Fuelled by the rapidly changing environment, the traditional reliance on individual knowledge and expertise is giving way to a new era where analytics and human skills work hand in hand. This is particularly true when it comes to demand forecasting. At the heart of this transformation is the understanding that demand forecasting is not a solitary task, but rather a symphony that requires a harmonious alignment of people, processes and tooling. Achieving maturity means moving beyond the rudimentary methods of the past, embracing sophisticated statistical models, and empowering people with an analytics and collaboration skillset, leveraged by the power of technology. 

Collaborative excellence in demand forecasting 

Cross-functional collaboration between different functions in the company emerges as a key catalyst in this journey. The days of siloed departments operating in isolation are over. Demand forecasting requires a united front, where marketing, sales, and operations converge to share insights and align strategies. Merging the various angles and contributions to enrich a solid baseline forecast, generated by a powerful forecast engine, fosters plan acceptance and plan quality. Adhering to a clear planning drumbeat throughout the organization, to gather and consolidate and forecast inputs, is essential to strengthen and truly embed the right planning behavior. Especially when a strong feedback loop is in place for in-depth review of the added value of each step in the forecast process. 

Transform demand forecasting in supply chain through intuitive tooling 

The shift towards easy-to-use, intuitive tools for capturing market intelligence is pivotal. The era of complex, unwieldy systems is fading, making room for platforms that empower users at every level of expertise. Tools should be configured in such a way that adding the right information can be done at the levels that fit the business specifics. For one case, for example, the customer-product detail level could be needed to capture key account promotional forecasts, while for other events enriching on country-product group level is required. 

Furthermore, tools providing clear demand insights drive effective forecasting. It’s not merely about collecting data; it’s about distilling it into actionable intelligence. The data-savvy organizations of tomorrow are those that can translate raw information into strategic foresight, enabling agile decision-making in a dynamic market. 

Grow and nurture forecasting and planning capabilities  

Reflecting on the people dimension, the future of demand forecasting should not depend on individual knowledge. Having the right mix in place of forecasting and planning knowledge, analytical skills, and collaborative behavior is crucial. Lasting success is about creating an interconnected ecosystem that outlives individual expertise, fostering a culture of continuous improvement and shared learning.  

Companies that keep a good eye on all four pillars can truly reach forecasting excellence: solid data hygiene, clear processes, fit-for-purpose tools, and getting the best out of people. The EyeOn Fast Scan provides organizations with a clear stick in the ground of current forecast maturity and the potential that can be reached. This can be used as a great starting point for taking the right improvement steps on each of these four pillars. 

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Building reliable futures: the vital role of data hygiene

Imagine trying to build a house without a solid foundation – it would be destined for failure. Similarly, forecasting without proper data hygiene is like building on shaky ground. One of the widely used metaphors is ‘shit-in = shit-out’, which quite literally points at the fact that entering bad quality data as an input to your model, will result in bad results.  

Data hygiene refers to the process of ensuring that data is accurate, consistent, and up to date. It involves cleaning, organizing, and validating data to eliminate errors and inconsistencies. Without this foundation, your forecasts are susceptible to inaccuracy, leading to misguided decisions and missed opportunities. The consequences of poor data hygiene are not to be underestimated. Inaccurate data can lead to flawed forecasts, resulting in suboptimal business strategies, overestimation or underestimation of demand, inefficient resource allocation, and possibly frustrated employees (for example sales colleagues might be unhappy with underestimations and therefore lower inventory). Ultimately, this can lead to financial losses, customer dissatisfaction, and damage to your brand’s reputation. 

How data hygiene enhances forecasting accuracy: 

1. Improved Decision-Making: Clean and reliable data allows decision-makers to trust the insights derived from forecasting models. This, in turn, empowers them to make more informed and strategic decisions that align with the organization’s goals.
2. Enhanced Operational Efficiency: Better data quality results in more accurate forecasts, which in turn enable better resource planning, optimizing inventory levels, and reducing the likelihood of stockouts or overstock situations. This efficiency extends to various business processes, contributing to a more streamlined and cost-effective operation.
3. Customer Satisfaction: Understanding customer behavior is at the core of successful forecasting. Clean data ensures a more accurate understanding of customer preferences and trends, allowing businesses to tailor safety stock levels in such a way that customer service level improves drastically.
4. Adaptability to Market Changes: The business landscape is ever-evolving. With accurate data, forecasting models can better adapt to changes in market conditions, consumer behavior, and external factors. This adaptability is crucial for staying ahead of the competition and seizing emerging opportunities.

Investing in data hygiene for precision demand forecasting

A proven method to implement effective data hygiene practices is to ensure that your employees receive proper training, and everyone has an incentive to adhere to standardized validation steps. Also, major steps can be made when investing in a tool that is capable of automating repetitive data extraction and manipulation steps. 

In the era of data-driven decision-making, solid data hygiene is the key to unlocking the next level of forecasting success and a crucial step to implement both basic statistical models and driver-based forecasting. Businesses that prioritize the cleanliness and accuracy of their data are better equipped to navigate uncertainties, adapt to market changes, and make strategic decisions that drive long-term success. As you embark on your forecasting journey, remember that the quality of your predictions is only as good as the quality of your data. Invest in data hygiene today to secure a more prosperous tomorrow.  

Curious about the status of your data hygiene and the potential it offers for forecast optimization? At EyeOn, we’ve developed the Fast Forecast Scan: a quick tool which provides you with rapid insights into the demand characteristics and forecastability of your business. As a first step we perform a thorough deep dive in your data and provide actionable data quality insights. With improved data quality, the Fast Forecast Scan provides you, within a few days, with data-backed insights on the highest possible forecast accuracy that can be reached and identifies the main opportunities for improvement. Witness the transformative power of the Fast Scan through our on-demand demo.

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Navigating the challenges: declining portfolio forecastability 

In the vibrant landscape of forecast and demand planning, portfolio forecastability faces turbulence due to various factors. Market shifts driven by geopolitical events, technological advancements, and unforeseen global crises introduce an element of unpredictability that challenges traditional forecasting methods. Evolving consumer preferences, shaped by trends, cultural shifts, and societal changes, add layers of complexity that demand planners must skillfully navigate. External factors, such as economic fluctuations and supply chain disruptions, amplify the challenge, making accurate demand anticipation increasingly elusive. 

The XYZ analysis, a renowned method of categorizing products based on demand volatility, vividly illustrates increased variability and reduced predictability of sales across nearly all industries in the last 12 months. For a growing part of company portfolios statistical approach is no longer sufficient.  More and more items need meticulous manual planning or the adoption of more agile and adaptive inventory strategies.

The path forward: agile and adaptive forecasting approaches 

In turbulent times, the limitations of relying solely on statistical forecasts become strikingly apparent. While statistical models excel in capturing historical patterns, they may struggle to swiftly adapt to sudden market changes. The assumptions underpinning these models—based on historical data and relatively stable conditions—can falter in the face of rapid changes. 

So, how can demand planners respond effectively to the challenge of declining forecastability in turbulent times? It demands a strategic shift towards agile and adaptive forecasting approaches, incorporating not only historical data but also real-time insights, forward-looking statistical methods, and the ability to swiftly adjust to changing circumstances. An increasing number of companies are dipping their toes into driver-based forecasting, a strategic approach identifying and leveraging key drivers influencing demand.  

This method employs machine learning techniques and artificial intelligence to enhance predictive accuracy. Driver-based approach builds on the statistical model foundation, recognizing that not all products or services are influenced by the same factors. By understanding the specific drivers affecting each portfolio component, demand planners can tailor their forecasting strategies accordingly. 

Driver-based forecasting relies on a detailed analysis of the various factors impacting demand for each product or service. These influencing factors can be categorized into both internal and external aspects. In specific industries, external factors such as weather conditions, economic indicators, and market trends play a significant role. Conversely, in different sectors, internal indicators like contract positions and order books carry more relevance. Incorporating these drivers into the forecasting model enhances the accuracy and relevance of predictions.

Difficulties in adopting driver-based forecasting 

The adoption of driver-based forecasting comes with inherent challenges. Organizations are required to invest substantially, leveraging advanced analytics tools that align with this methodology. Equipping demand planners with the requisite skills and establishing a resilient feedback loop for continuous enhancement are integral components of this implementation. Many supply chain managers grapple with the evaluation of the necessity for such an investment, recognizing the complexities involved in this decision-making process. It’s a decision that involves not only financial considerations but also a comprehensive understanding of the specifics and volatility of the demand dynamics, what statistical forecast still brings and where additional drivers would be required. 

In order to help companies to solve this puzzle, EyeOn came with the Fast Forecast Scan. It is a quick tool to provide rapid insights into demand characteristics and forecastability. The Fast Forecast Scan expedites the identification of improvement opportunities, guiding strategic decision-making on proper forecasting methods, be it statistical or driver-based. The scan unveils the highest possible statistical forecast accuracy for each product in your portfolio, identifying key improvement opportunities in your current forecast setup. Our experienced specialists identify items challenging to forecast with statistical methods and that can have potential for further driver-based analysis. Witness the transformative power of the Fast Scan through our on-demand demo.

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Empowering demand planners in the digital age 

Let’s face it: the job of a demand planner isn’t always the most glamorous or appealing to the younger workforce. For some the typical perception of a demand planner conjures images of someone endlessly crunching spreadsheet numbers in a dimly lit office, removed from the excitement of the market and customer interactions. In this age of rapid technological advancements, how can we free up vital time for the demand planner to interact with various stakeholders? 

One promising approach is to harness the wonders of modern technology and analytics to reduce the tedious aspects of the job. Instead of demand planners getting bogged down in manual number crunching, what if they could spend more time engaging in meaningful conversations with account managers, marketing, and customers? This is where the importance of best-in-class statistical forecasting models comes into play. By leveraging cutting-edge statistical approaches, demand planners can free themselves from the shackles of routine data manipulation, and instead focus their efforts on strategic thinking and dynamic decision-making.

Read our related blog: Declining forecastability: Can your portfolio still be forecasted?

Elevating demand planning through statistical models 

By automating the routine, repetitive tasks of demand planning, we create room for demand planners to add their valuable insights and creativity to the mix. They can build stronger relationships with account managers, gain a deeper understanding of customer needs, and refine the forecast when necessary. The demand planner of the future should be a strategic thinker, a collaborator, and a communicator, rather than just a number cruncher. 

Statistical forecasting isn’t about replacing human judgement; it’s about enhancing it. With the right tools and technology, demand planners can make informed decisions based on robust data-driven insights. Statistical models, equipped with the power of big data, adding machine learning techniques where needed, can provide a solid foundation for forecasts that demand planners can then fine-tune according to their unique domain knowledge, market insights, and experience into the process.

Reshaping the role of the demand planner

The art of demand planning is at a crossroads. It can remain a monotonous job focused on manual data crunching or it can transform into a dynamic, exciting role at the intersection of technology and human ingenuity. The future of demand planning is a blend of human effort and statistical effectiveness, and it’s an exciting path to be on. 

Are you ready to discover how statistics can build a solid foundation for your demand planning as well as bring more value to the role of your demand planners? The Fast Forecast Scan provides you with rapid insights into the main demand characteristics and forecastability of your business. It reveals the highest possible statistical forecast accuracy that can be reached and identifies the main opportunities for improvement. All in just a few days. Watch the on-demand demo of the Fast Scan.

The post Shaking up demand planning with statistical models appeared first on EyeOn.

However, traditional forecasting methods like time series forecasting often fall short of effectively utilizing this data. Planners often have to manually enrich the statistical forecast, which is time-consuming and introduces the risk of human bias.  

Fortunately, machine learning offers a solution by leveraging past and future data to identify demand drivers and enhance demand predictions. But a challenge arises due to the inherent black-box nature of powerful machine learning models. This is where Explainable AI (XAI) methods come into play, offering transparency and transforming the black box into a glass box.

Explainable AI (XAI)

During my research at EyeOn, I explored various Explainable AI (XAI) techniques and their alignment with the stakeholder’s needs in the demand planning process. I discovered that explanations are not one-size-fits-all; they depend on the interaction between the user and the explanation tool. Different stakeholders have unique requirements, which reveals a significant gap between the needs for explanations and the existing algorithm-focused approaches. 

By examining stakeholder needs, I found that model developers seek explanations to improve and refine models, planners require a solid understanding to support their decision-making and persuade others, and general managers seek confirmation of effective model utilization. Recognizing these diverse needs is crucial for successful implementation and acceptance during the planning process. 

Overall, I enjoyed putting these techniques into practice and gaining practical experience. It was nice to uncover each technique’s strengths and weaknesses and better understand different stakeholder’s needs and motivations. With the support of a welcoming and innovative team, I am optimistic about future opportunities and the positive impact we can achieve by leveraging this knowledge and these techniques.

Curious to Learn More? Get in Touch with Our Experts!

If you’re interested in learning more about these findings ,their practical implications or engage in further discussions, feel free to contact one of our experts. Want to know how we use this? Take a look at our driver-based forecasting framework. Together, we can continue advancing the field of explainable demand planning.  

The post Delivering the right message with Explainable AI in machine learning appeared first on EyeOn.

To improve accuracy, one could combine the stability of statistical methods with the flexibility of planners’ enrichments. However, we regularly observe situations where the planner encounters challenges in accurately assessing certain instances, which may lead to a decrease in the overall accuracy of the forecasts. This will leave the organization with too much inventory or result in a disappointed customer who cannot get the desired products.

In previous blogs, we discussed the thesis by Jochem Geurts about decisional guidance in forecasting.  This thesis showed the effectiveness of guidance towards better forecasting performance in an experimental setting.   

Providing planners with better support in decision-making

Within the thesis project of Loek Eggels, a master student in “Operations Management and Logistics” at the Eindhoven University of Technology, we researched the indicators leading to the added value of forecast enrichments and explored effective means of appropriately notifying the planners to provide better support in decision making.

The starting point of the thesis are numerous papers in the field of Behavioural Operations Management over the last 30 years. The literature has studied a large number of planner enrichments with mixed results to their effectiveness. Just like every other human, it has often been proven that planners are biased and do not operate like fully rational decision-makers. Recently, research has also been aimed at identifying important features that indicate the likelihood of an enrichment.  

In his thesis, Loek tries to answer the following research question: “What features are of great importance to the quality of forecast enrichments?” He answers this by analysing a large dataset with planner’s enrichments. The factors that focused on are the size and direction of the enrichment itself, the planner’s previous behaviour, the product category, and the time at which such an enrichment is executed.

This thesis uses an anonymized dataset from one of our customers to test and quantify the results. Advanced machine learning (LightGBM) and model explainability (SHAP) techniques are used to identify important features.  We discovered that features directly tied to the enrichment – its size, statistically forecasted quantity, and past enrichment performance – are critical. For example, an enrichment that reduces the forecast compared to the statistical forecast is more likely to be accurate. Nonetheless, the product category and planners’ biases also have a significant impact on the accuracy of an enrichment.

Practical implications of notifying planners

Based on these features, we can explore the practical implications for customers.  More specifically, we investigated when to notify a planner about a potential bad enrichment. There are a few guidelines one should adhere to in effectively alerting planners. All of us get bombarded by notifications on our phones throughout the day. When you receive many of them, you do not value their importance. The same holds for alerts during forecasting tasks. If you receive too many of them, you will discard them. 

Therefore, you would like to notify the planner when you are sure that the enrichment will be very inaccurate. Higher performance could be achieved based on the planners’ ability to incorporate the advice of these alerts. However, planners could at least reset the enrichment to the statistical forecast to maintain the forecast accuracy.

We can show the planner why the enrichment is predicted to be inaccurate, based on the Shapley values. The figure above shows the reasoning of the machine learning model behind a notification. It is triggered by the value of the enrichment size, the planner’s biases, and the hierarchy level at which the enrichment is executed.  This explanation should give planners the option to re-evaluate this enrichment and investigate how they can improve it. Ideally, planners can transform this advice into appropriate improvements.  When looking further, effective utilization of the feedback could create even better results.   

These findings underline the significant potential for enhancing forecast accuracy by preventing a small number of detrimental forecasts. The results open the door to more informed decision-making, reduced inventory costs, and more accurate forecasting.

Curious to Learn More? Get in Touch with Our Experts!

If you would like to know how our approach can improve the enrichment process in your company, please reach out to Loek Eggels, Dan Roozemond or Bregje van der Staak.

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Meta-learning will better capture your demand pattern

A straightforward and robust way of matching a statistical forecasting method to a series of historical demand values is to simply try them all and select the statistical forecasting model which achieved the best forecast accuracy during a rolling forecast. However, the main downside of this approach is that this requires a significant number of computations. In addition, the information used to select the statistical model is confined to the data of that single demand pattern.

What if we could somehow store the decision for one statistical model over all the others for every demand pattern? Meta-learning offers a way to do just that. Meta-learning in demand forecasting is a method that uses descriptive statistics of a demand pattern to match the way the pattern ‘looks like’, to a statistical model that offers a high accuracy and low bias forecast. Which comes down to a multi-class classification problem within the field of machine learning.

Assigning statistical forecasting model to a time series

We can utilize a two-step machine learning classification approach of assigning a statistical forecasting model to a time series. By first encoding a time series into context-independent descriptive statistics, we can train a decision tree classifier to assign a statistical forecasting model. In the second step, a multitask neural network handles the conditional question of which parameters the assigned model should use. Once a training set of observations has been labeled, both classifiers can be trained. Assigning a statistical forecasting model to a new time series comes down to calculating its descriptive statistics and allowing the classifiers to recognize the best-fitting model plus parameters.

One of the benefits of meta-learning is the reduced number of computations to be performed in exhaustively searching the performance during a rolling forecast test run each period. In addition, an increase in forecast accuracy is anticipated on very variable demand patterns, such as strongly intermittent demand. These two benefits are especially relevant today, where the ability to accumulate data and have access to an increasing number of demand patterns, increases the number of forecasts to be generated.

Staying ahead in the demand forecasting game

To stay ahead in the practice of demand forecasting, EyeOn continuously explores new techniques to further increase the value we can offer our customers. Recently our intern Robert Duijfjes explored the possibilities and added value that meta-learning can offer in demand forecasting across industries. He concluded that the added value is mostly visible in extremely volatile demand patterns, which is exactly the part of the portfolio where customers often encounter challenges. Applying meta-learning to these cases results in more focus and therefore a major time-save for the customer because the demand planners only need to perform forecast enrichments on a smaller part of the portfolio.

Next to meta-learning, we are exploring other options to improve your forecast performance. With these innovative methods we will be able to achieve improved forecast accuracy and bias, whilst handling larger portfolios.

To make sure our clients can benefit most from these new innovations, we’ve developed the Fast Forecast Scan: a quick tool that provides you with rapid insights into the demand characteristics and forecastability of your business. As a first step, we perform a thorough deep-dive into your data and provide actionable data quality insights. With improved data quality, the Fast Forecast Scan provides you, within a few days, with data-backed insights on the highest possible forecast accuracy that can be reached and identifies the main opportunities for improvement. Learn how the Fast Scan can help you reveal your forecast optimization potential.

Curious to hear more about meta-learning or other forecasting innovations? Reach out to our expert Rijk van der Meulen!

The post How can SKU time series improve forecasting accuracy with meta-learning? appeared first on EyeOn.

How to improve forecast accuracy: decide which products need human intervention

At EyeOn, we use the ABC/XYZ classification as guidance for planners to decide which products to focus on. Products are classified based on their volatility, from ‘stable demand’ to ‘high volatile demand’, and on their importance for the total turnover, for example ‘A products’ are important, whereas ‘C products’ are less important.

This ABC/XYZ classification results in nine quadrants, which are classified in terms of the human involvement needed.

• For those products that are important in terms of turnover and relatively easy to forecast (light green), a human planner can review the statistical forecast provided.
• For products that are not very important in terms of turnover, or are stable and medium important (dark green), we recommend using statistical forecasting only. Human involvement is not recommended for these products.
• Lastly, for products that are important for the turnover and hard to forecast (dark purple), we recommend performing manual forecasting. Human planners can focus on these products to increase forecasting accuracy. The same holds for the products that are new to the market (NPI) or in their end-of-life (EOL).

Enable planners to focus and make future adjustments smarter

By automating the forecast for most of your products, you enable the planners to focus on key products where their expertise is most needed and has the highest impact. These focused adjustments performed by the planners can be used to see where planners add value and can be analyzed and used to make future adjustments even smarter! Which, in the end, will give a significant boost to your forecast accuracy.

The road towards smart-touch forecasting

Do you want to learn more about this topic? Don’t miss our blog series Towards smart-touch forecasting, where we dive into the four building blocks to pave your road toward advanced smart-touch forecasting using cognitive insights.

Are you ready to discover how smart-touch forecasting can build a solid foundation for your demand planning as well as bring more value to the role of your demand planners? The Fast Forecast Scan provides you with rapid insights into the main demand characteristics and forecastability of your business. It reveals the highest possible statistical forecast accuracy that can be reached and identifies the main opportunities for improvement. All in just a few days. Watch the on-demand demo of the Fast Scan.

Need personalized advice? Reach out to our expert Bregje van der Staak!

The post How to improve forecast accuracy with smart-touch forecasting appeared first on EyeOn.

1. Re-instate statistical forecasting

The key assumption for statistical forecasting that “the past is a good predictor for the future” doesn’t hold anymore, which presents a new challenge in forecasting and demand management: Can we still generate a meaningful statistical forecast in our planning system, if past demand patterns are heavily disrupted? The answer is yes, and here are two methods how: 

Tetris correction  

This technique consciously fills plummeted sales gaps in the disrupted period to recover the demand pattern. It can also be used in case sales peaked exceptionally. However, the resulting statistical forecast might not be fully usable if it is expected that demand will again behave in a disrupted way in the future. To still generate a proper baseline in this case, that only needs limited manual enrichment, another method is available in our toolkit:  

The Rubber Duck 

This method applies future ‘disruption and recovery curves’ on the right aggregation level (e.g. country-product family), to reflect expected demand disruption throughout the selected portfolio scope. 

2. Optimize your working capital

Companies today are facing tremendous inventory challenges, balancing between controlling the stocks and maintaining their customer service levels; demand patterns have changed both up and down; inventory levels were based on the past stable situation. It is vitally important to bring focus and create a plan to cope with the challenges. What can you as a company do first to get the fast-working results without costly long-time efforts? 

The answer is simple – create insights in your main inventory, service level, and cost drivers. It is impossible to optimize your parameters without knowing the as-is situation. Use the insights to define the most effective prevention and counter measures. As a next step, restore supply chain balance and enhance the quick wins with smart analytics. Only then start regularly updating your inventory parameters based on the latest trends in demand and supply, leveraging the knowledge of the planners. Monitor the progress and the results to keep your settings optimized for the future.

3. Use your IBP process to manage the dynamics

Current business reality has taught us that being able to deal with dynamics is pivotal to be successful. A well-functioning IBP process is critical to reducing the latency in responding to events happening in the market and allows you to make high-quality decisions. IBP is a cross-functional business process involving finance, sales, marketing, and supply chain. Situations change fast. Grab new opportunities and have an agile response to the unexpected.   

Bring your IBP process to its full potential and keep evolving in line with business needs. How to get started:  

1. Assess & benchmark your current maturity 
2. Design & implement a fit for purpose IBP process 
3. Coach stakeholders on various levels in the organization 

4. Re-design your supply chain

Global supply chains with single sourcing have become a common set-up for competitive reasons over the last decades for many companies. Three main forces drive reconsideration of this set-up and the need to re-assess your supply chain configuration:  

1. The Covid crisis, with lockdowns, the closing of markets shows the vulnerability of global set-ups.
2. The growing politicization and uncertainty in free trade require more resilient supply chains.
3. The need to consider the environmental impact of the current supply chains. In the circular economy, shorter supply chains with integrated return flows are more sustainable.

Supply chain redesign starts with translating the business strategy into supply chain requirements. Consider building a digital model of the as-is supply chain (“digital twin”). This can be used for the optimization and evaluation of scenarios on various criteria like costs, service, environmental impact, and risks. Uncertainty asks for more frequent evaluation of the supply chain configuration. Leverage the developed digital supply chain model and build your capability. 

5. Accelerate the digital transformation

Digital technologies have changed our personal lives via WhatsApp, Instagram, Airbnb, Uber, and Spotify. This change has also a large impact on your value chain. Considerable investments are being made to enable the digital transformation in the planning domain, companies are taking big steps towards increasing the level of automation of their planning processes. Getting digital holds the promise of efficiency of tasks that once required substantial time and human effort. It also involves improving the quality of forecasts, plans, and decisions through mining large amounts of data to discover new insights that were previously inaccessible.   

Advancements need to be made in collecting and engineering data, implementing new tools that allow for more advanced analytics, preparing your organization, and building a data-driven culture. Start with developing a vision, select a business process to work on, and take it from there by running projects to explore benefits and learn data science tools that go beyond the existing planning tools, build capabilities, KPIs and data. 

Readjust your planning and forecasting to a post-pandemic world, start by getting in touch with our experts! 

What about the supply chain challenges in 2022? We got you covered!

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