Ask our experts about the EyeOn approach for driver-based forecasting: Contact us!

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Data latency is one of the biggest issues in supply chain decision-making. Too much time is lost from the moment an event happens until data becomes available (capture latency), until it has been analyzed (analysis latency), or until a decision has finally been taken (decision latency). Due to this loss of time from the occurrence of the event to acting on it, the value and impact of that decision have dramatically decreased.

We don’t just need to make the right decision, we need to make that decision really quickly. For this purpose EyeOn is developing smart-touch planning: with better technology we enable you to make faster decisions, thereby increasing the impact.

How smart-touch planning enables fast and informed decision-making in supply chain

We help you reach maximum quality data with minimum effort, provide a user-friendly ecosystem integrated with your own infrastructure, and enable your planners with best practices and AI insights for fast, informed supply chain decision-making. Smart-touch planning automates obvious decisions, makes recommendations where additional information is available, and flags special cases where human intervention is necessary.

Smart-touch planning is adapted to your company. It means making planning as simple as possible; adding complexity where needed to ensure the best possible forecast for your company and industry. Explainability is everything, to make sure that you get the most out of it. We focus on user-centricity and integrate smart-touch planning into your existing workflow.

This is what Smart-touch planning could look like:

An AI gives hints to users, enabling planners to make high-quality decisions.

Data engineering – the basis of future data analysis

We bring together data from all sources and connect it so that it forms the basis of future data analysis. We offer training to help you in collecting and connecting high-quality data and we provide standard templates to ease collecting the right data. Our Honeycomb software organizes data and provides insights & dashboards on data quality.

Better forecasting through feature engineering 

We take raw data and turn it into features that can be used for better supply chain decision-making. We combine data science techniques and existing libraries for feature generation with specific industry and customer knowledge to extract the most information. More useful features will give a better, more explainable, higher accuracy, and lower bias forecast. Using standard analyses and dashboards we identify which factors impact your business, giving you an even better understanding of your business dynamics and e.g. promotion effects.

Model ecosystem for effortless use

We are building a model ecosystem that provides a scalable infrastructure and set of best-of-breed models to predict, optimize, and explain. Both classic models (e.g. Holt-Winters) and more advanced models (Prophet, Catboost, ..) are available. Automated pre-configuration is included: the need to tune models is minimized – the aim is to enable everyone to use all models, effortlessly. This gets us great results quickly and gives the data scientists more time for in-depth analysis of individual cases. 

Enable your planners to be more effective

We have experience and unique technologies to give planner behavior feedback that enables you to increase the value provided by the planners. The goal is for planners and your company to benefit maximally from our smart-touch planning approach. We share best practices and standard insights to enhance planner behavior and decisions through training and technology.
We are currently productizing these insights, transforming dashboards and insights from our consultants to automated AI-based feedback.

Let’s discuss how smart-touch planning can improve supply chain decision-making, and make it easier and faster: Contact one of our experts! 

The post Improving supply chain decision-making: make better decisions, faster appeared first on EyeOn.

Read more in the Supply Chain Magazine’s trend interview (in Dutch) with EyeOn Managing Partner and Senior Consultant André Vriens.

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How to plan in a post-pandemic world: Five key focal areas in 2021

The post Seize opportunities with the help of digital transformation appeared first on EyeOn.

New technologies offer the opportunity to accomplish a more data driven supply chain. This made companies rethink their business planning processes. In this webinar we shared our vision on how to embark on the digital transformation journey and how to take the momentum of the Covid situation to take action.  

This webinar is part of the ‘Accelerate the digital transformation’ webinar series in which we touch upon the different building blocks of end-to-end supply chain transformation, with a webinar on the EyeOn approach regarding this topic, the next-generation IBP, and tool selection.  Also we will review a number of the current APS solutions on the market. Check out our upcoming events.

The post Webinar ‘Accelerate the digital transformation – Introduction’ appeared first on EyeOn.

Overcoming crisis with gains

The Corona crisis has dramatically changed the way companies around the world are managing their supply chains. Alongside with supply disruptions and manpower shortages, businesses encounter problems with the effectiveness of their forecasting capabilities during this turbulent period. Demand planners can’t rely on past data and, as previously, confidently use it to predict further development. Unpredictable and substantial short-term demand fluctuations overshadow the picture even more, distorting forecasting abilities and leaving supply chain departments without tools for steering the business. Business-as-usual is no longer an option for the majority of companies and they start looking for more advanced solutions.

Robust scenario analysis is the best solution for this volatile environment

EyeOn has numerous tools that go far beyond traditional history-based forecasting techniques. Our consultants have been working for years with various industries that have experienced diverse business challenges. Companies ask for our support to overcome these issues, advancing their demand planning capability to a whole new level. The level of uncertainty during the corona crisis is almost unprecedented. Our planning experts have no doubts that robust scenario analysis is the best solution for this volatile environment.

Find optimal safety stock levels and improve inventory management performance

One of our customers is a global leader in the area of dyes and specialty chemicals. The Corona wave impacted the company’s supply capabilities as well as demand potential. The traditional forecasting method that they used could not manage this level of turbulence and created a great challenge for the whole supply chain. This triggered their interest in EyeOn’s tools to improve their forecasting and inventory management practices. Our team was able to come up with the right solution in a matter of just a couple of weeks. EyeOn prepared a set of business scenarios for demand planning which incorporated various inputs from the customer as well as our deep understanding of supply chain in the process industry. It included a basic scenario and a range of additional more pessimistic or optimistic predictions based on a comprehensive analytical model. These scenarios were applied by the management as the basis for their planning. Each demand scenario included an inventory management roadmap that clearly explained how stock should be managed in each case. The approach helped to find optimal safety stock levels and improve inventory management performance. Thus, the company was able not only to maintain their forecast accuracy on a high level but also to manage their inventory in the most financially advantageous way.

Establish data-driven decision-making process

However, this was only the first step of the forecasting enhancement process. To help the customer navigate a rapidly changing environment EyeOn created a special planning solution. Anaplan supported by PowerBI served as a main integrated tool and simplified the evaluation of influence of different customers’ demand drivers. The solution combined with specially designed KPI dashboards provided insights regarding the real-time impacts of the Corona crisis and helped to reveal an accurate snapshot of the company’s day-to-day situation. The company was thus enabled to adapt its own scenario planning if the business environment changes. Under EyeOn’s supervision demand planners started to employ these new ways of working and in a couple of months the planning department was fully reinforced with the new powerful solution. Planning became the key function during the crisis period providing valuable demand and supply insight to the management team. As a result, a healthy data-driven decision-making process was established within the company. It helped them to avoid the devastating consequences of the ‘bullwhip effect’ and to keep inventory levels and working capital under control on all stages of their supply chain.

Conclusion

During a crisis, deep insight and understanding of the entire chain are key to handling it properly. EyeOn is equipped with world-class experts, cutting-edge solutions, and in-depth business knowledge. These factors help to quickly identify customer’s needs and to deliver robust solutions rapidly. During the last crisis, it became obvious that commercial survival depends on resilience and adaptability to fluctuations and disruptions. Readiness is everything.

Do you want to enhance your planning capability? We are happy to accompany you on this journey! Contact us now.

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Inventory reduction strategy

Many markets and companies are recovering from the disruptions COVID-19 has caused. Markets are opening up again and production facilities have resumed production. Faced with a high uncertainty on how the economy and markets will recover, CEOs are forced to cut costs and free up money by reducing stock. On the other hand it is increasingly important that customers are served at the desired level, thus a solid stock reduction strategy is required. This brings us to the question of this blog: inventory reductions, what are do’s and don’ts?

Don’ts!

1. Focus on overall lowering safety stocks. I more and more hear companies start to decrease their safety stock levels across all items with a certain percentage, even up to 30%! Don’t do it. It will definitely lead to lower service levels – amplified by the increased demand and supply uncertainty that most companies currently face – and a further increase of the bullwhip in the supply chain, with all its well-known consequences.
2. Set target (safety) stock levels using forecasts from stationary statistical forecast models. Demand during the pandemic is definitely not stationary. What to do instead? See number 4 in the list of do’s!
3. Spend time on modelling the impacts of the epidemic itself, how long recovery will take and if and when a “second wave” can be expected. Leave it to the experts!

Do’s

1. Increase production flexibility/decrease lot sizes. Creating “flow” and reducing lead times in your supply chain by lowering lot sizes will lead to a lower cycle stock, but make sure operational costs only grow marginally!
2. Reduce complexity/diversity product portfolio. Make sure you can deliver your important (A) items and reduce the number of unimportant, less value adding (C) items. A nice example from the pasta industry can be found by visting this page.
3. Reduce stock of slow/non-movers by writing them off, increase sales/marketing activity or stop production. But make sure you identify the real slow/non-movers!
4. Forecasting: extract real business drivers and underlying demand pattern and use them to forecast the “rubber duck” or “bathtub” curve.
5. Create E2E-visibility of stock levels across the supply chain. In multiple projects lately we identified many quick wins: double safety stocks in the supply chain were removed, and/or stock was balanced over locations having under- and overstock.

Need help for a quick start?

Is it that simple, just follow the above guidelines? No, every situation requires a tailored approach and not every “do” might be that effective. As a first step, it is important to know how your “inventory pie” is divided over the different sub inventory types (safety stock, cycle stock, strategic stock, transit stock, etc.) and across the supply chain. Based on that, take actions on the most promising stock type(s) and location(s). A nice example of how an inventory dashboard creates such insights, can found by visiting this page. Please feel free to reach out to us if you would like to discuss what your company needs as a first step, our experts look forward to support you along this challenge!

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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.

Proposed solution: rubber duck curve

The proposed solution is the rubber duck curve forecast adjustment. The figure below is called the rubber duck curve, because it has the shape of the beloved bath toy. The idea is simple and intuitive. The method divides the time horizon into four moments: regular sales, disruption, recovery, and new normal. The method assumes that the regular sales will follow the patterns of the statistical forecast in a stable environment. Then, the impact of Covid-19 kicks in, causing a decline in demand. A period of disruption starts. As measures to control the infection are eased, the sales are expected to recover. This recovery phase can be perceived in one period, or over a period of time. It is also possible that there is no recovery and the new normal picks up directly from the disruption phase. The new normal is defined as the period with new stable sales. From this point on, the statistical forecasting methods can take over again. Beware that the impact of disruption can also have an opposite effect. Thus, a positive effect on the sales.

Application and implementation

The method is a great concept, but how do you apply it to your forecasting process? To be able to apply this method you need to have knowledge about the expected impact of the virus. This knowledge is represented by three key moments in the curve: start of disruption, start of recovery, and start of the new normal. Each moment is characterized by a starting point in time, and an expected percental change in sales with respect to the forecast before the disruption started. If you have identified each moment, you can impute the expected behavior to your statistical forecast. The statistical forecast incorporates the trend and seasonal effects, so the future forecast has the right demand pattern adjusted with the knowledge about the expected impact.

The way in which these moments are specified is through a template. Information about the expected disruptions is gathered here. The template is flexible to allow the specification of each of the moments at the right hierarchy level. This ensures that the adjustments match the level at which companies can expect the disruption. Additionally, this allows for a fast adjustment of the entire portfolio, without having to provide an expected change at the lowest hierarchy levels.

Example

The figure below shows an example of rubber duck adjustments and a filled-in template. The first columns of the template indicate the hierarchy level which is impacted by Covid-19. Then, the next three columns indicate ‘when’ a new phase starts, and the last three columns indicate what the percental change is.  The graph shows that starting on May 2020 there is a change of -30% in the expected demand; the disruption phase has started. Then, in November 2021 the recovery period starts with a change of 15% in comparison to the reference forecast. Finally, the new normal starts on February 2021 and the sales are expected to stabilize again. From this moment on, the statistical forecasting method can take over again and rubber duck adjustments are no longer needed.

Benefits

Adding the rubber duck curve to the statistical forecast can provide a good foundation for future demand planning. Consequently, there is no need to adapt each individual forecast in an enrichment tool manually due to unrealistic mid/long term statistical forecast figures. Furthermore, the template can be updated easily during each forecast cycle based on the latest insights. Lastly, the rubber duck curve can be implemented fast and can be easily reapplied in case of a another Covid-19 wave. 

In conclusion, combining the power of statistical forecasting with the knowledge about expected impact on your business as a rubber duck curve, provides you with a Covid-19-proof method for forecasting.

Please feel free to contact us for more support!

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The vast majority of companies rely on statistical forecast models to predict their demand. These models use time series of historical sales to estimate future demand. A key assumption of these models is that the underlying system remains unchanged. In other words; the relationship between historical and future demand does not change. Clearly this assumption is violated during the current Corona crisis and as a result we should be very reluctant to rely on statistical models. The current circumstances present both a need and an opportunity to predict demand more dynamically. This requires different information than just historical sales; it requires tapping into other data sources that provide insights regarding future demand. One of these data sources to consider are the open orders from your order book.

What exactly are open orders?

Open orders refer to demand that has been placed in advance. In other words, future demand that is already known at present. Although many companies have access to open orders via their order book, this information rarely finds its way into the statistical forecast. At best, planners use this information manually to adjust the statistical forecast. It would be more efficient, however, if the statistical forecast could somehow already take this information into account. Sure, that sounds good, but how can we do this? Well, 18th century clergyman and mathematician Thomas Bayes provides us with an interesting toolkit to take on this problem.

Bayesian inference

Before we jump right into it, let’s take a moment to discuss the basics of Bayesian inference. Bayes’ Theorem provides us with a quantitative framework for updating our beliefs as the facts around us change or new information arises. This framework is captured in his famous equation:

Although the formula might look intimidating at first, what it essentially boils down to is this: whenever we receive new information, how much should we let it affect what we currently believe to be true? Does the new information support the initial belief, dispute it, or not affect it all? Are you starting to see how this method might help in incorporating order book data into the statistical forecast?

Applying the Bayesian line of reasoning to our forecast problem

The statistical forecast provides us with the initial belief regarding what the demand will be in the future. By contemplating the order book, however, we receive new information that will either support or dispute this initial belief. Using Bayes’ equation, we can then calculate the Posterior distribution. This distribution represents our belief regarding future demand after taking into account both the initial statistical forecast and the open orders. 

Example

Let’s clarify with an example. Imagine that your (initial) statistical forecast for one month ahead is 24. Moreover, your order book shows that, at present, you have 6 pre-orders in the system for one month ahead. Finally, the last piece of information that we need is an estimate of the probability that an order for next month is already known at present. Assume that this probability is equal to 50%. The question now becomes ‘How to use the number of pre-orders to adjust the initial statistical forecast?’. 

1. The first step is to fit a distribution with mean equal to the initial forecast (in our example 24). This distribution (in blue) represents our initial belief about what the demand for next month is going to be.
2. Next, we use Bayes’ rule to adjust the initial forecast. To put it plainly, in this example the order book tells us that future demand is likely to be lower than we initially expected based on the statistical forecast. Hence, the Bayesian forecast incorporates this information and lowers the statistical forecast (from 24 to 19). 

Final thoughts 

As a consequence of the Corona crisis, it is possible that your market is currently facing drops in demand, increases in demand, or significant demand shifts over time or between products. In any case, your order book might provide valuable information, signalling where your future demand is heading. With the Bayesian toolkit, you can incorporate this information automatically without having to check every order yourself.

Eager to learn more about this topic or curious if this method could also be valuable for your organisation? Please do reach out to Rijk van der Meulen.

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No-touch planning, automated generation of master data, robotic process automation. On the one hand advanced automation seems and is closer than ever.

Advances in general purpose AI (think DeepMind) or focused AI (voice assistants, photo pattern recognition) make it easy to imagine the application in supply chain planning is only a matter of time. In this light, episode 47of the ‘After On’ podcast is worthwhile the listen.

Still, we struggle on a daily basis with bad master data, erroneous or time-consuming Excel files, difficult to interpret solvers and cumbersome scenario planning. Maybe an automated supply chain is just one leap to far?

Take a step back and think about the technology we use every day. What’s the last time you’ve used physical maps to navigate? Go to the library? Remember the stock traders shouting on the exchange floor? Technology slowly advances and seamlessly intertwines with daily life. 

The same holds for applications in supply chain management. We just executed a deep dive in the level of automation we apply in analyses we execute for and in applications delivered at our customers. The degree of automation in a typical project has risen to a level we would not find say 10 years ago. 

For example, more and more steps of setting up a forecasting process are automated. Manual segmentation is not required anymore. Segmentation is automated, just like many of the steps to select the best forecast approach for each category.

 

 

In our work to reduce data latency through speeding up requirements propagation, demand trend recognition and shorten planning cycles, we see ever more opportunities to automate. Take a multi node divergent supply chain with many routing choices per node, with varying lead times and yields. A detailed understanding of critical starting material requirements requires a full plan propagation, with a latency of (at least) one planning cycle. An automated ad hoc propagation using predicted lead times and material requirements – learning from past routing decisions and outcomes – delivers predicted raw material requirements with accuracy fit for planning on demand, rather than once every cycle at best.

 

 

And it can work the other way around. An analysis of supplier performance giving insight of performance against planned parameter, triggered the development of an automated update and learning loop feeding supplier management, QC lab priorities, master data management and planning.

 

 

 
Automation based learning loop for supplier management 

Any step, no matter how small, brings the no touch supply chain a little closer. At minimum it provides answers to the questions asked. In many cases, though, it triggers efforts to significantly reduce data latency and implement true automated learning loops.

Want to learn more? Ready to take the first step and implement? Feel free to reach us through our website or read the document “Data Engineering” to familiarize with the approach EyeOn takes on making improvements in this area.

In striving for success, large companies have to continuously struggle against growing internal complexity. EyeOn helps our clients manage this complexity by designing, implementing and executing excellent planning processes as a discriminating factor for this success. In order to achieve this, we develop and share knowledge about top level planning and forecasting, with constantly demonstrable return on investment for our clients.

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