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

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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? 

Read this great blog:  

How can manufacturers manage disruption and improve productivity? By using advanced analytics for manufacturing, to understand the valuable information concealed within the data they already have!

Most manufacturers have already made the easiest and most obvious changes to improve their operations, leveraging traditional methods to boost their own productivity and simultaneously create a more dependable supply chain.

But the pressure to do even more with less is relentless, especially in a volatile business climate such as the one the global community is currently experiencing with the pandemic. Therefore, manufacturers must continually look for new ways to improve the productivity and profitability of their operations. Advanced analytics for manufacturing is a good place to start.

Optimizing Your Existing Assets

We’ve used this space to say this before. Here it is again: there’s an asset that many manufacturers have not yet optimized – their own data.

Some of you may want to argue that point but note that we’re calling for the optimization of deriving sound business decisions from data. It’s a high bar. You may have called a few meetings, written a few emails, maybe even scribbled on a whiteboard and snapped a photo. That’s not good enough.

Thanks to the power of the cloud and advanced analytics, manufacturers can put data to work, gathering information from multiple data sources and taking advantage of machine learning models and visualization platforms to uncover new ways to streamline processes from sourcing to sales.

Advanced analytics for manufacturing and production not only help manufacturers solve stubborn problems, they may reveal some they never knew about such as weaknesses in the extended supply chain or unprofitable production lines. Specifically, practitioners can use intuitive displays to drill into safety metrics, product quality, on-time delivery, cost efficiency, and much more. Here are some common advanced analytics use cases for manufacturers.

Quality Control

Quality control is key to the customer experience and to your bottom line. Over time, ineffective quality control processes will affect customer satisfaction, buying behaviors, and ultimately lead to lost market share. And the costs don’t stop there.

Poor quality control leads to more customer support costs, warranty issues and repairs, and less efficient manufacturing. Good predictive analytics, however, can provide insight into potential quality issues and trends before they become truly critical issues. You can use advanced analytics for manufacturing to understand defects by item, work center, work order, shift and reason code. Separate trends from one-off outliers.

Life Cycle Maintenance

For manufacturers with major investments in infrastructure and equipment, the ability to manage that capital outlay is critical. By analyzing metrics and data related to the life cycle maintenance of equipment, companies can predict both timelines for probable maintenance events and upcoming capital expenditure requirements, allowing them to streamline their maintenance costs and avoid critical downtime.

In short, knowing when a part is going to break reduces downtime and waste. By analyzing factors that drive the wear and tear of your devices and machinery, you gain transparency on the real lifetime of your products. According to McKinsey & Company’s 2017 report ‘Manufacturing: Analytics Unleashes Productivity and Profitability’, what is often termed predictive maintenance typically reduces machine downtime by 30% to 50% and increases machine life by 20% to 40%.

Supply Chain Optimization

We’ve saved supply chain optimization for last. This use case is especially relevant as we watch global supply chains cope with the damage caused by the COVID-19 pandemic. Start by simply mapping your supply chain to at least the second level and preferably the third. Your overall goal is to identify weaknesses and build resilience. As a practical matter, you will learn to anticipate the right time to produce orders or plan shipments to maximize on-time delivery and resolve storage issues. Analyzing the duration of individual processes and the interdependencies among them provides information about the impact of disruptions and potential options for avoiding those disruptions.

Bear in mind that supply chain excellence requires strong supplier relationships and a constant exchange of information with your suppliers to ensure materials and parts are where they need to be when you need them. Logility fosters collaboration – even outside your company – on a common platform to increase pipeline efficiency.

Managing Your Data

Let’s wrap up with a reminder that data-driven insights require holistic data management. In other words, you need to bring it all together to tease out the deep meaning and enjoy the benefits. The term of art is data warehousing. Because Logility provides data integration across departments and systems, essentially everything your business collects — big data, small data, on premise, off premise — you have the ability to develop business insights that follow your entire business process, not just pieces of it.

Data warehousing builds a common repository that allows you to work with your information with a myriad of information access and visualization tools. No matter how your team chooses to work with your enterprise data, data warehousing ensures everyone has the same set of metrics, rules and assumptions to help them meet common goals.

If you haven’t already, make advanced analytics for manufacturing a priority at your company. Start, or finish what you started. What better way to use the downtime none of us expected to have in 2020?

Want to learn more about tackling common supply chain challenges in the manufacturing industry. Check out our ebook ‘Top Supply Chain Challenges for Manufacturing Companies’.

Detective Joe Friday had a way with words. Even if you’ve never seen a Dragnet re-run in your life, or you’ve never heard of Joe Friday, his catchphrase, “Just the facts, ma’am,” is a good motto for running your business. The foundation of supply chain analytics is the desire to let objective, relevant information drive action – in other words, to empower and enlighten employees about data and to make decisions after they’ve looked carefully at “just the facts,” with the help of well-designed supply chain dashboards.

As humans we have an innate ability to use our neocortex (the part of the brain where abstract reasoning, imagination and mathematics are processed) to validate what our limbic brain (where emotions are processed) is “feeling.” Think about it – when we have a “gut feeling” about how business is going, the first thing we do is try to validate that feeling with data. The problem is we can’t take in and effectively analyze the terabytes of raw information now readily available; we’re just not built that way.

We need systems to filter out the extraneous bits, and, just as importantly, we need systems to present the most relevant information in ways we can easily assimilate. To do that well, a digital supply chain platform must place a premium on dashboard design.

It’s true that aesthetics and user experience seem like soft targets at first, especially if you consider yourself a numbers person. But in the case of a solid digital supply chain platform which offers a very broad spectrum of analytics capabilities, like Logility, the focus on the effective design of supply chain dashboards is all about letting form follow function. Here are four reasons why good design is essential for supply chain dashboards.

1. Well-designed supply chain dashboards make information consumption effortless

When a user glances at a well-designed supply chain dashboard, that user should get an immediate overview of how the company is doing relative to their responsibilities. Everything he or she needs to make the decisions you depend upon him or her to make should be right there, ideally on one easy-to-view screen. Not only should the dashboard be carefully designed for maximum comprehension, but so should each element on the dashboard. KPIs should leap out visually, as should any serious anomalies. That just won’t happen if you’re relying on poor design.

2. Well-designed supply chain dashboards lead viewers through consistent processes

As it turns out, we tend to take in visual representations of information in predictable ways. Most of us, for example, look first at the upper-right hand portion of a screen before glancing at other sections. Quality design can exploit these tendencies and, through thoughtful layout, lead an analytics dashboard user through a consistent process for consuming and acting upon a given set of data. The flip side, of course, is that poor design can prevent this kind of consistency from taking root.

3. Well-designed supply chain dashboards turns data into actionable information

We can all agree that data is dumb. In the raw, a large collection of data can lead knowledge workers astray, or in many cases, virtually paralyze them. That’s a terrible waste of your company’s greatest asset – human intelligence. A well-designed solution can take at least the preliminary layer of analysis off a worker’s plate. Think of something as simple as the judicious use of icons. Putting them in line with raw numbers to show the direction of trends can make scanning a set of metrics and choosing a course of action much simpler.

4. Well-designed supply chain dashboards make ROI possible

A poorly designed tool isn’t an asset, it’s a cost sink. It doesn’t matter if the tool is a $3.00 utility knife from the big-box DIY store or an enterprise-level software solution. If no one trusts the results, or if it’s hard to use, no one is going to turn to it when it’s time to get things done. We’re back to trusting our “gut feel” which seems to ignore the last 40,000 years of neocortex evolution. It’s impossible to reach ROI without validating the gut feeling. Quality design is only one step in the process of optimizing your data assets; it is the beginning of a long road to ensure that employees trust and use your company’s analytics.

So, there you have it – four ways that good design impacts information presentation and a little insight into what that means for the knowledge workers in your organization.

Causal forecasting shines a light on, and isolates, actual demand signals from market “chatter,” thus improving forecast quality. In our increasingly interconnected supply chains, potentially valuable information is hidden in plain sight, obscured by the chaotic flood of structured and unstructured data a business generates and consumes. Causal forecasting techniques can uncover complex patterns that are often missed, helping supply chain professionals focus on the truth and ignore market noise and irrelevant activity.

Now and then we hear from planners who claim immunity to the consistent clamor from today’s always-on global markets. A typical rebuttal might be something like, “Causal forecasting sounds great, but it’s not applicable to us. My company’s planning function isn’t disrupted by irrelevant inputs because we base replenishment on shipments, some seasonal adjustments and a dash of gut feel, nothing more.”

This approach is not sustainable. The race is on to deploy technologies such as machine learning and AI that are needed to convert massive, complex and disparate sets of downstream market data into insights that can improve execution across the enterprise. You can be proactive and use causal forecasting to leverage data you already own, model additional data sources that could help explain demand variability… or do nothing.

What Exactly Is Causal Forecasting?
First, what it’s not is a replacement for demand forecasting. In fact, it enhances basic demand forecasting solutions by leveraging machine learning and advanced analytics to provide more insight to improve forecast quality and demand response. Causal forecasting resides between mid- and long-range planning (typically the realm of time-series planning methods) and extremely short-term Demand Sensing technology (“What should I ship today?”).

Logility’s Causal Forecasting solution is an enterprise application for merging all relevant demand signals into a single source of truth and performing predictive and visual analytics that improve forecast quality and allow a business to take impactful action ahead of the demand curve. As such, causal forecasting is much more than basing inventory positions and replenishment schedules on shipment data. Rather, it is about leaning into complexities and understanding cause and effect, not playing it safe at the expense of margin.

Users of causal forecasting quickly learn that it’s more than forecasting trends and seasonality; it’s about identifying and measuring market signals, then using those signals to shape future demand. Imagine understanding market dynamics well enough to move beyond marginally effective reactions to actually influencing outcomes!

The Benefits Offered by Logility’s Causal Forecasting Solution
In short, forecast reliability and consensus planning will improve as a result of clearly understanding how market forces correlate with changes in true demand. Causal forecasting provides visibility and collaboration tools to create direct line-of-sight to real demand from customers as it happens, using this as vital intelligence to be fed back up through the supply chain.

Let’s apply it to a real scenario. You discover that during a given period your company’s sales into the retail channel are significantly higher than sales reported using POS data. Ultimately, the problem is a combination of an overly optimistic forecast and the wrong product mix. Inventory increased as overall customer service levels fell, and margins are set to erode as product is put on clearance.

To avoid this happening, a deeper understanding of causal factors is needed. For example, a planner using Logility’s Causal Forecasting solution to explore variability and causal relationships among stores, products, territories, inventory, raw material price fluctuations, pricing, promotions, competitive activity and consumer sentiment would discover that several brand extension launches were hindered by competitive activity, negative reviews on social media, raw material shortages and shipping delays. The planner could then immediately model adjustments and commit an improved forecast back to the master demand planning system.

Some Things to Bear in Mind
You may have determined already that causal forecasting works best in certain environments and does present some challenges. Industries such as Fast-Moving Consumer Goods (FMCG), Retail, Pharmaceuticals, High Tech and Chemical align well because they tend to share the same characteristics: inventory-driven, consumer-centric, price sensitive, with a reliance on global supply chains, and highly influenced by external factors like commodity prices. And they often share similar business and operational goals: cycle-time reduction, improved fill rates, improved customer service, better cash flow, elimination of over/under stocks, and waste reduction.

As for challenges, before you can have consistent access you need a data storage management strategy to ensure all relevant data can be gathered, housed, structured and blended appropriately. From there, Causal Forecasting allows users to explore multiple best-fit causal models and discover patterns of “explained variance” in the data. Powerful visualization tools allow users to understand the impact of what-if simulations. The final step is integration back to your master demand planning systems.

In summary, causal forecasting helps you aggregate the data you have with the data you need to improve the accuracy of your forecasts.

When asking the question, “How does machine learning improve supply chain performance?” it’s important to bear in mind that the proper goal of machine learning is not abdication of human responsibility for decision-making. Rather, it’s improving our individual and collective ability to make faster, better decisions by leveraging increased speed, accuracy and absence of bias. Our context here is supply chain planning and execution, but there is no reason to limit the scope of machine learning. 

The User Experience

When it comes to designing and creating technology solutions for supply chain analytics, this is not a throw-away idea buried in a long-forgotten PowerPoint presentation. Logility’s point of view is clear: we prefer to start with the user experience. We identify the optimal way for an expert to interact with data to improve decisions. We then work backwards to find the appropriate solutions that enhance the user experience so that the expert has more data at hand for making better decisions. The expert user interacts with data, reports, and forecasts. The variance from forecast identifies business processes that decision makers will want to understand and take management action on. 

Therefore, always start with a user’s needs and work backwards. 

The Influence of New and Emerging Technologies

A quick examination of some recent examples of the influence of what are still considered emerging technologies — such as machine learning and artificial intelligence (AI) — demonstrates that the human element always was, and in fact always must be, paramount. The early hype focused on harnessing the advantages that machines have over humans. Math, speed, brute force, impartiality: this was exhilarating stuff, but it was never about a diminished role for humans. 

In 2016, Lowe’s introduced LoweBot, an autonomous retail service robot in Lowe’s stores throughout the San Francisco Bay area. For the customer, LoweBot can find products in multiple languages and effectively navigate the store. As LoweBot helps customers with simple questions, it enables employees to spend more time offering their expertise and specialty knowledge to customers. Furthermore, LoweBot is able to assist with inventory monitoring in real time, which helps employees detect patterns that might guide future business decisions. Employees are receiving a double benefit: more time to focus on the strategic versus the mundane and real-time inputs to improve inventory management. What a fascinating example of how machine learning improves supply chain performance.

Or consider Walmart’s use of satellite photography and drones for scanning its warehouses. These technologies offer speed, accuracy, and attention to detail. They dutifully report what they find but offer no commentary. 

In 2015, IBM acquired The Weather Company. According to IBM’s website, businesses can now make better decisions by using weather data. “Our solutions connect newscasters, airline pilots, energy traders, insurance executives, state agency employees, retail management and more to the weather intel they need, on any device. We aggregate the deepest, richest data sets — both business and consumer — to deliver personal, reliable and actionable weather information, analytics and insight.” 

The Machine Learning Evolution

Using weather data as context, we’re living through a progression that looks something like this: 

  1. Everyone has known forever that weather impacts the sales of many goods and services. Absent ML technology, humans did the best they could to forecast demand and plan inventory. Mom’s lemonade-stand wisdom held sway: “Make one pitcher at a time while it’s raining.”
  2. Next was the advent of the descriptive power of big data, enabled by giving powerful computers access to vast troves of digital data on the Internet. Call it the era of correlations. As one Walmart executive put it, “We don’t know why berries sell better when the temperature is below 80 degrees. We just go with it. We stock more berries and sell more berries.”
  3. The next frontier: efficient machine learning implementations that enable machines to learn, refine and steadily improve the utility offered to decision-makers. 

The Next Frontier

What does that next frontier look like in practical terms? To start with, it means closing the loop by benchmarking results, providing feedback and refining plans, again and again. Ultimately, machine learning becomes a closed system when the data flows are highly automated, modeling is continuously updated based on the latest information, and the output of the data processing is an automated input to a business decision — like a forecasting decision or an inventory position decision.  

And it means that the benefits of automation are always balanced against the understanding that positive business results come from human actions. Machine learning has much to offer the modern-day supply chain, but we pesky humans continue to play the most important role. In simple terms, that ‘most important role’ is the cycle of observation followed by critical thinking followed by action.  

Interested in finding out more about machine learning as part of an overall supply chain planning strategy?  Take a look at our eBook The Role of Machine Learning in Supply Chain Planning

Among other aspects of a modern-day business, forecasting has become more complex, with many firms striving to incorporate product, pricing, discounts, channel, and other available data to improve accuracy. This increase in forecasting demand complexity and the associated massive increase in data volume requires a machine learning (ML) forecasting solution. In a nutshell, machine learning is a type of supervised or unsupervised artificial intelligence where software can learn without being explicitly programmed.

Simply stated, traditional forecasting methods cannot scale to handle the complex and data-heavy SKU-level analyses that today’s businesses demand. Because we work with clients every day who are faced with this challenge, we have developed purpose-built ML software for demand forecasting. Machine learning forecasting is very fast, allowing a company to generate hundreds of thousands of SKU-level forecasts in minutes. And with dashboard and report management services, you can get your results into action quickly using a system that has been designed for this type of enterprise-scale forecasting business case.

Are You Too Late to the Party?

If you’re worried that you’ve waited too long to invest in machine learning, don’t panic. Yes, it’s true that ML adoption is in full swing. For more than a decade, companies have used the power of machine learning to improve supply chain planning efficiencies and develop optimized supply chains. Automatic model switching to improve forecast accuracy is just one of many examples of the early use of ML to continually tune the digital supply chain and optimally leverage physical supply chain network performance.

According to research published by Supply Chain Insights, “CEOs expect supply chain leaders to prepare for digital business and want to know how they intend to develop capabilities and use advanced technologies like artificial intelligence to create a flexible, agile and responsive digital supply chain.”

In short, you aren’t early, but it’s never too late. The ML market has grown rapidly, but there’s more growth and more innovation ahead. You should be part of it. Start with doing some homework and learn what’s worked for other businesses like yours. This step will help you develop an ROI model to sell the initiative internally.

#1. Understand where machine learning can have the most impact

To estimate the return on investment from leveraging machine learning, you’ll need to explore several areas of opportunity:

  • Forecasting: Forecast accuracy is a top challenge for many companies and a quick-win application of ML could be the automated adoption of “best-fit” algorithms across your portfolio.
  • Supply Chain Optimization: Another high-value opportunity of ML is continually analyzing the state of your digital supply chain and automatically tuning planning parameters to meet customer requirements while maximizing company objectives.
  • Multi-Echelon Inventory Optimization (MEIO): Using the latest demand and supply information, machine learning can enable continuous improvement in your company’s ability to meet a desired customer service level with the lowest inventory investment.
  • Automation: Automate manual data efforts and processes, leaving more time for analysts to work on value-adding activities. Improving supply chain team efficiency is key in today’s tight labor market.
  • Risk mitigation: Detect problems earlier and gain a sustainable competitive advantage by proactively addressing potential disruptions.
  • New product/service development: Leverage new data sources to analyze phrases and market sentiment to develop more successful new products and services that provide stronger sales and higher profits.
  • Stay competitive: Your competition is likely investing in machine learning.

#2. Build the team; build experience

The right culture, organizational structure and skill sets are essential to realizing the benefits of machine learning. Look for:

  • Executive support – this plays an important role in building both the organizational power and vision for ML capabilities. Engage executives with business benefits, not the merits of “yet another IT project”.
  • Machine learning subject matter experts with a diverse set of skills including communication and influence to build support for ML across an organization.
  • Supply chain and business analysts who understand business needs, can assess the impact of changes, can determine the appropriate response and can communicate recommendations clearly.
  • Data scientists who can gather, analyze and interpret complex data used in business decision-making.
  • Artificial intelligence specialists to work on systems that gather information and formulate recommendations that automatically act.

#3. Choose the right digital supply chain platform

Does your supply chain platform support your plans to harness machine learning? Here are a few capabilities that your supply chain planning platform needs:

  • Process automation to free up resources to focus on strategic activities
  • Advanced visualization to support all your analysis requirements
  • Support for all levels of analytic maturity from descriptive through to cognitive
  • In-memory processing for robust analysis and fast response times
  • Cloud-based for fast deployment, security, reliability and scalability
  • Configurable user interface for cross-functional analysis requirements
  • Data warehousing for consistent, harmonized and managed data across your entire supply chain.

Now, stop waiting and get started. Do a little research, build your ROI case and your team, implement the right platform and course correct as needed. A year from now, you should look back on 2020 as the year you made great progress implementing machine learning to boost performance across your supply chain.

Learn more about the value of harnessing big data to create a digital supply chain transformation supported by advanced technologies such as machine learning –

A global pandemic was impossible to foresee as COVID-19 swept across our increasingly interconnected world. The challenging landscape, from store and factory closures to wild swings in inventory, has proven to be the ultimate test of strength for supply chain teams everywhere. Supply chain is now a household term. Companies are measured on their ability to meet demand, right-size inventory to preserve cash and identify potential risks before they impact operations.

There are many examples where companies have turned challenges into new opportunity (see the recent article in Supply Chain Dive, From visibility software to pallet picks: How Tillamook planned inventory as demand soared). We want to share more of these stories and, along with our sister companies Demand Solutions and NGC, developed the Disruption RX Virtual Supply Chain Summit series.

This complimentary virtual summit October 7-8, 2020 will showcase thought-provoking and transformative supply chain strategies that helped some of the most reputable brands turn challenges into opportunity by making better decisions faster.

Just a few of the brands presenting include:

Check out the agenda and register today!

Ask finance managers to name a necessary evil of their responsibilities and many will cite reconciling goods received against invoices not received (“GR-NI”). The GR-NI issue is time-consuming to manage but not exactly mission critical to finding new business.

Because of that it often gets demoted to the lowest of priorities. Not dealing with GR-NI, however, creates financial liabilities and can introduce significant risk to your business operations.

Automating the Cumbersome

The primary GR-NI concern is that a supplier delivers goods to the business – but the invoices for those goods never arrive in the Accounts Payable department. The AP team must pull lists of goods received and compare that to invoices in the AP database to find these gaps. These provisions may add up over time. And without an automated way to monitor and resolve discrepancies, the paperwork just keeps piling up. These piles have risk – liabilities are incorrectly stated, provisions in the GR-NI account tie up capital, supplier relationships may suffer, and credits may be missed.

However, with a simple software-based supplier reconciliation process using procurement analytics in place, an organization can automate this cumbersome process to save hundreds to thousands of man hours every year. And with the same system, it can improve its audit processes and even automate the process of supplier communication to resolve issues as they arise.

For example, one of our clients, a vegetable processor, works with over 2,000 suppliers and receives nearly 300,000 invoices every year. Using a manual approach to invoice reconciliation, they tied up four AP staff members for a week every month to reconcile dozens of statements from across 2,000 vendor accounts. Inadequate audit controls, misstated liabilities, and the frustration of dealing with vendors chasing invoice status all took their toll.

Implementing a new GR-NI process needs some focus and targets to be successful. For example, our client focused on critical preferred supplier relationships with high volume to start. This limited the initial number of suppliers to the 200 most critical ones.

A simple overview of their process is:

  • Creation of a database to integrate financial and orders data;
  • Routine upload of AP ledger(s) to the system;
  • Cross-referencing orders with invoices received;
  • Building chart visuals to highlight discrepancies by amount and supplier type;
  • Outputting supplier lists prioritized for review and contact; and
  • Tracking results in the system to improve overall supplier processes.

Key elements of the procurement analytics solution are:

  • A method to get statements into the system regardless of format and source;
  • Cross-link matching of companies and multi-level parent-child relationships resolved;
  • Automatic generation of statements in PDF and spreadsheet formats; and
  • Electronic report distribution to suppliers.

To maximize value, look for the operational areas in your company that can benefit from better processes. Tedious tasks like resolving goods received-not invoiced issues may not fall under your radar, but automating and improving these processes can deliver a serious positive impact. Logility is an ideal solution for supplier reconciliation processes.

A quick scan of the Internet will yield a long list of “the biggest-ever supply chain disasters”. A few of the most infamous are listed below. The common thread? Each of these events was completely devastating to the company concerned. Would access to better supply chain and IT performance information have helped avert or mitigate these failures? Maybe, but what is certainly clear from the examples is that the complexity of modern supply chains requires better visibility into every phase of production and distribution. And better visibility requires data which can support the best supply chain KPIs for your business.

Company Year Problem Impact
Nike 1999 Poorly implemented demand planning software mis-forecasts demand for branded sneakers $100M in lost sales and 20% drop in stock price in one year
Tri-Valley Growers 1997 Never completed ERP/SCM implementation Contributed heavily to company closure
FoxMeyer 1996 Failed SCM implementation causes shipping errors Bankruptcy
Adidas 1996 WMS failure causes Adidas to under ship by 20% Significant loss of market share. Huge loss of IT talent
Cisco 2001 Demand planning systems unable to forecast demand-slowing associated with economic meltdown $2.2B in inventory write-down and 20% drop in stock value

Every company today runs on data – the key to using your data is choosing the right metrics for visibility into your supply chain. While Key Performance Indicators (KPIs) may be reviewed quarterly or monthly, the speed with which supply chain operations occur makes a daily view of more tactical and operational metrics a growing necessity.

In our experience across hundreds of implementations, we find that while every company has its own unique dynamics that must be measured, most businesses fall into one of three operational types – and these types can be grouped by a focus on customers, products, or operational efficiency.

Service Excellence
Service Excellence companies make their primary focus the customer experience. In a consumer-oriented world, there has never been a stronger focus on service delivery. Amazon and Zappo’s typify companies that create competitive advantage and loyalty with their delivery and return arrangements. Generally, product companies that offer high value-add fall into this category.

Service Excellence companies should have their finger on the pulse of their customers and their competitors every day. Their supply chains must be flexible and responsive to customer and market needs. Management must be cognizant of issues of procurement, manufacturing, inventory, and distribution that impact satisfaction and loyalty.

The top five supply chain KPIs for Service Excellence companies are:

  • Call volume and queue
  • First contact resolutions
  • Item fill rate
  • Perfect order
  • Delivered full / on time

Product Excellence
Companies with a focus on operational excellence view all aspects of the supply chain holistically. The focus is driving costs out of each leg of producing and selling goods. Technology leaders like Apple are typically product oriented. These companies are generally focused on commodity goods and delivering the best value at minimal cost. Daily, Product Excellence companies are primarily concerned with the supply chain’s flow, its reliability, and its cost.

The top five supply chain KPIs for Product Excellence companies are:

  • Total cost of goods sold
  • Supply chain cost per unit sold
  • Labor utilization
  • Warehousing and transportation costs
  • Enterprise software performance

Operational Excellence
Lean organizations focus on eliminating waste in their supply chain at every stage. Following lean management principles, supply chain KPIs are mapped by seven categories of waste and inefficiency in a business: Excessive waiting, Overproduction, Rejects, Motion, Processing, Inventory, and Transport. Lean KPIs tend to be the most tactical and daily oriented. One of the keys to cost efficiency is anticipating supply chain problems early using system data and then acting before the problems impact financial performance.

The top five Include:

  • Asset utilization rate
  • Defect and acceptance rates
  • Queues and wait times
  • Stock levels
  • Safety stock

Your Supply Chain Type
So what orientation does your company have? Many of our customers say “well, a little bit of each,” and that’s completely normal. In fact, the Supply Chain Operations Reference (SCOR) model published by the Supply Chain Council outlines core attributes common to any business with a supply chain and associated core (or “Level One”) metrics.

SCOR Attribute Metrics Description Best for Type
Costs COGS, Total Supply Chain Cost, Productivity, Returns Rates, Carry Cost of Inventory Focus on measurement of total cost and its drivers Operational Excellence
Responsiveness Order Fulfillment Lead Times, Late Order, Order Accuracy, Inventory to Sales Ratio Focus on how quickly and effectively the company delivers goods to customers Service Excellence
Reliability Fill rates, Perfect Order, Delivery Quality, Inventory turns, Order Status Focus on getting the right product to the right customer at the right time Product Excellence
Flexibility Production Flexibility Focus on the ability of the company to adjust operational processes to react to changing market needs Service Excellence Product Excellence
Asset Efficiency Cash-to-Cycle Time, Inventory on hand, Asset turn, Repair and Maintenance Cycle Focus on effectively managing working and fixed capital Operational Excellence

There are two ways to choose your supply chain KPIs. But before that, your supply chain measurement program must be based on some idea of your end goals. Pick a particular attribute as the main emphasis. While other metrics can be measured, KPIs are only useful to the extent they can promote positive change in an organization, and most organizations can focus on only one or two areas at a time. Next, how do you assess performance? KPIs that show performance relative to industry benchmarks and peer performance are usually strategic KPIs that may be assessed monthly or quarterly. SCOR Level 1 Metrics are good examples of measures that have longer-ranging focus.

Many companies, however, are focused more on real-time/right-time metrics that can be updated daily and provide greater opportunity for faster reaction to operational issues and opportunities. These fall into three categories: demand, supply, and operations.

Daily demand characteristics that should be measured are changes in market conditions or leading indicators that suggest a shift in demand is moving through the supply chain. These metrics are designed to help address the ‘bull whip’ effect of small moves in end-buyer activity rippling back through the supply chain in unexpected ways. Demand KPIs should, by nature, be predictive. Historical sales and customer activity are key inputs to a more refined measurement or forecast of where sales are trending.

Daily supply KPIs are related to the interaction between your suppliers, procurement and production. With the world demanding more responsive supply chains, there is a greater focus on suppliers who can work with your firm’s capabilities and limits. If you are less responsive in your flexibility, then choosing suppliers who can augment your operations can be key.

Daily operational KPIs drill into machine and process performance. Queue times, rejection rates, and transport metrics such as cross-dock rates are key. The image below shows an example dashboard that managers can use to monitor supply chain performance daily. With its focus on shipment, this dashboard emphasizes service excellence. It provides a holistic view of the supply chain, process efficiency, and order outcomes.

Most companies can benefit from a review of their current metrics and KPIs. Are they being used effectively? Or are they just collecting dust? Has the organization already adjusted to these metrics so that measurements consistently fall within tolerances? If so, it may be good news for management bonuses. However, it should also signal to senior management that it is time to set the bar higher and look for newer diagnostics measures within the organization to help drive even better outcomes.

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