3-minute read 

How do we get to accurate forecasting? The truth is simple: Our forecast should be a reflection of the plans we develop and the assumptions we make on our demand drivers. In reality, creating a plan is one thing, while making a demand-driver-based forecast is another thing.

In a poll, we asked our network what you find the most challenging in making a realistic forecast. The response was clear. You struggle to define your sales plans and the assumptions on your demand drivers. And when you manage that, turning the agreed plans into a forecast is the second hurdle.

Because creating the plan and the forecast is already challenging, actually bringing focus to where you can improve and to learning from your performance are the least of your worries.

So, how do you get to accurate forecasting then? You will need all 4 elements to drive change. Let’s start with the most pressing challenge:

Defining your plans and assumptions

Every business will have a few big drivers of demand based on which you can make assumptions for the future. When an organization focusses on assumptions first, the reasoning that leads to  the forecast immediately gets less biased. To get there, we advise the following steps:

1. A version of an S&OP process is a prerequisite to ensure the decisions on plans and assumptions can take place.
2. A pragmatic solution to capture plans and assumptions is imperative to support your S&OP decision making.

With a focused process and a pragmatic solution to support it, you have the necessary foundation in place.

Turning your plans into an accurate forecast

Once you have the plans in place, how can you avoid that the forecast ends up simply being a copy of the past? The more focus you put on qualitative plans and assumptions, the less time you actually need to spend on creating a forecast. With techniques like driver-based forecasting the machine delivers the forecast for you. A machine learning forecasting technique incorporates decisive aspects such as your sales data, and past and future information about your demand drivers, into a forecast. At EyeOn we currently have multiple customer cases where the machine generated forecasts outperform the manual forecasts with a substantial workload reduction.

Spotting vital review areas

But is the machine always the better option? We recommend smart-touch planning. The best forecasts are the result of a machine doing the heavy lifting and planners stepping in where their expertise is needed. With the right insights, the planners instantly spot where they can make a difference. Eventually, the more the machine learns about the products, customer, and drivers, the less intervention is needed. Want some more inspiration on this, stay tuned for the launch of our EyeOn Promo app which offers actionable insights on your promotional plan based on machine learning.

Accurate forecasting: Learning from performance

Finally, planning and forecasting is not an exact science, but there must be more science to it than covered by manual forecasting. And like for every science, learning is imperative.

On the one hand, the machine learns from every single data point in combination with your qualitative plans and assumptions in a way no planner could match that.

On the other hand, also the planners need to learn. They should learn about how the machine delivers the forecast. We believe, a forecasting set-up should have machine learning elements at its core, but always in combination with explainability functionality. Next to the technical learning, the planners should also understand how they contributed to the forecast. You can create this awareness by bringing in concepts like cognitive insights. As soon as you adopt a mindset of continuous learning, forecasting isn’t a challenge anymore, but rather a way to unlock potential and achieve success.

Are you ready to take the steps towards a more realistic forecast? Please feel free to contact our expert Erik De Vos to discuss your situation and explore solutions together.

The post What is your biggest challenge in accurate forecasting? appeared first on EyeOn.

4-min read

Whether it is a knee-jerk reaction to the pandemic or just the result of reaching the tipping point of next-generation thinking, professionals are now asking deeper questions about the added-value of their jobs and contributions. How they spend their working hours is a big portion of that reflection. It has become increasingly important to find purpose in our work.

This is particularly true for the younger generation of employees who now make up 13% of the workforce. Colloquially known as Gen Z, Generation Z is defined as the demographic group born in the late 1990s to early 2010s. They are expected to make up one-third of the workforce by the end of this decade. They are ambitious, brimming with opinions and ideas, and care about different things than those who have come before them.

You may argue that our own forefathers said the same thing; however the accelerated pace of technological change has made the generational divide more marked. This group are entering the workforce with a natural appreciation of rapidly advancing technologies and an innate expectation to be able to put them to good use. In an overheated supply chain recruitment market, with high costs of onboarding and knowledge drain, managing the needs of these supply chain professionals is key to business success.

Closing the gap of supply chain professionals

A key characteristic of Gen Z is that they are noticeably more insistent on protecting their own boundaries and achieving alignment between their work life and their own value system. This can feel alien to previous generations. Like any good S&OP/IBP cycle, achieving success starts with figuring out how to close the gap.

Left unaddressed, Gen Z’s protection of their boundaries and defense of their moral code can quickly descend into a frustrated work environment and a speedier tendency to seek alternative employment. Showing your willingness to invest in upskilling and to adopt innovative practices to close the gap, is essential to keep young talent on board and engaged.

Define excellent performance

To manage Gen Z supply chain professionals, we must recognize their individual talents and respond to their needs. They are even more concerned with academic performance and job prospects than previous generations. They seek clarity on what to prioritize to deliver excellent performance.

To achieve maximum results, we need to acknowledge that their life programming has resulted in the expectation of a high degree of personal choice. To respect this, but still achieve the desired progress, we must offer:

• A clearly articulated end goal
• A list of any hard constraints to be observed en route, and
• A high level of freedom in how to get there

In granting this freedom, we must also realize that the responsibility to deliver on those expectations can feel like a heavy weight to a less experienced employee. Dashboarding to facilitate prioritization setting and to act as a safety net, for both parties, can help.

Clarify where to focus

As the only generation in our workforce to grow up in a social-media-fed world, where new stimulus comes every 10 seconds, you could be forgiven for thinking that Gen Z would be adept at handling a higher-paced environment.

However, the sheer volume of data available with limited time to process it, exacerbates pressure during a demand planning cycle. The responsibility to make intelligent decisions, against the backdrop of limited capability building and an abundance of data to wade through, introduces a high risk of data paralysis or burnout.

Executing non-core processes such as slogging through masses of data to support decision-making is unattractive work for a typical Gen Zer. They are digital natives, so they know that functionality such as cognitive planning and planning parameter automation exist, which could be doing a lot of this work, and so feel denigrated to have to manually do such work, time and time again. They would much prefer to focus on the value-added activities that rely on the human brain, such as critical decision-making or driving new product adoption.

Removing the grunt work, and supporting Gen Z with understanding your business priorities and providing clarity on where to focus is the number one gift you can give them in this volatile world. At EyeOn Planning Services we offer just that.

Accessible data science and capability building

At EyeOn we have developed our own Honeycomb platform, harnessing the power of cutting-edge data science techniques and putting them to work for our customers modelling industry best practices gathered from years of experience in multiple domains.

Within EyeOn Planning Services, our experts leverage this platform to execute advanced analytics for your critical planning processes, while supporting capability building and continuous improvement efforts. We highlight top priorities for attention in the subsequent planning cycle and offer reliable, secure, affordable services to do the data crunching for your team. Years of happy customers have reported higher forecast accuracy, an unbiased forecast, optimized inventory levels and higher customer service.

Win-Win

Our team of talented Planning Service Experts finds their purpose in delivering time-sensitive value adding insights and in driving years ahead innovation in our field.

By outsourcing to the experts, you are future-proofing your processes and relieving your own team of duties that cost them energy. That time and energy can be put to much better use, focusing on more purposeful work and boosting key value drivers for your business, such as new product introductions or promotions.

Now doesn’t that sound like a win-win gap-closing initiative? Reach out to our expert Sinead Counet to learn more or meet us at one of our upcoming events!

Remember to keep an eye on this page where we will address further supply chain challenges you will be facing this year.

The post Closing the gap: Retaining Gen Z professionals while meeting your supply chain objectives appeared first on EyeOn.

5-min read

In the Collins English Dictionary ‘Words of the Year’ list 2022, ‘permacrisis’ is at the top. Defined as: “an extended period of instability and insecurity, esp. one resulting from a series of catastrophic events”, it is an ugly reminder that accurately encapsulates today’s world as 2023 dawns.

The invasion of Ukraine has led to the biggest war in Europe since 1945 and the most serious risk of nuclear escalation since the Cold War. High inflation in many parts of the world, fuelled by soaring food and energy costs is posing the biggest macroeconomic challenge in the modern era of central banking. Fundamental pillars of the post-WW2 world order, especially in Europe – inviolable borders, restraint in threats with a nuclear arsenal, controlled inflation, and plentiful energy supplies – assumptions that have held for decades, have all been simultaneously shaken.

The combination of three shock dimensions has made this crisis profound. Dimension one is geopolitical and has roots in the rift on two fronts: the challenging of the post-WW2 world order by President Putin and the increasing divergence between the USA and China. The resolve with which Europe and the USA have responded to the Ukraine invasion has indeed widened the gap between ‘the West’ and the rest of the world.

The second dimension is the commodity shock as a result of the Russian invasion of Ukraine. The resulting sharpest energy price increase since the 1970s has led to a warp-speed reshaping of the global energy system. This is further compounded by Ukraine’s importance as an exporter of agricultural commodities, leaving many supply lines at peril with supply chain shortages looming.

The loss of macroeconomic stability marks the third dimension of this crisis. Demand fuelled by stimulus packages met with post-pandemic supply chain constraints and shortages, resulting in accelerating consumer prices. This dramatically worsened with spiking energy prices. Even the most drastic and broadest interest rate increases undertaken in the last 40 years by central banks, have not yet secured price stability. Inflation remains high, still uncomfortably close to double-digit numbers in many countries.

Should we expect supply chain shortages in 2023?

Fundamentally it will all depend on how these three dimensions, geopolitics, energy and commodity prices, and macroeconomic stability will evolve and affect each other. In the short term, the situation looks rather grim. Many areas in the world will be facing a recession in 2023. Economic difficulties in turn could exacerbate geopolitical risks. This is particularly prevalent in Europe. So far European governments have protected consumers with massive subsidies and price caps from the worst of the energy-price shock. The biggest geopolitical risk is Russia, unable to succeed on the battlefield it might try harder to exploit energy supply and cost vulnerabilities as many European economies are already on the edge of recession.

Overall macroeconomic stability remains the other major challenge in 2023. Global GDP growth is predicted to slow to 1.6% in 2023 from 2.8% in 2022. However global inflation will likely remain high at 6%. This will maintain pressure on further interest rate increases to curb inflation. The higher interest rates needed to dampen inflation will further sap consumer spending and impact increasing unemployment.

China in contrast will likely maintain a loose monetary regime with low-interest rates, seeking to stimulate GDP growth and boost trade. Nonetheless, China is entering 2023 enfeebled by policy mistakes (Covid-19) and a festering property crisis. As we have seen during the Covid-pandemic in 2021 a stuttering China will impact worldwide supply chains.

At the start of 2023, the US maintains a fundamentally stronger economic position in comparison to China and Europe. Although curbing inflation remains in focus and a mild recession is on the horizon, being a big energy producer, the country has benefited from the soaring energy prices globally.

Is there room for optimism?

There is some good news amidst today’s turmoil. Some countries and industries will prosper even in these difficult times. Resource-rich countries will continue to benefit from high commodity and energy prices. Concurrently the energy shock will accelerate the shift to renewables further. The IEA – International Energy Agency – is calling it “a turning point in the history of energy” that will quicken the clean-energy transition.

Simultaneously, the present situation will also advocate greater realism about the continuing role of fossil fuels. In particular, the role of natural gas as a bridge fuel in the energy transition is under scrutiny. Long-standing hypocrisies need to be confronted on a multilateral political level. Every crisis spawns new possibilities and the result, if tackled diligently, could be a global energy system that is more secure, greener, and diverse.

Turning chaos into opportunities: Are you ready for the challenge?

As highlighted above, a multitude of disturbances could impact your supply chain in 2023 and might result in supply chain shortages. Building a more robust supply chain environment seems to be a true panacea. Therein lies the question of “How to build a more resilient supply chain for your business in 2023?”.

Corporations on their part should embrace opportunities that will present themselves even in these difficult times. Transitioning from a reactive post-pandemic modus operandi to a proactive outlook-adjusted approach is the way forward.

As industries are rebalancing their supply chains in 2023, the art will be not to overreact when any of the above shock dimensions impact your business. Remember the long-term view, keeping strategic goals and fundamental trends in mind when making decisions. With proven expertise in navigating the challenges of global supply chain management, EyeOn advocates two ways of embracing the volatilities in 2023; Material allocation optimization as well as using a flexible resource pool, among other solutions. Sales and Operations Execution (S&OE) capabilities will support you in optimizing resource planning through the establishment of a dependable planning and execution framework. In addition, getting external support such as interim demand planning with skilled, experienced professionals and process outsourcing are robust methods of mitigating risk.

Technology that prioritizes data and enhances visibility can greatly help in managing and optimizing supply chains from end-to-end. Furthermore, the right technologies will enable you to make better-informed decisions, faster and with much more confidence.

In short, we anticipate a year of ongoing changes and disruptions that will keep supply chains on the volatile side. However, supply chain leaders should not take this as an excuse to remain reactive. Instead, now is the best time to turn threats into opportunities.

At EyeOn we help you navigate these challenges and support you in taking decisive actions to ensure your supply chain will thrive beyond this ‘permacrisis’. Feel free to get in touch with our expert Alexander Wenk, or any of our other specialists to discuss how you can navigate these disrupting times effectively.

Remember to keep an eye on this page where we will address further supply chain challenges you will be facing this year.

The post Anticipating supply chain shortages in an age of ‘permacrisis’ appeared first on EyeOn.

In our previous blogs, we’ve seen how a forecasting and demand planning assessment can give actionable insights that enable the planning team to focus on moving towards smart-touch forecasting and increase the quality of their decisions. Furthermore, we have some first results on the effect of decisional guidance: It generally improves forecast accuracy, but a planner’s willingness to accept the guidance can vary.

Planner types

Just like there are many different kinds of people, there are many different kinds of planners. We will highlight three examples:

• An optimistic planner adjusts too heavily, typically in an upward direction, often decreasing accuracy.
• A planner can furthermore show anchoring behavior. The adjusted value is in the right direction, but the adjusted value is too close to the statistical forecast (increasing accuracy, but not attaining the full potential).
• Finally, an overreacting planner adjusts in the right direction but overshoots the actual demand.

A theoretical data scientist might now simply say: “Easy! We identify which kind of planner we’re dealing with, and accept or reject their enrichments based on a fancy machine learning model, thereby maximizing the accuracy of the final forecast”. This is, however, not a productive approach: It is pitting the human and the machine against each other, instead of empowering planners to make the best possible forecast. Instead of ‘human versus machine’, we could be maximizing the potential of ‘human with machine’.

So, why do we care about planner types?

Personalized feedback

Imagine for a moment that you’re talking to two planners, let’s call them Anna and James. Anna is a typical optimistic planner: She has a positive outlook on life in general and that filters through to the demand planning enrichments he makes. James on the other hand is an anchoring planner, and a bit more cautious.

In the real world, talking with Anna and James would be a very different kind of conversation. Similarly, if we want to improve their impact, a planning system should give very different kinds of feedback. To Anna, when she adjusts a forecast upwards, the machine may suggest: “Are you sure? Dramatic upward adjustments like this have shown to be overly optimistic in the past. Please consider both the size and the timing of the uplift.” To James, when he adjusts a forecast upwards, the suggestion may be: “Are you sure? If you have good reasons to increase the forecast, the uplift will probably be more than you think.”

‘Smart-touch forecasting’ through automated enrichments

The examples above are a natural first step. Once a planner has made their adjustment and the system sees a potential for significant improvement, we give targeted feedback. This approach puts the planner in charge and leaves it up to them to accept or reject the machine’s suggestion. This is a small first step to take: When the recommendations make sense and prove their value, over time demand planners will come to trust the machine.

That’s when it’s time for the next step: automated enrichments. Taking as input the time series data, historical enrichments, and other internal and external drivers, we can use machine learning techniques to automatically recommend enrichments. A planner can then focus on validating those suggestions.

With modern advances in the explainability of machine learning models, techniques such as Shapley values (e.g., SHAP), and local surrogate models (e.g., LIME), we can create an understanding of the behavior and outcome of the machine learning models. For instance, the planner can see what inputs, trends, and drivers have caused the recommendation. This further enhances trust in the algorithms and enables the user to make an educated judgment call on the validity of the recommendation.

Conclusion

In the current day and age, with a ‘war on talent’ in the job market, our aim should be to empower and engage our planners. With a step-by-step approach from cognitive insights in a forecasting and demand planning assessment, through personalized feedback, and finally automated enrichments, we can truly enable smart-touch planning. By giving our people the right tools and the right information, they can have a tremendous impact.

If you have questions or would like to discuss your enrichment process, please contact Dan Roozemond, or get in touch with us.

The post Putting the ‘smart’ in smart-touch forecasting appeared first on EyeOn.

As described in our earlier blogs, forecast data enrichment processes are complex. It is not always clear who in the demand planning team adjusted and for what reasons. While in many companies the number of forecast items to take into account has grown exponentially, the experienced ‘old school’ planners who knew the ins and outs of each planning item are gradually phasing out. Today’s demand planning teams would therefore benefit greatly from being supported in a smart way to make effective and focused forecast adjustments. Providing these effective and focused data enrichments is one of the key steps of our cognitive insights approach to improve your forecast by nudging the planner to make better enrichment decisions:

1. Create awareness
2. Assess previously performed enrichments
3. Guide planners in providing effective enrichments
4. Automate predictable enrichments where possible

This blog focuses on guiding planners to provide effective enrichments. But how to achieve this? The concept of decisional guidance provides the answer to that question.

Decisional data enrichment guidance

Decisional guidance was introduced in literature in the ’90s already: Mark Silver defines it as the way a planning system supports the decision-maker with structuring and executing the decision-making process. Within intentional guidance, literature distinguishes two types: informative guidance and suggestive guidance (Montazemi et al., 1996; Fildes et al., 2006).

Informative guidance: Giving a planner unbiased, relevant information without any suggestions on actions to take. Example: “Based on historical sales, average demand was 598 products”.

Suggestive guidance: Proposing a specific action to the decision maker. Example: “Based on historical sales, the system advises to adjust the forecast for March from 123 to 598 products. Do you accept this change?”

As research on decisional guidance in the context of planning and forecasting is lacking, we decided to perform a study on how decisional guidance can be implemented in planning systems to improve the performance of judgmentally adjusted forecasts. Jochem Geurts, master student Operations, Management and Logistics of Eindhoven University of Technology, performed this research.

The research of Jochem consists of a data analysis on a dataset of one of our customers, and an experiment in which we determine which form of guidance works best in what situation.

Data analysis

To validate the added value of forecast enrichment, we used a dataset of one of our customers. The dataset showed the current process of judgmentally adjusting statistical forecasts. Jochem found that in the current situation, on average planners do not improve the forecast performance. Specifically, adjustments made for products categorized as low to medium volatile decreased the forecast performance. This decline in accuracy is mainly due to the already high quality of the statistical forecast (average forecasting error of 29% for statistical forecasts versus 42% for the enriched forecasts). Adjustments made on volatile products did improve the forecast performance. Next to the distinction between the volatility of the products, Jochem also looked at the direction of the adjustment. The analysis showed that planners are good at choosing the direction of the adjustment, but they have difficulties predicting how much higher or lower the new forecast should be. These two findings are combined in the experiment in which decisional guidance is applied.

Experiment

The experiment showed that decisional guidance has a positive effect on forecast accuracy. When making a distinction between informative and suggestive guidance, we showed that both forms of guidance have a positive effect on forecast accuracy and there was no big differences between the effect of these two different types. For products with medium to high volatility, decisional guidance on the size (how much) significantly improved the forecast performance. For products with low volatility, decisional guidance on the adjustment (direction) showed nearly the same effects in terms of performance as decisional guidance on the size . It can be useful to use decisional guidance on the adjustment as extra check for planners if they are certain to adjust the forecasts of products with low volatility.

Next, the experiment shows that the participants were more intended to fully accept advice which involved small changes compared to large adjustments. Since large adjustments typically add most value to the forecast performance, we recommend to be careful with proposing many small adjustments as decisional guidance. It might lead to a decreased willingness to accept the large adjustments as advice. These results will be used to further improve the forecast data enrichment process.

Decisional guidance: towards smart-touch forecasting

The promising results of both the analysis and experiment confirm that providing the right decisional guidance to planning teams on making- and accepting changes can significantly improve your forecasting performance. This is a great step towards true ‘smart-touch forecasting’.

If you would like to know how decisional guidance can improve the data enrichment process in your company, please reach out to Bregje van der Staak.

The post How decisional guidance helps planners make effective data enrichments appeared first on EyeOn.

Boosting your supply chain demand planning capability will allow you to improve the quality of your demand plan, quickly sense plan deviations, and effectively take action to get a grip on demand. But where to start? Many companies find it difficult to pinpoint the root causes limiting current performance, evaluate whether performance issues are related to the enrichment process, and how to prioritize improvement initiatives.

What you need is an objective assessment. This is one of the key steps of our cognitive insights approach to improve your forecast by nudging the planner to make better enrichment decisions:

1. Create awareness
2. Assess previously performed enrichments
3. Activate planners in providing effective enrichments
4. Automate predictable enrichments where possible

A clear overview and concrete action steps to improve your supply chain demand planning capability

We help customers by objectively assessing their current demand planning capability including the impact on supply planning and inventory management. The outcome of the assessment is a clear view of current strengths and weaknesses, identification of quick wins and a signed-off roadmap that describes concrete steps to improve the demand planning capability.

Depending on the needs of your company, the assessment can entail a number of components: a quantitative demand and forecasting benchmark based on a statistical data analysis, or a qualitative demand management review based on the actual state and interviews. You can find more details on the forecasting and demand planning assessment on our website.

The cognitive component in the forecasting and demand planning assessment

In our forecasting and demand planning assessment we focus on the cognitive component. We have a structured approach to assess where planners add value to the supply chain demand planning process. With our assessments, we leverage our dashboards focusing on cognitive insights using forecast performance metrics such as forecast value add. With these dashboards, we provide the planning community with insights into which enrichments were effective in the past and which did not lead to increased forecasting performance.

The following insights can be derived from the dashboards:

• What is the overall impact of the enrichments on the quality of the forecast;
• For which products the forecast deteriorates with enrichments;
• Where does Sales and/or Marketing improve the performance;
• Is there a structural bias under- or overshoot?

Our visualisations in PowerBi will turn the demand data into actionable insights that enable the full planning team to focus and take high quality decisions. By analysing past performance, the dashboards provide advice that can be used in the current demand planning cycle.

We focus on the following insights:

• Impact: We show the planning team the impact of the enrichments performed in previous cycles. We indicate how many products have been enriched, the impact on forecasting performance and the type of adjustments made (negative, positive or no enrichment). By providing these insights, the planning team is able to get insight into their past actions and the effect it has on forecasting performance.
• Ineffectiveness: By focusing on the top products, customers or regions, a planner can find out which groups’ forecast accuracy decreased, how many products where changed and what kind of changes were made. By showing the ineffective enrichments, we are able to bring focus in the enrichment process. These products shouldn’t have been enriched as the enrichments had a negative effect on forecasting performance.
• Classification: By using ABC / XYZ analysis, we determine where to focus enrichments on and what products to leave to statistical forecasting. For each of the ABC/XYZ categories, we show the impact on forecast accuracy. By highlighting this, we help the planning team to focus on the products that are difficult to forecast and need human attention.

Whether it’s during the assessment or as part of our planning service, we make sure to discuss together with your planning team how to use the insights on cognitive enrichment. This way we take your team along in how to make effective enrichments, enabling you to overcome human biases such as optimism in the planning process.

Are you interested in learning more about forecast and demand planning assessment, the cognitive component or our Planning Services dashboards, feel free to reach out to us! 

Read part four of this blog series where we talk about how decisional guidance can help planners make effective enrichments.

The post Supply chain demand planning & forecasting assessment with a cognitive component appeared first on EyeOn.

In our first blog, we introduced our vision on smart-touch forecasting. A key element of this approach is what we call ‘cognitive forecast enrichments’, in which we nudge the planner to make better enrichment decisions.

To take the planner along in providing effective and efficient enrichments, we recommend the following four steps:

1. Create awareness
2. Assess previously performed enrichments
3. Activate planners in providing effective enrichments
4. Automate predictable enrichments where possible

This blog focuses on creating awareness.

Realizing the pitfalls of human forecasting behavior

The forecast enrichment process of many companies is still in the early stages. It is often unclear which person made the enrichments, or the forecast history including the different enrichments is not properly tracked. By analyzing the enrichment process and its different steps, we aim to uncover the pitfalls of human forecasting behavior. However, how do we analyze the enrichment process?

Performing a quantitative analysis is one of the first steps to evaluate your enrichment process. In such a quantitative analysis, we touch on the following topics:

By taking a detailed look at the (type of) enrichment process in place, the stakeholders involved, the tools that are being used, and the continuous improvement cycle, we can identify the pain points in the process that can be improved by means of for example coaching or process re-design.

Building enrichment capabilities

Next to the qualitative analysis, awareness can be created by means of a training session. We provide a one-day training ‘Smart-touch forecasting’ which is designed to introduce the key concepts and core requirements needed to design and implement robust forecasting enrichment processes. This will drive your business performance by balancing the use of advanced analytics with focused value-adding enrichments. The EyeOn master class ‘Smart touch forecasting’ can both be facilitated in-house and as a standard master class in which multiple companies participate simultaneously.

After creating awareness, it is important to also act and assess your enrichment performance. We will discuss in our next blog post how we propose to evaluate the added value of your enrichment process.

If you have questions or would like to get more information on how to create awareness in your organization, feel free to contact Bregje van der Staak!

Read part three of this blog series where we talk about forecasting and demand planning assessment with a cognitive component.

The post Create awareness on human forecasting behavior appeared first on EyeOn.

Due to increasing data availability, advanced data science techniques such as machine learning have become a powerful tool to reduce forecasting bias and create more accurate statistical forecasts. However, in practice, human planners still include additional information, not known to the statistical forecasting algorithm, to come to a final forecast.

Integrating planner enrichments to reduce forecasting bias

Despite efforts over the last decade to improve statistical forecasting capabilities and include more input data, this forecast enrichment process is still of eminent importance in most companies. But to really complement the statistical forecast the enrichments should be of high quality. However, research shows that planners do not always add value to the statistical forecast when enriching, and can even make the forecast worse. This inaccurate enriched forecast causes production re-planning that creates purchasing, financing, and scheduling difficulties, next to service level issues and imbalanced inventories. Only by enriching in a structured and focused way planners can truly add value.

At EyeOn we believe that we can integrate the planner enrichments in a smart way. By creating smart-touch planning solutions, we can automate the forecasting process for products that are easy to forecast, we can provide recommendations where it is more difficult, and flag where human intervention is needed. We call this process smart-touch forecasting.

As shown in the figure below, planners obtain a statistical forecast from the planning system and receive feedback on their enrichment behavior while interacting with the planning tool. This combined effort of planning system and planner results in the best forecasting performance.

The challenge of human bias

When evaluating the enrichments of human planners, we need to be aware of their cognitive biases. As humans are not capable of dealing with too large amounts of data, they can provide suggestions that are irrational.

Decreasing human forecasting bias with data-driven nudging

Therefore a key element of our smart-touch forecasting approach is what we call ‘cognitive forecast enrichments’, in which we nudge the planner to make better enrichment decisions.

In order to take the planner along in providing effective and efficient enrichments, we recommend the following four steps:

1. Create awareness
2. Assess previously performed enrichments
3. Activate planners in providing effective enrichments
4. Automate predictable enrichments where possible

The road towards smart-touch forecasting

The blog series ‘Improved Forecasting Using Cognitive Insights’ will dive into each of these four building blocks in the coming months. Each month, we will present a part of our approach in building an effective forecasting enrichment process. Read here the next blog on creating ‘Awareness’!

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.

If you have questions or would like to discuss your enrichment process, please contact Edward Versteijnen!

The post Reducing forecasting bias through smart-touch forecasting 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.