Daily Demand Scenarios

In this Videoblog, we will explain how time series forecasting has emerged as a pivotal tool, particularly at the daily level, which Smart Software has been pioneering since its inception over forty years ago. The evolution of business practices from annual to more refined temporal increments like monthly and now daily data analysis illustrates a significant shift in operational strategies.

Initially, during the 1980s, the usual practice of using annual data for forecasting and the introduction of monthly data was considered innovative. This period marked the beginning of a trend toward increasing the resolution of data analysis, enabling businesses to capture and react to faster shifts in market dynamics. As we progressed into the 2000s, the norm of monthly data analysis was well-established, but the ‘cool kids’—innovators at the edge of business analytics—began experimenting with weekly data. This shift was driven by the need to synchronize business operations with increasingly volatile market conditions and consumer behaviors that demanded more rapid responses than monthly cycles could provide. Today, in the 2020s, while monthly data analysis remains common, the frontier has shifted again, this time towards daily data analysis, with some pioneers even venturing into hourly analytics.

The real power of daily data analysis lies in its ability to provide a detailed view of business operations, capturing daily fluctuations that might be overlooked by monthly or weekly data.  However, the complexities of daily data necessitate advanced analytical approaches to extract meaningful insights. At this level, understanding demand requires grappling with concepts like intermittency, seasonality, trend, and volatility. Intermittency, or the occurrence of zero-demand days, becomes more pronounced at a daily granularity and demands specialized forecasting techniques like Croston’s method for accurate predictions. Seasonality at a daily level can reveal multiple patterns—such as increased sales on weekends or holidays—that monthly data would mask. Trends can be observed as short-term increases or decreases in demand, demanding agile adjustment strategies. Finally, volatility at the daily level is accentuated, showing more significant swings in demand than seen in monthly or weekly analyses, which can affect inventory management strategies and the need for buffer stock. This level of complexity underscores the need for sophisticated analytical tools and expertise in daily data analysis.

In conclusion, the evolution from less frequent to daily time series forecasting marks a substantial shift in how businesses approach data analysis. This transition not only reflects the accelerating pace of business but also highlights the requirement for tools that can handle increased data granularity. Smart Software’s dedication to refining its analytical capabilities to manage daily data highlights the industry’s broader move towards more dynamic, responsive, and data-driven decision-making. This shift is not merely about keeping pace with time but about leveraging detailed insights to forge competitive advantages in an ever-changing business environment.

 

The Cost of Spreadsheet Planning

Companies that depend on spreadsheets for demand planning, forecasting, and inventory management are often constrained by the spreadsheet’s inherent limitations. This post examines the drawbacks of traditional inventory management approaches caused by spreadsheets and their associated costs, contrasting these with the significant benefits gained from embracing state-of-the-art planning technologies.

Spreadsheets, while flexible for their infinite customizability, are fundamentally manual in nature requiring significant data management, human input, and oversight. This increases the risk of errors, from simple data entry mistakes to complex formula errors, that cause cascading effects that adversely impact forecasts.  Additionally, despite advances in collaborative features that enable multiple users to interact with a common sheet, spreadsheet-based processes are often siloed. The holder of the spreadsheet holds the data.  When this happens, many sources of data truth begin to emerge.  Without the trust of an agreed-upon, pristine, and automatically updated source of data, organizations don’t have the necessary foundation from which predictive modeling, forecasting, and analytics can be built.

In contrast, advanced planning systems like Smart IP&O are designed to overcome these limitations. Such systems are built to automatically ingest data via API or files from ERP and EAM systems, transform that data using built in ETL tools, and can process large volumes of data efficiently.  This enables businesses to manage complex inventory and forecasting tasks with greater accuracy and less manual effort because the data collection, aggregation, and transformation is already done. Transitioning to advanced planning systems is key for optimizing resources for several reasons.

Spreadsheets also have a scaling problem. The bigger the business grows, the greater the number of spreadsheets, workbooks, and formulas becomes.  The result is a tightly wound and rigid set of interdependencies that become unwieldy and inefficient.  Users will struggle to handle the increased load and complexity with slow processing times and an inability to manage large datasets and face challenges collaborating across teams and departments.

On the other hand, advanced planning systems for inventory optimization, demand planning, and inventory management are scalable, designed to grow with the business and adapt to its changing needs. This scalability ensures that companies can continue to manage their inventory and forecasting effectively, regardless of the size or complexity of their operations. By transitioning to systems like Smart IP&O, companies can not only improve the accuracy of their inventory management and forecasting but also gain a competitive edge in the market by being more responsive to changes in demand and more efficient in their operations.

Benefits of Jumping in: An electric utility company struggled to maintain service parts availability without overstocking for over 250,000-part numbers across a diverse network of power generation and distribution facilities. It replaced their twenty-year-old legacy planning process that made heavy use of spreadsheets with Smart IP&O and a real-time integration to their EAM system.  Before Smart, they were only able to modify Min/Max and Safety Stock levels infrequently.  When they did, it was nearly always because a problem occurred that triggered the review.  The methods used to change the stocking parameters relied heavily on gut feel and averages of the historical usage.   The Utility leveraged Smart’s what-if scenarios to create digital twins of alternate stocking policies and simulated how each scenario would perform across key performance indicators such as inventory value, service levels, fill rates, and shortage costs.  The software pinpointed targeted Min/Max increases and decreases that were deployed to their EAM system, driving optimal replenishments of their spare parts.  The result:  A significant inventory reduction of $9 million that freed up cash and valuable warehouse space while sustaining 99%+ target service levels.

Managing Forecast Accuracy: Forecast error is an inevitable part of inventory management, but most businesses don’t track it.  As Peter Drucker said, “You can’t improve what you don’t measure.”  A global high-tech manufacturing company utilizing a spreadsheet-based forecast process had to manually create its baseline forecasts and forecast accuracy reporting.  Given the planners’ workload and siloed processes, they just didn’t update their reports very often, and when they did, the results had to be manually distributed.  The business didn’t have a way of knowing just how accurate a given forecast was and couldn’t cite their actual errors by group of part with any confidence.  They also didn’t know whether their forecasts were outperforming a control method.  After Smart IP&O went live, the Demand Planning module automated this for them. Smart Demand Planner now automatically reforecasts their demand each planning cycle utilizing ML methods and saves accuracy reports for every part x location.  Any overrides that are applied to the forecasts can now be auto-compared to the baseline to measure forecast value add – i.e., whether the additional effort to make those changes improved the accuracy.  Now that the ability to automate the baseline statistical forecasting and produce accuracy reports is in place, this business has solid footing from which to improve their forecast process and resulting forecast accuracy.

Get it Right and Keep it Right:  Another customer in the aftermarket parts business has used Smart’s forecasting solutions since 2005 – nearly 20 years!  They were faced with challenges forecasting intermittently demanded parts sold to support their auto aftermarket business. By replacing their spreadsheet-based approach and manual uploads to SAP with statistical forecasts of demand and safety stock from SmartForecasts, they were able to significantly reduce backorders and lost sales, with fill rates improving from 93% to 96% within just three months.  The key to their success was leveraging Smart’s patented method for forecasting intermittent demand – The “Smart-Willemain” bootstrap method generated accurate estimates of the cumulative demand over the lead time that helped ensure better visibility of the possible demands.

Connecting Forecasts to the Inventory Plan: Advanced planning systems support forecast-based inventory management, which is a proactive approach that relies on demand forecasts and simulations to predict possible outcomes and their associated probabilities.  This data is used to determine optimal inventory levels.  Scenario-based or probabilistic forecasting contrasts with the more reactive nature of spreadsheet-based methods. A longtime customer in the fabric business, previously dealt with overstocks and stockouts due to intermittent demand for thousands of SKUs. They had no way of knowing what their stock-out risks were and so couldn’t proactively modify policies to mitigate risk other than making very rough-cut assumptions that tended to overstock grossly.  They adopted Smart Software’s demand and inventory planning software to generate simulations of demand that identified optimal Minimum On-Hand values and order quantities, maintaining product availability for immediate shipping, highlighting the advantages of a forecast-based inventory management approach.

Better Collaboration:  Sharing forecasts with key suppliers helps to ensure supply.  Kratos Space, part of Kratos Defense & Security Solutions, Inc., leveraged Smart forecasts to provide their Contract Manufacturers with better insights on future demand.  They used the forecasts to make commitments on future buys that enabled the CM to reduce material costs and lead times for engineered-to-order systems. This collaboration demonstrates how advanced forecasting techniques can lead to significant supply chain collaboration that yields efficiencies and cost savings for both parties.

 

Learning from Inventory Models

In this video blog, we explore the integral role that inventory models play in shaping the decision-making processes of professionals across various industries. These models, whether they are tangible computer simulations or intangible mental constructs, serve as critical tools in managing the complexities of modern business environments. The discussion begins with an overview of how these models are utilized to predict outcomes and streamline operations, emphasizing their relevance in a constantly evolving market landscape.

​The discussion further explores how various models distinctly influence strategic decision-making processes. For instance, the mental models professionals develop through experience often guide initial responses to operational challenges. These models are subjective, built from personal insights and past encounters with similar situations, allowing quick, intuitive decision-making. On the other hand, computer-based models provide a more objective framework. They use historical data and algorithmic calculations to forecast future scenarios, offering a quantitative basis for decisions that need to consider multiple variables and potential outcomes. This section highlights specific examples, such as the impact of adjusting order quantities on inventory costs and ordering frequency or the effects of fluctuating lead times on service levels and customer satisfaction.

In conclusion, while mental models provide a framework based on experience and intuition, computer models offer a more detailed and numbers-driven perspective. Combining both types of models allows for a more robust decision-making process, balancing theoretical knowledge with practical experience. This approach enhances the understanding of inventory dynamics and equips professionals with the tools to adapt to changes effectively, ensuring sustainability and competitiveness in their respective fields.

 

 

The Methods of Forecasting

​Demand planning and statistical forecasting software play a pivotal role in effective business management by incorporating features that significantly enhance forecasting accuracy. One key aspect involves the utilization of smoothing-based or extrapolative models, enabling businesses to quickly make predictions based solely on historical data. This foundation rooted in past performance is crucial for understanding trends and patterns, especially in variables like sales or product demand. Forecasting software goes beyond mere data analysis by allowing the blending of professional judgment with statistical forecasts, recognizing that forecasting is not a one-size-fits-all process. This flexibility enables businesses to incorporate human insights and industry knowledge into the forecasting model, ensuring a more nuanced and accurate prediction.

Features such as forecasting multiple items as a group, considering promotion-driven demand, and handling intermittent demand patterns are essential capabilities for businesses dealing with diverse product portfolios and dynamic market conditions.  Proper implementation of these applications empowers businesses with versatile forecasting tools, contributing significantly to informed decision-making and operational efficiency.

Extrapolative models

Our demand forecasting solutions support a variety of forecasting approaches including extrapolative or smoothing-based forecasting models, such as exponential smoothing and moving averages.  The philosophy behind these models is simple: they try to detect, quantify, and project into the future any repeating patterns in the historical data.

  There are two types of patterns that might be found in the historical data:

  • Trend
  • Seasonality

These patterns are illustrated in the following figure along with random data.

The Methods of Forecasting

 

Illustrating trending, seasonal, and random time series data

If the pattern is a trend, then extrapolative models such as double exponential smoothing and linear moving average estimates the rate of increase or decrease in the level of the variable and project that rate into the future.

If the pattern is seasonality, then models such as Winters and triple exponential smoothing estimate either seasonal multipliers or seasonal add factors and then apply these to projections of the nonseasonal portion of the data.

Very often, especially with retail sales data, both trend and seasonal patterns are involved. If these patterns are stable, they can be exploited to give very accurate forecasts.

Sometimes, however, there are no obvious patterns, so that plots of the data look like random noise. Sometimes patterns are clearly visible, but they change over time and cannot be relied upon to repeat. In these cases, the extrapolative models don’t try to quantify and project patterns. Instead, they try to average through the noise and make good estimates of the middle of the distribution of data values. These typical values then become the forecasts.  Sometimes, when users see a historical plot with lots of ups and downs they are concerned when the forecast doesn’t replicate those ups and downs. Normally, this should not be a reason for concern.  This occurs when the historical patterns aren’t strong enough to warrant using a forecasting method that would replicate the pattern.  You want to make sure your forecasts don’t suffer from the “wiggle effect” that is described in this blog post.

Past as a predictor of the future

The key assumption implicit in extrapolative models is that the past is a good guide to the future. This assumption, however, can break down. Some of the historical data may be obsolete. For example, the data might describe a business environment that no longer exists. Or, the world that the model represents may be ready to change soon, rendering all the data obsolete. Because of such complicating factors, the risks of extrapolative forecasting are lower when forecasting only a short time into the future.

Extrapolative models have the practical advantage of being cheap and easy to build, maintain and use. They require only accurate records of past values of the variables you need to forecast. As time goes by, you simply add the latest data points to the time series and reforecast. In contrast, the causal models described below require more thinking and more data. The simplicity of extrapolative models is most appreciated when you have a massive forecasting problem, such as making overnight forecasts of demand for all 30,000 items in inventory in a warehouse.

Judgmental adjustments

Extrapolative models can be run in a fully automatic mode with Demand Planner with no intervention required. Causal models require substantive judgment for wise selection of independent variables. However, both types of statistical models can be enhanced by judgmental adjustments. Both can profit from your insights.

Both causal and extrapolative models are built on historical data. However, you may have additional information that is not reflected in the numbers found in the historical record. For instance, you may know that competitive conditions will soon change, perhaps due to price discounts, or industry trends, or the emergence of new competitors, or the announcement of a new generation of your own products. If these events occur during the period for which you are forecasting, they may well spoil the accuracy of purely statistical forecasts. Smart Demand Planner’ graphical adjustment feature lets you include these additional factors in your forecasts through the process of on- screen graphical adjustment.

Be aware that applying user adjustments to the forecast is a two-edged sword. Used appropriately, it can enhance forecast accuracy by exploiting a richer set of information. Used promiscuously, it can add additional noise to the process and reduce accuracy. We advise that you use judgmental adjustments sparingly, but that you never blindly accept the predictions of a purely statistical forecasting method.  It is also very important to measure forecast value add.  That is, the value added to the forecast process by each incremental step.  For example, if you are applying overrides based on business knowledge, it is important to measure whether those adjustments are adding value by improving forecast accuracy.  Smart Demand Planner supports measurement of forecast value add by tracking every forecast considered and automating the forecast accuracy reports. You can select statistical forecasts, measure their errors, and compare them to the overridden ones.  By doing so, you inform the forecasting process so that better decisions can be made in the future. 

Multiple-level forecasts

Another common situation involves multiple-level forecasting, where there are multiple items being forecast as a group or there may even be multiple groups, with each group containing multiple items. We will generally call this type of forecasting Multilevel Forecasting. The prime example is product line forecasting, where each item is a member of a family of items, and the total of all the items in the family is a meaningful quantity.

For example, as in the following figure, you might have a line of tractors and want forecasts of sales for each type of tractor and for the entire tractor line.

The Methods of Forecasting 2

Illustrating multiple-level product forecasts

 Smart Demand Planner provides Roll Up/Roll Down Forecasting. This function is crucial for obtaining comprehensive forecasts of all product items and their group total. The Roll Down/Roll Up method within this feature offers two options for obtaining these forecasts:

Roll Up (Bottom-Up): This option initially forecasts each item individually and then aggregates the item-level forecasts to generate a family-level forecast.

Roll Down (Top-Down): Alternatively, the roll-down option starts by forming the historical total at the family level, forecasts it, and then proportionally allocates the total down to the item level.

When utilizing Roll Down/Roll Up, you have access to the full array of forecast methods provided by Smart Demand Planner at both the item and family levels. This ensures flexibility and accuracy in forecasting, catering to the specific needs of your business across different hierarchical levels.

Forecasting research has not established clear conditions favoring either the top-down or bottom-up approach to forecasting. However, the bottom-up approach seems preferable when item histories are stable, and the emphasis is on the trends and seasonal patterns of the individual items. Top-down is normally a better choice if some items have very noisy history or the emphasis is on forecasting at the group level. Since Smart Demand Planner makes it fast and easy to try both a bottom-up and a top- down approach, you should try both methods and compare the results.  You can use Smart Demand Planner’s “Hold back on Current”  feature in the “Forecast vs. Actual” to test both approaches on your own data and see which one yields a more accurate forecast for your business. 

 

Looking for Trouble in Your Inventory Data

In this video blog, the spotlight is on a critical aspect of inventory management: the analysis and interpretation of inventory data. The focus is specifically on a dataset from a public transit agency detailing spare parts for buses. With over 13,700 parts recorded, the data presents a prime opportunity to delve into the intricacies of inventory operations and identify areas for improvement.

Understanding and addressing anomalies within inventory data is important for several reasons. It not only ensures the efficient operation of inventory systems but also minimizes costs and enhances service quality. This video blog explores four fundamental rules of inventory management and demonstrates, through real-world data, how deviations from these rules can signal underlying issues. By examining aspects such as item cost, lead times, on-hand and on-order units, and the parameters guiding replenishment policies, the video provides a comprehensive overview of the potential challenges and inefficiencies lurking within inventory data. 

We highlight the importance of regular inventory data analysis and how such an analysis can serve as a powerful tool for inventory managers, allowing them to detect and rectify problems before they escalate. Relying on antiquated approaches can lead to inaccuracies, resulting in either excess inventory or unfulfilled customer expectations, which in turn could cause considerable financial repercussions and inefficiencies in operations.

Through a detailed examination of the public transit agency’s dataset, the video blog conveys a clear message: proactive inventory data review is essential for maintaining optimal inventory operations, ensuring that parts are available when needed, and avoiding unnecessary expenditures.

Leveraging advanced predictive analytics tools like Smart Inventory Planning and Optimization will help you control your inventory data. Smart IP&O will show you decisive demand and inventory insights into evolving spare parts demand patterns at every moment, empowering your organization with the information needed for strategic decision-making.