thinking would have teams placing deep buys of highly promotional items to be ready for the onslaught of in-store shopping starting the week of November 22, 2020. But recent sales data has shown us the public is still hesitant to return to malls, however they fully embrace the digital experiences offered by many retailers. 

New market dynamics should spur the retail industry to seek new ways of measuring performance. It may not be the news you were hoping for, but traditional retail success metrics are fast becoming obsolete in today’s economy. The Internet has changed how business is done, but brick-and-mortar businesses remain. In fact, go-to-market strategists have elevated their game by adopting a channel-agnostic focus as opposed to a binary physical-versus-digital orientation.  

It’s difficult to state the case more clearly and succinctly than Matthew R. Shay, president and CEO of the National Retail Federation, does here: While retail continues to evolve and adapt to changing consumer preferences and new technologies, it is increasingly critical to develop newer, more relevant metrics to accurately value and measure retailers. The current suite of metrics were built for a time that no longer exists. The lines between channels have blurred beyond recognition, making it challenging to properly attribute a sale with these outdated metrics…we need a common set of updated metrics that more accurately measures retailer performance and captures the full value that retailers are creating.  

The processes, tools, and analytics that we’ve relied on in the past — like sales and inventory turns — will no longer be sufficient to move us forward. At the same time, our fundamental goals have remained constant: providing compelling customer offerings, optimizing inventory placement, and converting that inventory into cash.   

This predicament reminds me of the situation faced by Oakland A’s GM Billy Beane and his quant-jock sidekick Bill James in 2002, when the New York Yankees were spending $140M a year on players to the A’s $40M. Knowing they couldn’t match New York’s money, Beane and James attacked the problem with metrics. Throw in a Michael Lewis book and a Brad Pitt movie, and you have Moneyball.  

It’s become somewhat fashionable to find flaws with Lewis’s portrayal, but Beane’s creativity and bravery aren’t questioned. Without completely discarding traditional baseball performance measurements like home runs and RBIs, he set out to find new metrics that would help him build a competitive team of largely undervalued players. On-Base Percentage, for example, turned out to be a strong predictor of success. His commitment never wavered. When the A’s manager balked and played expensive, free agent veterans instead of Beane’s hand-picked “affordable” players with a knack for getting on base, Beane traded away the veterans.  

Retail needs a Moneyball moment. It’s time to leverage the vast amount of data available to us and create revolutionary ways of measuring value, gaining insights, and driving customer demand. Logility is at the forefront of this movement with its next generation of analytics, called Next Genalytics.  

Next Genalytics isn’t a one-size-fits-all prescription for retailers. There is no mandate to focus on X, ignore Y, etc. Rather, the concept has built-in flexibility. The metrics you choose to develop, and track will likely be based on corporate lifecycle stage (i.e. maturity) among other variables. A young business may determine that customer acquisition and sales per unique customer are critical drivers of success while a mature business might focus on free cash flow. 

The point and the promise of Next Genalytics is this: there are modern, proven tools to help deliver on your analytics needs. Technology is not the limitation. Imagination, creativity and perseverance constitute the first hurdle. The second is capturing the data you desire.  

Relying solely on traditional retail performance metrics won’t work. Time for action. Now, let’s play ball! 


The Future of Retail Metrics. Sides, Marsh, Hobbs, Furman. Deloitte Development LLC. 2019. 

A Roadmap for Designing an Enterprise that Thrives During Supply Chain Disruptions

We’ve arrived at mile markers 7, 8 and 9 on the road to building lasting resilience and agility into your extended supply chain. Thanks for sharing the journey with me.   

You’ll recall that the catalyst for this 4-part series was a renewed sense of obligation and urgency among Logility’s many seasoned supply chain and analytics professionals to share what we’ve learned over the years. As they say at Farmers Insurance, “We know a thing or two because we’ve seen a thing or two.” (Full disclosure, we thought we had seen it all, but 2020 has no equal.) 

Earlier this year we created a task force charged with documenting a program management methodology that would take CEO-driven enterprise-wide objectives and produce a “resiliency roadmap” that all can follow. The idea was to package our learning with a structure that you can leverage and adapt. It’s 12 steps, and in the first two posts I covered steps 1 through 6:  

  1. Assessment 
  2. Justification 
  3. Vision 
  4. Explore 
  5. Collaborate 
  6. Elevate

Now let’s cover steps 7 through 9. 

  1. Business Continuity. A long time ago in a business climate far, far away, business continuity was an IT responsibility, often paired with terms like disaster recovery, fail-over, recovery time objective, and recovery point objective. These were the glory days of the pessimistic approach to confronting supply chain disruptions. Success was defined as not dying. 

    At Logility, we applaud companies that have in recent years taken a broader, more optimistic view of business continuity. Traditional risk assessment remains essential, but companies should seek competitive advantage during challenging times. One way to do this is by strengthening and augmenting the supply chain.  

    Before introducing steps 8 and 9, Visibility and Change Management, let’s step back for a moment and critique the 12-step framework. It’s tempting to view each step as discrete and strictly chronological. But the world is messier than that. In reality, Visibility and Change Management permeate or at least influence most of the other steps on our journey.  
  1. Visibility. An enterprise seeking to fortify its supply chain must constantly strive for better data visibility across the entire network. We’ve observed that a certain “lunch bucket” mentality is useful. This step shouldn’t be left to theorists and data scientists. Improved visibility — and therefore improved decision-making — comes from practitioners tirelessly seeking novel insights from reliable data. 
  1. Change Management. This is where all consulting companies begin and end their presentations. There’s a reason for that. Perfect process and perfect technology won’t matter if employees are confused and unwilling to embrace new roles. In other words, culture eats strategy for breakfast. There will be strategy and ownership changes. Be honest. Communicate early and often. The good news is everyone can be empowered to make changes based on objective rather than subjective analyses.  

Dry Ice Dry Ice Baby 

For homework, think about how you would approach the massive disruption hitting the world’s “cold chain” as COVID-19 vaccines move through final approvals. At the time of writing, here are just a few of the logistical challenges that lie in wait: 

  • Vaccines like to be kept cool, but cooling requirements aren’t uniform. The Pfizer candidate for COVID-19 has to be kept at -70 degrees Celsius, 50 degrees colder than any current vaccine. That will be an issue for developing countries and for rural areas in the developed world. 
  • Dry ice is frozen carbon dioxide. What’s going to happen to the carbon dioxide supply? 
  • Dry ice can be dangerous to handle. 
  • Transportation is one thing, but what about storage? Consider what storage in California while wildfires are raging will be like. 
  • Coordination of construction and maintenance of databases that track who’s received what vaccine, where and when.  
  • Most vaccines are likely to require two doses. The whole effort must be repeated within weeks. 

Next up is the final installment of this 4-part series, where we’ll cover steps 10 through 12. 

You’re committed to rolling out an analytics platform across your supply chain and you have tons of data. So how do you turn that data into actionable information?

There is a continuum in terms of the presentation of data that allows for continuous sophistication in understanding and interpreting data. There are many ways to view data, but those that are particularly useful in supply chain analytics are reporting, score carding, dashboarding and benchmarking. Once these are embedded, deeper, more granular data analysis can be performed with the power of advanced analytics.

Reporting and Score carding

The simplest form of looking at data is the all-too-familiar reporting. Back in the day, reporting consisted of numbers printed out on green bar paper, but today’s analytical reports are far more detailed and dynamic than in the past. For instance, a modern report for a manufacturer might display all the data about transportation providers as usable information, in a scorecard format. Factors such as on-time delivery, freight cost per unit shipped, and transit time are assigned metrics and weighted averages to help users determine how well carriers are performing overall.


Operations managers and executives who want a quick, daily overview of what is happening in their supply chain use dashboards to provide information in near real time to help users understand what is happening within their network. This supports them to make proactive decisions to remedy problems as they occur. Where reporting is really like looking in the rear-view mirror, dashboards are used to see what’s going on now, and make it easier for users to identify trends and exceptions, and to intervene before something goes wrong.

Dashboards can be implemented by companies to track all their real-time data and provide detailed reports of information such as claims as a percent of freight cost, space utilization, fuel efficiency, or vehicle time utilization. Essentially, the dashboard determines whether a transportation system is effective. Significant cost savings can occur when KPIs are monitored and the data is accessible in a format that allows users to make informed decisions. The biggest advantage of using dashboards to present data is the time-saving factor – dashboards give companies the advantage of allowing users to make decisions without having to wait for someone to pull and send reports.


An analytics platform for the supply chain also allows for benchmarking. Comparing data on factors such as freight rates and on-time delivery percentages against peers allows companies to gain a more complete picture of their performance in the marketplace. Take freight rates, for example. Rates have fluctuated with the disrupted economy, so your job is really to assess whether a deal offered by a vendor is good or not. By benchmarking carriers against each other, you can easily see who is offering you the best deal. Consistently monitoring and making informed decisions based on data means you are fully leveraging your analytics platform.

Advanced Analytics

As you become more mature in your supply chain analytics adoption, you will be able to apply predictive and prescriptive analytics to find patterns in historical data that yield insights into future risks and opportunities in your supply chain and transportation networks. This predictive analysis capability uses real-time, data-driven insights to speed up decision-making and help create an optimized and responsive supply chain.

So as you think about rolling out an analytics platform, think in terms of the applications, not the data. Think about what reports are important and how scorecards could improve your visibility into data. Develop dashboards to give you a look ahead and use scorecards and benchmarking to make sure that you are getting the best deal from your vendors and suppliers. And work towards using advanced analytics to focus on improving your forecasting and finding trends and patterns in your data to determine, and plan for, what might happen in the future.

Take a moment to read this real-world example of how a Logility customer solved complex supply chain challenges with analytics and collaboration.

A Roadmap for Designing an Enterprise that Thrives During Supply Chain Disruptions

In part 1 of this series, we talked about the consensus among supply chain practitioners, pundits, and technology providers that minor supply and demand disruptions flare up constantly, the result of actions and reactions across complex, highly tuned, and interdependent global value chains. This has become cliché…the sneeze in Shenzhen that becomes a bad cough in Cupertino. Most companies can cope with these speed bumps, some better than others.

However, major sustained turmoil — like that caused by COVID-19 — quickly separates companies into bankruptcies, survivors, and thrivers.

As supply chain professionals, we at Logility have real-world knowledge gained from helping companies improve the consistency of supply, react to changing consumer behavior, and use analytics to deliver market-driven insights. We noticed that while thrivers are often surprised, and even harmed by major disruptions, they are resilient. They bounce back under adverse conditions. More to the point, inside their supply chains they have levers. They have easy access to accurate market data, they gather and analyze it quickly, they distill the key insights and, as a result, they enjoy options.

As Seth Godin says, “Flexibility in the face of change is where resilience comes from.”

We felt an obligation to share our observations, so in March 2020 we built a task force charged with creating an operational model and program management methodology that would take CEO-driven enterprise-wide objectives and bring them to life in a persistent, systematic roadmap. It’s 12 steps, and in part 1 we covered steps 1 through 3.

1. Assessment
2. Justification
3. Vision

Now it is time to look at steps 4 through 6, beginning with Explore.

4. Explore. When it comes to evaluating modern supply chain technologies, it’s important to be well-informed, curious, and realistic. The market is saturated with breathless assertions about the benefits of solutions based on Artificial Intelligence, Machine Learning and Predictive Analytics.

Some of those claims are true, some are more hat than cattle. Beware the hype-cycle. Remember, supply chain professionals are being asked to deliver tangible results. That’s your day job. Refer back to your Assessment — many companies have yet to create a comprehensive digital twin. That is eminently achievable with modern systems like Logility, so start there, and keep laser-focused on network visibility and the integration of planning and execution.

5. Collaborate. There are two facets to this step. The first is having an honest discussion about what it means to add a new project to everyone’s plate. Remember, this is not an IT project. It’s a business transformation project. Collaboration inside a silo is worthless, at times even harmful. We believe your innovation strategy must include a strategic partner.

It follows that, as a key partner, we need to continually refine our development and implementation disciplines to perfectly balance rapid value creation with reduced risk. This is much more than a product roadmap exercise or a debate about agile versus waterfall project management methods.

We must never let the platform interfere with your success. To borrow an analogy from manufacturing, picture a veteran machinist reviewing a CAD drawing for a new product. If you hear the machinist say, “Well, here’s another engineer that’s never set foot on the shopfloor,” then the organization failed the test for designed-for-manufacturability. Upstream failed downstream. The same principle applies to supply chain technology development and implementation.

6. Elevate Integrated Business Planning. This will remind many of you of Sales & Operations Planning. But there are new expectations in this realm, and they are focused on creating speed, trust, and resiliency. Specifically, your organization must embrace the value of a granular view of the extended enterprise. Can you analyze trends at the SKU level and drill into constraints on the shop floor? If so, then pronouncements from the War Room will be “virtually vetted,” and not the result of panic or guesswork.

Challenge yourself to think beyond the traditional Available-to-Promise metric and ask if an action meets the criterion of profitable-to-promise. You need to examine revenue, cost and margin impacts of every scenario under consideration. Some of these decisions will be automated, others will require exception-based intervention.

There you have a sketch for steps 4 through 6 in Logility’s 12-step process for building a resilient enterprise. Watch this space for steps 7 through 12. In the meantime, send us questions and comments. And for homework, spend some time thinking about this statement: “Planning is dead. Don’t waste your time telling the market what you want; instead, watch it like a hawk every day and take what it gives you.” Do you agree? Disagree? Somewhere in the middle perhaps?

Register for the first webcast of our four part series that will cover this 12-step roadmap for building a resilient enterprise. We will work our way through each step as we explore how to create a business that can respond and pivot at the pace of disruption, as well as seize opportunities presented by shifting market forces. Register here.

What does this mean for your supply chain?

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. Earlier this week 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 to prove its products do not contain any material, in whole or part, sourced from XUAR. If suspected and unable to show verifiable proof, the importer has three options:

1) Take the products out of the US market and export them somewhere else.

2) Destroy the merchandise.

3) Abandon the merchandise.

All three are costly, both financially and in the minds of consumers. Today’s consumers want to support brands that are ethical towards the treatment of workers and sustainable, good for the environment. Many companies tout their Corporate Social Responsibility initiatives however few can measure the impact of their supply chain beyond the first or second tier in the network. How the cotton is picked, or the yarn is spun, where the tomatoes are sourced, or silica is mined is more a matter of faith. The vast majority of companies do not have the capability to show evidence that each tier of their supply chain meets the ethical and sustainable standards they espouse, and their customers expect.

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.

How ready are you?

Continue Learning:

Spinning up a Digital Thread to Help Brands Achieve Supply Chain Resiliency, Compliance and Sustainability

Let’s journey back to late 2019 and early 2020. Under the umbrella of Sustainability, key players in the global fashion industry were talking about implementing lot traceability technologies, but in general the timelines were somewhat “comfortable.” There was no compelling event on the horizon. That would change.

At that point, “Sustainable” was largely a marketing term, and many fashion brands were using it haphazardly and irresponsibly. It reached the apex of the hype-cycle and consumers were naturally growing skeptical and cynical of its promotion without verification. Worried about alienating customers, many brand owners took new interest in traceability to understand the environmental impact of facilities, partners, and materials used.

The onslaught of COVID put pressure on many supply chains, including fashion. It forced the adoption and acceleration of eCommerce and spurred new ways of looking at stores as multi-purpose assets. In addition, COVID revealed the need for increased downstream fortification as sources — from growers to spinners to mills — became less reliable. The industry recognized the need for a digital thread connecting all members of the value chain, and lot traceability would be an important component.

Then came the moment of stark clarity, interestingly enough in numerical form: 406-3. (No, that’s not the score of the Georgia Tech football team’s historic victory over Cumberland College. Coach John Heisman’s Engineers eked out a victory in that game by a score of 222-0.)

The other shoe dropped in September 2020 when, by a vote of 406-3, the United States House of Representatives passed H.R. 6210 — the Uyghur Forced Labor Prevention Act. As currently drafted, the bill will block the import of any goods into the U.S. that cannot prove the merchandise is free of material and labor inputs originating in China’s Xinjiang region (XUAR).

Chinese cotton is extremely important to the fashion industry. As much as 85% of the cotton used in the industry is from the XUAR. In the US, 24% of all imports of cotton textile and apparel come from China. While the bill and related Withhold Release Orders did not go as far as banning all cotton and yarn products from China into the United States, it puts the industry on notice that cotton and other products from this region are becoming controversial due to human rights concerns. In other words, more action could come.

As the bill wound its way through House committees in the spring and summer, some apparel brands decided to hedge their bets and began to cut ties with companies that source materials from the region. In July, Patagonia said it would end sourcing from Xinjiang, and two months later, H&M Group confirmed plans to sever ties over the next year with a mill associated with a textile producer linked to Uyghur abuses.

But it was the September 22nd vote that grabbed everyone’s attention, in part because the bill imposes a guilty until proven innocent burden for importers. Complying will force the fashion industry to adopt sophisticated lot trace technologies.

Unlocking this level of verification will require a greater level of traceability than fashion is accustomed to. This is an early morning wake-up call. Most companies only deal with their Tier 1 vendors, because that’s where they have the relationship. The finished garment company sells to one of the retailers in the U.S., and that retailer has really no relationship with the fabric mill. They might have nominated that fabric, but they really have no idea where the yarns are coming from, much less even where the cotton is coming from.”

Thus, thanks to COVID and geopolitics, the industry now recognizes the growing importance of a resilient supply chain. Mapping and strengthening the supply network is one thing; but businesses now must take a more important step—completely eliminating and replacing XUAR-based fabric sources.

In the wake of these growing concerns, we launched in December 2020 a digital supply-chain 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 record of importer of record back through the finished goods factory, fabric mill, yarn supplier, and cotton source. Transactions are validated at every tier using PO’s, 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 arising for apparel companies, traceability will be key in meeting consumer demands. While supply chain professionals in the fashion industry knew that a sophisticated lot traceability solution would be critical for backing up sustainability claims, no one predicted that the path would include a pandemic and aggressive state-versus-state government action. 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.