What is supply chain inventory optimization?

Inventory optimization is essentially a gigantic scale of demand and supply that companies trading goods must constantly balance. It involves strategically managing stock levels to maximize efficiency, minimize costs, and ensure customer needs are met. The goal is to have the right inventory available – not too much to result in extra storage costs in warehouses, but enough to buffer against unexpected disruptions.

Considering the many factors that could tip this scale in one direction or the other, finding the perfect balance is a challenging task. But not an impossible one.

What are the key elements of inventory optimization?

Inventory optimization is a multifaceted process that involves various players and stages. However, it can be broken down into three core areas: demand forecasting, safety stock management, and logistics. In other words, to stay efficient, businesses must anticipate future sales, determine the right stock levels, and ensure seamless distribution of goods.

Demand forecasting

This is the process of estimating future demand for a product, based on data. Even if we can’t predict the exact future, businesses can get close by combining historical sales data, customer and financial analytics, and external factors like seasonality and economic conditions.

Safety stock management

Closely tied to a product’s future sales, safety stock is an optimal inventory buffer that prevents stockouts while minimizing excess inventory, ensuring the company meets customer demand. It also acts as a cushion against supplier and production delays, demand fluctuations due to seasonal or unexpected events, and constraints during frozen horizon periods.

Logistics

A company may have sufficient stock to meet demand but not have it in the right locations. This is where logistics come into play. A well-organized flow of goods moving from production lines to various warehouses and distribution centers ensures that stock levels are strategically distributed, making inventory more efficient and accessible where it is needed most. Easy to say but hard to do, considering how complex it is to orchestrate shipment scheduling while also maximizing loads and choosing the best routes to save costs.

The challenges of traditional inventory optimization

Current inventory levels, storage capabilities, seasonal trends, good longevity, future promotional campaigns, supplier lead times and schedules – many are the factors to consider to always have the right amount of products ready to fulfill the market’s crazy demands.

Plus, even if we could make almost perfect demand predictions, another big challenge arises: having fast turnaround times to immediately act on, depending on the changing conditions. All of these factors make inventory optimization a highly intricate and demanding task, especially in the face of:

Fast-moving consumer demand

In the era of Amazon’s next-day shipping, consumers expect rapid delivery times, making it challenging for companies to predict and respond to shifting demand patterns quickly.

Increased competition

With even more players in the market, companies must find ways to stay ahead by improving their inventory management to meet customer expectations while controlling costs.

Supply chain disruptions

Unpredictable disruptions can halt the flow of goods, leading to stockouts, delays, and the need for costly contingency plans.

Technological challenges

It may sound shocking, but 67.4% of supply chain managers use Microsoft Excel to manage inventory, demonstrating how many are still relying on outdated methods instead of leveraging more efficient, automated technologies.

Inventory optimization strategies

This is an overview of some of the many strategies companies can implement to avoid the stockout and overstock nightmare.

ABC analysis

The ABC of any inventory optimization strategy is, of course, knowing your product, and this is what this method is all about. It classifies products based on their significance to the business, grouping them into three categories: A, B, and C. Category A includes the most valuable and critical items, whereas Category C consists of the least significant ones. By using this system, businesses can better manage inventory, ensuring they maintain appropriate stock levels and focus on the products that provide the greatest value to both their customers and the company.

Rulex has a long history of helping major retailers and supply chains implement this very strategy. See how easy it’s to calculate the most profitable and best-selling products in Rulex Platform in this dedicated webinar: Improving business strategy with ABC segmentation – Webinars – Rulex Community

SKU rationalization

Once a company has identified its Category C products through ABC analysis, the next step is to determine whether these SKUs (Stock Keeping Units) should be discontinued, modified, or retained in the product lineup. For example, a company might choose to keep a specific SKU because, despite its modest sales, it’s a niche product which cannot be found elsewhere, and consequently drives customer loyalty.

To streamline inventory in specific product categories, many retailers use Rulex Platform’s powerful Assortment Optimizer tool. This tool extracts and generates replacement rules from frequent itemsets, helping businesses determine how to replace items with equivalent alternatives for maximum benefit. By strategically removing products and replacing them with these alternatives, Rulex Platform helps businesses optimize their assortment, minimize revenue loss, and prevent customers from switching to competitors.

Demand forecasting

We have already mentioned the importance of estimating demand to align supply with market needs. This is another strategy that major companies have implemented using Rulex Platform. For example, Rulex supported a global pharmaceutical company in accurately forecasting sales for several flagship products through an all-in-one solution, which encompassed data pre-processing, modeling, and forecasting within a single platform.

To learn more about demand forecasting and its real-life applications, read our dedicated article.

Safety stock tuning

Downstream demand forecasting is the Holy Grail of safety stock tuning. In simple terms, it’s about determining the optimal amount of inventory to maintain based on both certain and uncertain factors (e.g., confirmed orders, expected orders, item availability, ect.).

Rulex has developed a solution that does exactly that. Using a series of simulations, it analyzes whether and how often the planned stock level could be exceeded. For example, if the estimated safety stock is set at 100 apples, but 20% of simulations show demand surpassing this amount, we will face a 20% risk of stockout if we don’t set a higher safety stock.

Replenishment optimization

Once safety stock levels are set, they must be carefully monitored to ensure smooth operations. Replenishment optimization is the strategy that makes this possible by managing restocking at the right time and in the right quantities. It ensures that inventory is replenished efficiently to meet demand while minimizing costs associated with stockouts, overstocking, and storage.

To address restocking challenges, Rulex has designed the Network Optimizer. This tool balances the workload distributed over a network, determining the amount of material to move and where to move it. Keep reading to see how this works in a real-life scenario.

Transport optimization

Good logistics ensure that products are where they should be when they are most needed. To help companies achieve this goal, Rulex offers an extremely powerful Transport Optimizer. Known as, Rulex Axellerate, this generates shipment schedules that help keep costs and delays down.

With extremely rapid computation times, Rulex Axellerate can create end-to-end, long-term transportation plans in under an hour. When applied to a major logistics network, the solution reduced expedited shipments by 70%. Watch the video to see how Rulex Axellerate works.

In addition to these two vertical solutions, Rulex offers a proprietary, general-purpose solver capable of tackling virtually any optimization challenge – from work shift scheduling to dock optimization — even across multiple domains simultaneously, such as production and packaging.

Real-life success: how Rulex transformed inventory optimization

At Rulex, we’ve worked with some of the world’s largest production and supply chain leaders to revolutionize inventory management. One standout case is a Fortune 50 supply chain company seeking help to maintain optimal stock levels across multiple warehouses and sites.

Traditional planning systems left up to 20% of planning decisions to be handled manually – an inefficient, time-consuming process. Rulex stepped in with an advanced replenishment solution, automating over 90% of these previously unmanaged decisions. With its powerful optimization tasks, Rulex reduced under – and overstocking by 8%, cutting costs by $100K per day in a single market region and lightening the planner workload by an impressive 75%.

By seamlessly adapting to real-world complexities, Rulex’s replenishment solution worked as a game-changer in creating a balanced, cost-effective, and agile supply chain.

Find out more about this specific case study and others in our e-book.

The road to better inventory planning and optimization

If you sell products, knowing where your inventory is, who wants to buy it, and where it needs to go is essential to your business’s success. However, this knowledge alone may not be enough without the right, flexible solutions to act on it.

With a vast array of predictive techniques and optimization solvers, Rulex provides supply chains with a powerful advantage, enabling highly customizable solutions for smarter inventory management.

What mathematical optimization actually is, why it is important for companies, and why it’s so damn complicated

Optimization is the process of enhancing operational efficiency and performance by identifying the best-fit solution for a specific business scenario. It typically involves setting a well-defined goal, such as reducing operational costs or increasing revenue, while considering many factors and navigating multiple constraints.

In the context of logistics, for example, a company must determine the optimal combination of shipments while considering factors such as delivery times, costs, and availability of drivers and goods. Constraints can be soft, such as preferable delivery dates, which may allow flexibility albeit with penalties, or hard, such as insufficient inventory levels, which cannot be violated.

There are many ways these factors can be combined to reach the desired outcome, and optimization tools aim to find the optimum, or near-optimum, solution in order to maximize efficiency.

Modeling

This phase involves translating the business problem into mathematical formulas, which are then converted into a constraint matrix for the solver algorithm to process and find the optimal solution. Formulating business problems mathematically is a highly complex and time-consuming task. Additionally, transforming these formulas into a constraint matrix generally requires programming skills.

Solving

At this stage, the solver algorithm attempts to find an optimal feasible solution based on the constraints defined earlier. However, in most real-world problems, current solver algorithms on the market can take hours or even days to find a solution – and sometimes, they don’t succeed at all. This often happens when there are too many hard constraints, making an “optimal” solution virtually unattainable.

The most frustrating part is that when a feasible solution cannot be found, these algorithms provide no explanation, offering no insight into which constraints could be adjusted to achieve a solution. They operate like a “black box” that simply rejects the input, leaving us to start over from scratch.

How Rulex revolutionizes mathematical optimization

Rulex has just released a new advanced optimization tool that revolutionizes the traditional approaches, reducing time and complexity in both the modeling and solving phases. A part of Rulex Platform’s suite, Build & Solve is a powerful optimization task where business experts can define the problem along with its hard and soft constraints (Build), then apply an advanced algorithm to find a solution (Solve), all within a reasonable time frame and without the need for advanced technical skills.

In the Modeling (or Build) phase, no mathematical formulas are needed. Users can define problems using logical syntax within familiar spreadsheets, while the Build & Solve task itself automatically generates the constraint matrix from the business data and spreadsheets.

This is already a significant advantage. But there’s more.

In the Solving phase, the Build & Solve algorithm is extremely rapid. While traditional tools may take hours or even days, Rulex can find a feasible solution within seconds or minutes. If a solution is not found on the first attempt, the tool identifies which specific constraints are preventing a feasible solution, thus allowing experts to quickly assess and address the issue. This approach significantly reduces the need for repeated, time-consuming calculations and accelerates the path to a viable solution.

In addition to offering all the essential tools for gathering and pre-processing data, Rulex Platform also provides comprehensive capabilities for post-processing and visualization, allowing results to be presented to end-users exactly as and where they need them.

Real-world applications of Build & Solve

Build & Solve is not some future proof-of-concept; it’s already in production at numerous global companies, particularly in supply chains and manufacturing, where it is efficiently optimizing processes as we write. One of its most successful applications is in scheduling, ensuring that tasks and actions within a plan align with specific business objectives.

Scheduling optimization:

The tool was used to develop a solution for a Fortune 50 company, optimizing production efficiency by simultaneously tackling both product and packaging planning. This was a highly complex challenge, given the diverse constraints – ranging from managing different batch sizes to coordinating multi-stage orders and machines that cannot operate simultaneously.

The company’s experts were able to define the problem using logical syntax in spreadsheets, avoiding the need for complex mathematical formulas. Thanks to the tool’s user-friendly interface and rapid processing, the solution was deployed in under a month. Now, production planning can be optimized in less than 10 minutes, significantly outperforming traditional manual methods in both speed and accuracy, resulting in a threefold increase in production efficiency.

More in-house optimization tools

While Build & Solve serves as a general purpose optimization tool, Rulex also offers specialized tasks for specific supply chain and logistics challenges, including warehouse and transport optimization. With these tools, the problem framework is already established; users simply need to customize it with their specific case details and data.

Avoid getting stuck down rabbit holes

Tailored around business needs, Build & Solve offers extremely rapid and efficient problem resolution, so you don’t get stuck down rabbit holes.

Do you have a business challenge that feels insurmountable? Bring your case to us, and together we’ll find a solution. Schedule a free consultation with one of our experts today!

What is decision intelligence?

Decision intelligence (DI) combines cutting edge technologies such as AI and mathematical optimization with human expertise to create a cohesive framework for answering key questions, such as: What will be the outcome if I take this action today, given the current context?; What actions should I take now to maximise the likelihood of achieving my goals?; What strategies can I employ to reduce costs?

Listed in their top 10 strategic technology trends of 2024, Gartner defines DI as a “practical discipline that improves decision making by explicitly understanding and engineering how decisions are made, and outcomes evaluated, managed and improved by feedback”. DI is, therefore, no different from the decision flow that you and your team create when making a decision during a meeting.

But, is DI just a buzzword? McKinsey predicts that 70% of businesses will rely on decision intelligence by 2030. So, it may be worth having a further look into the topic.

How is decision intelligence different from business intelligence?

The first question that may arise when hearing about DI is how it differs from business intelligence (BI) – the practice of presenting relevant, historical business information and querying data in visual formats. Decision intelligence can be seen as BI 2.0, diving much deeper into the inner workings of data.

Suppose you want to analyze your company’s sales. While business intelligence reveals past sales trends, decision intelligence goes further by explaining why sales fluctuated and identifying which factors were less influential. This insight helps address business challenges more effectively. In other words, DI is an action-oriented practice that provides the additional knowledge needed to elevate your business operations to new levels.

How decision intelligence platforms work

Decision intelligence platforms orchestrate large volumes of data into a unified view, leveraging cutting-edge technologies like eXplainable AI to support intelligent decisions, and then executing and automating decision flows, saving you time.

These platforms must be designed to scale solutions easily and incorporate human expertise and feedback. The key is that decisions made using decision intelligence platforms should not only be superior in quality, accuracy, and effectiveness but also augmented by a hybrid system of advanced technologies and human judgement. To facilitate this human-machine collaboration, transparency is crucial, offering clear insights into decision-making processes, so users can understand and evaluate any automated decisions.

“Inform with accurate data, decide with advanced and explainable tools, and repeat with automation” is the mantra of good decision intelligence. Listed among the top decision intelligence platforms by Gartner on the market, Rulex is an innovative platform system where these three phases are all integrated into the same workspace. This means you can create solutions to improve decision-making starting from your in-house data and following the entire process step by step.

Collecting information with agility

Call it what you want; we call it data agility, meaning the capacity to collect, aggregate, and harmonize data smoothly from any source and format. But Rulex Platform’s agility capabilities do not stop there.

It also ensures your data is of top quality through a combination of data quality technologies, from traditional data cleansing to rule-based validation and AI-driven self-healing. Not only can these multiple approaches literally tackle any data quality issue, but, equally important, they can be handled and applied by citizen developers, without the need to constantly stress the IT team for help and support.

A full decision intelligence toolkit

When data preparation is done, let the fun begin. Decision making is not only about data: it’s about reaching your organization’s goals, with data as a key ingredient.

Rulex provides a comprehensive suite for an outcome-based approach. Whether optimizing logistics, making informed credit scoring decisions, or gaining insights into your product landscape, virtually any solution can be built in Rulex Platform. Additionally, its WYSIWYG graphical interface and transparent technology enable users without technical skills to design solutions independently.

Native eXplainable AI – Generates output in the form of understandable if-then rules, leading to peak performance while ensuring transparency, compliance, and adherence to ethical AI standards. Rulex’s proprietary XAI technology has resulted in multi-million-dollar savings for global corporations in areas such as customer loyalty and fraud detection.

Mathematical optimization – Finds optimal solutions for complex business scenarios by managing intricate physical and logical constraints defined in common spreadsheets. Global supply chains use this tool to minimize costs and maximize revenues in areas such as deployment, dock, and transport optimization, and even simultaneously optimizing multiple areas like warehousing and work shifts.

Business rule engine – Serves as a powerful tool for defining, enhancing and applying business rules to data. It allows users to write business rules in a simple Excel file using intuitive syntax. It has been used for advanced master data validation, lending and credit risk management, and strategic supplier diversification.

What-if scenario simulator – Forecasts the behavior of complex processes and recommends precise modifications to achieve desired outcomes without costly real-life experiments. It has been employed to reduce energy consumption, predict equipment maintenance, and improve retention strategies.

AutoML – Simplifies and accelerates data analytics through a user-friendly, guided workflow. By automating time-consuming tasks like data preparation, pre-processing, and feature selection, it facilitates seamless experimentation with multiple machine learning models. It is widely used by global financial organizations for applications such as credit scoring, wealth management, product cross-selling, and up-selling.

Decisions are human, repetition can be automated

Once you have built your decision flow and are satisfied with it, the final step is to automate it. Automation ensures smooth data flow across systems, providing up-to-date analytics, and streamlining company processes, ultimately saving time. Rulex Platform provides end-to-end workflow automation, covering everything from data collection and integration to analysis and reporting.

Finally, while many software solutions struggle to integrate with existing infrastructure without causing major disruption, Rulex excels. Both the cloud and server versions of Rulex offer a comprehensive catalog of REST APIs, facilitating smooth connections with multiple systems and services. To further enhance collaboration and efficiency, Rulex Platform provides all the necessary features to support the creation of DevOps pipelines, integrating the software with existing tool-chains.

Empowering your decisions

Making decisions is not new; we, as humans, have done it for millions of years. However, the uncertainty of today’s market and geopolitical landscape demands a quicker pace. Making faster and better decisions is crucial for maintaining a competitive edge. Rulex Platform can be your trump card, empowering your company while seamlessly integrating with your existing infrastructure and systems.

Have you received a discount from your favorite clothing brand? Business rules were probably involved in the decision-making process. Often brands set business rules that award discounts every time a certain value is reached by the customer. But who defines and executes rules in a company? Basically, who rules the rules?

What are business rules?

Business rules play a crucial role in companies’ operations and processes, as they guide the everyday decision-making within the business. They express business goals, guidelines, regulations, performance requirements, etc. while automating processes.

A rule can be formulated to indicate a particular course of action to be followed under specific circumstances or to prevent certain actions from happening.

For instance, e-commerce analysts can set business rules to apply customer discounts or for pricing optimization, while financial institutions employ them to improve decision-making for risk management: they suggest to bank clerks whether a loan should be granted or not.

Moreover, business rules streamline operations. They can be set to conditionally process documents and invoices or to route customer service calls.

Business rules keep things going while helping maintain consistency across the organization.

Business rule engines: who rules the rules

Translating activities into concrete business logic is a great deal for companies in terms of efficiency and accuracy, as business rules reduce manual data entry, lowering the risk of potential errors, while automating repetitive tasks.

But who executes business rules, especially in complex environments where multiple constraints are involved?
That’s where business rule engines (BREs) come in.

A business rule engine is a piece of software that runs the rules on the provided business data, and if any condition
matches then it executes the corresponding actions, automatically responding to real-time situations.

Most rule engines, like Rulex Platform, don’t do just that. They also define new rules by using machine learning algorithms, integrating the existing rules provided by business experts.

Heuristic rules vs AI-generated rules

Heuristic rules are those that have been extrapolated from personal experience. Industry experts
define the decision logic after a careful analysis of historical data. They may compare new and old data patterns,
monitor new and real-time data, make assumptions to fill in the gaps, and then come up with a set of business
rules to apply to the business process.

AI-generated rules, on the other hand, are extrapolated by machine learning algorithms starting from the historical data of the business. In Rulex Factory, the heart of Rulex Platform, for example, these types of rules take the form of an if-then expression.

These two different types of rules can be merged together in the decision process, to achieve the best possible outcome.

How to manage business rules in Rulex Factory

Let’s take a look at how Rulex Factory manages business rules.

As business rules often have many dependencies, constraints, and mathematical formulas, Rulex has developed a
simple syntax that enables non-technical users to express these rules in a simple tabular format, such as an MS Excel file. With a simple drag and drop, you can then import the file into Rulex Factory and apply it to your business data.

If you have very simple rules, there is an even quicker way of managing rules. Once you have dragged and dropped
your data onto Rulex Factory’s canvas, just connect a Rule Manager task. This user-friendly task enables you to apply any pre-defined business rules to your data in a simple if-then format.

In the example, we added the business rule that defined which transport to use depending on the weather and working mode:

Weather = Sunny AND Smart Working = No, THEN Transport = Bicycle

There are two conditions, weather and smart working, which define the output, bicycle.

How to create business rules in Rulex Factory

Manually extrapolating the best rules from historical data is not always an easy task. However, specific algorithms
can analyze the data and generate business rules for us.

In Rulex Factory, we use the Logic Learning Machine (LLM), Rulex’s proprietary algorithm that produces clear and understandable if-then rules.

No technical expertise is required, and business users can create rules using the LLM with ease and confidence.

In the example, we connected the LLM task to our data, selected the attributes we want to use as input (e.g. weather conditions and working mode) and those that represent the output of the analysis (e.g. suggested method of transport), and computed the flow. By connecting a Rule Manager task to the LLM task, we can then easily check the AI-generated rules.

All the benefits of business rules engines

Rulex Factory is used every day by large-scale supply chains and financial institutions, and these are some of the main advantages pointed out by our clients:

• Improved efficiency:
  Programming business rules into workflows saves lots of time by automating tasks.
• Reduced complexity:
  Business rules are represented in simplified formats (if-then) that do not require coding skills.
• Increased consistency:
  Updates to business rules can be immediately applied without modifying the software code.
• Enhanced compliance:
  It makes it easy for businesses to comply with industry regulations and GDPR.
• Boost business agility:
  Enabling faster changes makes it possible to react more quickly to new opportunities.

Milano, 1 March 2023 – The University of Milano-Bicocca and the tech company Rulex have signed an agreement to strengthen their current research collaboration, extending it to new projects and fields of study, and to open a training partnership.

At the heart of the agreement is the application of Rulex Platform, an advanced data management tool, to the medical field, in particular hepatology. The project contacts are Damiano Verda, Head of Data Science at Rulex, and Alessio Gerussi, lecturer in gastroenterology at Milano-Bicocca University.

The collaboration dates back to 2019 and has already delivered exceptional results in the medical field. The most recent results were published in Liver International, in March 2022, in the article The application of machine learning in primary biliary cholangitis, which was also chosen to be the cover of the issue. This was followed by two publications: Artificial intelligence for precision medicine in autoimmune liver disease in Frontiers in Immunology (November 2022) and LLM-PBC: Logic Learning Machine-Based Explainable Rules Accurately Stratify the Genetic Risk of Primary Biliary Cholangitis in the Journal of Personalized Medicine (September 2022), which involved the collaboration of IRCCS (Humanitas Clinical and Research Center).

Damiano Verda comments on the research: “The work carried out with Milano-Bicocca University until now, which will draw new impetus from the recent signing of the agreement, has allowed us to test the potential of Rulex Platform and of the Logic Learning Machine model in the important and delicate field of medicine. Naturally, we are happy to have obtained important initial results, but we are even more determined to imagine new paths of growth and development, strengthened by what we have learned and will continue to learn”.

Other points of the agreement include extending current combined research to new fields of study, the launch of Industrial PhD programs, and participation in the Rulex University Program. The program offers university students free online training to build data analysis skills, which are increasingly a fundamental requirement in many lines of work, including medical research.

Modern supply chain networks

Modern supply chains are complex networks of resources, people, and companies that are involved in the production and delivery of a product to the customer. From raw materials to the end-product and final buyer, many steps need to be orchestrated within a given time and while coordinating several stakeholders.

In today’s global market, supply chain networks can span across different countries and continents, supplying goods to a multitude of customers. It goes without saying that in such an intricate web of producers, suppliers, and couriers supply chain management is key to ensuring the journey of goods runs according to plan and customers get what they want and when they want it.

Supply chain management

People working in the sector know that managing market demand and constraints (time, human resources, infrastructures, stocks, etc.) can be extremely challenging. If one single link breaks, such as a truck breaking down or an electrical blackout in a warehouse, it affects the entire chain.

But firefighting is actually the condition in which supply chain managers work every day because, even if nothing breaks, there are always new orders or last-minute deliveries that need to be factored into the planning.

The last couple of years has been particularly difficult as global disruptions have to be managed on top of daily smaller disruptions. “Disruptions are the new normal” is what we hear from supply chain experts more and more.

First, the Covid-19 pandemic and, later, the war in Ukraine have caused serious damage to Western logistics, which had to face (and still is facing) several difficulties, from a lack of raw materials to driver shortages and rises in fuel and energy prices. In 2021 alone, supply chain disruptions increased by 88%.

Supply chains are therefore seeking solutions to increase their resilience, enabling them to react to ever-changing circumstances and allowing the continuity of operations and services. That is where supply chain optimization can lend a helping hand.

Supply chain optimization process

A successful supply chain optimization process includes three different stages and a mix of various optimization techniques.

Supply chain design:

The phase involves strategic decisions related to the supply chain’s network design, such as the location of warehouses, the choice of suppliers, and the product flows to and from suppliers and buyers.

Supply chain planning:

The purpose of this phase is to balance demand and supply by creating a well-crafted deployment plan. It includes techniques such as the combination of demand forecasting and inventory management, to best prepare for future market requirements.

Supply chain execution:

This focuses on the day-to-day roll-out of the supply chain plan. It includes activities from tracking inventory levels to executing orders, from picking goods to truck loading and sending shipments.

It’s the execution phase that deals with those everyday challenges and sudden changes we have spoken about before, which require prompt adjustments to the plan. Let’s see an example of a data-driven optimization solution designed for this specific phase.

Transport optimizer: an example of data-driven optimization

Rulex Axellerate is our in-house optimizer for supply chain transportation.

This area of logistics refers to the movement of products from one location to another, involving the orchestration of a variety of places (warehouses, distribution centers, retailer shops, final customers’ doorstep), stakeholders (planners, distribution center staff, couriers), and resources (stocks, pallets, trucks, and other transport methods).

Rulex Axellerate creates the optimum transportation plan for every single day within a required planning horizon, dealing with thousands of shipments and multiple constraints at the same time (vehicle/route limitations; distribution center opening days; available docking bays, etc.). Moreover, Rulex Axellerate can factor in new orders and disruptions, producing brand-new optimum schedules for the current and following days in minutes.

Benefits:

Better and proactive planning means that shipments can be managed more efficiently bringing a series of great benefits:

• Enhanced service levels: reducing delays increases customer satisfaction and brand reputation.
• Increased profits: advanced delivery programs reduce the risk of penalties and allow to negotiate better deals with couriers
• Improved working conditions: avoid planner burnout, by removing stressful manual planning and constant firefighting.
• Reduced fuel costs and C02 emissions: optimizing truck loads makes it possible to use fewer trucks and so less fuel. On this topic see our on-demand webinar “The logistics’ green challenge”.

Choosing the right data-driven optimization software

As we saw, data-driven optimization can be particularly helpful in offering tools for improving the management of supply chain operations. However, how do supply chains choose the software that is most suitable for their business? We have collected some best practices from the several productive discussions we had with our clients in the sector:

1. Focus on business needs

Always choose the technology that can adapt to your supply chain needs. Ask questions about the scalability and flexibility of the solution, as you need a technology that can be quickly customized to the changing circumstances of your supply chain and is able to grow together with you.

2. Value human-centric technology

Disruptive software will not do any good to your supply chain if it is not easy to understand. To achieve real empowerment of managers and planners, supply chains must opt for a technology that is user-friendly and clear to non-technical profiles, with transparent processes and results.

3. Start from top-quality data

Supply chains may have the best technology at their side, but if they feed it with low-quality master data, it will not take them anywhere, if not to fictitious stockouts, distributions, delays, and penalties. Conscious of this issue, we have empowered our clients with tools to improve data quality, including a data-driven solution, Rulex RDC, which can quickly improve the quality of master data, reaching 100% accuracy in minutes.

To know more about data-driven optimization, and to see for yourself how it can benefit your supply chain, visit our supply chain page.

Predicting customer churn using machine learning has proved effective in identifying potential churners and developing successful retention strategies for the financial sector.

The process of digital transformation has opened up great new possibilities for banks and credit institutions. Online and mobile banking make it possible to reach every customer with an electronic device, no matter where they are in the world.

But as the market broadens, competition increases, any digital bank may potentially be a competitor. And since they are no longer obliged to stick to local banks, customers have a huge range of options when deciding where to open a bank account.

Furthermore, if a bank’s portfolio is uncompetitive in terms of offer and price, or its services are run poorly, it is likely to lose customers. This phenomenon is called churn.

Customer churn: facts and stats

In a nutshell, customer churn refers to customers who stop using a company’s products or services within a certain timeframe. If it happens over a short period, from several days to a month, we call it “hard churn”. It is known as “soft churn” if it happens between a couple of months and a year.

Customer churn has become a real struggle for international banks. According to recent stats, the annual attrition rate for banks in North America is about 11%. This means that banks spend considerable amounts of money and energy signing up new customers just to balance their books. Moreover, attracting and winning over new customers is extremely expensive, something like 6 or 7 times more than retaining existing ones.

In the digital era, banks must therefore focus their energies on developing an effective retention strategy to keep as many customers as possible. In this article, we’ll discuss what causes customers to leave and how to calculate churn prediction using machine learning.

Why customer churn happens

Customers may leave their bank for various reasons. Unfortunately, according to 1st Financial Training Services, 96% of unhappy customers don’t complain; 91% of them don’t explain why they are unhappy and simply leave. Based on our clients’ experience, we have drawn up a list of the main reasons why people decide to switch banks.

• Poor service

Good quality service is the basis for any solid business. But some businesses don’t understand how important it is until they start losing customers. A recent study reported that almost 9 in 10 customers abandon a firm because they experience poor customer service.

• Poor product-market fit

In the age of online banking, customers are constantly looking for better options. This means that if a bank can’t offer a good range of innovative, affordable products, not only will it be unlikely to find new customers, it will certainly lose existing ones.

• Slightly off-key product offers

Even when banks have a competitive product portfolio, they may not know their customers well enough and end up offering slightly off-key products, thereby lowering customer engagement.

• Difficult user experiences

Online banking websites that aren’t user-friendly and are difficult to navigate are a real pain for customers. Not to mention mobile banking apps that crash frequently, interrupting money transfers and online payments.

Predicting customer churn using machine learning

In today’s highly competitive market, successful banks are those which fully embrace digital transformation. Not only do they provide digital services and products, but they also use data-driven technology to enhance their decision-making process.

By exploiting the full potential of customer data, banks can better understand client behaviors and learn churn patterns from past records. This allows them to predict their customers’ future movements and respond accordingly.

Let’s see how.

• Managing customer data with advanced analysis tools

Banks need to smarten up their analytics if they want quick, accurate insights on their customers. But how? Spreadsheets alone aren’t enough to prevent banks from losing valuable pieces of information. They are ineffective when big volumes of data, from different sources and in multiple formats, are involved.

Equipped with an effective data analysis tool like Rulex Platform, banks can easily merge all their customer data from a wide range of databases into a single place in the same format. This facilitates data analysis, allowing banks to learn client behaviors and implement an effective retention strategy.

• Tracking churn patterns from historical records

Many different drivers cause customers to leave their banks, and they vary across life stages and demographics. Drawing on a bank’s historical data, machine learning models can generate insight on churn patterns. This helps business experts make predictions on possible future churners.

Rulex’s eXplainable AI (XAI) has proven particularly effective in the financial services sector. Rulex’s XAI quickly analyzes historical records and produces explainable outcomes regarding which customers are more likely to churn and why. On high-confidence predictions, Rulex technology’s success rate is between 90 and 99%.

• Increasing customer satisfaction with AI

Banks need to engage with their customers on a deeper level to improve customer retention. Knowing your clients means, for example, being able to offer them on-key products at the right time or enhance their experience. Once again, AI technologies like Rulex’s XAI can come in handy by suggesting the best ways to improve customer satisfaction.

After pinpointing customers who are likely to churn, Rulex’s XAI proposes corrective actions to prevent them from leaving. For example, for customers dissatisfied with service, Rulex’s XAI will suggest improvements to its quality. The corrective action will be to place customers who contact the support center twice or more into a special priority queue, so their calls are handled more quickly.

By using historical records and applying similar types of corrective action for all their customers, one of our clients was able to reduce churn rate by almost 3%, equal to a revenue increase of 11%.

If you feel it’s time for your bank to tackle the issue of customer churn, visit our Financial services page and get in touch with our experts for a free consultation.

Data-driven technology can help supply chains adopt green logistics by optimizing goods transportation.

As environmental concerns grow amid a general call for sustainability, an increasing number of companies are integrating green practices into their systems. The transportation sector is directly involved in environmental issues, as transport emissions are a major contributor to climate change. Alongside the energy sector and industry as a whole, transportation is in fact responsible for approximately 70% of global emissions.

Of all the different modes of transport responsible for 72% of global transport emissions, road vehicles lie at the heart of the problem. So, changing the way we use road vehicles now could have a hugely positive impact on the planet. If people begin using their cars less and governments invest more in green public transport, how can private businesses contribute to decarbonizing transport?

Taking supply chain logistics as our case study, we’ll show how new technology can make goods transport cleaner, without sacrificing efficiency and speed of service.

Better planning for greener transport

Emissions produced by supply chain transportation are linked to how far products travel and how they get there. Supply chains can really make a difference to the environment by optimizing product transportation. One way to achieve this is by optimizing truck loads to cut down the number of trucks on the road every day. This lightens supply chains’ carbon footprints by minimizing fuel consumption and CO₂ emissions.

In a nutshell, better planning leads to greener transport. But it is not as easy as some might think. People working in the field know that the world of transportation is quite unpredictable. Changes to shipments and priorities, trucks breaking down, heavy road traffic, distribution center closures, a lack of drivers, and so on…  all these small but relentless events can disrupt transportation managers’ plans, affecting green practices and the overall level of service.

How new technology makes a difference

Unpredictability, however, does not mean that an optimized transportation plan cannot be achieved. This is where new technology comes to transportation managers’ aid. Rulex Axellerate is a new, advanced TMS system which identifies the best way to aggregate loads and maximize capacities, minimizing the number of trucks needed for deliveries.

Unlike standard TMS systems, Rulex Axellerate has very short computation times, meaning that it can factor in disruptions and sudden changes. Just received a new order? In a matter of minutes, transportation managers can run a new plan that takes the new order into consideration. So, the best and greenest transportation plan is always guaranteed with Rulex Axellerate, as are substantial cost savings. Having fewer trucks on the road means that supply chains spend less money on fuel. This is particularly important now that petrol prices are skyrocketing, massively increasing transportation costs. Green practices really pay back when supply chains integrate them into their production system.

Building a better working environment

The unpredictability of the transportation and warehousing sectors can make work incredibly stressful for drivers and distribution center staff. Not surprisingly, burnout is an everyday occurrence. According to the U.S. Bureau of Statistics, the number of people quitting the sector has been steadily rising in recent years. This is where new technology like Rulex Axellerate comes in handy once again. An optimized transportation plan can indeed make the job less stressful and increase worker satisfaction.

• No one enjoys driving in traffic, especially if you have to drive for work. As we said before, an optimized transportation plan means having fewer trucks on the road every day. This improves traffic safety and makes driving less stressful for truck drivers.
 
• During peak logistics seasons, distribution centers may turn into chaotic places. New orders can come in at any time, and there may not be enough staff to load and unload trucks, so work can become physically challenging and even dangerous. By counting on a tool like Rulex Axellerate to factor in schedule changes, distribution center managers can organize work shifts better, thereby increasing workplace safety.

Joining the road to sustainability

While transport emissions are expected to grow rapidly, new technology like Rulex Axellerate helps supply chains make transport cleaner, more sustainable, and people-centric by improving the working environment and increasing worker safety. And all this can be done without sacrificing the supply chain service’s efficiency and speed, and while minimizing costs. Want to join the road to sustainability, bringing both green and social benefits to your supply chain? Check out the Rulex Axellerate page, and book a demo.    

As the applications for data-driven technology increase in everyday life, questions around ethical and responsible AI are emerging in the public arena.

Every time we play a song on Spotify, watch a video on YouTube, or order a takeaway online, we leave data footprints. Companies collect and use them to feed AI tools, which empower their business by helping them make better decisions. By using customer data, for example, companies can understand people’s behaviors and run better targeted marketing campaigns.

But “with great power there must also come great responsibility”. When AI-based decisions have a major impact on people’s lives, as in the case of receiving a bank loan or extra medical care, companies have a responsibility towards customers in terms of fairness and transparency. In other words, AI-based decisions should not be biased and, as required under the GDPR, they must be explainable – meaning accompanied by clear explanations.

In fact, if AI has the potential to help organizations make better decisions, why not make them fairer?

This is the question we’ll explore in this article, as we discuss cases of bad and good AI technology.

Discriminating data

AI solutions haven’t always been proven to work for the public good. They have been found to generate biased and unfair decisions in many circumstances. But how can data-driven technology discriminate against people? The reason lies in the principle of machine learning itself. AI systems learn how to make decisions by looking at historical data, so they can perpetuate existing biases. In other words, if the data contain biases, then the output will do too, unless appropriate precautions are taken.

In 2015, Amazon’s AI recruiting tool turned out to be biased against female applicants. It penalized resumes where the word “women” appeared, such as in “women’s chess club captain”. Since the tech industry is historically a male-dominated world, by learning from historical data, Amazon’s tool was preferring male candidates over women.

But that is not the only case. In 2019, an algorithm widely adopted in the U.S. healthcare system was proven to be biased against black people. The algorithm was used to guide health decisions, predicting which patients would benefit from extra medical care. Learning from historical data, the tool perpetuated long-standing racial disparities in medicine; its results favored white patients over black patients.

Whoever designed the aforementioned algorithms did not care about explaining the AI-based decisions, and did not realize the gravity of their mistakes, compromising brand credibility.

Biases can also occur if there is a lack of complete data when building an AI tool. In fact, if data are not complete, they may not be representative, and the tool may therefore include biases. This is exactly what has happened to many facial recognition tools. Because they weren’t built on complete data, the tools encountered issues with recognizing non-white faces. Particularly notorious is the case of the iPhone X, whose facial recognition feature was defined racist because on many occasions it failed to distinguish between Chinese users.

Responsibilizing AI

As more cases of biased AI appear, responsible AI becomes a real necessity. In 2019, the European Union started tackling the problem by publishing a series of guidelines for achieving ethical AI, The Ethics Guidelines for Trustworthy Artificial Intelligence. Major tech companies such as Google and Microsoft have already moved in this direction by releasing their responsible AI manifestos. The road to responsibilizing AI is still long, but every business can play its part. Companies can adopt different approaches to enforce fairness constraints on AI models:

1. FIXING THE ROOT PROBLEM – IMPROVING DATA PREPARATION
   Since most cases of bias in AI are produced by biased historical data, improving the data preparation phase can fix the root problem. In this phase, human operators can identify both clear and hidden discriminatory data, and evaluate if the data are representative of the group taken into consideration.It’s important that these operations are performed by domain experts, as they have a better understanding of the problem. Since business experts might not have a data science background, no-code and simple data preparation tools, like Rulex Platform, become a must.
2. OPENING AI – MAKING OUTPUT EXPLAINABLE
   Adopting eXplainable AI (XAI) over black box AI does make a huge difference. XAI tools produce explainable and transparent outcomes. This means business experts can understand and evaluate the outcomes, and detect and delete possible biases from automated decisions.
   “A good decision could improve your business today, but an explained decision could bring you to better understand and improve your processes in the future”, says Enrico Ferrari, Head of R&D Projects at Rulex. He has worked side-by-side with firms for many years now, striving to innovate their decision-making process.
   “We were working with eXplainable AI when the concept was still unknown to the wider public, creating solutions with a very high level of explainability and transparency like Logic Learning Machine (LLM) – an algorithm that produces outcomes in the form of IF-THEN rules. In 2016, our commitment to explainable technology was recognized by MIT Sloan, which honored us for having one of the most disruptive technologies.”

The path towards ethical and responsible AI may not be easy, but it is vital for companies who want to grow their customers’ trust and safeguard their rights and privacy, avoiding risky gaffes which may affect their credibility.