Manufacturing: 4 Keys to Smarter Demand Forecasting
Demand Forecasting in the Manufacturing Industry: Key Points to Master In the manufacturing industry, poor demand forecasting doesn’t just show up as a bad number in an Excel spreadsheet, it materializes as costly overstock, production stoppages, missed delivery deadlines, and unhappy customers. The stakes are concrete, immediate, and often massive. Yet many supply chain teams continue to manage their planning with inadequate tools or overly manual processes. So, how do you build a robust demand forecasting approach in a manufacturing context? Here are the key points to consider. 1. Understanding the Specifics of Manufacturing Demand Demand in the manufacturing industry doesn’t look like demand in distribution or retail. It has its own characteristics that make forecasting more complex: Long cycles and extended lead times: when a component’s lead time exceeds several weeks, mid-term forecast accuracy becomes critical. A mistake today is paid for dearly three months down the road. Largely derived demand: demand for raw materials or components depends directly on finished goods forecasts. This means forecasts must be cascaded through Bills of Materials (BOMs). Seasonality and promotional effects: sales campaigns, RFQs, or industrial cycles can create demand spikes that are hard to anticipate without the right data. A heterogeneous product portfolio: some items are predictable best-sellers, others are spare parts with sporadic or intermittent demand. No single forecasting method fits all. Recognizing these characteristics is the first step toward selecting the right forecasting models and best practices. 2. Choosing the Right Forecasting Methods Based on Demand Profiles One of the most common mistakes is applying a single method across the entire catalog. Yet every demand profile deserves a tailored approach. Regular, stable demand: exponential smoothing methods (Holt, Holt-Winters) deliver strong results in the vast majority of cases. They reliably capture trend and seasonality. Intermittent or sporadic demand: for spare parts or slow-moving SKUs, specialized methods such as Croston or Syntetos-Boylan can outperform classic moving averages. That said, given the difficulty of forecasting such demand, it’s better to focus your energy on inventory management. New products or launches: without historical data, you need to rely on analog references, market studies, or collaborative forecasts involving sales teams. Automation and machine learning: for portfolios spanning thousands of SKUs, automating forecasts through dynamically selected algorithms (based on past performance) is a major performance lever. The goal isn’t to use the most sophisticated method, it’s to use the most appropriate one for your business, with minimized and measurable forecast error. 3. Incorporating External Signals and Cross-Functional Collaboration Statistical algorithms, however powerful, cannot anticipate everything. In the manufacturing industry, ground-level intelligence carries significant value: Sales forecasts: sales teams hold valuable insight into active deals, pricing negotiations, or customer churn. These signals need to feed into the forecasting process. Customer data and firm orders: in a B2B context, order books or long-term contracts can dramatically reduce uncertainty over certain planning horizons. Macroeconomic and industry trends: industrial production indices, commodity prices, or sector-level activity can serve as useful leading indicators. The S&OP process (Sales & Operations Planning): structuring a monthly consensus meeting between sales, marketing, operations, and supply chain teams helps reconcile different perspectives and land on a single, shared forecast. Demand forecasting is not a siloed activity, it’s a cross-functional process that gains in accuracy when the whole organization contributes. 4. Measuring Performance and Driving Continuous Improvement An unmeasured forecast is an uncontrolled forecast. To improve, you need to put clear KPIs in place and track them consistently. MAE (Mean Absolute Error): MAE represents the average deviation between what you forecasted and what actually sold, regardless of the direction of the error (over- or under-forecast). Avoid MAPE, it encourages under-forecasting and blows up on low-volume items. Bias: measures whether forecasts are systematically over- or under-estimated. Persistent bias is the sign of a structural issue that needs to be corrected. Segment-level tracking: analyzing forecast accuracy by product family, sales channel, or planning horizon helps identify priority areas for improvement. Forecast Value Added (FVA): FVA is a performance metric that measures the effectiveness of each step in the forecasting process. Unlike MAE, which measures overall error, FVA answers a strategic question: “Did human intervention actually improve accuracy, or did we just waste time?” Beyond metrics, the goal is to build a culture of continuous improvement: analyzing significant variances, understanding their root causes (exceptional events, data entry errors, wrong model), and adjusting forecasts or processes accordingly. Conclusion: Turning Forecasting Into a Competitive Advantage In the manufacturing industry, demand forecasting is much more than a technical exercise, it’s a strategic lever. Better accuracy means less tied-up inventory, fewer stockouts, more stable production schedules, and ultimately, stronger profitability. The good news is that modern tools now make it possible to automate a large part of this work, test dozens of models in seconds, and focus team energy where it adds the most value: analysis, decision-making, and collaboration. Ready to take it further and improve your forecast accuracy starting now? Discover how SKU Science can help you industrialize your demand forecasting process — simply, quickly, and without a heavy IT project.
New Product Lifecycle Module: Seamless Product Transitions, Made Simple
Streamline your product transitions Automatically transfer historical data and preserve forecast accuracy Introduction Launching a new product or phasing one out should never feel like an uphill battle. Yet every phase-in or phase-out comes with risks: stockouts, excess inventory, lost revenue, and increased pressure on teams. All of this is often made worse by unreliable data at the very moment when decisions matter most. Our new Product Lifecycle Management module changes the game. It ensures smooth, controlled transitions with accurate forecasts available from day one. You can anticipate better, decide faster, and protect your performance with confidence. Navigating the challenges of product transitions Planners know it well: a poorly managed product transition can be costly. Here are the main pitfalls to avoid: Unmanaged end-of-life phases: excess inventory that ties up cash. Underestimated new product launches: missed sales opportunities from day one. Channel variability: different forecasts per country, store, or e-commerce channel that are difficult to replicate accurately. Lack of usable historical data: making it impossible to build reliable forecasts for new products. Manual updates: a major source of errors whenever a product code changes or marketing adjusts the timeline. The result is often the same: delayed decisions, reduced visibility, and reactive rather than strategic planning. An intelligent module to manage the entire product lifecycle Our new product lifecycle management module centralizes what planners previously had to handle manually. It connects end-of-life products with their successors while ensuring full continuity of forecasts. Each transition becomes transparent: you can clearly visualize the links between old and new products, manage transition timelines, and keep a complete history of changes. This approach provides full control over your product portfolio, from SKU creation to product retirement. Automating phase-in and phase-out processes One of the key strengths of the module lies in its automation capabilities.When a product reaches end of life, you simply assign its successor. The system automatically transfers the relevant data — including historical data and associated segments — to the new product. Forecasting models adapt seamlessly to real-life conditions: Automatic creation of forecasts for the successor product Dynamic transfer of historical data to ensure forecast continuity Preservation of links between products during the transition period (phase-in / phase-out) The result: fewer errors, smoother transitions, and stable forecasts — even when marketing calendars change. Advanced multi-channel forecasting management In many organizations, a single product can have multiple forecasts depending on sales channels, geographic regions, or customer segments. This level of granularity is essential, but often difficult to manage. Our module automates this complexity.For example, if a product has five separate forecasts by channel, the successor will automatically inherit all five. Each channel keeps its own sales dynamics, without manual duplication or data loss. This approach preserves local accuracy while significantly reducing operational workload. Creating new products based on existing ones Another common use case is launching a new product that has no direct predecessor but shares similar characteristics with an existing one.The module allows you to create a new product based on the data of an existing item, without establishing any formal link between them. This makes it easy to accelerate product launches while starting with reliable, experience-based forecasts. More reliable forecasts for better decision-making Thanks to these automations, product transitions become an opportunity rather than a constraint. You gain: Greater accuracy, with realistic forecasts from the very first planning cycles More agility, through instant creation of new products Peace of mind, as teams no longer need to manually manage hundreds of SKUs The results are tangible: reduced excess inventory, fewer stockouts, and improved visibility into future performance. Conclusion Product transitions should never put your sales plan at risk. With this new module, you can manage the entire product lifecycle from a single interface, without data loss or forecasting disruptions. Give your products the future they deserve. Discover the module in action and start managing your product lifecycle with confidence.
SKU Science Achieves SOC2 Type II Certification
SOC2 Type II Certification: A Guarantee of Trust and Security In a context where data security and regulatory compliance are at the forefront of business concerns, obtaining a SOC2 Type II certification is not just a bonus, it’s a necessity. At SKU Science, we are proud to announce that we have obtained this prestigious certification, further reinforcing our commitment to data security and the protection of our clients’ sensitive information. What is SOC2 Type II Certification? The SOC2 (Service Organization Control 2) certification is a rigorous standard that assesses a company’s ability to manage data according to strict criteria of security, availability, integrity, confidentiality, and information protection. While the Type I certification validates processes at a specific point in time, the SOC2 Type II certification goes even further by evaluating these processes over an extended period of several months. This means that we not only comply at a specific moment but also maintain strong and consistent security standards over time. Why is this certification crucial for our clients? As a SaaS solution in demand forecasting, SKU Science processes sensitive and strategic information daily. The SOC2 Type II certification ensures that all the data you entrust to us is protected by rigorous security processes. Here’s what this means for our clients: – Enhanced Security: We implement strict controls to prevent data breaches, alterations, or unauthorized access. – Ongoing Compliance: Our practices are evaluated over time, meaning we maintain a consistent level of compliance day in and day out. – Increased Trust: The SOC2 Type II certification is recognized globally, reassuring our clients of our ability to handle their data with professionalism and care. The impact of this certification on your business For companies in the supply chain sector, where the flow of information must be seamless and precise, a security breach can have dramatic repercussions. By choosing SKU Science, you’re opting for a certified solution that adheres to the highest security standards. This allows you to focus on optimizing your operations without worrying about the protection of your data. This certification also provides a competitive advantage: it positions us as a trusted partner, capable of meeting the demands of large enterprises as well as SMEs concerned about the security of their information. Conclusion: a certification, a commitment to the future At SKU Science, SOC2 Type II certification is not an end in itself, but a step in our ongoing commitment to excellence in data security. We will continue to enhance our processes to ensure that our clients have access not only to the best demand planning and inventory optimization solutions but also to a secure infrastructure that meets their expectations. By partnering with us, you benefit from a reliable, proven, and certified solution designed to offer you complete peace of mind.
New Partnership with Demand Driven Technologies
New Partnership: Intuiflow by Demand Driven Technologies and SKU Science Elevate Demand Planning Excellence ATLANTA–(BUSINESS WIRE)–Demand Driven Technologies, an emerging leader in supply chain management solutions, is thrilled to announce its strategic partnership with SKU Science, a frontrunner in fast and effective demand planning and forecasting. This collaboration brings together two industry-leading innovators to empower businesses with unparalleled agility and resilience in their supply chains. Embedding SKU Science’s cutting-edge forecasting capabilities into Demand Driven Technologies’ Intuiflow software will enable users to harness the power of data-driven decision-making and achieve optimal results in their Sales & Operations Execution and Planning processes. (S&OE/S&OP). Seamless Forecasting with Intuiflow’s Demand Planning Application With Intuiflow’s Demand Planning application, users can generate accurate and timely forecasts effortlessly. Leveraging historical data and analyzing 644 statistical combinations, the platform automatically identifies the best forecast at any level. Custom machine learning models tailored to specific datasets offer additional options, providing a truly personalized forecasting experience. Foster Ongoing Enhancements and Collaboration The integration of data from various stakeholders and departments allows for the establishment of unified demand plans, fostering enhanced collaboration throughout the organization. Regular assessments of business performance can be made by comparing it to both the fiscal year budget and the previous year’s results. Utilizing Intuiflow’s Dashboard feature, users can seamlessly compare actuals to the annual budget, enabling insightful financial improvements. Erik Bush, CEO of Demand Driven Technologies, Comments: “Our Intuiflow solution is quickly becoming the Supply Chain Planning solution of choice for both large enterprise and mid-market clients. Our partnership with SKU Science enables us to further extend the supply chain performance improvements our clients are achieving.” Stephane Leclercq, CEO of SKU Science, Adds: “By introducing advanced demand planning capabilities to Intuiflow, we are enabling businesses of all sizes to supercharge their operational efficiency and strategic agility. Now, they can enjoy an even more holistic and intuitive suite of tools to drive their success.” For more information on the groundbreaking capabilities of this partnership, please send an email to contact@skuscience.com. About Demand Driven Technologies: Demand Driven Technologies is a leading provider of supply chain management solutions, offering innovative tools and methodologies that enable businesses to adapt and excel in today’s complex supply chain environment. With a customer-centric approach, Demand Driven Technologies empowers organizations to achieve optimal inventory levels, reduce lead times, and improve overall supply chain performance. About SKU Science: Launched in 2018, SKU Science has rapidly become a trailblazer in supply chain management. Harnessing the power of modern web technologies and Big Data, SKU Science delivers solutions that are not only robust but also user-friendly and easily deployed – a perfect fit for businesses of all sizes. With this fusion of cutting-edge technology and ease of use, SKU Science has established itself as a prime mover in the realm of demand planning. Free Trial – Fast & Simple demand forecasting solutionEasy and affordable – No credit card requiredTry SKU Science now ! SIGN UP FREE
Reducing stockout impact with aggregate forecasting
Many companies do not have a process for archiving or tracking stockouts, even though they can have a significant impact on sales. This article will outline how organizations can minimize the effect of stockouts on demand forecasting through the use of aggregate forecast methods and actual sales data. While it would be preferable to use actual demand rather than historical sales by eliminating stockouts, our experience indicates that most companies have not yet reached this stage of maturity in their supply chain management. Free Trial – Fast & Simple demand forecasting solutionEasy and affordable – No credit card requiredTry SKU Science now ! SIGN UP FREE Benefits of using aggregated data for demand forecasting The best practice in supply chain management to achieve greater forecast accuracy is to calculate forecasts at an aggregate level. Here are some reasons why demand forecasting KPIs typically improve with this approach: – Increased sample size: By aggregating data from multiple sources, you increase the sample size of your data, which can improve the accuracy of your forecasts. A larger sample size can provide a more representative view of the population, allowing for more robust and reliable predictions. – Reduction of noise: Aggregating data can help reduce the effects of noise and outliers in the data. For example, if you’re forecasting demand for a product, an unusually high demand from a single customer or location might skew the data and lead to an inaccurate forecast. By aggregating data across multiple customers or locations, you can reduce the impact of these outliers and produce more accurate forecasts. – Better understanding of underlying patterns: Aggregating data can help you identify patterns and trends that you might not see when working with individual data points. For example, if you’re forecasting demand for a product across multiple stores, you might notice that demand tends to be higher on weekends or during certain months of the year. By identifying these patterns, you can create more accurate forecasts. – More explanatory power: Aggregating data can give you a more complete understanding of the factors that influence demand for a product or service. For example, by analyzing data from multiple stores, you may be able to identify both a seasonal pattern and store-specific characteristics that affect demand. Combining these two factors can provide more explanatory power and more accurate forecasts. Effective data grouping techniques for accurate demand forecasting Now let’s see what type of data we could use to group our data in order to apply our forecasting models. – Product data such as product size, weight, color, price, brand, segment, category, or any other field characterizing a product can be used to aggregate data. – Store data can also be used to understand how demand for a product varies across different store locations. This can include information such as store size, layout, and demographics of the surrounding area. – Sales channel is another example of information that can be used to organize your data before computing the forecast since the demand for specific channels can be very different. – Marketing data such as promotions or advertisements that were run for the product, can help understand the effect of these activities on demand. Therefore, grouping data using these fields or a combination of these fields can lead to better accuracy when forecasting demand. This table shows the average error rate in % for forecasts computed at a specific aggregate level Improving demand forecasting accuracy through aggregate data and stockout analysis If historical sales data is impacted by stockouts, it can lead to inaccuracies in demand forecasting because stockouts can cause fluctuations in sales that do not reflect true underlying demand. As seen earlier, working with aggregate data can help improve the accuracy of demand forecasting in this situation for a few reasons: – Smoothing effect: Aggregating sales data from multiple sources can help to smooth out fluctuations in sales caused by individual stockouts. For example, if sales data from one store is affected by a stockout, it can be offset by sales data from another store where stockouts did not occur. – Better understanding of the impact of stockouts: By aggregating data across multiple stores, you can also gain a better understanding of how stockouts are impacting demand. This can help you to identify which products are most affected by stockouts, as well as which stores or regions are more affected, you can use this information to plan and manage your inventory. – Reduced variability: Stockouts can cause significant variability in sales data, which can be especially problematic for statistical forecasting methods that rely on historical data. By aggregating data, you can help to reduce this variability, which can improve the accuracy of your forecasts. It’s important to note that even when working with aggregate data if stockouts are a frequent and persistent problem, it can make the forecasting process more challenging, and it’s important to consider ways to address the root causes of stockouts to improve forecast accuracy. Free Trial – Fast & Simple demand forecasting solutionEasy and affordable – No credit card requiredTry SKU Science now ! SIGN UP FREE Gains on forecasting KPIs for our customers As discussed earlier, there are several possible options for aggregating data to calculate the aggregate forecast, each with a specific error rate. It is important to consider several options and choose the most appropriate one for your needs. Forecast KPIs such as bias and error will help you identify the best level of aggregation for your calculations. However, it can be challenging to split aggregate forecast data into underlying forecasts for each period, which can be time-consuming and may result in inaccuracies. SKU Science has developed a fully automated feature that computes underlying forecasts for each period, making it easy to compare error rates and choose the relevant aggregate level. Our customers have been using this feature to lower their error rates and improve the accuracy of their demand forecasting. The impact can be significant, as we have seen up to 20%
Challenge your sales team’s forecasts with KPIs
To improve the performance of your supply chain, it is essential to have the right tools to support demand planning within your S&OP. Thus, you can limit stockouts and keep your inventory at a reasonable level while ensuring a high level of service. A key part of demand planning is getting the right forecast. Very often, these forecasts come from salespeople and distributors, however, the quality of these forecasts is frequently questioned by supply chain professionals. Focus on forecast KPIs for sales teams In practice, it is necessary to measure the quality of these forecasts (the famous KPIs). But it’s a difficult task to achieve with Excel. One can safely write, that there are as many ways of evaluating these forecasts that there are companies. We provide a performance tracking table below, to quickly identify items or levels in the organization requiring corrective action. SKU Science provides a solution to educate all players in the supply chain to improve forecasting. It is possible to recreate this table by hand in Excel, but it is a complicated task and requires monthly maintenance. Several customers have asked us to be able to compare the forecasts provided by their sales departments with those calculated by our platform.Hence, on this table, you can see 2 types of forecast. All the KPIs are calculated for the 2 two types of forecasts (depending on the lead times of your supply chain, otherwise it doesn’t make any sense) and compared to each other to calculate the added value of your sales team. Measure the forecast value-added of your teams Your teams spend time forecasting, but it only makes sense if they really improve the forecasting from a tool like SKU Science or some other platform. By comparing these data, you finally know if you are adding value, which must be your only goal. Concretely, how does this happen?From historical demand data, the platform computes forecasts for the last cycles. During each cycle, a new external forecast was uploaded from Excel onto the platform. These rolling forecasts from both the platform and sales representatives are archived at each new cycle. We analyze the forecast data with one month difference for each period in the example below.Instead of focusing on forecasts at the SKU level (you can own a lot of them), we study forecasts at a more macroscopic level, such as a territory or a warehouse (in this case Paris). This exercise is replicable at any level. The platform allows us to effortlessly obtain KPI tables calculated from quantities or financially valued. In the example below, a weighted KPI table is generated from the revenue of each SKU. This remains the best option to analyze KPIs and have a real impact on the company.Each calculated forecast KPI has 3 rows. • SKU Science: indicates the values concerning the platform forecasts.• User: indicates the values for the forecast from the sales department and uploaded to the platform.• Value added: represents the improvement or degradation made by the user compared to SKU Science. Forecast value-added and weighted KPIs It is easy to see in this table that the teams’ added value related to the forecast accuracy, in red, is 4% lower than the one obtained by the platform. Therefore, it is necessary to take corrective actions during the next cycles, to turn this figure into a positive. We will share some advice in another article to be successful. Without improvement in the next cycles, it is preferable not to change any platform’s forecasts and to avoid wasting your teams’ time. Another important piece of information that can be extracted from this table is the average difference between the actual turnover for each period and the forecast bias. Here we see, thanks to the negative sign, that the sales department underestimates on average $ 634k per month compared to the actual turnover. Shortages are to be feared on certain items in the Paris area without a good inventory policy. A positive sign would indicate a tendency to overestimate quantities, which would inevitably result in an increase in inventory level. The ideal situation would be to have a percentage bias close to zero. In general, it is good practice to analyze forecast KPIs against turnover or against the margin generated by the company.For some key articles, however, it may be interesting to analyze this table from the perspective of quantities. Free Trial – Fast & Simple demand forecasting solutionEasy and affordable – No credit card requiredTry SKU Science now ! SIGN UP FREE
How to set up a demand forecasting process
Forecasting demand is always a means to an end, not the end itself. A forecast is only relevant if it enables a supply chain to take appropriate actions. A good forecasting model should allow your supply chain to improve its service level, plan better, reduce waste, and overall costs. In this blog article, we introduce the 4-dimension demand forecasting framework to help you define the appropriate process for your organization. When setting up a demand forecasting process, you will have to set it across four dimensions: granularity, temporality, metrics, and process. 1. Granularity You should first work on determining the right geographical and material granularity for your forecast. ?️ Geographical. Should you forecast per country, region, market, channel, customer segment, warehouse, store? ? Material. Should you forecast per product, segment, brand, value, raw material required? To answer those questions, you have to think about the decisions taken by your supply chain based on this forecast. Remember, a forecast is only relevant if it helps your supply chain to take action. As an example, let’s assume you need to decide which products to ship from your plant to your regional warehouses. In that case, it might be a good idea to aggregate demand per each region allocated to a warehouse and forecast demand directly at this geographical level. To forecast the demand allocated to a warehouse, it is a bad practice to use historical orders as they are impacted by logistic constraints. Instead, you should forecast the warehouse demand based on what should be served from the warehouse if there were no constraints (in other words, forecast the demand coming from the geographical region that should be served by the warehouse). Free Trial – Fast & Simple demand forecasting solutionEasy and affordable – No credit card requiredTry SKU Science now ! SIGN UP FREE 2. Temporality Once you know at what granularity level you will be working on, you should pick the right forecasting horizon and temporal aggregation (time bucket). Many supply chains stick to forecast demand 18 or 24 months ahead, although you may need to pick a limited horizon to focus on. ?️ Temporal Aggregation. What temporal aggregation bucket should you use (day, week, month, quarter or year)? ? Horizon. How many periods do you need to forecast (one month, six months, two years)? Again, you should answer these questions by thinking about what your supply chain is trying to optimize/achieve and the lead times involved with these decisions. For example, let’s assume your suppliers (or production plants) need to receive monthly orders/forecasts three months in advance. In that case, you know you should work on monthly buckets and with a horizon of 3 months (M+1/+2/+3). You can also skip M+4 (or at least not focus on it). If you need a forecast to know what goods to ship from your central warehouse to your local warehouses, you should focus on a horizon equivalent to your internal lead time. ❗ Models and Forecasting Horizon. Statistical models can easily produce forecasts over a very long horizon (theoretically infinite). This is not the case with machine learning models. So, you might have to stick to statistical models for long-term forecasting. 3. Metrics Usually, practitioners overlook the question of forecasting metrics. Choosing the right metrics for a forecasting process/model is actually quite simple and will have profound impacts on the resulting forecasts. Depending on the metric selected, you might give too much importance to outliers (the RMSE flaw) or risk a biased forecast (the MAE flaw). Here are a few pieces of advice to choose the right forecasting metrics: ❌ Avoid MAPE. Many practitioners still use MAPE as a forecasting metric. This is a highly skewed indicator that will promote underforecasting. ✅ Combine KPIs. Choosing a combination of KPIs (such as MAE & Bias) will often be a good compromise enabling you to track accuracy and Bias while avoiding most pitfalls. ✅ Track consistent Bias. If a consistent bias (over/under forecasting) is observed on an item, something is probably wrong with the model/forecasting process. ? Weighted KPIs. You can try weighting each product (or SKU) in the overall metric calculation based on its profitability, cost, or overall supply chain impact. The idea is that you want to pay more attention to SKUs that matter the most. Beyond the math, it is important to align the forecasting KPIs to the required material and temporal granularities. For example, let’s assume you are interested in ordering goods from an overseas supplier with a lead time of 3 months. In that case, you should measure accuracy over a forecasting horizon at months +1,+2, and +3–or even better, calculate the cumulative error over three months–instead of merely looking at the accuracy achieved at month+1. 4. Demand forecasting process Now that you know your material and temporal aggregation, horizon, and metrics, you can set up a process. This process should be defined through three specific aspects. – Stakeholders. Who will review the forecast? Bringing different points of view to the table – using various information sources – will help create a more accurate forecast. – Periodicity. When do you review the forecast? Updating your forecast more often might improve its accuracy (as you have fresher data at hand). Updating it too often might create chaos as you overreact to demand changes and consume too many resources for a limited added value. – Review Process. How do you review the forecast? At the core of any forecasting process, there should be a measurement of the forecast value-added. Tracking each team member’s value-added will enable you to improve the forecasting process efficiency (and refine the relevant forecasting periodicity and stakeholders). ? Forecast Value Added Framework. A forecasting process framework that tracks each team/process step’s added value compared to a benchmark (or the previous team’s input). It was imagined and promoted by Michael Gilliland in the 2010s (see his book here). Our advice for your demand forecasting process – Short-term forecast. Let’s assume you need to decide every week what to
6 tips to get reliable demand forecasts post-COVID-19
The COVID-19 crisis has impacted all demand forecasting models, whatever your industry. Depending on the type of items you’re dealing with (low vs high demand) or your industry sector (retail, food, health care, utilities, logistic, manufacturing etc.), some measures need to be taken in order to plan (relatively) accurately for the months to come. The question is: “How should we treat actual demand data for March, April, and May 2020?” Demand during COVID-19 and its impact on the forecast model (Season & Trend model) Well, as you can imagine, there is not a single answer to this question, but we will try below to list all available options. We’re pretty sure that one will fit your business. The first obvious option is to tag these values as outliers. If you decide to treat March to May 2020 demand data as outliers, you then have several options for your forecast calculations. • You can remove them from the forecasting window. • You can implement some tactics to reduce their weight in your forecast models. • You can use historical data from previous years to replace these particular periods, by taking an average from 2017 – 2019 as an example. • You can use calculated forecast data from your model to replace these particular values. We will explain soon in another article how to quickly obtain your forecasts using this method with SKU Science. Demand during COVID-19 replaced with forecast data (Season & Trend model) Once your tactical choice is made – and taking into account that we are still in a VUCA world – you may consider allowing your forecast baseline to quickly adjust to changes in demand by increasing forecast parameters sensitivity. Dealing with Q1 and Q2, 2020 demand data will not just help you to properly plan for the rest of the year but also for Q1 and Q2, 2021. Indeed, for those with a seasonal demand, it is likely that your forecast models will “learn” a COVID-19 seasonality and predict a demand drop in March – May 2021. For some industries, flagging the lockdown months as outliers is not always the best option, because those sales could represent a new structural change that is likely to persist into the future – video conferencing solutions and medical teleconsultations spring to mind. Another example is an increased consumption of flour that could remain at this higher level in the months to come, as home cooking has gained the upper hand over fast food and eating out. Therefore, it is important for demand planners to know their industry so that they can properly interpret historical demand. In short, flagging recent demand as outliers is counterproductive if your customers have now changed their long-term behavior due to the crisis. Try our fast & simple demand forecasting solutionSign up for free to SKU Science today!Pre-loaded sample date – No credit card required SIGN UP FREE Another tactic to optimize your sales – while optimizing your working capital – would be to maintain low inventory levels for end products while maintaining a good level of raw material and WIP to allow for a rapid production ramp-up, in order to stay ahead if a demand increase arises. In conclusion, there is no forecasting silver bullet that is effective for all businesses. The demand over the last 3 months has been exceptional: it is likely that, in most cases, the post-COVID-19 demand will be different from the demand we have experienced during the last 3 months. Hence – except for some essential industries (e-commerce and food etc.) – it would be a safe bet to alter the demand data for March, April, and even May. If you currently use a statistical or a black-box algorithm it might be time to review how your forecast engine works to prevent it from overreacting to the COVID-19 crisis.
What is the right inventory policy for your business?
Whatever your activity, optimizing your inventory management is key to reducing the many associated costs, such as capital investments, risks, deterioration, insurance, personnel, logistics, and infrastructure. The ease or difficulty of accurately predicting a demand through the correct demand planning process, depends on your business activities. Some items may show a good forecast accuracy while others can demonstrate intermittent demand and are therefore difficult to predict.Therefore, understanding the different inventory policies allows you to choose the most suitable system in relation to the cost strategy associated with each item. An inventory system sets out “how many” parts to order or manufacture. There are three options: – Lot for lot: in which the order exactly matches the consumption requirements for the period– Fixed order quantity: for example, this might depend on the packaging (or multiples of these lots)– n periods of supply: a quantity large enough to meet demands during these n periods. An inventory policy also specifies “when” to place purchase orders or manufacturing orders. Two options are referenced: – Order point: the order is triggered when the stock falls below a certain threshold (for example, an order is made when there are fewer than 10 items available in stock).– Periodic replenishment: The time between two orders is fixed (for example, an order is placed every Monday morning). In this article we will cover two commonly used policies: – The order point (fixed replenishment)– Periodic replenishment (variable quantity) Try our fast & simple demand forecasting solution Sign up for free to SKU Science today!Pre-loaded sample date – No credit card required SIGN UP FREE Order point policy (fixed replenishment) This method is used when an order of the same quantity is placed with a supplier (or a production order) every time the level of available stock falls below a predetermined threshold. See example below: If 3, or fewer than 3, items remain in stock, a replenishment order is generated and will always be fixed at 10 items. Benefits: This is a reassuring policy that allows you to place an order whenever necessary. Limitations: It does not allow you to group orders with the same supplier, which can incur additional costs. Periodic replenishment (of variable quantity) This is a “fixed schedule” replenishment system, where the time between two replenishment orders is the same.The quantity ordered during each period is always different, and is based on the stock target and the inventory position at the time of the order.In the example below, we show an order placed every 5 periods, so as to reach a final stock level equal to 13. Benefits: The periodic replenishment is the most commonly used policy, because it allows you to group orders with the same supplier. This method benefits both your organization and your supplier by limiting exchanges, and by optimizing planning operations such as limiting the number of deliveries, and the loading of trucks or other means of transportation. Limitations: A future article dedicated to safety stock, will demonstrate that this policy is, in fact, riskier than the order point policy because of activity problems that can arise from depletion. Indeed. if you are already out of stock on Monday and you can only place your order on Friday, you will be 4 days without stock, since it is impossible to order between two periods. This is likely to result in lost revenue and missed sales opportunities. This kind of problem does not exist with the order point (fixed replenishment) method. Other inventory policies are possible Many other inventory policies can be envisaged. Thus it is possible to replenish a fixed quantity, during each period. It suffices to set the trigger for a minimum order threshold. These differing policies can be adapted according to the needs of each activity. However, the formulas for calculating safety stock would then be much more complicated to implement. You can get more details on the different inventory management policies and demand forecasting methods in the book Data Science for Supply Chain Forecast, written by Nicolas Vandeput. Try our fast & simple demand forecasting solution Sign up for free to SKU Science today!Pre-loaded sample date – No credit card required SIGN UP FREE
How to detect outliers for improved demand forecasting
Most supply chains expect some demand variability and therefore, one must choose the correct forecast model, as can be seen in our previous articles. Regardless of the nature of this variance, exceptional factors may happen, and can seriously impair the reliability of a given model. We call these data “outliers”. These outliers result firstly from exceptional demand, such as stock liquidations, temporary stops in production, or external restrictions, which may be due to logistical or infrastructural constraints making both the composition of the stock or the fulfillment of customer orders temporarily impossible. Even though some demand observations are real, it does not mean they are not exceptional and shouldn’t be cleaned Secondly, there are also mistakes and errors, which are obvious outliers. If you spot these kinds of errors or encoding mistakes, you need to implement process improvements in order to prevent them from happening again. Considering the negative effects outliers can bring to your business, knowing how to detect them is essential and there are some techniques that can be used to address this issue. Try our fast & simple demand forecasting solution Sign up for free to SKU Science today!Pre-loaded sample date – No credit card required SIGN UP FREE Winsorization This first idea is a rather simplistic approach of defining a certain minimum and maximum range in which the data will simply be disregarded. Statistically, this margin is defined as the percentile, which means the value below which x% of the observations in a group will fall. For example, 99% of the demand observations for a product will be lower than its 99th percentile. This approach may be efficient, but it can also result in some problems, such as detecting fake outliers in a dataset without outliers (see figure 1 below); or in the case of real outliers, it doesn’t sufficiently reduce the value keeping it at a level way above our expectation. It is an approach that ends up requiring a very accurate critical analysis by the planner, so it is not as efficient and reliable as it should be, as it would take out the high and low values of a dataset, even if they aren’t exceptional per se. Figure 1: Winsorization of a simple dataset without outlier Standard Deviation Another approach would be to use the demand variation around the historical average and exclude the values that are exceptionally far from this average, according to a certain range between two thresholds centered on the demand. Figure 2: Distance to the mean With this method, in a situation without outliers, we do not change any demand observation (we keep all values in this case), and in another situation with an outlier (see Y2 below), we do not change the low or high demand points but only the actual outlier (from 100 to 49 in the figure below). Figure 3: Outlier detection based on standard deviation (Normalization) Although it may seem to solve all the issues arising from the Winsorization approach, the actual limitation will arise when you have a product with a trend or seasonality. In this case, its historical average will not accurately represent the actual average for that given period, and consequently the thresholds may not correctly limit a possible outlier. Error standard deviation In order to solve the drawbacks from the Standard Deviation and Winsorization let’s go back to the definition of an outlier. An outlier is a value that you didn’t expect. In other words, it is a value far away from your prediction (i.e. your forecast). To spot outliers, we must therefore analyze the forecast error and see which periods are exceptionally wrong. If we compute the error for our forecast we would obtain a mean error and a standard deviation. You can refer to the book Data Science for Supply Chain Forecast, by Nicolas Vandeput to see real-life examples and more details. The figure below illustrates our results obtained from our forecast. Figure 4: Outlier detection via forecast error This smarter detection method, which analyzes the forecast error deviation instead of simply the demand variation around the mean, is able to flag the outliers much more precisely and reduce them back to a plausible value. Therefore, this should be your preferred method for accurate demand forecasting. Try our fast & simple demand forecasting solution Sign up for free to SKU Science today!Pre-loaded sample date – No credit card required SIGN UP FREE
