Food and Beverage Archives

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.

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.
External Finished Good/Raw Material Supply Analysis: Use network optimization to build tariff costs and supplier capacity shortages into your impact analyses.
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.
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.
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.
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.

The traditional formula of minimizing costs and maximizing efficiency is no longer enough – instead, tradeoffs need to be made. Higher costs are an unfortunate reality in the current supply chain environment, and they’re likely to persist despite attempts to contain costs in areas that can be controlled.

This realization for supply chain leaders creates new questions about which strategies can genuinely optimize their operations while offsetting changing demand and supply disruption. Of course, they know resilience is needed to succeed in today’s dynamic business environment. But it’s also well understood that intelligent decision-making that drives always-on, dynamic, and flexible supply chain planning is equally critical.

With a supply chain network design and optimization solution, businesses can find a significant advantage in balancing the variables that get in the way of optimizing complex supply networks and stabilizing product flows.

Five Reasons for Adopting a Supply Chain Network Design and Optimization Solution

Suppose a product can be manufactured in multiple plant locations, and there’s a potential warehouse only 50 miles away from 60% of your customers that regularly order it. How would you know if the revenue potential of shifting locations outweighs any cost savings or increases – or better yet how are your logistics activities impacted?

Often, businesses do not have a clear-sighted way of determining whether such an optimization tactic is beneficial, especially at the moment the opportunity arises. Instead, supply chain leaders must wait for their executive team to sign off on the suggestion after months of financial analysis – and lost potential for revenue growth.

However, the adoption of a supply chain network design and optimization solution can change the outcome for five reasons:

First: Comprehensive and credible insights. When information and insights come from a place of credibility, getting buy-in from executive leadership, business partners, and even the sales and marketing organizations is easier to accomplish. A solution that brings together data from across the business ecosystem and visualizes it in a compelling way from a common environment provides a consistent source of truth that keeps everyone in your organization on the same page. As a result, the entire enterprise can be fully educated on the latest opportunities and risks in demand, supplies, costs, and goods movement performance and make actionable decisions that deliver optimal outcomes.

Second: Real-time analysis and visualizations. A supply chain model created and set on the back burner six months ago cannot be picked up, dusted off, and acted on. Market dynamics change, and the model needs to be redesigned and deliver value just as quickly – instead of in weeks or months. Predictive analytics and data visualization based on near-real-time enterprise data can help determine the feasibility and outcome of the original model and uncover any shift in plans that can further optimize operational efficiency and revenue growth.

Third: Repeatable network design. A reference database and benchmarking tools eliminate the need to build a new model from scratch every time while leveraging your company’s current and forecasted demand with near-real-time access to information. Repeatable network design means time and cost savings; in fact, 10-20%¹ in operating costs can be saved when network modeling becomes a routine activity rather than an event that happens once every three years.

Fourth: Integration across a common digital platform. The goal of supply chain integration is to support the seamless exchange of information across all network tiers; through planning, resource management, execution, and measurement, and throughout the product lifecycle. Being tightly coupled to a digital planning platform, a supply chain network design and optimization solution can form a close working relationship focused on improving response, accelerating production time, and minimizing cost and waste.

Fifth: Ease of use. The solution brings decision-making around sourcing, production, and logistics further down the organizational chart. All roles – from supply chain executives to data scientists and analysts – can leverage business-wide information and intuitively build data visualizations to get accurate insight on whether you should move forward, how to improve your negotiating position, and when is the best time to start. In addition, your business can reassess its decisions as market dynamics change.

The Start of a Powerful Era in Supply Chain Design

The more global supply chains are under stress, the more pressure to optimize the entire network from all sides. Clearly, it’s time for a holistic approach centered around comprehensive and trustworthy insights, real-time analytics and visualization, up-to-date reference databases and benchmarking, and a platform that enables integrated processes, collaboration and decision-making.

But the concept is not to tear down the supply chain. Instead, with network design and optimization, your business can evolve the ecosystem whenever and wherever it needs to transform, to meet customer expectations while fulfilling business demand for genuine optimization for the long term.

Connect with our supply chain specialists today to understand more about optimizing your supply chain by answering complex questions and making decisions faster.

1. CSCMP EDGE Leadership Panel Survey, 2022

Key Takeaways:

At a time of intense supply chain disruption, 57% of organizations say they lack visibility into their supply chains
Robust, resilient supply chains utilize AI, ML and automation
These technologies power the modern supply chain, saving money and time and increasing profitability
Make quicker, more informed decisions, reduce risk and inventory, and offer better customer service

Accurate Forecasts, Lower Supply Costs, and Faster Cash-to-Cash Cycles

As the last few years have shown, supply chain disruptions are unpredictable. They are also tough to manage without a proactive approach and the right technology. It’s worth noting that 79% of companies with high-performing supply chains – those that operate in the most efficient and cost-effective way across all key supply chain processes to most effectively match demand to supply – show greater revenue growth than their average industry competitor. These high-performing companies weather supply chain disruptions better, make speedier decisions, and perform more accurate forecasting.

What’s propelling these successes are automation, artificial intelligence (AI), and machine learning (ML). These are terms we are all accustomed to seeing batted around in our space, so it’s important to understand what they bring to supply chain management and what kinds of problems they can help supply chain leaders solve.

For example, more than half of today’s companies say they have poor visibility into their supply chain, and 63% say they have no technology to monitor their supply chain performance, therefore those businesses that embrace a digital supply chain management platform will leap over their competition.

In addition to making more money, organizations that have an optimized supply chain have lower supply costs, less than 50% of the inventory, and cash-to-cash cycles that are more than three times faster than those that do not. In this article, we’ll discuss the roles that AI, ML and automation play in the supply chain and how they can help your business thrive in an era of seemingly endless disruptions.

Transforming the Supply Chain Through Technology

The pandemic and its associated lockdowns and closures created unprecedented supply chain challenges. Market volatility has reigned ever since, highlighting the need for flexibility and agility. In response, companies have been taking a hard look at their supply chains with an eye toward resilience. To that end, digital supply chain platforms have become the weapon of choice to tackle supply chain disruptions.

With their integrated end-to-end approach and complete visibility, a digital supply chain platform can deal with the opportunities and constraints of business functions ranging from sourcing and procurement to sales. AI can analyze huge volumes of data, grasp relationships, give visibility into operations, and support better and speedier decision-making. This is a real game changer. But the benefits of AI, ML, and automation don’t stop there. Let’s look at how each can create a money-saving, money-making, resilient supply chain.

AI can help you improve demand forecasting and inform integrated business planning (IBP)

AI technology mimics human intelligence and is programmed to “think” like a human, rationalize, and take human-like action. With AI, you can improve demand forecasting and inform integrated business planning as well as revolutionize your supply chain in every area:

Planning. AI gives you complete transparency through end-to-end visibility and offers end-to-end margin optimization.

Procurement. With a digital supply chain platform, you benefit from full data integration with suppliers and the ability to optimize recipes for raw materials based on their forecasted pricing.
Production. The insights provided by AI allow for agile production planning and scheduling.
Distribution. With the real-time information provided by AI, you’ll be able to optimize routing, freight contracting, and vessel sharing to reduce both costs and the environmental impact of your organization.
Sales and marketing. AI enables unified, more accurate price and demand forecasts as well as providing improved transparency and granularity, so you know the integrated margin per sale.

Those companies that were early adopters of AI-enabled digital supply chain management improved logistics costs by 15%, inventory levels by 35%, and service levels by 65% when compared with those without a digital supply chain. Now let’s look at ML.

ML can help you find new opportunities to optimize your supply chain

ML is a branch of AI. It’s the part that uses data and algorithms to mimic the way humans learn. It’s an essential component of your supply chain – statistical methods train algorithms to classify things, make predictions, and uncover critical insights that drive decision-making throughout the entire enterprise.

ML continuously analyzes your supply chain data to find opportunities for optimization. Using constraint-based modeling, the algorithms identify the factors affecting supply chain accuracy. The improvements can be felt across the entire supply chain:

Improved forecasting. Humans just can’t crunch data like machine intelligence. A digital supply chain platform can quickly analyze large, diverse data sets to improve demand forecasting accuracy.
Better collaboration. ML analyzes data across different networks so you can reduce freight costs, enhance performance in supplier delivery, and reduce supplier risk. By identifying the factors that impact efficiency within supply chain tiers, you can better collaborate with suppliers to find the optimum solution.

Know how your supply chain is really performing. ML combines unsupervised learning, supervised learning, and reinforced learning to continually analyze data so you can get real insights into how your supply chain is doing and where you can improve for more efficiency.
New product demand forecasting. When you have ML in your supply chain, it considers the causal factors that influence new product demand. The algorithms combine a pragmatic approach with advanced statistical models to enable accurate demand forecasting and data-driven decisions about inventory.

Supply chain management enhanced with AI improves forecasting accuracy while increasing granularity and optimizing stock replenishment. Forecasting errors can be reduced between 20% and 50%. Sales lost due to product unavailability can be reduced up to 65%, and you can achieve inventory reductions of 20% to 50%. ML can enable R&D cost reductions of 10% to 15% and reduce time-to-market by 10%.

The last piece of your supply chain management technology is automation. It’s all about increasing supply chain efficiency. Let’s explore.

Automation can help you make your supply chain more efficient, while reducing costs

Automation in the supply chain uses digital technology to streamline processes by boosting task efficiency. This is how it’s used to give you that boost while reducing costs and helping you scale your business:

Manual task automation. Your staff likely has better, more valuable things to do than process documents and manipulate data. You can save a lot of time and money and make your supply chain run more smoothly, and your employees can spend more time building relationships with customers.
Visibility. Visibility means everyone, no matter where they are in your network, can see the same information for a single source of truth. This helps eliminate order and delivery errors. Visibility into shipping information means your customers can follow their orders with real-time information.
Better customer service. Customer expectations are constantly increasing. Automation means achieving faster delivery times, more competitive pricing, and the real-time order information your customers demand.

Automation in the supply chain manifests as increased fill rates, decreased cycle times, increased warehouse throughput time, lower labor and operational costs, reduced errors, and improved inventory management. All three of these technologies – AI, ML, and automation – work together to build a supply chain that can reduce the impact of supply chain disruptions, all while improving general company performance.

The Logility Platform delivers better business outcomes and more resilient supply chains

The Logility® Digital Supply Chain Platform lets you seize new opportunities, sense and respond to changing market dynamics, and more profitably manage complex global supply chains. We use the power of technology to deliver a digital, sustainable supply chain that powers the resilient enterprise.

This means better sourcing decisions, more accurate planning, accelerated cycle times, improved precision, and increased operating performance – everything you need to weather supply chain disruptions and constraints – while meeting your environmental, social and governance (ESG) goals.

It’s time for a digital, sustainable supply chain. Reach out to our specialists today to discuss how we can put these technologies to work in ways that matter to your business.

Make over your supply chain to get visibility into potential future disruptions.

There’s always a lot at stake in supply chain management. Today’s unprecedented supply chain disruption challenges have led to executives looking for new ways of managing their supply chains, not just for today, but to prepare for future unpredictability. Technology leapt to the forefront as visibility into the entire supply chain became essential.

Lessons learned since the pandemic are the key to surviving new challenges. Prior to the pandemic, efficiency meant just-in-time manufacturing, but disruptions exposed the flaw in this approach. Production halts, container and transport shortages, and port logjams are just a few of the factors eating away at both efficiency and profits. What’s a supply chain manager to do? Let’s talk about supply chain disruption challenges and what you can do now that will also serve you in the future.

What are the Big Things Still Disrupting Supply Chains?

Circumstances seem to change on a daily basis. This port is clogged. Now that one is moving but another port is having difficulties. It never seems to end, and experts say we can expect at least another year of supply chain disruption challenges.

COVID-19 didn’t start it all, but it certainly exacerbated serious supply chain disruptions that emerged during the trade wars in 2018 and 2019.

The pandemic shut down ports and factories. This slowed down the movement of goods across the globe. Public health issues meant it was difficult to reopen the economy even partially, and we saw periodic opening and closing of both facilities and ports.

Then no one anticipated the strong bounce-back in consumer demand. The issue became trying to move gallons of water through a straw. This wasn’t helped by the fact that so many of the goods came from far away, which was just another opportunity for disruption to rear its ugly head.

Then came Russia’s invasion of Ukraine

The Russian invasion has triggered supply chain disruptions and other obstacles that are hampering vital logistics and trade route implementation. And because of the catastrophic impact on the global food supply, as well as food prices, more than a billion people could be pushed into famine and destitution. Between them, Ukraine and Russia account for approximately one-third of global wheat production.

Sanctions, blocked Ukrainian ports, and stalled transportation routes have also disrupted the China-to-Europe supply chain. This means supply chain leaders have had to shift from using trains back to using ocean freight and getting goods to market takes longer.

It’s been an undeniable mess, and while you can’t always predict supply chain disruptions, it doesn’t mean you can’t be better prepared, with disruptions big and small now an accepted inevitability. It’s time to make over your supply chain to meet and overcome obstacles as well as create new flexibility to help you prepare for the future.

The Power to Predict Supply Chain Disruptions

It’s been a tough few years, and it’s hard to see a light at the end of the tunnel. But it’s time to accept that the world is in a constant state of flux. It’s hard to plan and confidently predict supply chain disruptions, so to sustain growth and create resilience in these uncertain times means developing agility. Agility means having the ability to respond quickly and think outside the box when necessary.

Rethink just-in-time inventory policies: There could be another pandemic or another war. If a large-scale event happens and you don’t have back stock, you’re a threat to your own supply chain. It’s important to note that a lot of today’s worst supply shortages happen in supplier sub-tiers, where manufacturers lack visibility. You need transparency beyond the first tier so you can assess their vulnerability. Mitigating risk may mean finding new suppliers, redesigning supplier networks, revising inventory targets, building safety stock, and diversifying your sourcing strategies.
Test what-if scenarios: Build multiple “what-if” scenarios so you can test quickly and observe what parts of the supply chain repeatedly fail. You can’t mitigate risks that you can’t see, and if you can’t see the problems, you can’t find solutions. This multiple-scenario testing may seem daunting, but it’s a great way to get insights into vulnerabilities that would impact your supply chain, as well as determining the impact of various costs and constraints on the supply chain network.
Optimize network design: Optimize your complex supply chain network quickly and easily by addressing your most complex supply chain challenges such as determining how many facilities you should have and where they should be in order to meet your desired service levels and optimize costs.
Share more data with suppliers: You don’t have to share everything, and you shouldn’t, but you should disclose data when conditions call for it. Working together with your suppliers can expose areas of supply chain disruption you hadn’t considered. If you are hesitant about sharing data, there are third-party firms that can analyze your supply chain, pinpoint weaknesses, and give you recommendations. However, the right supply chain management platform will give you the machine learning, artificial intelligence, and advanced analytics you need to perform your own in-depth analysis and recommendations.
Evaluate supplier options beyond China: There was a time – in the 1950s and 1960s – when it seemed like everything was made in Japan. By 1979, China was one of the globe’s fastest-growing economies. In 1980, manufacturing in China began surpassing the world’s industrial powers one at a time, and in 2010, became number one when it overtook the United States. This can be part of your scenario planning, to consider looking beyond China’s sphere of influence to expand your vendor base.
Build redundancy where possible in your supply chain. If your organization relies heavily on China or Eastern Europe, consider reshoring. Or you could near-shore to Mexico, Latin America, or other countries in Southeast Asia or the north of Europe. Be cognizant of other vendor issues that can cause supply chain disruptions such as lack of regulatory compliance, poor human rights, and environmental records – you don’t want to be doing business in another XUAR.

None of us has a crystal ball, which means we’re all preparing for an uncertain future. Only complete visibility into all tiers of your supply chain can give your organization a resilient, agile, and profitable tomorrow.

Logility Provides Transparency and Agility to Address Supply Chain Disruption Challenges

The Logility® Digital Supply Chain Platform accelerates the sustainable and resilient digital supply chain by leveraging advanced analytics, ML, and AI that empower your business with the visibility you need to create a supply chain that weathers disruptions with agility.

This means better sourcing decisions, more accurate planning, accelerated cycle times, improved precision, increased operating performance – everything you need to weather supply chain disruptions and constraints – while meeting your ESG goals.

We help organizations sense and respond to changing market dynamics and more profitably manage their global businesses to become resilient, sustainable enterprises. It’s time for a digital, sustainable supply chain. Reach out to our specialists today to learn more.

Supply chain organizations are like any other part of a business – they’re always moving, changing, and trying to produce and deliver outcomes faster, more efficiently, and to everyone’s satisfaction. But even the most high-performing supply chains have process, infrastructure, and technology gaps that can become problematic if not addressed early.

According to Gartner¹, fewer than 10% of companies have mastered a balance between cost management, customer service, risk mitigation, and growth investment. The reason is apparent: keeping pace with every shift requires more than implementing a new application or technology to an already growing patchwork of digital assets.

To get out of this rut, businesses must let go of their traditional assumptions, processes, and decision-making behaviors and start restructuring their operations at the foundation and upward with a digital supply chain platform that provides network optimization.

The Core of Today’s Supply Chain Challenges

After nearly three years of unprecedented supply chain turbulence, businesses and their extended partner networks are still encountering risks posed by an abundance of economic, social and environmental challenges. Labor strikes, geopolitical conflicts, capacity and supply shortages, and an increase in natural disasters – these disruptive events and more are magnifying how woefully unprepared organizations are to handle dramatic shifts.

Common struggles include:

Data to build the supply chain network model is distributed over many systems and not normalized for immediate use
Some necessary data is unknown, such as freight rates and labor costs
Current tools for network design are not agile enough to offer multiple solutions to a challenge and allow decision makers to embed their own knowledge
Current methods for network design are not easily repeatable, not fast, and not optimized

Poor modeling and the inability to act on decisions can result in a loss in cost efficiencies whenever the supply chain is updated – whether adding to the original design or unintentionally adopting a new practice. That is a large chunk of money being left on the table by anyone’s standards.

Your business can take ownership of these savings by treating every change as an opportunity to evolve your supply chain design. Even small changes such as identifying an alternative supplier is a great moment to determine how to optimize your entire supply network’s synergy by evaluating the total landed cost and net service impacts to the system and placing the decision in the context of a dynamic plan.

The Solution: Continuous, Repeatable Supply Chain Network Design and Optimization

The solution is not to tear down the supply chain and rebuild it whenever you want to transform. It’s just too complex and time consuming to do a project on a monthly, quarterly, or semi-annual basis that is usually undertaken every 3.7 years, on average. Instead, supply chain leaders need to view supply chain redesign as a continuous initiative with no beginning or end date.

This is where a supply chain network design and optimization solution is tremendously relevant. The technology allows you to automatically analyze and measure failures and inefficiencies to drive actions that modify or improve the network’s design. The integration of machine learning, predictive analytics, data visualization, and interactive dashboards also empowers you to rethink your operations on a more granular level.

You can reach decisions that drive continuous network design and optimization – not short-term gains at the expense of the future – for example:

Scenario evaluation and simulation to adjust the network-wide master schedules and distribution plans in response to curtailment of production in regions heavily impacted by labor and capacity shortages
Smooth transition to new ways of working remotely, enabled by shared visibility, messaging, and remote collaboration within a single version of the operating schedule
Scheduling of operations in labor-intensive factories that accommodate new government regulations and industry requirements

By supporting a comprehensive approach to continuous, repeatable network design and optimization, a network optimization solution as part of a digital supply chain platform helps realign organizational goals by reframing decision-making as an opportunity to fulfill demand more profitably. This dramatic shift in decision-making helps avoid the traps and limitations of functional approaches to cost reduction and efficiency gains. Now, supply chain leaders like you can develop network operating diagnostics for awareness and systemic analysis of major sources of monetary, resource, and inventory waste and risk.

From Everyday Decisions to Long-Term Optimization

For every supply chain leader, recent disruptions have reinforced the reality that no decision is too small. Yet, organizations that adopted supply chain network optimization have realized rapid payback from their choices as they continuously optimize their network’s balance between supply and demand for the long term.

Learn more about repeatable network design and optimization, and how Logility’s digital supply chain platform can help, by following this link.

¹ Gartner Research, “Network Diagnostics and Planning Excellence Are at the Heart of Supply Chain Cost Optimization’; June 2020

Respond quickly, maximize benefits, and identify supply chain opportunities and threats.

Key Takeaways:

Supply chains need technology, but putting that technology to best use is the province of humans
AI and ML are tools, not solutions
It’s human judgment and ingenuity that will save the day, but the right data is vital
Technology provides transparency and visibility across the entire supply chain, something humans cannot get on their own
Balance between humans and technology is essential to anticipate and solve the supply chain disruptions that are all too common today, and who knows what’s coming next?

Supply chain disruptions. They grab daily headlines, and the question always on the table is how to get the visibility and agility needed to respond in time. Things can’t go on as they did pre-pandemic. No matter how good it all looked on paper, the pandemic made weak links obvious: supplier shortages and backed-up transportation networks, among other things. It also exposed worker exploitation in lower-tier vendors that shed light on serious ESG miscalculations.

Smart organizations have embraced supply chain management platforms that provide complete transparency. The best solutions that address supply chain opportunities and threats leverage artificial intelligence (AI) and machine learning (ML) to provide the data and analytics necessary to manage the modern supply chain.

But technology alone is not the answer. The real path forward combines AI with human intelligence to maximize benefits as well as identify opportunities and threats. AI can provide the essential data component; it’s useless when it comes to collaboration, negotiation, and forming relationships with suppliers. While AI mimics human thought, it’s no replacement for the human judgment that’s vital to successfully implementing your digital supply chain platform. In this article, we’ll explore how to leverage humans and technology to best advantage.

The Human Component of Digital Supply Chains

Technology is flashy and captures attention in the boardroom. A vision of a completely automated and seamlessly integrated supply chain may be presented, where machines use AI and ML to adapt, solve problems, and respond to supply and demand changes. There’s usually no mention of humans in these discussions unless it’s about how they won’t (someday) be needed any longer.

Will this sort of supply chain ever become a reality? Maybe. But algorithms won’t solve every problem, and a sophisticated demand forecasting system isn’t much use if humans ignore its outputs. The value of a digital supply chain platform depends on the ability and willingness of the people in an organization to take action in response to the data.

It’s the symbiosis between digitization and humans that creates the real chance to identify supply chain threats and opportunities. Designed to repair complex and fractured processes, algorithms can only provide information – it takes a human with logistics expertise to handle any exceptions and provide a quick response to changing circumstances.

In short, AI can increase speed and accuracy in number crunching, data handling, and supplier vetting. It can help optimize transportation routes, warehousing, and stock management and handle other specialized but mundane tasks. It can also increase customer satisfaction by improving ease of use, reducing work steps, and offering new solutions for customers to improve their own supply chain visibility.

Your supply chain platform and its associated technology is a tool. To be fully effective, the unique cognitive skills of supply chain management professionals are essential. So, how do you achieve the right balance?

How to Leverage People with Technology

There’s no doubt modern technological solutions are revolutionizing and improving supply chain performance. AI and ML are vital to gather data, crunch it, and learn from it. Automation is great, but it only takes you so far. It’s humans who use this data to make the rapid decisions today’s supply chain disruptions demand.

A recent study asked supply chain industry professionals what they thought was the right balance between humans and technology. Their opinion in 2019 was that 60% technology and 40% human expertise provided the optimum balance. That didn’t, however, discount the role of humans – it’s that their tasks are changing as technology takes on manual processes. Humans still are the best solution when strategic thinking and decision-making are needed.

In 2021, the new ideal balance was 57% technology and 43% humans. This isn’t a huge shift, but it marks an awareness that technology cannot solve all problems, no matter how advanced. Human ingenuity is a major factor in handling supply chain disruptions successfully.

The pandemic was a major factor increasing the need for technology to support humans. AI was a valuable tool in decision-making, providing usable data, helping optimize carrier networks, and identifying ways to increase operational efficiency. But it was humans who had to decide which recommendations would best serve their organization.

Technology, People, and ESG

Through the use of AI and ML that search and track patterns, organizations can keep a closer eye on and better meet their commitments to environmental, social, and corporate governance. While many companies tout their sustainability initiatives, without the data to back it up, it’s nothing more than a greenwashing exercise.

It’s about visibility and the transparency that it provides. Opaque procurement methods and multi-tier supplier networks mean it is impossible for a human to do the data analysis needed, much less make sense of it. This is where a digital supply chain platform proves invaluable.

Successfully Merging People and Technology in the Supply Chain

Here’s the bottom line: Every organization needs a robust digital supply chain platform. Every organization also needs human expertise. They both provide value, and one cannot be successful without the other.

Even without supply chain disruptions, the frailties that emerged with the pandemic need solving. The data insights provided via AI and ML are invaluable to accelerate decision-making, reduce costs, increase agility, and gain deeper insight across the supply chain. This results in improved forecast accuracy, planning confidence, and a way to continuously monitor supply chain performance.

A supply chain management platform also provides a way to visualize, evaluate, and optimize planning, sense demand, and plan out life cycles. On the supply side, you gain the ability to plan and optimize manufacturing, manage vendors, control quality, and meet compliance and ESG commitments.

With all of the data and the tools to use it effectively, humans can make faster, better decisions based on a completely transparent supply chain. At their very core, supply chains are about human connections – partnerships, collaborations, and even competition. These are all vital to a successful supply chain.

In short, technology should empower people. It’s the balance between human expertise and the information and automation provided by technology that will mark the agile, resilient, and successful supply chain.

The Technology You Need to Support Your Human Capital

Logility’s digital supply chain platform allows you to trace your supply chain end to end. That gives your supply chain experts the visibility and data they need to optimize every part of the supply chain, preserve and increase revenue, and deepen customer and supplier relationships. Our digital supply chain platform utilizes AI, machine learning, and automation to help you easily track and analyze your supply chain, so you always have optimal performance and key insights.

Make better decisions and deliver better outcomes. Contact us today.

Feeling far from bubbly? Get the insights beverage companies need to meet today’s biggest supply chain challenges.

Key Takeaways

Clogged ports, packaging shortages, and a lack of CO² are all issues plaguing the beverage industry
A shortage of resin for plastic bottles is exacerbated by a high tax on imports
The big guys bought up all the aluminum cans because predictive analytics provided valuable insight
For beverage businesses to survive, it’s time to digitally transform, gain visibility across the entire supply chain, and get the data they need to mitigate future disruption

Port congestion, shipping delays, and other supply chain disruptions are affecting industries across all economic sectors, and that includes beverages of all types. For wine and spirits companies, for example, the glass bottle shortage and shipping glitches are two of the biggest problems. Sales remain strong, but producers, importers, and retailers are all scrambling to stock the most popular releases, particularly during holiday seasons. Better long-term planning, supply planning, and just-in-time logistics are essential to address beverage industry supply chain challenges.

If the pandemic taught the supply chain nothing else, it highlighted the necessity for the consumer-focused beverage industry to embrace the value of data. Data is the foundation of a successful supply chain now and will become even more valuable over time as companies lean on their decision support platforms with predictive and prescriptive analytics.

Getting the data needed requires an investment in both supply chain talent and technology. Only the right technology will make data and analytics actionable and accessible for teams across all supply chain tiers. The insights gained through data lead not only to smoother supply chain operations but also provide game-changing business intelligence and decision support. In this article, we’ll look at the most common beverage industry supply chain challenges and the digital solutions needed to solve them.

Supply Chain Disruptions Affecting the Beverage Industry

The beverage industry is suffering from the same supply chain disruptions as most other industries. Clogged ports, a shortage of raw ingredients, Russia’s invasion of Ukraine, fires, and floods have all served to challenge the smooth flow of goods. But there are some problems unique to the constantly shifting landscape of the beverage industry.

• A shortage of aluminum coupled with high demand

China has cracked down on pollution, which has resulted in a 10% to 20% decrease in aluminum production. This is severely hampering the beverage industry, which is dependent on aluminum cans. The fact that demand has increased exponentially in recent years hasn’t helped. During the pandemic, consumers who were unable to quaff their favorite beverage in a restaurant, brewery, or bar had to drink their beverages from cans.

Major can manufacturers also cater to large companies, such as Coca-Cola and PepsiCo. As well as other bigger manufacturers, they predicted the shortage and made sure they would have what they needed. This means that smaller manufacturers were left out in the cold. And there’s no end in sight.

• A shortage of carbon dioxide

Carbon dioxide is in short supply in the U.S., affecting carbonated beverages of all kinds from sparkling water to beer. This disruption in the CO² supply chain is not expected to get better in the short term. This shortage is caused by a couple of factors.

Gas contamination is hitting all CO² users hard. Denbury, an independent energy company, sources CO² from Jackson Dome, a natural carbon source field in Mississippi. When they decided to use their CO² supply for enhanced oil recovery as well as drill additional wells for CO², contamination occurred with the introduction of hydrocarbons that included benzene. Supply is reduced because not all suppliers can filter out the impurities.

Additionally, plant closures for annual maintenance mean even more CO² is being pulled out of the supply chain. Ammonia plants are closing for maintenance as well, and they are a source of food-grade CO². While these annual closures can be predicted, the issues with Denbury have made logistics planning incredibly difficult.

• A plastic resin shortage

When it comes to plastic resin, there’s a global shortage, and this is expected to continue for the foreseeable future. Demand is high, labor is in short supply, and prices have skyrocketed. Plastic manufacturers face production delays and even shutdowns because of supply chain shortages. Water bottlers in particular are suffering after the reinstatement of a tax on imported resins – $1.80 per every $1.

• Issues in trucking and shipping

Because beverages have a high turnover rate and consumers expect on-demand availability, retailers need smaller quantity shipments with more frequent delivery. This requires beverage manufacturers and distributors to be logistically agile. That requires less-than-truckload shipping, which means transport appointments, which aren’t required by full truckloads. This can result in delayed final delivery, which is where just-in-time logistics comes in.

And surprisingly, stolen cargo has been added to this list of beverage supply chain challenges. Beverage goods can be resold online and aren’t traceable. Port and facility congestion has made theft easier and comprised 14% of the thefts in 2021. This presents yet another supply chain disruption for the beverage industry.

It isn’t all doom and gloom, though. Even small manufacturers can gain an advantage over their competitors with the right decision support technology. How do you think Coke and Pepsi managed to snatch up all the resin? With a digital platform that provided the data they needed to predict the shortage before it happened.

Data Powers the Modern Beverage Supply Chain

You can’t have a resilient, effective, robust supply chain without data. You can’t plan and you can’t predict. Your supply chain remains opaque as you struggle to get supplies to meet demand. An out-of-control supply chain threatens everything from your daily operations to sales, fulfillment, and ultimately the viability of your company.

Of course, getting the right product to the right customer is the priority and the challenge, but to remain competitive, the beverage industry must embrace digital solutions that simplify decision-making and improve both profit and productivity. There’s a need for more flexibility and agility across the entire supply chain, better collaboration, and complete visibility to identify potential bottlenecks and predict supply chain shortages and other issues before they have a detrimental effect.

A transparent supply chain means you get a real-time view into what’s happening across your supply chain, even if you have several manufacturing or distribution sites. It provides a single source of truth for your entire business and gives you the data you need to make the right decisions by predicting and mitigating problems before they happen.

A digital supply chain platform can give you what you need for:

Annual and long-term planning
Demand planning, demand sensing, life cycle planning, and causal forecasting
Inventory planning and optimization
Supply and manufacturing planning and optimization, vendor management, sourcing management, and compliance management
Supply chain network optimization

And of course, serving all these planning solutions is good data management, which, along with machine learning and artificial intelligence, accelerates decision-making, provides deeper insights, and increases supply chain agility. Effective supply chain data management will:

Ensure inventory is optimized to meet service goals and service level agreements
Reduce last-minute, expensive shipping due to errors
Increase perfect orders
Ensure your organization is more demand-centric
Reduce exceptions and firefighting
Increase customer satisfaction through improvements in product availability.

Many of today’s beverage industry leaders are embracing digital transformation, which leads to better business outcomes. With beverage industry supply chain challenges and disruptions showing no signs of easing any time soon, it’s time to be proactive rather than panicky. Use this as an opportunity to define, refine, and re-align your supply chain to weather disruptions today and create a more secure future.

Technology Built for Beverage Supply Chain Success

Logility provides supply chain solutions that allow you to trace your supply chain end to end. We give you the visibility and data you need to optimize your supply chain to weather any disruption. Our platform utilizes AI, machine learning, and automation to help you easily track and analyze your supply chain, so you always have optimal performance and key insights. Make better decisions and make more profits. Contact us today.

For more reading on the biggest challenges facing the beverage industry today, and the best digital solutions to address these challenges, check out this eBook: A Recipe for Resilience: Strategies to Strengthen Your Food and Beverage Supply Chain

Why do we build logistics models?

This is obviously a rhetorical question. But I ask it because modeling often takes a detour into the land of debilitating detail. And by debilitating, I mean an enormous analytical time sink — think months. I am often asking clients whether they wish to:

A) Model the precise general ledger costs for logistics?

B) Make a well-researched decision?

If you chose “A” you can stop reading because the rest of this blog post is about why that will lead you down the wrong path.

At the surface “A” and “B” seem to follow one another. If I am making a good model, am I not accurately modeling my future logistics spend? Yes, with a big BUT… the precision required to make a perfect model of your financial spend can often lead you to a create a model that is erratic. Let us look at this a little deeper and see where the “precise” and the “good” deviate in a classic logistics model.

What is a Good Logistics Model?

A good logistics model is designed to predict the future. Yet a modeler will always start with a calibrated baseline. And the “calibrated” part of this refers to accounting costs — those costs found in an organization’s financial database. The theory goes that if a model shows the same costs as the current state, then we can trust that it will show appropriate differences when changes are modeled.

A logistics model can be as complex or simple as a modeler wishes, however it always needs to be believable and grounded in the actual costs of a system. This blog post should make one thing clear to the modeler: the search for “accounting” level accuracy can stand in opposition to your actual goal — making a supply chain decision. It will not only cost you time to build this “perfect” supply chain model, but it will also imbed imperfections into the very mechanism of the model.

What Makes Fiscal Accounting Accuracy Popular?

People gravitate to their accounting numbers because of comfort — pure and simple comfort. General ledgers do not need to be explained. They reflect actual expenditures — they are immovable facts of history. They are also safe. Executives and managers alike believe their general ledgers. No one gets sent out of a conference room for repeating known accounting numbers to a group. But the actual spend last year has lots of little aberrations.

How Can Accounting Costs Lead You Astray?

Accounting costs can look very detailed and accurate; take an entry for an individual shipment of your product, for example. You can see the units, weights, and most importantly, the costs. These costs go directly into the accounting system. These show exactly what was paid net of discounts, accessorial, and anything else that might be tacked on.

This means that for every origin and destination that has shipment activity, we should have a highly accurate cost for the organization’s shipment down that lane… right?

Let me share experience from hundreds of modeling exercises. If we treat a shipment table as the definitive cost for each lane, we run into three problems with our model:

1. Lack of statistical significance

2. Heterogeneous data

3. GAAP accounting methods

Let’s delve into each one of these.

1. Statistical Significance — If I have a number, how can it be wrong?

Let’s say you have data on hundreds, or even thousands, of trucks you paid for last year. How could this vast amount of real data go wrong? When you break them down by lane, season, and method of purchase, thousands of data points might turn into five to ten data points for a given lane — or sometimes only one shipment. As a reminder from that long-forgotten statistics class, statistical significance for a single variable starts at seven data points — just to be roughly correct.

Now go further and ask yourself:

– Does your shipment data have a mix of spot and contract shipments?

– Are there data points for every season? Note: trucking in some regions has significant seasonality.

– Is there selection bias? Your buyers or your 3PL might be taking advantage of opportunistic contracts— trucks that were cheap for a single event, but do not reflect the market price next year.

You may have a wealth of data in aggregate but not at the level you need. Here is a way to test its value to a model — take a sample set of data for a given region and given season. Find the average and standard deviation by individual origin and destination. The variation will probably be large — I base this on my experience. The danger is that this variation is dropped from analysis once the average is found. Do some statistical sniff tests, you will probably be very disappointed in the value of this data in predicting your future spend on a lane-by-lane basis.

Figure 1 below visually displays what historic data looks like in an actual model. The total cost of the network exactly matched the accounting cost of $6.97MM. You can see three warehouses and almost every warehouse shipped to each destination city. We should have lots of real historical date, no need to fill in the blanks.

Here is where it goes wrong, Figure 2 is the same data set but optimized for the lowest cost supplier. Great, see how I saved $948K? Now look closely, find all the crisscrossed lines. Find the case where a customer node is right near the Alabama warehouse but is shipped from the Nevada warehouse.

actual historic shipments logistics model — Figure 1 – Actual Historic Shipments

lowest cost logistics model — Figure 2 – Optimized to lowest cost lanes based on historical data

If it was not obvious before, you should now realize that optimization software acts like a passive aggressive child. If it can follow your instructions exactly and return nonsense, it will. In this case the analyst could go to each lane that did not make sense and exactly match it to actual average truck costs. Our wealth of data falls apart because lack of statistical significance throws off our model — we cannot rely on hundreds of records that present a solid average and we have not even started to add lanes that do not have any historical data.

2. Heterogeneous Data — how do you fill in the blanks?

The purpose of a logistical model is to answer “what-if” questions. This means something is going to change — a new port, new warehouse location, new route to the customer. Inevitably, key transportation lanes will not be found in the historical data. Modelers generally use three methods: quotes from carriers, regression analysis, and benchmark (market) data. Each of these methods will create three very different sets of data.

Carriers can provide quotes on contract and spot rates for a defined set of lanes. These will not match your historical costs because the carrier is predicting the future costs at the same time they are trying to secure your business. Many things might not match your past discount rates and accessorial charges. But with effort and enough quotes, you can get these to be “OK.” It will take time and you will have to do it every time you make a change in the model.

Regression analysis will turn a pile of data into a statistically significant formula. But it will also have inherent errors. Truckload rates are not uniform across any geography. An extreme example would be a port city like Los Angeles; more loaded trucks go out of Los Angeles than in. You will find that the cost from Phoenix to downtown LA is 42.5% the cost of a truck from LA to Phoenix. This is an extreme example, but you get the idea – regression formulas average out a lot of market subtleties.

Figure 3 is an example of regression data input into the model. It makes the pretty picture you want to see — every customer is served out of its nearest warehouse. You can see the purple line showing the smooth lines of demarcation between service areas. This is easy to explain but is not correct.

The third data set is benchmark or market data which is really another measure of historic data, but it has the advantage of being historic data across hundreds of companies and millions of shipments. It will not match a company’s historic data precisely. But if everyone is buying from the same market you can assume that everyone’s rates will regress to the same market average.

In Figure 4, you will see the customer-to-warehouse assignments are not as clean as the regression. But this is the real world and reflects the optimum use of the freight market. For example, trucks from Illinois going south are cheaper per mile than Alabama going north and east. This may be harder to explain to someone with this picture, but it has the advantage of being the most right.

Benchmark data also has the advantage of being the same set of data for known and unknown lanes You do not have to create a Frankenstein-like data model of mixed sources to fill in all the data you need.

regression-based costs logistics model — Figure 3 – Optimized to regression-based costs

market-based costs logistics model — Figure 4 – Optimized to market-based costs

See the table below to describe the usefulness of different data sources.

Combining two or more sets of heterogeneous data across thousands of lanes requires a miracle of analysis to get them coordinated to the point that they do not mislead your analysis.

3. Cost Accounting — How can my numbers be wrong when they match my P&L?

Accounting is for accountants. Their objective is to balance all account totals at the end of each period. There are a lot of ways that transportation invoices are reconciled to their actual costs inside this time frame. These include corrections that might be taken at different times than the actual shipment or include discounts either ascribed to a shipment or again taken at a different point in the period.

Manipulating all the costs, corrections, and discounts to match each shipment can be an enormous task. If you total all the truckload, LTL, and Parcel shipments into a subtotal by mode, you can believe that summed number. However, if you want each of thousands of individual shipments to be properly costed, you have a lot of work to do and might need to get your accounting department to spend some quality time on your project.

What Should I Be Doing Instead?

The way to build a good supply chain model is to rely on long-term market figures and averages. You can buy them from any number of rate boards or Logility provides its own proprietary data set with its SaaS subscriptions. All you need do is calibrate these numbers to your business by “benchmarking” them against what you do know. Scale the market rates up or down and proceed to answering your question.

The true value of a logistics model will be that it trades off high-cost modes like LTL against low-cost modes like FTL. Or that inventory will be traded off against transportation. As long as your ratios are calibrated, you will get the right answer.  And a good supply chain leader will value the right and defensible answer over detailed accounting precision — and they will appreciate that you answer the question sooner rather than later.

Additional reading:

Answering the One-Day Service Challenge
“True Greenfield” – Answering the Distribution Service Time Challenge
Three Ways Center-of-Gravity Analysis Studies Lead You Astray

It’s a complex problem, but you can successfully optimize inventory levels with the right approach and technology.

With all of today’s supply chain disruptions, and new ones no doubt lurking around the corner, companies without optimized inventory are risking overpaying and underperforming. During the normal course of business, inventory managers face the challenge of managing potentially thousands of items. All of these have their own characteristics with their own complex calculations. The key to meeting today’s challenges is proactive, strategic inventory management.

Proactive inventory management is not possible without insight across the entire supply chain. You need not only a sound methodology but also a transparent look into the end-to-end supply chain. Otherwise, you risk not only quickly rising inventory levels but a shortage of spare parts.

Supply chain disruption is a complex issue. The big events – COVID, weather and climate events, and Russia’s invasion of Ukraine – dominate the news cycle. What’s under the surface is under-reported, including transportation bottlenecks, labor shortages, and vendors going out of business. These events have become more common, and to react to new disruptions, real-time adjustments are needed. Let’s look at how to optimize inventory levels in a time of seemingly never-ending supply chain disruption.

Start With the Customer Experience

There’s no doubt that the customer is king, whether it’s a company or a consumer. From raw materials to manufactured parts to final products, everything throughout the supply chain must be managed to get the customer what they want when they want it, and they often demand real-time tracking information.

What happens if there is no stock? Stock-outs don’t only damage your relationship with your customers, they damage your brand and label it unreliable. Not to mention that you’ve potentially just sent business to your competition.

This costs you, not the least when it comes to opportunity. You’ve lost not just one transaction but future transactions with that customer. An inventory management system that prevents stock-outs takes a lot of the uncertainty from inventory planning and helps you keep a healthier cash flow – it ensures you have the products in stock that have high customer demand.

Disruptions and other uncertainties in your supply chain will always present issues. How will your company manage the tradeoff between large inventory investment and controlling costs?

Inventory Optimization Challenges

The overarching challenge is to optimize inventory levels while at the same time improving and maintaining a great customer experience and reducing variable costs. Making the right decisions depends on your answers to these questions:

To cushion against disruption, how do you set safety stock levels?
Have you assessed the risk of your inventory turning out to be too expensive to carry or rendered obsolete?
Are you willing to make trade-offs between service levels and safety stock, and what are they?
Do you have enough demand predictability to make service level trade-offs? Do you want to fill orders regardless of your inventory costs?

To meet inventory optimization challenges, setting appropriate levels to absorb disruptions requires some tradeoffs.

Managing Safety Stock

In the last couple of years, you’ve likely noticed that demand can drastically change in the blink of an eye. This makes safety stock management incredibly important as a proactive approach to inventory management. It establishes the minimum amount of on-hand inventory that acts as a buffer for demand surges or shortages in supply.

Safety stock is a simple way to prevent lost orders, which reduces the risk of stock-outs and guarantees you can fulfill orders. But having too much inventory poses a financial risk, and reducing inventory is tricky – it requires a deep understanding of which drivers have the most effect on your supply chain, and then removing them in a way that improves overall efficiency.

It’s more important than ever to have enough stock for orders and to establish accurate levels for automated reordering.

The Value of Multi-Echelon Inventory Optimization

Multi-echelon inventory optimization (MEIO) goes beyond ordinary inventory optimization to optimize stock locations and amounts across all sites and nodes in your supply chain. The right MEIO approach automates stocking and replenishment and enables rich “what-if” scenario analysis to analyze tradeoffs between costs and service levels. Using machine learning, it also identifies stocking patterns for seasonal products or new product introductions. Through robust visualizations, MEIO dashboards and event-driven notifications help improve usability, user adoption, and user efficiency.

MEIO uses time-tested advanced mathematical algorithms to accurately model inventory flows through the interdependent stages and locations of a supply chain, and it analyzes historical behavior under all conditions. The model helps create an optimal configuration of inventory buffers and locations adequate to handle any degree of demand and supply uncertainty, seasonality, etc. while achieving desired service levels for minimum cost.

With MEIO and the right digital supply chain platform, you can realize the benefits of lower working capital, a reduction in the burden of logistical cost, savings from decreased obsolescence, and increased revenue because you’ll have fewer permanently lost sales orders. But how do you get there?

It Takes Real-Time Visibility to Optimize Inventory Levels

It doesn’t matter what industry you are in, real-time visibility over your inventory is more critical than ever. A robust inventory planning and optimization solution within your supply chain platform will give you the visibility you need across your entire supply chain, including facilities and goods in transit. You’ll get a crystal-clear picture of just how much stock you have and where it’s located, so you can make sound decisions about material movements and meet demand.

Combined with demand planning and supply planning systems, you’ll have better insight into both demand spikes and your ability to fulfill orders. With the right platform, you’ll get robust analytics that provide visibility into inbound inventory shipments and customer order status. If you’re a manufacturer, that means visibility into supplier orders, goods in transit, and if they will meet your production schedules. You’ll know in real time if you must source materials from a different supplier or reroute a shipment.

Building and maintaining a competitive advantage requires embracing innovation and technology. Without modern digital solutions and best practices, what’s left? Inefficient manual processes, standard ERP system functionality that doesn’t give you what you want, and spreadsheets. This isn’t going to get you where you need to go. The modern enterprise requires a modern solution. Inventory optimization is just good business. Wondering how to optimize inventory levels? It takes the right digital solutions.

Inventory Optimization Solutions That Meet Business Goals

Logility provides a digital platform with supply chain management solutions that allow you to trace your supply chain end to end. You get the visibility and data you need to optimize your inventory in a way that preserves and increases revenue and allows you to protect and deepen customer relationships. Our platform utilizes artificial intelligence, machine learning, and automation to help you easily track and analyze your supply chain, so you always have optimal performance and key insights. To make better decisions and more profits, contact us today.

Industry: Food and Beverage