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. 

The post Next level demand forecasting in supply chain: Balancing key pillars appeared first on EyeOn.

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.

The post The next level of demand forecasting: why solid data hygiene is the key to success appeared first on EyeOn.

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.

The post Declining forecastability: Can your portfolio still be forecasted? appeared first on EyeOn.

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.

2-minute read 

Today’s reality: Short-term flexibility does not do the trick anymore

In today’s reality the logistics industry faces significant challenges from both the demand and supply planning side. For example, global disruptions cause bullwhip effects with higher peaks and dips in warehousing demand, while at the same time it is getting more difficult to find the right personnel when these peaks arise. Due to increasing inbound-outbound imbalances, occupancy has structurally increased and it is harder to find additional storage space. Consequently, logistics warehousing operations cannot be executed as efficiently as in the past. Some logistics companies now even confront their customers with additional ‘occupancy surcharges’ when occupancy exceeds certain levels.

Fire prevention: Bring proven forecasting and planning best practices to the logistics industry

The logistics industry seems to be in a similar situation as many of their manufacturing and retail customers were in few decades ago: facing a clear need to embrace forecasting and planning. High-quality forecasting and logistics planning can absorb the imminent demand and supply pressures without the need for heavy capacity investments or uncontrolled short-term costs. Simply by preventing short-term issues before they arise. For example, securing enough additional flex workers for a foreseen peak in demand, or freeing up storage space prior to an expected increase of overall stock levels.

The good news is that it does not have to take decades to make a step up in this area. Logistics can profit from the proven forecasting and planning best practices that have been developed for their customers throughout the years. EyeOn Planning Services has bundled these best practices and related data science capabilities in recurrent services for its customers. This allows logistics companies to immediately harvest the benefits, while following a steep learning curve on how to effectively acquire actionable insights in ‘what happened’ and how to accurately forecast and plan ‘what is coming’.

Logistics planning Leaders vs laggards

Frontrunner logistics companies recognize the need to absorb best-practices in forecasting and planning. They see that now is the moment to plan further ahead and get ready for the years to come. By doing so these companies will experience more control and efficiency, ensuring a smooth operational process on the short-term, and preventing the need for costly firefighting.

This will give them a clear competitive advantage compared to industry peers sticking to old habits!

Ready to boost your fire prevention? EyeOn Planning Services can help you. Contact us now!

The post Stop firefighting in warehouse logistics planning appeared first on EyeOn.

Today’s increasingly volatile market requires dynamic and accurate forecasting, but good demand planners are hard to find, and even harder to keep! Planners are rethinking what meaningful work looks like for them – and wading through thousands of line items each forecast cycle to produce reliable forecast numbers, often does not fit that bill. Machine learning can help.

There is an abundance of data available today, but the sheer volume of it makes for days of processing effort, or in practice, just accepting that a lot of your data is under-utilized, especially in forecasting. But what if you could truly leverage the predictive value of your data to generate high-quality forecasting outcomes quickly? What if your planners could instead spend their time focusing on enriching only a limited number of forecast exceptions while closely collaborating with business?

In this webinar we show how machine learning can use the relevant demand drivers for your business and learn from their historical data to enhance the predictive power of your forecast. Creating a value-adding machine learning model is challenging, but we explain how our approach to get started instantly using Planning Services gradually builds up your forecasting capabilities. We share real-life cases on how our approach has proven to be successful in practice.

Below is a snippet from the webinar. Watch the complete on-demand webinar here!

Watch the complete on-demand webinar!

The post On-demand webinar: Leverage your data with driver-based forecasting appeared first on EyeOn.

Data is everywhere in today’s world. Every single event in your supply chain generates data, and the opportunities to utilize all that data seem endless. At the same time, there are many tools and technologies out there claiming to solve all your data problems. No wonder many professionals feel overwhelmed!

Which key opportunity can you unlock by using your data better? Reducing latency! Quicker data collection, better dashboards, and suitable tools help you get the right information on the right person’s desk at the right time – effectively reducing the delay from event to decision, and thereby dramatically increasing the impact of your decisions. Machine learning can help.

Results from a benchmark conducted by EyeOn in 2021 show that:

1. companies are most eager to start applying machine learning in the area of demand forecasting
2. only 10% are actually doing this at that moment.

While the goal for each company is straightforward – trying to improve forecast performance with less effort spent by demand planning, sales, and marketing altogether – there is no one-size-fits-all solution that can do the job.

Do you want to improve your forecast performance and accelerate decision-making in the supply chain? Watch our video on driver-based forecasting!

What is driver-based forecasting?

Let’s start from the core. Why do companies want to apply machine learning? This is because traditional forecasting methods, such as time series forecasting, do not take into account impactful demand drivers. In many industries, there are additional drivers that have a far greater effect on demand than for example seasonality.

In machine learning models we incorporate data that relates to these demand drivers, with the aim to learn from their impact in the past in order to reflect this in the forecast. Depending on the industry, internal or external (market) drivers are more relevant to the forecast. In general, internal drivers often have a focused impact on forecasting, such as planning a promotion for a certain range of products at a specific customer or introducing a new product to your biggest customer.

Creating a value-adding machine learning model takes effort and time but can really have a big impact on your forecasting performance. Two of our projects have shown that by applying driver-based forecasting the final forecast (including all manual input from sales and marketing) can be improved by even 12%pt of forecast accuracy and can bring bias within the 2% bandwidth. Another big advantage is that since the most important known demand drivers are already taken into account, only exceptional changes are needed to come to a final forecast.

How to start applying machine learning in forecasting?

As mentioned in the introduction, it is not easy to start applying machine learning in demand forecasting in a proper way. Tooling and knowledge are the biggest hurdles that companies face.

IT departments often have a long-term IT roadmap and need to see how applying machine learning in demand planning would fit in there. On the other side people currently involved in the demand planning process often do not have the technical capabilities to build a small proof-of-concept themselves.

Surprisingly, most companies do not think that the quality and availability of their data should be a roadblock. Let’s take the example of a company, with access to suitable tooling, people with the right skill set, and high-quality data available, then still there is another hurdle to overcome and that is the question of what it will bring? Preparing a proper business case is crucial.

Luckily, that is something where EyeOn can be of help. We can build a proof-of-concept in a very short amount of time. Our data scientists build and apply machine learning models in a very pragmatic and robust way. The proof-of-concept offers you:

• Quick insights into the value of demand drivers on the forecastability of your business
• The maximum forecast performance that can be reached by using driver-based forecasting
• A quantified improvement potential versus current forecasting methods (in efficiency and effectiveness)

The proof-of-concept shows great potential and you would like to grasp the benefits immediately? Our Planning Services team is ready to build and operate your driver-based forecasting process from the start. In the meantime you can work on getting tooling and knowledge to the required level, so we can transfer the operation back to your internal organization.

In this video we further explain the EyeOn driver-based forecasting proof-of-concept:

We are here for you!

Do you want to improve your forecast performance? At EyeOn, 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. Witness the transformative power of the Fast Scan through our on-demand demo.

You prefer to speak to one of our specialists? Please reach out to us and see how driver-based forecasting can be of added value to your organization: Contact Erik de Vos, Willem Gerbecks, or André Vriens!

The post How to make a next step in forecasting with machine learning appeared first on EyeOn.

EyeOn offers a framework to leverage the full potential of your demand planners. In this webinar we presented how our framework can guide your demand planners.

Watch the webinar recording below to learn more, or contact us directly!

The post Demand planning 2.0 – Take control of your role! appeared first on EyeOn.

A backorder is an order for a good or service that cannot be filled at the current time due to a lack of available supply. The item may not be held in the company’s available inventory but could still be in production, or the company may need to manufacture more of that product.

Advantages of having backorders

• It reduces costs: As a beginner retailer, you should avoid overstocking products as they are not direct cash. For instance, by setting a target service level of 80% it is clear that 20% of demand will be intentionally back-ordered in order to save on inventory expenses. When focusing on back-ordering there is no need to pay for any extra storage space, because whatever is getting in, consumers have already bought it and this directly cuts costs.

• It shows the customer pattern: Back-ordering tells us a lot about consumer behavior and how they perceive the product. When customers continue to submit orders even though products are out-of-stock, you can look into the statistics and analyze how much stock you need in the future.

Disadvantages of having backorders

However, back-ordering can bring some disadvantages with it. In the picture below it is shown that customers have two options when their order cannot be immediately satisfied:

Not having enough products in stock can cause:

• Loss of competitive edge: Your loyal customers can decide to buy from your competitors because they have the product in inventory, while you are out of stock.
• Customer frustration: Customers may decide to wait until you deliver them the right product or they might get a similar product that you have in stock. But, they will leave with disappointment and frustration.

Here are a few steps to follow to reduce your backorders and prevent losing customer loyalty and revenue:

1. Get real-time data on your inventory levels: The first step to avoid backorders is to have a system that accurately tracks warehouse inventory levels and generates real-time alerts. You should discover the inventory run-out with no delay.
2. Forecast your demand: Collecting data on your historical sales and forecasting future demand helps to keep the right amount of products in your inventory.
3. Create a list of potential reasons for backorders: Investigate “why” there could be a backorder in your system. The reasons could be for example:
   a. Supply capacity: The company does not have enough capacity to internally/externally supply that product.
   b. Quality issue: There’s a product or raw material issue that prevents the order from being satisfied.
   c. Logistics: The demand is unsatisfied due to transportation issues.
   d. Obsolete product: The customer ordered a product that is not being produced anymore.
   The list of what elements can create backorders might differ according to the type of business.
4. Create insights: Once you have accurate data and each backorder is assigned to a reason code it’s time to create insights and break down the problem. Creating visuals is a guide to finding and solving the weaknesses that are causing backorders. Additionally, it is a way to monitor how the backorder trend is progressing in time.

In supply chain management it is hard to imagine how you can be effective at inventory management and customer satisfaction without a strong grasp of your underlying backorder KPIs. A dashboard provides an at-a-glance window into your business performance and enables you to manage all those orders of out-of-stock products. Learn more about dashboarding!

Curious how we turn your data into actionable insights? Our powerful visualizations will turn your data into actionable insights that facilitate both planners and managers to focus and make high-quality decisions, faster. Do you make data-driven decisions with confidence? If not (yet), please get in touch with our specialists to see how we can support you.

The post What is a backorder and how can it be reduced? appeared first on EyeOn.

The solution to this increasing level of uncertainty is robust scenario analysis. The EyeOn Planning Services Team helps our customer from the chemical industry with creating different demand scenarios. By combining customer’s business input, we succeeded to map the effect of potential demand changes on safety stock levels and interpreted inventory performance. Offering thus to our customer, a road map of how inventory should be managed according to each demand scenario.

The urgency agile responses is evident, however towards which direction? This is exactly where EyeOn Planning Services can make an impact; for more information explore the Planning Services website, or contact our colleague Bohdana Shumanska.

The post Creating different demand scenarios appeared first on EyeOn.