Building robust supply chain analytics capabilities is more important than ever.
Economic pressures including rising fuel costs, global expansion, off-shore low cost competition, and tight manufacturing capacities continue to stretch a supply chain team’s ability to reduce costs while meeting ever-increasing customer expectations. To complicate matters, finding and retaining qualified supply chain talent continues to be an issue. Top supply chain talent tends to migrate to companies that have invested in modern solutions that allow users to spend their time creating value versus shifting through spreadsheets to format, align, validate and report on last week’s data.
Supply chain analytics is the application of mathematics, statistics, predictive modeling and machine-learning techniques to find meaningful patterns in the vast mountains of data produced by enterprise systems and external sources. Tapping into both structured and unstructured data sources, advanced analytics help you draw conclusions about your demand, inventory, production and distribution operations to quickly drive more informed business decisions. An important goal of a supply chain analytics initiative is to enable better business decisions that improve operating results and allow you to be more responsive to customer needs.
Interest in supply chain analytics continues to grow at an exponential rate. Research has shown a strong correlation between more advanced analytics and higher Return
on Investment (ROI). Higher analytics maturity levels often lead to improved product quality, increased revenues and service levels while decreasing inventory.
This white paper dives into the importance of building robust supply chain analytics capabilities to support profitable revenue growth and exceptional customer service, and offers three easy-to-use checklists to help get you started.
In today’s supply chain business environment, improving insights to critical advanced analytics metrics via data visualization has become a critical element responsible for driving performance for any company.
Improving your organization’s ability to consume data in an intuitive, conventional manner to facilitate understanding can be the difference from taking action at the right time and avoiding potential disruptions due to lack of visibility.
It is becoming increasingly important to automate as much as possible, augment the human team with more insights and more data while incorporating visualization aspects that accelerate the decision making process.
This is done by tapping into new data sources, leveraging artificial intelligence, and machine learning. Then organizing that data and identifying new patterns that a human could not identify in a reasonable amount of time without AI. These insights are gathered from rich data, providing stronger information than from traditional data sets.
A strong visualization of insightful data is important to a business’s success with AI and advanced analytics. Visualizations create an engaging user experience that presents information in a highly intuitive way. Currently, there is a transition in the talent pool, and it is important to keep up with the digital natives entering the workforce and their expectations. How they navigate data and scenarios is different from someone who has been in that role for 20 years and grew up with data presented in a different way. Visualization of data and serving information in a more intuitive way is key.
By automating decisions where we can, and providing the needed analysis to determine the best approach, given what we know, we are able to free up the personnel to implement the work and think about the business more creatively.
Capturing big data for insightful, advanced analytics can be a hefty task, but is equally as important as the presentation of that data. The strong visualization of meaningful data is crucial to a business’s success with artificial intelligence and analytics. It creates an engaging user experience that presents information in a highly intuitive way. This paper explains the importance of visualization of data and what critical metrics can be improved with data visualization.
Supply chains are moving faster and faster and the complexity of data needed to manage them is growing exponentially. To quickly turn tremendous volumes of data into actionable insight, an increasing number of organizations are now turning to innovative digital supply chain planning platforms that are powered by artificial intelligence (AI) and machine learning (ML).
On its own, supply chain planning (SCP) provides the critical set of business processes that companies rely on for optimizing the delivery of goods, services, and information to their customers. Focused on balancing supply with demand, SCP manages real-time demand commitments, “what-if” scenario analysis, inventory optimization, and sales and operations planning (S&OP), among other functions, according to Gartner.
With increased speed in the supply chain, skilled labor is more difficult to find and retain, and customers are harder to please. Add in trade wars, economic uncertainty, and the risk of supply disruption to the equation, and you wind up with a perfect storm of supply chain challenges that—when combined with other hurdles—require the right mix of software and advanced technology to solve.
When companies incorporate artificial intelligence and machine learning into their supply chain planning activities, everyone wins. Layering advanced technologies like AI and ML into supply chain planning creates new capabilities and propels companies that want to stay ahead of the rapidly changing business landscape. Innovative digital supply chain platforms that are powered by AI and ML, combined with SCP can uncover hidden opportunities, identify potential risks and accelerate decision making – from product concept to customer availability, and all points in between.
In this Making the Case guide, we explore the opportunities that these advanced technologies offer and show how you can get the most out of the potential they offer. AI and machine learning are powerful tools that will continue to propel the supply chain, and in partnership with valuable supply chain practitioners.
Supply chain organizations are still at the early stages of making the most of advances in digital supply chain technology, including the use of a digital twin. In many ways, you could argue the industry is mostly using the astonishing capabilities of computing and internet connectivity to do what they did before – just faster and cheaper. With a more connected world, there is an increased amount of supply chain data available. This increase in data is incredible but also can be daunting. That’s why smart companies focus not just on gathering data, and figuring out how to filter and analyze it, but how to use it to make better, faster, more informed decisions with a digital twin. Since data management is not usually the core business of companies engaged in manufacturing and/or selling goods, most need to rely on a trusted technology partner (such as Logility) to help them make the most of this brave new world.
Artificial intelligence and machine learning can handle more parameters than most are aware. Typically, there’s more than one “right” answer, and the software helps get the best answer for the current strategy. With this artificial intelligence, companies can then employ a digital twin that will assist in analyzing different scenarios. With these scenarios available, companies can explore the impact of changes with little effort and no cost.
Accurate, up-to-date data is vital to achieving these advanced insights. This affects how long it takes to identify something that happened in the supply chain that needs to be addressed – or something that is likely to happen. With readily available insights companies can then use AI, machine learning and advanced analytics to solve problems.
With this increase of information and artificial intelligence, a refined plan is no longer a nice-to-have, but a necessity. Companies are no longer limited to monthly, weekly or daily reports, they can refine their strategy through the entire process, up until the product is headed to the customer.
Every supply chain team must optimize inventory investments with service level performance. Without proper balance, organizations run the risk of excess inventory and trapped working capital, or too little inventory and lost sales. Finding the right balance is critical to boosting service levels while reducing inventory costs. This research examines the current state of inventory optimization adoption, identifies the top pressures driving inventory optimization initiatives, and provides a breakdown of capabilities by industry.
Through advanced “what-if” scenario evaluations based on the use of Digital Twins, Multi-Echelon Inventory Optimization (MEIO) has shown to reduce inventory investment between 10 – 30% while improving customer service levels.
An overwhelming majority of respondents rely on their ERP system (77%) to manage inventory. This highlights a significant opportunity for companies to further reduce working capital through the use of more advanced solutions.
Inventory Optimization Study Overview
Logility partnered with Elastic Solutions, a leading provider of B2B marketing research services, to conduct a study – Leveraging Inventory for Profitable Growth – and now offering you you the results in this free survey! 100% of survey respondents participated voluntarily.
Over 100 survey respondents, representing more than 10 industries, participated in this study. All are located in North America.
One key takeaway: Multi-Echelon Inventory Optimization – the ability to optimize raw material, WIP, and/or finished good simultaneously across all nodes in the network– is how 48% of the respondents currently optimize inventory. Only 3% stated that they don’t perform any kind of inventory management or optimization.
Download the entire 13-page report to learn more.
Retailing is currently undergoing one of its most seismic shifts with further changes imminent. “Omni Channel,” “Multi Multi” and “Cross Channel” are being replaced by a “Unified Commerce” approach. By 2020, it is likely that shoppers will pay for home delivery. Pure-play retail will largely cease to exist; and truly self-serve stores will become a reality.
And have no doubt, customers expect reliable fulfillment. Recent research shows that nearly 50% of global shoppers are influenced by a retailer’s ability to offer convenient collection points for online purchases. Click & Collect is no longer a nice-to-have, it’s a prerequisite that is driving further changes in retail operations, merchandise planning and velocity. Two decades ago, omni channel retailing emerged to enable
customers along the many paths that lead to purchase, from a store’s website to its brick-and-mortar locations. Today, retailers are weaving those paths into a cohesive journey—a strategy known as Unified Commerce.
Unified Commerce clearly means customers shop brands, not channels. To deliver on a Unified Commerce strategy, retailers must build a business model that leverages a harmonious integration of retail processes and systems to provide full transparency of consumers on the back end and seamless customer experiences on the front end, regardless of the journey to purchase.
To successfully transition from omni channel to Unified Commerce, retailers must evaluate their capabilities, assess customer touch points, and create a plan for moving forward. Download this white paper, first in a three-part series, to learn more.
The path toward Unified Commerce requires both an agile retailing and merchandise planning approach plus new ways of thinking.
Shoppers don’t think in channels, and you shouldn’t think of your selling channels as individual entities either. Ecommerce, mobile and point of sale (POS) are all part of your shoppers’ experience with your brand. Plus, interaction with your brand will likely start before the shoppers reach the store or open their packages.
For example, shoppers may discover your brand on social media whilst checking out a key opinion leader’s or influencer’s post. After a couple of clicks, they can either buy the item on their mobile, tablet or desktop, or go to the store to get it. This process has to be frictionless or conversion drops.
Forward thinking, creative retailers are now starting to use sentiment and artificial intelligence (AI) to gain insights from their customers or loyalty members to test products before they come to market to influence merchandise planning and buying decisions earlier in the process. Retailers have an abundance of customer data that they can harness and use and also make their loyal customers feel engaged in the brand by asking for product feedback and providing access to user groups. Therefore, by reducing the risk of poorly developed and planned ranges and focusing on new ranges for key customers, retailers can increase average transaction values and customer loyalty to the brand.
So, what could a unified commerce customer experience look like for your company?
Download this white paper to find out. Part two of a three part series, this piece presents two additional building blocks of a unified commerce platform: how to create a unified customer experience, and how to build customer loyalty by using personalization.
Retailing is currently undergoing one of the most radical seismic shifts with further changes imminent. A comprehensive retail demand planning foundation is key for success.
In order to deliver the very best shopping experience, as a retailer you must deliver what your customers want. For the last few decades, retailers have used different retail demand planning systems for each area of their business, with virtually no integration. This has meant time-consuming and costly attempts to analyze each silo of information, including inventory positions. However, the inaccuracies of working this way has led to a greater cost—and thus falling customer satisfaction levels as failure to effectively communicate across all channels of sale virtually guarantees an inability to deliver a valuable customer experience.
By adopting a Unified Commerce culture, with an improved technology stack, your retail business can react to consumer demands in real time. Not in stock in-store? No problem, arrange a delivery direct to the customer’s door. Bought online and want to return to store? Simple, optimized inventory solutions mean that the location of the product is tracked and the inventory automatically updated.
In this third white paper in series of three, learn why embracing Unified Commerce and having a single plan that uses advanced analytics, artificial intelligence and machine learning to automate retail demand planning – including merchandise planning, assortment, allocation and replenishment – is the key to success.
You will learn how Unified Commerce can leverage all of your processes and technology into one cohesive platform that can be accessed anywhere, anytime, working with every single one of your customer engagement points to provide an exemplary experience, whenever and wherever your customers choose to shop.
Evaluating demand forecasting analytics solutions can be a confusing and complex process. However, knowing the right questions to ask can greatly reduce the risk of selecting the wrong solution provider.
Over the last decade, advanced analytics has been at or near the top of the list of priorities for many executives and information technology professionals throughout the world. Organizations have made major investments in database technologies including CRM, ERP and other transactional systems that run the operations of a department, division and subsidiaries. As a result, most organizations have access to valuable data and are looking for ways to leverage demand forecasting analytics for a competitive advantage. Transforming raw data into meaningful and useful information enables more effective strategic, tactical, operational insights and decision-making.
A recent survey revealed that organizations that place greater emphasis on the solution provider evaluation process tend to have a more successful project. Not surprisingly, these organizations also consistently outperform their competitors in the marketplace. The true value of a solution for demand forecasting analytics lies in the insight it provides on business operations and the opportunities it creates for better decision making. Understanding the business requirements for an advanced analytics project is crucial to the selection strategy that aims to align with the overall business objectives while addressing the needs of business users. Failure to integrate business requirements into the selection process can result in substantial cost overruns or, even worse, project failure.
With hundreds of advanced analytics tools available in the market, a sound selection strategy that integrates business requirements accurately is more important than ever. Download this white paper, Five Questions to Ask before Selecting an Advanced Analytics Solution Provider, to help you on your journey.
Excelling at strategic supply chain management, including planning and optimization, creates a recipe for fast, efficient and profitable operations, providing a competitive advantage for any food or beverage company.
As a food manufacturer, you face a big list of tough supply chain challenges including long raw material lead times, volatile commodity price fluctuations, safety and quality issues, demand uncertainty and seasonality, high promotional activity, product perishability, frequent new product introductions (NPIs), exacting distribution requirements, complex manufacturing constraints, strict legal and regulatory requirements, and increasing customer expectations.
Without a doubt, the supply chain organizations in food and beverage companies face mounting pressure to reduce costs, add more value and do more with fewer resources. A convergence of strategic supply chain management, people, process, data, and technology capabilities, including those listed below, makes the timing ripe to take your food and beverage supply chain to the next level. Once your supply chain planning foundation is established, these six areas are the logical places to focus if you want to develop more mature supply chain practices and add more value to your organization:
- Automation through artificial intelligence and machine learning capabilities
- More mature and user friendly supply chain planning and optimization solutions
- Proven supply chain optimization algorithms
- Access to richer internal and external structured and unstructured data
- Technology savvy workforce—Millennial/Generation Z
This white paper outlines steps you should take to reap the greatest harvest from strategic supply chain management investments including: supply optimization; multi-echelon inventory optimization (MEIO); manufacturing optimization; advanced sales and operations planning (S&OP); digital transformation; and artificial intelligence and machine learning.