Predictive Analytics in Supply Chain Management. Boosting Supply Chain Analytics with AI

Predictive analytics is one of the most promising technologies in supply chain management (SCM). It has been around for decades, but it’s only recently that predictive analytics solutions have become mainstream and affordable enough to be used by small and medium-sized enterprises. With the help of predictive analytics, companies can fine-tune their supply chains in ways that simply were not possible before. As data becomes more prevalent, so does the use of big data and predictive analytics to predict customer behavior and improve supply chains. The data collected from the past can be used to find patterns and make predictions. Predictive analytics and similar AI-based solutions have been introduced into SCM to boost supply chain analytics, generate better forecasts for demand, optimize inventory levels, and reduce costs by reducing waste.

What is predictive analytics? How can predictive analytics help your supply chain? And what are some challenges you might face when implementing forecasting solutions? This article will answer all these questions.

TL;DR

• Predictive analytics is a valuable technology in supply chain management that has become more affordable and accessible to small and medium-sized enterprises.
• By leveraging predictive analytics, companies can anticipate customer demand, optimize inventory levels, and reduce costs.
• Predictive analytics in supply chain management utilizes data and advanced algorithms to forecast future demand and optimize inventory.
• It helps companies maintain the right amount of inventory, reduce operational costs, minimize downtime, optimize fleet and route planning, and improve logistics network optimization.
• To implement predictive analytics in your supply chain, contacting Nexocode data engineers for assistance.

How Is Data Analytics Used in the Supply Chain?

Companies working along the supply chain are already using various data analytics in their operations. Most of the analytics currently in use are descriptive in nature. Descriptive analytics is a set of tools for summarizing data and spotting trends. This type of analysis does not predict the future but instead describes what has happened in the past based on historical facts, figures, statistics, and so forth. Diagnostic analytics intends to give insights into why something happened.

Every analytic process begins with the collection of data. Data can come from various sources, including sales and purchase forms, invoices, delivery notes, CMRs, customs documents, etc. Supply chains are complex and therefore produce lots of data. The challenge is to process this data and use business intelligence analytics to understand what happened, why it happened, and make the best business decisions about the future.

Predictive analytics takes the data another step further, answering questions about the future - what will happen? Information needs to be transformed into something more useful for predictive analysis purposes - it’s typical for this step not to occur until after descriptive analytics solutions have been implemented because companies often don’t even realize how valuable insights can be found in their historical data sets.

The most powerful and impressive data science analytics is about gaining foresight and an intelligent action plan with prescriptive analytics. Prescriptive analytics is predictive analytics with a purpose; it’s about making predictive models and using them to determine the best course of action for future events.

Cognitive analytics aims to replicate human thought processes and behavior, and they can be used to address challenging, complicated questions. These analyses are capable of taking into account factors such as context when interpreting findings. To perform this, cognitive analytics uses artificial intelligence, particularly machine learning and deep learning, to become more intelligent over time. Cognitive technologies can comprehend, reason, learn, and interact like a person, with far greater efficiency and speed than humans can. This may considerably cut down on the amount of labor needed by staff to generate these reports and analyses and allow people outside the data science team access to the results.

Predictive Analytics

Predictive analytics is one of the most popular branches of statistics within the data analytics field. The idea behind predictive modeling is to make use of past data to predict future events or behaviors, which companies can use for the better supply chain management. The supply chain is also important in other industries, e.g. chemical. Read more about the challenges and opportunities of using AI in the chemical supply chain HERE.

As the name implies, predictive analytics takes past data and uses it to make predictions about future events. In a broader sense, historical information is utilized to create a mathematical model that reflects significant patterns and trends.

Several different algorithms and statistical methods might be used for predictive modeling, including:

• regression analysis,
• correlation analysis,
• classification techniques,
• segmentation techniques,
• time-series models,
• deep learning technologies.

The recent advancements in artificial intelligence technologies and the growing availability of these novel technologies also triggered advances in methodologies used for building models. Time series modeling and deep learning techniques have taken predictive analytics to the next level.

When deciding which predictive model works best for your business, it’s essential to consider where you’re starting because different organizations have different datasets available when modeling takes place - small businesses without rich supply chain databases might need predictive solutions that do not require large amounts of training data. In contrast, larger entities would benefit more from complex machine learning projects involving deep neural networks. It is also important to note that data augmentation methods, techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data make it possible for companies to expand their data sets. The data investigation step conducted by experienced data scientists is crucial before moving further with the adoption of predictive analytics solutions.

Solutions that use predictive analytics are beneficial for supply chain management companies. They are the perfect way to boost supply chain analytic capabilities and turn data into actionable insights about what’s going on in a business at any given moment and predict future trends, which can be used to make better decisions moving forward.

The following steps illustrate how predictive analytics works within a company:

Data collection - every predictive analytics process begins with the collection of data. Data can come from various sources, including sales and purchase forms, invoices, delivery notes, CMRs customs documents, etc. Supply chains are complex and therefore produce lots of data. This step involves gathering anything related to your organization’s operations, including sales reports, trade deals, supplier contracts, etc. Some organizations also collect social media activity, whether forecasts or other information, from public databases.

Data preparation - machine learning algorithms need clean and organized data for proper training/modeling, so it’s essential to remove all non-relevant fields before feeding them into predictive models. Preprocessing steps conducted by data scientists help in getting clean data by eliminating noise and outliers. The more quality data is provided into predictive models, the better insights they can produce.

AI Proof of Concept - once you have the necessary data, predictive analytics starts to take over. The Proof of Concept step usually focuses on further data investigation and initial modeling to see what benchmarks are reachable with the lowest investments.

Modeling - predictive modeling solutions can be implemented in several ways, depending on companies’ predictive analytics requirements and goals. Predictive models are built based on historical data sets to predict future events or behaviors. This is done by building predictive analytic models with various machine learning algorithms, training based on historical data, and testing model outcomes with test data sets. This step also involves fine-tuning the model and the benchmarks it can achieve on historical data.

Deployment - once tested, the model is ready for deployment to production environments. This step usually covers integration with other production systems and data sources. Predictive solutions need constant monitoring, which means that companies have to monitor the effects of the models and fine-tune them to changing environments to maximize results.

How Is Predictive Analytics Transforming Logistics and Supply Chains?

Supply chains have evolved significantly over the last few years, and so has predictive analytics. With predictive analysis solutions being widely available at affordable prices and reasonably easy to integrate with other systems for small businesses, it’s not surprising that more and more enterprises want to implement this technology into their operations to boost supply chain management efforts.

Predictive analytics is pervasive among big brands whose sales volume can easily reach hundreds of billions annually. They know how important data is when it comes down to making the right business decisions about inventory levels, production needs, etc., which also happen daily across all departments involved in an organization’s supply chain.

Predictive analytics has become so popular because it allows organizations to make smarter decisions about their supply chains than they would otherwise have been able to do on their own through traditional means. It’s not just businesses interested in predictive data science: governments worldwide are starting to use advanced predictive tools for their goals and purposes.

Big data analytics applications within the supply chain cover the whole flow - from suppliers and procurement through production, logistics, sales, and the end customer. Some of the most popular predictive solutions in supply chain management include predictive maintenance, planning, and forecasting.

Predictive Analytics Use Cases in Logistics and Supply Chain

Demand Prediction

Forecasting is about anticipating future events based on patterns found in historical data sets; it’s mostly about finding a suitable mathematical model that accurately forecasts future trends and predicts what will happen given specific variables or conditions. It helps to indicate everything from sales volumes of individual products, market demands, seasonal fluctuations, etc.

Predictive analytics gives companies the ability to predict future customer demand. This is one of the most significant advantages predictive technology offers. It allows organizations to take steps before an actual increase in sales occurs, not after customers start complaining about missed deadlines and lost revenue opportunities). Demand forecasting can predict future market trends and supply accordingly, helping in enterprise resource planning. As an example, the predictive model could help companies estimate the demand for their products in a specific region, so they could either expand production or look for partners with spare capacities who could provide additional units at certain times when sales are expected to increase.

One of the predictive analytics use cases in supply chain management based on demand forecasts is truckload shipping forecasting, which considers all significant variables for freight transportation. It can be used to predict demand for shipment services, identify factors that are likely to affect future volumes of goods being transported, etc. Companies could plan accordingly and book additional capacity if necessary while avoiding bottlenecks during peak seasons when everything seems to happen at once.

Production Scheduling

Predictive modeling is advantageous when it comes to production planning and scheduling. By considering all available data from past sales history, demand forecast, etc., companies can make sure that they have the right amount of materials on hand for production within a given time frame. Companies can use supply chain analytics to plan their production activities. This is possible through demand planning, forecasting, and optimization applications. The results of these predictive models are combined with other relevant data about costs, capacities, etc., which give companies the ability to determine how much inventory they need for each product at specific times or regions throughout a year.

In addition to predicting material needs based on expected sales volumes in future periods, predictive analytic models also allow organizations to react quickly if something goes wrong during the supply chain management process. Such as a significant customer deciding not to place an order or a supplier failing to fulfill their contractual obligations. In these cases, predictive solutions help businesses avoid the overproduction of products no one wants by identifying potential problems before they occur and making adjustments accordingly.

Many products are manufactured in batches with the same production line used for various purposes. Scheduling with predictive models helps companies find the best production plan that considers different steps of the process and their duration and demand for the particular product. Production scheduling based on supply chain analytics helps optimize batch manufacturing by streamlining decisions on which product should be produced and planning for cost-effectiveness.

Inventory Optimization

Inventory management is one of the most critical processes that predictive analytics can improve. This use case allows companies to make the most out of their supply chain management processes. Having too much inventory in stock can be costly, while not having enough for expected sales could mean losing potential customers. The predictive model helps organizations maintain just the right level of supplies at all times - which usually means lower investment costs and less waste due to overproduction or understocking.

Companies adopt supply chain analytics to determine how much inventory should be kept on hand based on historical data about customer behavior patterns combined with upcoming events such as holidays or an end-of-season sale period which might cause increased purchases of particular items.

Inventory management and preventing stock-ups, especially for perishable goods such as food and pharmaceutical products, is essential in any supply chain management process. In these cases, predictive analytics can prove incredibly useful since the model can adapt forecasts based on incoming data from sales reps, future demand, or other relevant sources for ensuring the smooth running of operations.

Related case study: Optimizing drug distribution and inventory activities for a hospital pharmacies networkTo improve current large-scale procurement processes, a pharma company approached us to use applied analytics to stock and distribute drugs among US hospitals.Our challenge? Maximizing savings by streamlining the procurement of medication across the hospital network and their pharmacies. Read more about this case study.

Predictive Maintenance

A predictive analytics solution can help supply chain managers reduce operational costs and downtime by identifying potential problems before they occur. In addition to predictive analysis for production planning and scheduling, companies can use predictive models to simplify the maintenance process, helping avoid expensive breakdowns that could have been prevented with little preparation.

Predictive maintenance is one of the most popular supply chain analytics applications that offer businesses a competitive edge by optimizing productivity levels while minimizing operational costs at all times.

Predictive equipment monitoring solutions help businesses reduce costs associated with unplanned downtime by enabling them to schedule repairs ahead of time rather than dealing with unexpected equipment breakdowns that result in production delays or excessive product waste caused by outdated machinery parts etc.

Predictive Fleet and Route Planning

Predictive analytics applied to logistics networks offers many opportunities for supply chain managers to boost the performance of their business. Focusing on optimizing deliveries and transportation companies can reduce costs associated with poor planning or delays caused by bad weather, traffic jams, etc. In addition, they have the opportunity to increase customer satisfaction levels and optimize inventory management, resulting in more profit from sales in general.

Predictive fleet optimization solutions help supply chain businesses find new ways to combine important supply chain metrics and data from different sources such as vehicle location information, delivery time estimates based on historical data about distances covered per day, and other relevant metrics that affect the route planning process.

Related case study: Delivering a dedicated IT system to manage and sell freight deals and plan transportationA major Polish logistics company approached us to create a dedicated IT system to handle their core business process – managing and selling logistics deals.Our challenge? The key challenge in the logistics sector is cutting the time of concluding deals to an absolute minimum. The tool has to be very responsive and help in the smart matching of carriers and freight, fleet management, and other logistics operations. The platform helps shipping agents minimize fuel consumption, maximize operational efficiency, and optimize fleet performance by matching multiple loadings on a similar route with a single carrier. Read more about this case study.

Logistics network optimization systems based on predictive models help network managers and supply chain partners reduce transit time and fuel consumption for goods between warehouses or points on sale. In predictive routing models, factors like expected travel times are combined with ongoing events specific for each company - e.g., available fleet, drivers schedules, cargo, loading places, holidays, etc.

Predictive analytics capabilities can help logistics providers optimize their routes by identifying road segments where traffic tends to slow down or gets congested - this way, they would have a better understanding of how long it takes them to transport a certain amount of cargo on specific roads without having any surprises along the way. Predictive modeling is also helpful when reacting quickly if unexpected events occur, such as extreme weather conditions requiring changing routes or temporarily altering schedules. Check more predictive analytics case studies.

Related case study: Implementing AI model to optimize routes and timelines of deliveriesA company from the logistics sector approached us to create a custom AI model that optimizes routes and the scheduling of deliveries.Our challenge? The key challenge here was to prepare a dedicated AI-based system designed for carriers to optimize delivery time depending on the destination address. Thanks to the model we managed to reduce failed and late delivery rates by 30%. Read a detailed case study of this project.

Cost and Pricing Optimization

Prices for many goods and services fluctuate daily – going up or down depending on supply and demand. For example, gasoline prices are usually highest during holidays or on the weekends when demand is high.

For manufacturers, predictive analytics can be used to optimize pricing strategies by identifying optimal price points based on historical data about product sales volume at different prices and market conditions such as currency exchange rates, inflation, etc. In addition, a predictive system can help companies minimize the risk from possible “pricing mistakes,” which could have been caused by human error during manual calculations, delays in obtaining factual information that was needed to set prices appropriately, etc.

Supply chain managers can use predictive models to create a baseline model that considers historical supply chain data and produces an accurate prediction about what will happen if certain conditions remain unchanged (e.g., no change in product price). Predictive models provide businesses with an automated process to determine their best competitive advantage - e.g., should they choose discounted prices? or increase their margins? By predictive modeling, companies gain deep insights into how different factors affect buying decisions – such as price changes or promotional campaigns - which helps supply chain professionals adapt pricing strategies accordingly and increase revenue from sales even further. AI has some special superpowers when it comes to price optimization. Dynamic pricing models are the most impressive here, as they base on predicting supply and demand together with multiple other data points that impact the final price and clients’ decision-making. Head over to our article on dynamic pricing in logistics and article on applying dynamic rate management in FTL transportation.

Prices of raw materials are constantly changing due to various factors. Up-to-date predictive analysis can identify patterns and provide companies with insights into future costs, which helps manufacturers plan production and producers update their pricing models and sales activities better in the long run, thus maximizing their profits.

Predictive analytics applied to the supply chain can help businesses find new ways of maximizing profits without losing sight of optimal customer experience by satisfying customer demand and long-term sustainability and the increased sales volume in general.

Supply Chain Risk Management

Many businesses implemented various initiatives such as outsourcing manufacturing and product diversity to gain cost and market share. These tactics are effective under stable conditions, but they may make a supply chain more prone to various types of disruptions caused by unpredictable economic cycles, consumer preferences, pandemics, and other natural and man-made disasters. Supply chain leaders apply different supply chain risk management (SCRM) strategies.

Supply chain companies adopt predictive analytics for risk management to identify possible risks that may cause disruptions along the supply chains. The popularity of social media and the sea of data we all share create new models that utilize big data analytics and help mitigate supply chain disruptions. A company may use social media data about strikes, fires, or bankruptcies to monitor supply chain disruptions and take proactive steps before its competitors by mapping supplies chains and recording social data on strikes, fires, and bankruptcies.

Related case study: Developing a logistics platform offering real-time visibility and integrations with different carriersOne of our clients was seeking to improve the global supply chain optimization productOur challenge? Providing visibility and data transmission for maximum efficiency and control. We supported solution development for end-to-end execution of logistics activities in Supply Chain Management at the PO/SKU level, including PO creation, stock management, suppliers and distributors management, consolidation and load planning, carrier allocation, documentation, and final delivery. Read more about this case study.

Customer Experience

Predictive models help companies gain insights into customer behavior and therefore have the potential to improve customer experience. Computer models can identify what customers are likely to buy next and when they may cancel or return a product. Predictive analytics in supply chain management algorithms can identify predictive patterns and trends about buying personas, which enables companies to recommend products or offer personalized pricing based on the information they have gathered from customers.

This strategy helps consumers and retailers retain existing customers while attracting new ones by delivering differentiated product recommendations that are more likely to appeal to them than other options. Predictive analytics can be used to identify customer segments, which will make it easier for businesses to adjust supply chain networks and product prices according to demand at different price points or introduce new products on the market if certain types of buyers are more likely to purchase them.

With predictive analytics, businesses can gain deep insights into their clients’ buying behaviors, preferences, needs, etc. As a result, there is an opportunity for sales professionals to develop effective marketing campaigns targeted at specific groups of consumers who will most likely buy particular items offered by the company. This information helps managers understand how their marketing campaigns impact customer buying decisions, allowing them to adjust future marketing strategies accordingly. For example, predictive analytics can help improve the predictive capabilities of business intelligence systems by understanding changing consumer behavior patterns and analyzing product returns - e.g., why did some people stop using a product? What made others switch from one brand to another?

Predictive analysis can also provide businesses with more insight into social media data, such as mentions on Twitter, Facebook, and other products for supply chain professionals to ensure quality standards based on actual feedback from consumers without delay. Analyzing word clouds is an effective way of identifying trends in real-time.

Benefits of Introducing Predictive Analytics Solutions in Supply Chain Management

Predictive analytics in supply chain management positively affects the overall predictive capabilities of these systems, helping businesses predict future demands and avoid missed sales opportunities. The competitive edge predictive analysis offers crucial for many companies operating along the supply chain, including manufacturing, retailing, procurement, logistics, and distribution, marketing and sales, etc., which have been heavily investing into AI solutions.

The benefits brought by predictive analysis can be categorized into four major categories:

• Improved decision-making,
• Risk reduction and business resilience,
• Cost savings and revenue maximization
• Increased customer satisfaction.

When you use historical data about your company or industry to create reliable forecasts, this helps you make better decisions about inventory levels, staffing requirements, etc., which leads directly to improved financial performance and minimizes losses from lousy planning.

Predictive analytics offer optimization benefits across various levels throughout the entire supply chain network - from suppliers through warehouses to retail stores. This approach helps businesses achieve higher performance than traditional decision-making techniques based on descriptive analytics and past data experiences. It allows them to adapt business processes more quickly than before while avoiding disruptions along the supply chain.

Prediction solutions help companies better manage risks in supply chains by identifying potential disruptions before they happen. Predictive analytic models are beneficial for companies that want to reduce costs associated with demand forecasting errors and minimize risk exposures such as lost sales, stockups, or stockouts. Predictive analysis can identify patterns that lead to this risk by analyzing demand forecasts against what is produced, warehoused, and shipped out of distribution centers. This information helps businesses understand whether they have enough products on hand to meet current demand levels vs. their sales projections.

Predictive analytic models help businesses gain deep insight into consumer behavior patterns to understand changing consumer needs and provide effective marketing campaigns targeted at specific groups of customers. This strategy allows companies to increase their sales figures while minimizing revenue losses from unsold goods or unwanted discounts on products that no longer appeal to customers.

Supply chain analytics can help companies improve their businesses’ bottom lines by delivering superior customer service through predictive insights on clients purchasing behavior patterns, better chances to retain existing customers while attracting new ones with targeted marketing campaigns based on predictive analysis on social media keywords related to the product, etc.

Challenges Supply Chain Managers Face When Implementing Predictive Analytics

Simple predictive analytics solutions are already in use for a while. Transferring to more complex and more powerful machine learning solutions for forecasting is connected with significant challenges:

Limited Access to Historical Data

The big challenge at the moment is that companies need to have access to vast amounts of historical data for predictive analytics models to work effectively. Predictive analysis requires high volumes of reliable input data historically collected across various business units within an organization or between different organizations within supply chain networks. The volume and quality of available information vary from company to company based on their size, geographical distribution, and already employed IT solutions. It is not uncommon that early in the process, data investigation operations need to take place conducted by a skilled data scientist. Businesses need to invest time and resources into collecting strategically relevant information about their business processes or industry. We advise starting with low investment AI Design Sprint workshops to strategically approach data collection and kickstart predictive modeling implementation.

Lack of 360° Vision

Many businesses still rely on disparate legacy solutions that lack integration among systems throughout the entire supply chain network. Software that supports the operations is distributed and does not cover processes as a whole. Solutions from different vendors are often not compatible with each other, making it harder to merge data across platforms, or worse, there is no integration. The lack of a 360° view of the supply chain presents one of the biggest challenges predictive analytics in supply chains face today.

Lack of AI-skilled Professionals

On-site, only the industry’s top companies have data scientists and machine learning experts to advise them on all things related to machine learning implementations. There is a lack of predictive modeling experts on the market. The supply far outweighs demand, making it difficult for businesses to find and hire predictive analysts with solid expertise in business domain knowledge, data science, mathematics, or statistics. Many companies fail at implementing predictive analytics solutions because they do not have enough qualified employees who can carry out complex AI projects. If your company is on the lookout for an experienced AI development team, contact nexocode experts.

Future of Supply Chain Analytics - Custom Prescriptive Analytics

The predictive analytics solutions in SCM are based on machine learning algorithms that can recognize patterns, cluster data into different groups, and make predictions with a certain degree of accuracy. Artificial intelligence is at the heart of predictive analytics capabilities in supply chains today. AI methods have been introduced to automate demand forecasting, production planning, and optimizing inventory levels across all channels, all with limited or no human assistance or intervention. The introduction of these tools has brought several benefits: reduced costs through lower wastage; better customer satisfaction by providing accurate forecasts which help companies avoid stock-outs; shorter lead times from suppliers to end customers due to improved demand visibility.

Thinking big, predictive analytics should not just address past data problems but also look into the future to proactively act on or anticipate future events. In the future, predictive analytics in supply chains will be powered by custom prescriptive and cognitive solutions - instead of simple predictive analysis software. The goal should be to generate prescriptive insights that are accurate enough for decision-makers to bring about actions or changes in business operations before it is too late and opportunities have been missed. Companies need prescriptive models capable of taking multiple variables at play, including sales reports, manufacturing data, transportation information, weather forecasts, consumer sentiment on social networks, and other external factors that may impact supply chains.

Cognitive analytics and customized applications tailored to the needs of a particular business is the future in logistics and supply chain software. With an ever-growing universe of available information across various digital channels, accessible through Internet technologies, companies could soon start using customized prescriptive and cognitive analysis solutions to take all available data and convert them into an innovative action plan. In the near future, we expect AI-powered predictive analytics tools to become mainstream across different industries and business verticals, including retailing and manufacturing sectors.

Take predictive and prescriptive machine learning to the next level with nexocode - contact us today! Our team of AI experts has extensive experience in developing predictive analytics solutions and other AI-based software solutions for logistics & supply chains.