The Science and practice of predictive analytics is well established and rapidly gaining ground in the public and private sectors. It’s no longer considered magic because we now have advanced analytics systems that harness and organize massive amounts of disparate data and model that big data in ways that allow humans to be proactive and make informed decisions.

How would your supply chain decision-making be enhanced if you had the power to harness the data of the past into decisions for the future using predictive analytics modeling?

What are Predictive Analytics for Supply Chain?

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.

How can big data lead to supply chain optimization?

Let’s examine two popular applications: supply chain optimization and baseball. 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, the big data conundrum.

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’s predictive analytics capabilities 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 predictive 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 up to $800,000 savings within the first 18 months. In addition, the company points to a two FTE reduction (about $300,000 annually), and faster decision-making by business managers.

The Baseball Analogy

Now let’s consider the case of building a winning team in baseball, and the use of sabermetrics. The premise of sabermetrics is that the historically most common measure of performance, the runs-batted-in percentage (RBI), was an incomplete measure of the likelihood that a team would win a championship. Baseball statisticians, now called “sabermetricians”, have figured out how to use all the available data on a player’s performance to make a better decision on which players to combine onto a team. This is a great example of predictive analytics applied to baseball.

You may have seen this portrayed in the movie “Moneyball”, where the proponent of sabermetrics was trained in economics and computer programming, was arguing for the managers of the baseball team to try a different and more scientific approach. The sabermetrician focused on a new metric: On Base Percentage (OBP). The sabermetrician found that getting on base by any means is foundational — you cannot be batted in if you are not on base. Sabermetrics Identified the first link in a chain of causal events that lead to success: focus on getting on base by any means including walks and being hit by the pitcher.

Even more important, the Moneyball sabermetrician knew that decisions on which players to recruit and who to trade were constrained by budget and availability. With other teams pursuing recruits with the highest RBI statistic, the sabermetrician knew there was an arbitrage opportunity — recruit older players with high OBP, even if they were near the end of their careers and being traded away by other teams because their RBI statistic had dropped off.

The dominance of sabermetrics in modern baseball is analogous to how big data and advanced predictive analytics is now coming to dominate modern supply chain optimization. Combining more and more data into a large ecosystem affords a broader analysis space, leading to new insights that would never be found if the data processing were not blended and automated. Identifying conditions that have a larger-than-average value multiplier (arbitrage) is more powerful when the search for arbitrage is informed by big data. And in both baseball and supply chain management, you need financial metrics in order to choose the best strategy.

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.

No customer experience is more disappointing than not receiving an order as promised. Whether it is a damaged product, a shipping delay, or a complex chain of handoffs and rerouting, any mishap can damage market share, revenue growth, and customer relationships beyond repair.

Most B2B and B2C brands believe faster order processing, more frequent inventory planning, and “good enough” promise dates can help avoid this situation.

Yet they still struggle with contradictory pressures related to the cost savings of just-in-time manufacturing and inventory optimization, the economic realities of raw material shortages and inflation-induced pricing, and growing customer demand for sustainably made products.

To achieve that perfect balance between demand fulfillment and cost-effectiveness, you need a digital supply chain platform that provides the processing power, data management and integration, and analytics necessary to analyze each order against product availability, capability to fulfill the order, and profitability of the order promise.

Here are three ways to boost your customer satisfaction with automated order promising.

1. Fast Commitment to Accurate Promise Dates

Suppose your supply chain planners work 24/7 reviewing every purchase, slotting it in the packing and shipping schedule, and notifying suppliers when more products should be manufactured. In that case, customer orders are most likely to be fulfilled as promised. However, this is not a cost-efficient or sustainable arrangement.

Your planners still need to go home, even if orders don’t stop coming in. As a result, it is all too easy for product orders to exceed inventory availability, leading to overpromised fulfillment and delayed delivery. And let’s face it: no one wants to reach out to a customer with that unwelcome news.

What if promise dates are processed in real-time without human intervention? As orders are received, the digital supply chain platform automatically matches the order to existing or planned inventory (ATP), and if there is none to allocate it will then check capacity and materials availability (CTP) to provide accurate delivery dates, in real-time, that your customers can rely on.

2. Reasonable Delivery Dates Without Surprises

With automated order promising, your supply chain planners can benefit from the real-time analysis of every single order against business constraints.

The platform accepts orders from ERP, eCommerce sites, customer service areas, and dealer/customer portals. These orders will be immediately processed first running an ATP check and if nothing is available for the order then running a CTP check.  CTP analysis considers business constraints on inventory,  production schedules, capacity, lead times, materials and other supply chain parameters which affect the ability to provide the product to the customer.  The customer is then informed immediately with a realistic expected delivery date.

When planners come in the next day there should be no surprises, no ‘oh no we can’t do this’ in the 20 days our default promise date was set to.  Now consider this additional stability in your planning organization; no longer living a life of calling customers with bad news on delayed orders and no more constant fire-fighting and expediting.

This accurate and real-time communication at the point of the transaction brings tremendous value to your customer experience. Your customers will know right away what to expect and are allowed to accept or reject the new order and delivery agreement. Furthermore, your supply chain gains the flexibility to avoid being overburdened by unreasonable delivery dates, so you can respond in ways that keep customers satisfied and loyal.

3. Early Order Arrival with Greater Visibility

By running automated order promising in the background, your business gains a connected ecosystem of eCommerce sites, order-taking applications, and your ERP that can capture orders accurately and provide visibility into your company’s capacity to fulfill them. But more importantly, your planners can derive data-driven insights to evaluate different options for improving customer satisfaction.

Suppose a customer called in an order for 500,000 units of one of your more popular products. A planner would need to check in a distribution center (DC) nearest to the customer’s location to see if the right quantity is available. If not, the production manager would need to determine whether a facility has the capacity to manufacture the product quick enough to meet the promised delivery date.

This traditional process flow depends on people to process the request, research availability and capacity, and get goods all in one place to fulfill an order. It’s manual, laborious, and too slow for a world accustomed to immediate answers and results.

Automated order promising eliminates all those drawbacks. Based on predefined business rules, the technology can rapidly pull real-time inventory information from the closest DC. And if the requested quantity is not available, the system automatically expands the search to surrounding DCs and considers trucks expected to drop shipment to that initial DC. In addition, the customer can be notified on whether the order will be delivered on time or at a new date.

And here’s the best part: all of these tasks can happen in a matter of minutes, not days. Furthermore, by automatically expanding your inventory, the customer’s need is met while you reduce costs by avoiding a short production run, expedited shipping, and hours of work that employees could use for more strategic activities.

From Guesswork to Informed Order Promising

Ten years ago, it may have felt natural to have supply chain planners make assumptions about lead times, expected order volumes, and future order capacity based on seasonality, buying behaviors, and customer consumption. But nowadays, businesses are encountering trend lines that are no longer as stable, slow-moving, or predictable as they used to be.

With the automated order promising capabilities of the Logility Digital Supply Chain Platform, your business can navigate every unexpected – and expected – twist and turn in your supply chain operations.

As a result, your planners can better position inventory, honor shipment dates, and ensure orders remain profitable – all while keeping customers satisfied and loyal.

How can your business benefit from the automated order promising capabilities of the Logility Digital Supply Chain Platform? Find out by watching our on-demand webcast “Automated Order Promising to Improve Customer Satisfaction.

For decades, engineer-focused software has dominated the supply chain network design world as well as the optimization world in general. Originating in the 90s, these pieces of software have become increasingly powerful, but also increasingly complex and “touchy.” The requirements for excellent data, highly trained engineers, and significant time to model has become prohibitive to rapid decision-making.

Gaming simulation is an alternative to this method. It uses a business rules engine that reflects design best practices as well as market-based data that is often better than historical data

Logility has embraced gaming simulation for the supply chain and made it an integral component of our network optimization solution. The gaming engine acts as a continuously running algorithm to find the optimum flow of product through a supply chain from supply nodes to demand nodes (customers). Users do not need to run an optimization routine, nor do they need to wait for solvers to return an answer. All they need to do is make a change — add a node, move a node, add a connection between nodes — and the gaming engine presents a whole new simulation. 

This capability allows our network optimization solution to be highly productive. It supports work-shopping ideas with executives as well as doing very fast sensitivity studies. See gaming simulation for the supply chain in action below.

Logility’s network optimization solution helps you answer important questions about redesigning and optimizing your supply chain, such as whether you are shipping to the right locations, where you should put your next warehouse, or whether you should offshore or near-shore.

Leverage Your Own People and Their Expertise 

Logility’s network optimization solution offers an intuitively usable interface that lets you democratize network design, leveraging your own people and their expertise. 

Talk to our supply chain experts today about evolving your supply chain design with network optimization. 

Check out other great content covering network optimization: 

A key factor in the success of Logility’s network optimization solution is the use of reference data. 

At Logility, we believe that using market reference data is more statistically sound than relying on historical data from one customer. Yes, this data is comforting in that it reconciles back to accounting numbers. However, it is misleading when you try to predict what you will spend in the next period. Historical/company data is subject to: 

  • Lack of statistical depth — less than 20 data points for any given cost 
  • Accounting irregularities — shipments and payments that are discounted or readjusted later in the year 
  • Purchase bias — truckloads or shipments that made conditions favorable such as an available truck or a back-haul opportunity 

But most of all, building a data model from scratch takes time — lots of time. 

We believe users should be able to generate results immediately and be able to benchmark a model against their known costs. Think of our network optimization reference data as a tariff that you can adjust to match your businesses behavior. 

Logility’s network optimization solution comes loaded with: 

  • Truckload rates 
  • LTL rates 
  • Parcel rates 
  • Intermodal rates 
  • Labor rates 
  • Lease rates 
  • And more 

Nathanael Powrie, EVP of data analytics at supply chain consultancy firm Maine Pointe, highlights the value of reference data available in Logility’s network optimization: 

“It is a game-changer to have the platform pre-loaded with transportation, warehouse and labor-cost reference data sets that represent accurate historical averages that provide the best indication of relative prices for our scenarios.”  

Hear Nathanael discuss how Logility’s network optimization solution is driving faster, better supply chain decisions. 

Get Faster Time to Value with Network Optimization 

Logility’s network optimization solution supports today’s supply chain leaders to ask and answer questions that reduce the complexities of supply chain design, increase efficiencies and help accelerate confident decision-making.  

Talk to our supply chain experts today about delivering better business outcomes with network optimization. 

Supply chain issues have put many companies into crisis mode. Avert crises and manage your supply chain more efficiently with optimized allocation of constrained supply.

It is challenging to deploy products quickly and efficiently when dealing with insufficient stock to support demand, or when a customer asks for different product compositions or assortments. While supply chain woes have existed for decades, the last few years of supply chain crises have tested the mettle of companies in every industry. Quick reactions and responses are essential, but that’s impossible without a proactive approach to a rapidly changing landscape.  

What lies ahead in 2023 (and beyond) requires an intelligent supply chain strategy to help you automate and accelerate your allocation processes to ensure the right product assortments arrive in stores and back stock that’s held in distribution centers is right sized. In support of your strategy, you need solutions that help you rapidly address shortages and avert crises.  

Supply Shortages Continue 

It seems counterintuitive, but even though the number of cargo ships was drastically reduced at various points in the last two-and-a-half years, ships sat at ports waiting to be unloaded due to labor shortages – a problem that persists today and contributes to ongoing supply shortages that exacerbate supply chain disruption.  

Transportation and shipping issues aren’t the only problem, of course. The computer chip shortage impacting the auto industry was capacity related – fires in a manufacturing plant, not enough factories to produce the chips due to increased demand, and limitations within the industry to accurately forecast demand for various types of chips meant many carmakers were forced to suspend production. 

Supply constraints are nuanced and complex and these are just two examples of how such constraints can play out on a global scale. We know you’re looking for better ways to work around short-supply challenges in your supply chain’s day-to-day operations – here are four ways that the Logility® Digital Supply Chain Platform addresses those challenges with intelligent, optimized allocation. 

Solution 1: Fair share – everyone gets something 

The fair-share methodology distributes or allocates a quantity based on the values already at low levels, essentially prorating it based on the existing values.  

This could be used for something as simple as a complete flat spread if there was no demand or no available inventory at any level. It could be used to make sure everyone gets roughly the same amount of inventory. There could be some rounding up or down here and there, but essentially there is a flat-spread distribution across the different entities that are being allocated to. 

For example, let’s consider one servicing location that distributes to three destination locations. Using fair-share distribution, you’re going to look at the forecast for each of the destination locations. You can do a fair-share or push proration distribution that would take the available inventory and spread it to the locations in the same ratio as their forecast, so everyone gets a ratioed amount of what they were forecast to get.  

The fair-share distribution is best used when you don’t have prioritization of any kind, whether by location, account, customer, or demand type. You may also want to use this method if you have no additional product scheduled to arrive, ensuring every customer receives something because you won’t be able to fulfill later with additional supply. 

Solution 2: Demand prioritization – first come, first served 

The opposite of fair share, this method looks at prioritization: How can you satisfy demand when you want to prioritize certain locations? The Logility platform allows you to use demand prioritization among accounts, regions, customers, channels, or locations, depending on your priorities, to determine where constrained inventory should be sent.  

You can prioritize different types of demand, such as regular price forecasts or promotional forecasts, and move onto lesser priorities from there. This gives you a lot of flexibility and insight into how the prioritization behavior takes place.  

Let’s say you’re looking at a plan over a 12-month period, and you see some constrained inventory. You can easily identify late fulfillment, lost revenue, lost demand, or a demand drop that are a result of supply constraints.  

The platform also allows you to look at various scenarios so you can see the impact on your plan before you commit to it. You can see a demonstration of this function in action on our webcast Crisis Averted: Optimize Allocation of Constrained Supply and view the immediate impact of prioritization on revenue and service levels. 

Demand prioritization is best if you have tiered customers or groups or locations (known priorities), you need to maximize metrics like profitability over equitability, or you need to short supply for the present but have more products coming in future. 

Solution 3: Rules-based allocation – consider multiple factors 

While fair-share and demand prioritization are mirror images, the rules-based allocation method allows you to take multiple criteria, order them sequentially and recommend quantities for distribution.  

For example, an apparel company might pick certain colors and sizes of a single style to distribute. The selected criteria create a workflow – a set of saved allocation criteria configured based on business needs. These criteria can be based on the product line, the user, the organization, or however your list needs to be configured to meet your business requirements.  

You can also use rules to automate this process. For example, if you’re allocating something from a particular vendor, use criteria A, but if you’re allocating something from a different vendor, use criteria B – or even use it with the same vendor. 

You could indicate that if there is a new item, use criteria A; if this is considered an existing item, use criteria B, and so on, and you can set this up to run automatically and unattended. There are many criteria you can consider for your workflow.  

The user would go in after the fact, review the allocation, make any adjustments they want, and then either approve or adjust the allocation to complete the process. 

The rules-based allocation method is best used if you want to consider multiple factors rather than forecast priority or fair share. If you’re not sure whether more product is coming in, you can cover part of your distribution and let need dictate the rest. 

Solution 4: ‘Open source’ – flexible configuration 

While it’s not an official term, ‘open source’ sums up what’s possible using this methodology – it’s the epitome of configurability. Not only is the methodology or the logic the system uses completely configurable, but it can be used to distribute products and the source and supply of what you’re distributing can also be configured.  

Let’s look at one scenario. You can use prioritization, or the “greedy” (first come, first serve) criteria. Customer one is your highest priority, so they are served first. Customer two gets the balance of what’s left over, and, in the “greedy” methodology, customer three gets nothing.  

You can also configure this to whatever prioritization logic you need. You are not limited to the inventory or supply or product that’s been the focus so far. If you’re looking at available capacity over time, for example, you can allocate that just as you did with your products. You can prioritize which products take precedent over other products to make sure they’re used to fulfill unused capacity first before moving on to other products. This is useful for key products you want to prioritize for production – new products, highest margin products etc.  

Because of its completely flexible configurability, you could use it in any situation or when none of the other methodologies can effectively optimize allocation of constrained supply.  

Make Better Supply Chain Decisions That Align with Business Goals 

These four methodologies for optimizing allocation of constrained supply can be used to deal with inventory challenges in a way that matches the goals of your organization while you deal effectively with any supply chain crisis. It’s not a one-size-fits-all proposition, but the data you’ll have available through the Logility platform means you’ll make better decisions and improve your overall supply chain strategy. The methodologies, which are all part of the platform, can be used as you grow and adapt to change. 

Just like supply chain challenges, these solutions aren’t specific to any vertical or industry. Constrained supply is a universal problem, and Logility has the solutions. From integrated business planning to demand and supply planning and optimization, to vendor management and the advanced analytics you need, we’ll deliver a digital, sustainable supply chain that powers the resilient enterprise. Hone your competitive edge and overcome any disruption. Your supply chain won’t fix itself, and there’s no time to waste. Let’s get started

To see the Logility platform in action and solving for the supply challenges covered here, watch our on-demand webcast Crisis Averted: Optimize Allocation of Constrained Supply 

Solve for demand planning confidence, despite economic uncertainty. 

Lurking beneath the relentless wave of supply chain disruptions are challenges managing supply and demand, as peaks and valleys in orders mean a longer shelf life for warehouse inventory. It’s a tug-of-war that can cause inventory carrying costs to soar.   

Now, higher interest rates and material prices threaten to increase inventory carrying costs, adding to the pressure on both supply chain and finance leaders to protect margins. And as markets shift, companies must find new ways to quickly reduce costs everywhere they can, and ultimately do more with less. 

Accurate, long-term forecasting is crucial to efficient supply chain management and driving down costs. But supply chain professionals today require the means to understand short-term demand to help make faster decisions based on what happened days – or even hours – ago. And this can’t be done manually with high volumes of SKUs and vendors in the mix, combined with the complexities of global trade. 

Demand sensing – a methodology using artificial intelligence (AI) to improve near-future forecasts – leverages up-to-the-minute data to optimize inventory and improve customer service levels by reducing the latency between planning decisions and the most current activity within your supply chain. 

Going Beyond the Traditional Paradigm for Demand Forecasting 

Traditional demand forecasting relies on historical sales data that is often sufficient for mid- and long-term planning horizons but is less accurate when it comes to short-term planning. Using machine learning (ML) and natural language processing (NLP) capabilities, demand sensing AI can incorporate a much broader range of demand signals and very current supply chain data, creating the most accurate possible daily forecasts to respond to real-world events. 

By applying demand sensing technology, your company can progress toward inventory management that lowers carrying costs and improves the responsiveness of your supply chain. Take, for example, Nike. By implementing demand sensing using real-time data and analytics, they improved supply chain speed and flexibility, and margins are getting a boost as well. 

How Artificial Intelligence Helps Navigate Supply Chain Disruption and Gives You a Competitive Edge 

Dealing with supply and demand fluctuations is challenging. Stockpiling inventory is costly, and the pandemic, wars, political tensions, and inflation have rendered historical data ineffective (along with your spreadsheets). World-class organizations are applying supply chain demand sensing AI, and here’s why. 

Ensure you have a clear view of market trends and potential risks. Organizations today must have a clear view of the most current trends so they can adjust forecasting and product development to match. They need early warnings of potential product or service issues. In short, demand sensing helps you adjust quickly to unpredictable market needs with optimized inventory.

Understand your current buyer behavior to rapidly respond to demand spikes. Your customers have more choices than ever when it comes to what they buy and whom they buy it from. Along with the product they want, they expect personalized and responsive service. By leveraging near real-time data, your business will be better able to meet your customers’ needs. Demand sensing technology provides the means to truly analyze current demand signals, reduce signal latency across the supply chain and improve response times. 

Establish better supply chain visibility so you can improve service and product offerings for competitive advantage. What kind of visibility will add value to your business? You need to  be able to see across events and promotions. You need the ability to evaluate external factors such as weather patterns, geopolitics, inflation – even social media sentiment – to improve forecast accuracy. You need to gain insight into competitive positioning and improve service and product offerings. And you need a single platform to service these needs. 

Improve Forecast Accuracy with a Broader Range of Demand Signals 
  • Begin with granular, historical data  

Analyze sell-in demand data but use a shorter time horizon and factor in shipping history.  

  • Then incorporate every other piece of data 

This includes customer orders, consolidated POS information, and channel data, including your social media. This can work as an early warning system for future disruptions as well as predicting trends.  

  • Add external data sources 

One of the things that makes demand sensing so accurate is that it includes data points that aren’t included in traditional forecasting. Depending on what’s going on in the world at the time, explore adding data that includes: 

  1. Macroeconomic factors, including a country’s GDP, the stock market, employment data, inflation, the geopolitical climate, and so on. All these things can affect demand. 
  2. Competitor data such as promotional discounts and stockouts. This type of information lets you adjust what you offer to gain a competitive edge.  
  3. Weather data may seem an odd thing to include, but if your sales are seasonal, short-term weather changes can impact demand and sales.  

Incorporating data that reflects the current realities of your supply chain, including a broader range of demand signals, improves your speed and agility in responding to supply chain disruptions. 

AI-First Demand Planning eBook

Learn “How Human-Machine Collaboration Cuts Costs, Error, and Implementation Time” in this free eBook.

Free eBook
The Logility® Digital Supply Chain Platform Offers Demand Sensing Technology 

Demand sensing uses artificial intelligence to take market-based demand data and translates it into information you can use to improve short-term forecast accuracy by 30% or more compared to traditional methods of time-series forecasting. This means faster demand response times, elevated customer service levels, balanced inventory, and improved profitability. 

With Logility’s digital supply chain platform, you have the technology you need to craft a modern, data-driven, and managed supply chain network with all the benefits it brings. 

From integrated business planning to demand and supply planning and optimization to vendor management and the advanced analytics you need, we’ll deliver a digital, sustainable supply chain that powers the resilient enterprise. Hone your competitive edge and overcome any disruption. Your supply chain won’t fix itself, and there’s no time to waste. Let’s get started

Key Takeaways 

  • Economists expect slower economic growth in 2023, but still debate whether a global economic recession will officially emerge. 
  • Despite the challenges of the last two years and current economic fears, only 12% of companies have stepped up to protect themselves against supply chain disruption. 
  • Organizations that fail to plan are more vulnerable to avoidable risks – vendor failures, operational shortfalls, and devastated revenue growth. 
  • It’s not too late: the impact of a downturn can be significantly muted with five digital updates to supply chain operations, to help future-proof your supply chain. 

Five crucial actions to improve your supply chain financial performance

Resilience, flexibility, and visibility are the keys to future-proof your supply chain. The entire operation needs to remain highly coordinated, but it must also pivot quickly to respond to market dynamics, such as changes in customer preferences, demand, and supply availability. 

Digital supply chain platforms are well designed to handle these challenges. The technology enables organizations to modernize their operations with five essential capabilities, leveraging a combination of artificial intelligence, machine learning, process automation, and predictive and prescriptive analytics. 

1. Plan for true demand 

On a single platform, supply chain organizations can predict future changes in demand and evaluate their impact across three dimensions: demand planning and optimization, causal forecasting, and demand sensing. Each demand analysis serves as a building block toward significantly more accurate predictions that are attuned to product portfolios, target markets, and product lifecycle stages – regardless of complexity. 

This information offers transparent guidance into market demand to optimize outcomes for new product introductions, product phase-outs, and short product and promotion life cycles. For instance, a plastic packaging products manufacturer can optimize its capital investments by using forecasting data and increasing its visibility across current business gaps and future requirements. With just a 10% improvement in forecast accuracy, the company can raise service levels between 5% and 95% and reduce lead times by 40%.3 

2. Protect margins and safeguard supply 

Understanding the structure of the supply network and how it extends across multiple tiers of suppliers and subcontractors is critical to avoid, absorb, or navigate supply chain disruptions. With access to data from across the business ecosystem and vendor systems, supply chain organizations can gain these insights to measure variability in demand, lead time, production capacity, throughput performance, resource availability, and logistics plans. 

This probabilistic approach to supply planning enables companies to enforce accountability, control, and compliance across the supplier network with proactive alerts, reporting, and key performance indicators.  

Consider a global hospitality supplier that wanted to secure a consistently available inventory of personal care products at a lower cost. A probabilistic view of its supply projections allows the business to gauge emerging risks and opportunities against its corporate financial goals and service level expectations. Suppliers can also use this information to quickly pinpoint gaps in product availability and scale production capacity to reach agreed-on inventory levels while calculating prices that are fair for both sides of the vendor relationship. 

3. Create adaptive, agile supply chains 

Like organisms, supply chains continually evolve to meet the needs and expectations of an increasingly dynamic marketplace. And all forms of business planning must be equally flexible – from sales and operations planning (S&OP) to sales and operations execution (S&OE) and financial and strategic planning. 

In-memory digital twin technology embedded in a digital supply chain platform helps you prepare for disruptions by enabling fast planning comparisons, simulations, and “what if” scenarios in a highly integrated manner.  

Take, for example, a packaged food company looking to relaunch its brand. The business leverages a digital twin to get a 360-degree view of the supply chain, so it can streamline its distribution network and centralize network planning. Plus, decision-makers discover that it’s easier to identify and analyze risk and ensure mitigated and prompt responses to disruptions. As a result, the campaign was successful, with 100 million snack cakes delivered to stores within four months and flying off the shelves. 

4. Align inventories to free up cash 

Out-of-date spreadsheets, process workarounds, and orders placed outside of supported channels have always been more commonplace in the supply chain than they should be. But during a global economic crisis, there is no room for these error-inducing, non-transparent practices of an out-of-control inventory situation.  

By shoring up inventory management with a unified digital supply chain platform, companies can avoid having a surplus of one product, stockouts of high-demand items, and a considerable chunk of cash tied up in aging backstock. They can run multiple inventory optimization scenarios based on existing and forecasted changes to the supply chain and potential postponement and investment strategies. In return, the business quickly understands the causes of inventory fluctuations and their impact on costs and service levels.  

By using this approach to right-size inventory and free up working capital, supply chain organizations can fuel critical business initiatives that may have otherwise been placed on the back burner due to cost. Suppose a furniture manufacturer decided to adopt a digital supply chain platform during a time of product line expansion and inflation-induced pressures. The company soon learns that it can move to a production facility closer to its customers to improve inventory turns by 36%, achieve greater product availability, and reduce lead time by 30%.4 

5. Prepare for an economic recovery 

For every economic downturn, a recovery is waiting to reveal itself. And companies that developed comprehensive action plans before a financial crisis will likely seize new market share once it is over.  

A practical action plan uses a digital supply chain platform to optimize all aspects of the supply chain. With the data gathered, supply chain organizations can further streamline their supplier network, automate processes to manage operations more confidently, and eliminate wasteful spending and practices to meet new market conditions. More importantly, they can battle a growing number of SKUs and planning complexity, paired with business growth. In most cases, this capability can bring an 80% decrease in aged product write-offs and 99% higher service levels, as well as support decisions on growth and strategy.6 

Overcome economic uncertainty with the right strategy

Supply chain organizations cannot stop a recession from happening. But they can take the right steps now to safeguard their supplier network, operations, and revenue models. And when economic conditions begin to bounce back, they would have eliminated any weaknesses that could get in the way of acting on new opportunities immediately. 

That’s the beauty of adopting a digital supply chain platform. The integrated platform gives supply chain organizations the data-driven insights, visibility, and analytics and automation tools they need to collaborate efficiently, make better decisions faster, and improve customer and employee satisfaction – no matter the economic conditions. 

And given the potential for more disruptions, there is no moment too soon for innovative thinking and the right technology. Let’s get started

Sources 

2. “Risk, Resilience, and Rebalancing in Global Value Chains,” McKinsey & Company, 2020. 

3– 4, 6. Logility customer benchmarking data. 

As we navigate through the end of the year and the holiday season, we’re also looking ahead to 2023. We know we’re going to see ongoing disruption. We know the chances of recession are growing. We know our clients need more and more ways to take control of their supply chain operations, end to end. Check out four of the areas we’re advising our clients to be thinking about heading into the New Year, to help prepare their supply chain for 2023.

supply chain for 2023
  1. Governments may start to intervene1. So far, governments have left companies to manage the supply chain challenges on their own – for the most part. But there is increasing awareness and concern that supply chain challenges can be viewed as issues of national security. Some posit that governments are more likely to intervene in 2023 to address issues caused by the ongoing geopolitical events and rising inflation. We’ve already seen an example of this in late 2022, when the Biden administration acted to block the strike of thousands of railroad workers across the United States – an event that could have been catastrophic for U.S. industry and the economy. We are advising clients to: 
  • Utilize scenario planning in cases such as when to onshore versus offshore to evaluate sourcing options in the wake of government intervention 
  • Clean data and documentation to make managing government intervention easier 
  1. Last year’s shortages = next year’s oversupply2. The beginning of the year saw us dealing with supply shortages as product was caught up in containers, as suppliers dealt with capacity challenges. Then we saw a glut of inventory hit the shelves in late summer when those bottlenecks were addressed. Retailers had to slash prices to deal with the excess. As the economy continues to head toward a recession, we expect consumer demand to decline in 2023. We’re advising clients to: 
  • Shift their focus to demand to avoid another oversupply situation 
  • Incorporate causals to ensure they are accounting for external influences to demand – accurate demand forecasts are going to play a major role in this ability 
  • Utilize demand sensing solutions to help detect shifts in buying patterns and rapidly adjust plans 
  1. A surplus of ships is likely3. We expect shipping costs to drop in 2023 as we see a rush of new vessels next year. Some 28% of the current installed fleet capacity is on order; just under half of that is expected to be delivered over the course of next year. That could make container rates lower and cause shipping costs to drop. We’re advising clients to: 
  • Re-evaluate their network design to ensure they’re taking advantage of changing lane rates and optimal distribution network design
  1. Don’t expect the labor market to improve4. Labor issues raised many challenges in 2022, and the outlook remains unsteady, especially in the shipping industry, as we head into 2023. Labor actions like strikes in the U.S., U.K., Germany, South Africa, and South Korea were abundant in 2022. We expect workers to continue seeking pay increases to keep up with inflation and improved benefits overall. The Russia-Ukraine war and China’s rising Covid lockdowns add to the congestion problems in 2023. 
  • Start evaluating effective ways to enable your knowledge workers as you prepare your supply chain for 2023; automate routine tasks and provide decision support tools so that more time can be focused on resolving issues that matter for the business. 

For other information on how to prepare your supply chain for 2023, we suggest additional reading: 

Sources:

A Critical Capabilities document is a comparative analysis that scores competing products or services against a set of critical differentiators identified by Gartner. It shows you which products or services are a best fit in various Use Cases to provide you actionable advice on which products/services you should add to your vendor shortlists for further evaluation.  

Gartner understands supply chain technology leaders need to take their desired planning maturity and these Use Cases into account when selecting SCP solutions. In the 2022 “Critical Capabilities for Supply Chain Planning Solutions” report, Gartner underscores the high-stakes nature of such an assessment.  Demand planning and end-to-end (E2E) enterprise planning have the lowest range of vendor scores, while digital planning has the highest range of vendor scores. 

We believe this particular report helps address the needs of supply chain executives competing in economically ambiguous and conflicting environments, as they’re rethinking their supply chain planning function with different levels of digital maturity and diverse mixes of capabilities.  

So how can businesses determine which vendor best meets their needs? Gartner advises, “supply chain technology leaders responsible for evaluating supply chain planning solutions: 

  • Focus your SCP solution evaluation on the relevant Use Cases by identifying your current “as is” and future “to be” maturity requirements. 
  • Compare your business requirements with their importance to your target use cases by reviewing the importance given for each critical capability for the target use case. 
  • Align your requirement importance with those of the critical capabilities for a specific Use Case by challenging the organization to determine which use cases it really needs to support in five years.” 

With this approach, Gartner ranked Logility second highest in four out of five Use Cases which are Demand Planning, Supply Planning, E2E Enterprise Planning and E2E Multienterprise Planning among SCP solution providers in its 2022 “Critical Capabilities for Supply Chain Planning Solutions” report. In fact, Logility attained 4.8 for Demand Planning and Supply Planning Use Cases out of a possible score of 5. 

Gartner Critical Capabilities for Supply Chain Planning

Gartner compared vendors with 15 critical capabilities across five SCP Use Cases that relate to various stages of their supply chain maturity model. Gartner suggests using this report as a companion to Magic Quadrant for Supply Chain Planning Solutions as it provides deeper insight into providers’ product and service offerings by extending the Magic Quadrant analysis. 

Innovating the Future of Modern Supply Chain Planning 

When navigating a steady stream of supply chain disruptions and shifts, businesses require an intuitive ability to sense change continuously, analyze scenarios quickly, and pivot activities effectively to help ensure peak operational performance. This competitive advantage is best supported by a digital supply chain platform evolving with the latest artificial intelligence, machine learning, and automation functionalities. 

Today, Logility provides a comprehensive, cloud-architected supply chain management platform – the Logility® Digital Supply Chain Platform – that helps customers manage critical planning processes. To date, we have expanded our platform of integrated business planning; data management; demand, supply, and inventory management; and product planning, traceability, and lifecycle management to include supply chain design and network optimization. The addition allows our customers to optimize their complex supply chain network quickly and easily and achieve rapid time to value. By weighing their supply network’s risks and opportunities, supply chain organizations can make strategic, operational, and tactical decisions based on data visualization and “what-if” scenario analysis – presenting real-time insights on impacts to cost and service. 

Delivering a Single, Complete Platform 

By seizing new opportunities for innovation and partnership, we provide solutions that maximize our customers’ potential growth and revenue, and ability to serve customers fully in an increasingly digital marketplace. In addition, these top priorities can be accomplished with environmental, social, and governance (ESG) transparency that consumers, government auditors, and business partners and stakeholders expect. 

By capturing data from the entire supply chain, the Logility® Digital Supply Chain Platform can turn supply chain and business data into a tool for transparency, risk mitigation, collaboration, and ethical decision-making. Our customers can run multiple “what-if” analyses of best- and worst-case scenarios and every possibility in between. Numerous use cases can also be quickly modeled to automatically pinpoint opportunities to balance simplicity, efficiency, and speed with ESG stewardship. 

Fulfilling Supply Chain Needs – Today and Tomorrow 

Logility’s latest innovation and partnership efforts have stepped up the supply chain game for our customers. 

When serving the needs of supply chain contributors, managers, and directors and their trading partners, we strive to create a platform environment that delivers the flexibility and use-case support that supply chain organizations seek. For our customers, this means the power to not only execute SCP fundamentals, but also advance their ability to identify exceptions requiring extra attention and turn them into opportunities.  

Learn more about how supply chain planning solution providers are compared by reading the 2022 Gartner report, Critical Capabilities for Supply Chain Planning Solutions 

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. 

Gartner does not endorse any vendor, product, or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of the Gartner Research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. 

It’s time to evolve your supply chain from one-size-fits-all to purpose-built. Start your journey with the top six use cases for network optimization.

Do your business leaders believe they’ve locked down your supply chain network with the right partners, processes, technologies and contingencies that can overcome next week’s challenges as well as next year’s? This belief may be fraught with risk. A potent combination of incorrect assumptions, changing business needs and unforeseeable disruptions will eventually degrade the network that has been painstakingly created, resulting in supply chain rigidity, sluggishness, missed opportunities and higher costs. 

Now, consider a different vision for how a supply chain can be designed and how it can operate. Instead of placing a premium on hedging against an unpredictable future, think about having, at your fingertips, the ideal supply chain for fulfilling your next order. And then a different but still ideal network for the following order –  ideal in the sense of the optimal balance of cost, speed, risk mitigation, growth, customer satisfaction and meeting environmental, social and governance commitments.  

It’s time for business leaders to let go of traditional assumptions, processes, and decision-making behaviors and start restructuring their operations to achieve maximum efficiency and flexibility. Fortunately, the necessary technology exists today. 

Answer Your Toughest Network Design Questions 

Network optimization puts continuous supply chain design at your fingertips with an intuitive, usable interface that lets you and your own people (rather than hired consultants) configure and explore design alternatives continuously. Powerful scenario analysis identifies optimal locations for future plants, warehouses or 3PL locations and quickly presents impacts to cost and service in an interactive environment.  

Check out the 6 top uses cases for network optimization below.

  1. Manufacturing Analysis: Optimize the total landed costs of production decisions. Balance your capacity constraints and operational requirements in one model. Determine the optimal scenario that minimizes total production, freight, inventory costs and meets service requirements. Truly understand the impact of near-shore and off-shore scenarios. Case in point: a consumer electronics company made a critical off-shore/near-shore decision and determined that the long-term risk and operational impact of near-shoring in Mexico outweighed the cost advantage of production in China and Vietnam. 
  2. External Finished Good/Raw Material Supply Analysis: Use network optimization to build tariff costs and supplier capacity shortages into your impact analyses. 
  3. Emerging Market Analysis: Build a roadmap and playbook that addresses the trade-offs based on shifts, dips and spikes in demand. Use cost-to-serve analyses as part of evaluating the profitability of entering and exiting markets. Build “optionality” into your supply chain by designing in ways to grow, expand, or react to variability. 
  4. Disruption Response: Don’t just react to a supply chain emergency—treat emergent disruptions as a chance to think strategically about your logistics network. Model the new flow paths around a port slowdown or a supplier’s capacity shortages. Understand the total impact of new routes and identify the optimum supply and logistics partners. Case in point: an industrial goods importer modeled the impact in time and cost of bypassing Long Beach during a slowdown by delivering to a Mexican port and directly railing containers to the central U.S. 
  5. Logistics Analysis: Identify ways to trade expensive modes and services for faster and more profitable ones.  Understand and quantify the viability and benefits of solutions such as pooling, delaying strategies, and 3PL and 4PL offerings. Case in point: a German-based appliance manufacturer re-designed their US network to take advantage of consolidating import operations into a limited number of ports but also maintained a complex parts and service network. 
  6. Distribution Network Strategy: Explore and design aggressive distribution strategies that balance cost and customer service goals. Examples include optimizing the flow and profitability of an existing market, determining how and where to employ omni-channel techniques, and taking advantage of new technologies like on-demand warehousing or fulfillment services. Case in point: a mattress eCommerce company designed a supply chain to manage explosive growth and identified zone skipping as a risk-proof way to expand and save 33% on their last-mile costs. 

By enabling a comprehensive approach to continuous, repeatable network design, network optimization helps realign organizational goals by reframing decision-making as an opportunity to fulfill demand more profitably.  

This new decision-making paradigm helps avoid the traps and limitations of functional approaches to cost reduction and efficiency gains. Now, you can visualize an optimum network, communicate a future state to your organization, and rapidly drive to decisions and action. 

Talk to Logility’s supply chain experts today – we are ready to help you answer complex questions and make faster decisions with network optimization. Or see the solution in action here.