Inventory overstocking can harm both financial stability and operational efficiency. When an organization is overstocked, it ties up capital in excess inventory that might not sell, increasing storage costs and the risk of inventory obsolescence. Additionally, the funds used to purchase the excess inventory could have been better invested in other areas of the business, such as marketing or research and development. Overstocking also hampers cash flow, as money is locked in stock rather than available for immediate operational needs. Managing inventory effectively is critical for maintaining a healthy balance sheet and ensuring that resources are optimally allocated. Here is an in-depth exploration of the main causes of overstocking, their implications, and possible solutions.

1 Inaccurate Demand Forecasting

One of the primary causes of overstocking is inaccurate demand forecasting. When businesses rely on outdated forecasting methods or insufficient data, they can easily overestimate demand, leading to overstocking. A prime example is the clothing industry, where fashion trends can change rapidly. A well-known fashion brand recently faced challenges after overestimating demand for a new clothing line based on flawed data analysis, leading to unsold inventory.

To address this issue, companies can implement new technologies that automatically select the best forecasting methods for the data, incorporating trends and seasonal patterns to ensure accuracy. By improving forecasting accuracy, businesses can better align their inventory with actual demand, leading to more precise inventory management and fewer overstock scenarios. For instance, a Hardware retailer using Smart Demand Planner reduced forecasting errors by 15%, demonstrating the potential for significant improvement in inventory management​​​​.

2 Improper Inventory Management

Effective inventory management is fundamental to prevent overstocking. Without accurate systems to track inventory levels, businesses might order excess stock and incur higher expenses. This issue often stems from reliance on spreadsheets or inefficient ERP systems that lack real-time data integration.

State-of-the-art technologies provide real-time visibility into inventory levels, allowing businesses to automate and optimize reordering processes.  A large electric utility company faced challenges in maintaining service parts availability without overstocking, managing over 250,000 part numbers across a diverse network of power generation and distribution facilities. The company replaced its outdated system with Smart IP&O and integrated it in real-time with their Enterprise Asset Management (EAM) system. Smart IP&O enabled the utility to use “what-if” scenarios, creating digital twins of alternate stocking policies and simulating performance across key performance indicators, such as inventory value, service levels, fill rates, and shortage costs. This allowed the utility to make targeted adjustments to their stocking parameters, which were then deployed to their EAM system, driving optimal replenishments of spare parts.

The outcome was significant: a $9 million reduction in inventory, freeing up cash and valuable warehouse space while sustaining target service levels of over 99%​

3 Overly Optimistic Sales Projections

Businesses, especially those in growth phases, may predict higher sales than they achieve, leading to excess inventory intended to meet anticipated demand that never materializes. An example of this is the recent case with an electric vehicle manufacturer that projected high sales for its truck but faced production delays and lower-than-expected demand, resulting in an overstock of components and parts. This miscalculation led to increased storage costs and strained financial resources.

Another automotive aftermarket company struggled to forecast intermittently demanded parts accurately, frequently resulting in overstocking and stockouts.  Using AI-driven technology enabled the company to significantly reduce backorders and lost sales, with fill rates improving from 93% to 96% within just three months. By leveraging Smart IP&O forecasting technologies, the company could generate accurate estimates of cumulative demand over lead times, providing better visibility of potential demand scenarios. This allowed for optimized inventory levels, reducing storage costs and improving financial efficiency by aligning inventory with actual demand​.

4 Bulk Purchasing Discounts

The appeal of cost savings from bulk purchases can prompt businesses to buy more than needed, tying up capital and storage space. This often leads to storage challenges when excess stock is ordered to secure a discount.

To address this challenge, businesses should weigh the benefits of bulk discounts against the costs of holding excess inventory. Next-generation technology can help identify the most cost-effective purchasing strategy by balancing immediate savings with long-term storage costs. By implementing Smart IP&O, MNR could accurately forecast inventory requirements and optimize its inventory management processes. This led to an 8% reduction in parts inventory, reaching a high customer service level of 98.7% and reducing inventory growth for new equipment from a projected 10% to only 6%.

5 Seasonal Demand Fluctuations

Difficulty in aligning inventory with seasonal demand can result in surplus stock once the peak sales period ends. Toy manufacturers, for example, might produce too many holiday-themed toys only to face low demand after the holidays. The fashion industry frequently experiences similar challenges, with certain styles becoming obsolete as seasons change. The latest technologies can help businesses anticipate seasonal demand shifts and adjust inventory levels accordingly. By analyzing past sales data and predicting future trends, businesses can better prepare for seasonal fluctuations, minimize overstocking risk, and improve inventory turnover.

6 Supplier Lead Time Variability

Unreliable supplier lead times can lead to overstocking as a buffer against delays. If lead times improve or demand decreases unexpectedly, businesses may have excess inventory. For example, an auto parts distributor might stockpile components to mitigate supplier delays, only to find lead times improving suddenly.

Advanced technology can help by providing real-time data and predictive analytics to manage lead time variability better. These tools allow companies to dynamically adjust their orders, reducing the need for excessive safety stock.

7 Inadequate Inventory Policies

Outdated or incorrect inventory policies, such as faulty Min/Max settings, can lead to over-ordering.  However, using Modern technology to regularly review and update inventory policies ensures they align with current business needs and market conditions. By keeping policies up-to-date, businesses can reduce the risk of overstocking due to procedural errors. A recent case study demonstrated how a major retailer used Smart IP&O to revise inventory policies, resulting in a 15% reduction in overstock​​.

8 Promotions and Marketing Campaigns

Misalignment between marketing efforts and actual customer demand can cause businesses to overestimate the impact of promotions, resulting in unsold inventory. For example, a cosmetics company might overproduce a limited edition product, expecting high demand that doesn’t materialize. Leveraging Smart IP&O can help align marketing initiatives with realistic demand expectations, avoiding excess stock. By integrating marketing plans with demand forecasts, businesses can optimize their promotional strategies to better match actual customer interest.

9 Fear of Stockouts

Companies often maintain higher inventory levels to avoid stockouts, which can lead to lost sales and unhappy customers. This fear can drive businesses to overstock as a safety net, especially in industries where customer satisfaction and retention are crucial. A notable example comes from a large retail chain that significantly increased its inventory of household goods to avoid stockouts. While this strategy initially helped meet customer demand, it later resulted in excess inventory as consumer purchasing patterns stabilized. This overstocking contributed to a profit drop of nearly 90% in the second quarter, largely due to markdowns and the clearing of excess stock.

To mitigate such situations, businesses can utilize advanced inventory planning and optimization tools to provide accurate demand forecasts. For instance, a leading electronics manufacturer used Smart IP&O solution to reduce inventory levels by 20% without impacting service levels, effectively reducing costs while maintaining customer satisfaction by ensuring they had the right amount of stock on hand​​​​.

10 Overcompensation for Supply Chain Issues

Businesses may overstock to safeguard against ongoing supply chain disruptions, but this can lead to storage issues. For instance, a tech company might stockpile components to avoid potential supply chain hiccups, resulting in surplus inventory and increased costs. Advanced systems can help businesses better anticipate and respond to supply chain challenges, balancing the need for safety stock with the risk of overstocking. A technology firm used Smart IP&O to streamline its inventory strategy, reducing excess stock by 20% while maintaining supply chain resilience​​.

11 Long Lead Times and Unreliable Suppliers

Prolonged lead times and unreliable suppliers can lead businesses to order more stock than needed to cover potential supply gaps. However, less critical Items that are forecasted to achieve very high service levels represent opportunities to reduce inventory.  By targeting lower service levels on less critical items, inventory will be “right size” over time to the new equilibrium, decreasing holding costs and the value of inventory on hand. A major public transit system reduced inventory by more than $4,000,000 while improving service levels using our cutting-edge technology.

12 Lack of Real-Time Inventory Visibility

Without real-time insights into inventory, businesses often order more stock than necessary, leading to inefficiencies and increased costs. Smart IP&O enabled Seneca companies to model demand at each stocking location and, using service level-driven planning, determine how much to stock to achieve the service level we require.  By running and comparing different scenarios, they can easily define and update optimal stocking policies for each tech support rep and stockrooms.

The software has provided field technicians with evidence they did not have before, showing them their actual consumption, frequency of part use, and rationale for stocking policies, using 90% as the targeted service level norm.  Field technicians have embraced its use, with significant results:  “Zero Turns” inventory has dropped from $400K to under $100K, “First Fix Rate” exceeds 90%, and total inventory investment has decreased by more than 25%, from $11 million to $ 8 million.

In conclusion, overstocking seriously threatens business profitability and efficiency, leading to increased storage costs, tied-up capital, and potential obsolescence of goods. These issues can strain resources and limit a company’s ability to respond to market changes. However, overstocking can be effectively managed by understanding its causes, such as inaccurate demand forecasting, prolonged lead times, and unreliable suppliers. Implementing robust AI-driven solutions like Smart IP&O can help businesses optimize inventory levels, reduce excess stock, and enhance operational efficiency. By leveraging advanced forecasting and inventory optimization tools, companies can find the right balance in meeting customer demand and minimizing inventory-related costs.

Demand planning goes beyond simply forecasting product needs; it’s about ensuring your business meets customer demands with precision, efficiency, and cost-effectiveness. Latest demand planning technology addresses key challenges like forecast accuracy, inventory management, and market responsiveness. In this blog, we will introduce critical demand planning trends, including data-driven insights, probabilistic forecasting, consensus planning, predictive analytics, scenario modeling, real-time visibility, and multilevel forecasting. These trends will help you stay ahead of the curve, optimize your supply chain, reduce costs, and enhance customer satisfaction, positioning your business for long-term success.

Data-Driven Insights

Advanced analytics, machine learning, and artificial intelligence (AI) are becoming integral to demand planning. Technologies like Smart UP&O allow businesses to analyze complex data sets, identify patterns, and make more accurate predictions. This shift towards data-driven insights helps businesses respond quickly to market changes, minimizing stockouts and reducing excess inventory.

Probabilistic Forecasting

Probabilistic forecasting focuses on predicting a range of possible outcomes rather than a single figure. This trend is particularly important for managing uncertainty and risk in demand planning. It helps businesses prepare for various demand scenarios, enhancing inventory management and reducing the likelihood of stockouts or overstocking​.

Consensus Forecasting

Modern manufacturing is moving towards an integrated approach where departments and stakeholders collaborate more closely. Collaborative forecasting involves sharing insights across the supply chain, from suppliers to distributors and internal teams. This approach breaks down silos and ensures that everyone is working towards a common goal, leading to a more synchronized and efficient supply chain​.

Predictive and Prescriptive Analytics

Predictive analytics forecasts future outcomes based on historical data and trends, helping businesses anticipate demand fluctuations. For example, Smart Demand Planner (SDP) automates forecasting to adjust inventory and production levels accordingly​.

Prescriptive analytics goes further by offering actionable recommendations. Smart Inventory Planning and Optimization (IP&O), for instance, prescribes optimal inventory policies based on service levels, costs, and risks​. ogether, these tools enable proactive decision-making, allowing companies to both predict and optimize their responses to future challenges.

Scenario Modeling

Scenario modeling is becoming a key part of demand planning, enabling businesses to simulate different scenarios and assess their impact on operations. This method helps companies create adaptable strategies to effectively handle uncertainties. Smart IP&O enhances this capability by offering What If Scenarios that allow users to test different inventory policies before implementation. By adjusting variables like service levels or order quantities, businesses can visualize the effects on costs and service levels, empowering them to select the optimal strategy for minimizing risks and controlling costs​​.

Real-Time Visibility

As supply chains become more global and interconnected, real-time visibility into inventory and supply chain activities is crucial. Enhanced collaboration with suppliers and distributors, combined with real-time data, enables businesses to make quicker, more informed decisions. This helps optimize inventory levels, reduce lead times, and improve overall supply chain resilience​.

Multilevel Forecasting

This involves forecasting at different levels of the product hierarchy, such as individual items, product families, or even entire product lines. Multilevel forecasting is vital for businesses with complex product portfolios, as it ensures that forecasts are accurate at both the micro and macro levels​.

Demand planning is a decisive aspect of modern supply chain management, offering businesses the ability to enhance operational efficiency, reduce costs, and better meet customer demands. Leveraging advanced platforms like Smart IP&O significantly improves forecasting accuracy and inventory management, enabling swift responses to market fluctuations. Automated statistical forecasting, combined with capabilities like hierarchy forecasting and forecast overrides, ensures that forecasts are accurate and adaptable, leading to more realistic planning decisions. Additionally, with tools like scenario modeling, businesses can explore various demand scenarios across their product hierarchy, facilitating informed decision-making by providing insights into potential outcomes and risks. This approach allows businesses to anticipate the impact of policy changes, make better decisions, and ultimately optimize their inventory and overall supply chain management, staying ahead of key trends in the process.

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.

​

​​In a highly configurable manufacturing environment, forecasting finished goods can become a complex and daunting task. The number of possible finished products skyrockets when many components are interchangeable. A traditional MRP would force us to forecast every single finished product, which can be unrealistic or even impossible. Several leading solutions introduce the concept of the “Planning BOM,” which allows the use of forecasts at a higher level in the manufacturing process. In this article, we will discuss this functionality in Epicor Kinetic and how you can take advantage of it with Epicor Smart Inventory Planning and Optimization (Smart IP&O) to get ahead of your demand in the face of this complexity.

Why Would I Need a Planning BOM?

Traditionally, each finished product or SKU would have a rigidly defined bill of materials. If we stock that product and want to plan around forecasted demand, we will forecast demand for those products and then feed MRP to blow this forecasted demand from the finished good level down to its components via the BOM.

Many companies, however, offer highly configurable products where customers can select options on the product they buy. As an example, recall the last time you bought a cellphone. You chose a brand and model, but from there, you were likely presented with options: what screen size do you want? How much storage do you want? What color do you prefer? If that business wants to have these cellphones ready and available to ship to you in a reasonable time, suddenly, they are no longer just anticipating demand for that model—they must forecast that model for every type of screen size, for all storage capacities, for all colors, and all possible combinations of those as well! For some manufacturers, these configurations can compound to hundreds or thousands of possible finished good permutations.

There may be so many possible customizations that the demand at the finished product level is completely unforecastable in a traditional sense. Thousands of those cellphones may sell every year, but for each possible configuration, the demand may be extremely low and sporadic—perhaps certain combinations sell once and never again.

This often forces these companies to plan reorder points and safety stock levels mostly at the component level, while largely reacting to firm demand at the finished good level via MRP. While this is a valid approach, it lacks a systematic way to leverage forecasts that may account for anticipated future activity such as promotions, upcoming projects, or sales opportunities. Forecasting at the “configured” level is effectively impossible, and trying to weave in these forecast assumptions at the component level isn’t feasible either.

Planning BOM Explained This is where Planning BOMs come in. Perhaps the sales team is working on a big B2B opportunity for that model, or there’s a planned promotion for Cyber Monday. While trying to work in those assumptions for every possible configuration isn’t realistic, doing it at the model level is totally doable—and tremendously valuable.

The Planning BOM can use a forecast at a higher level and then blow demand down based on predefined proportions for its possible components. For example, the cellphone manufacturer may know that most people opt for 128GB of storage, and far fewer opt for upgrades to 256GB or 512GB. The planning BOM allows the organization to (for example) blow 60% of the demand down to the 128GB option, 30% to the 256GB option, and 10% to the 512GB option. They could do the same for screen sizes, colors, or other available customizations.

The business can now focus its forecast at this model level, leaving the Planning BOM to determine the component mix. Clearly, defining these proportions requires some thought, but Planning BOMs effectively allows businesses to forecast what would otherwise be unforecastable.

The Importance of a Good Forecast

Of course, we still need a good forecast to load into Epicor Kinetic. As explained in this article, while Epicor Kinetic can import a forecast, it often cannot generate one, and when it does it tends to require a great deal of hard-to-use configurations that don’t often get revisited, resulting in inaccurate forecasts. It is, therefore, up to the business to come up with its own sets of forecasts, often manually produced in Excel. Forecasting manually generally presents a number of challenges, including but not limited to:

• The inability to identify demand patterns like seasonality or trend.
• Overreliance on customer or sales forecasts.
• Lack of accuracy or performance tracking.

No matter how well configured the MRP is with your carefully considered Planning BOMs, a poor forecast means poor MRP output and mistrust in the system—garbage in, garbage out. Continuing along with the “cellphone company” example, without a systematic way of capturing key demand patterns and/or domain knowledge in the forecast, MRP can never see it.

Smart IP&O: A Comprehensive Solution

Smart IP&O supports planning at all levels of your BOM, though the “blowing out” is handled via MRP inside Epicor Kinetic. Here is the method we use for our Epicor Kinetic customers, which is straightforward and effective:

• Smart Demand Planner: The platform contains a purpose-built forecasting application called Smart Demand Planner that you will use to forecast demand for your manufactured products (usually finished goods). It generates statistical forecasts, enables planners to make adjustments and/or weave in other forecasts (such as sales or customer forecasts), and tracks accuracy. The output of this is a forecast that goes into forecast entry inside Epicor Kinetic, where MRP will pick it up. MRP will subsequently use demand at the finished good level, and also blow out material requirements through the BOM, so that demand is recognized at lower levels as well.
• Smart Inventory Optimization: You simultaneously use Smart Inventory Optimization to set min/max/safety levels both for any finished goods you make to stock (if applicable; some of our customers operate purely make-to-order off of firm demand), as well as for raw materials. The key here is that at the raw material level, Smart will leverage job usage demand, supplier lead times, etc., to optimize these parameters while at the same time using sales orders/shipments as demand at the finished good level. Smart handles these multiple inputs of demand elegantly via the bidirectional integration with Epicor Kinetic.

When MRP runs, it nets out supply & demand (which, once again, includes raw material demand blown out from the finished good forecast) against the min/max/safety levels you have established to suggest PO and job suggestions.

Extend Epicor Kinetic with Smart IP&O

Smart IP&O is designed to extend your Epicor Kinetic system with many integrated demand planning and inventory optimization solutions. For example, it can generate statistical forecasts automatically for large numbers of items, allows for intuitive forecast adjustments, tracks forecast accuracy, and ultimately allows you to generate true consensus-based forecasts to better anticipate the needs of your customers.

Thanks to highly flexible product hierarchies, Smart IP&O is perfectly suited to forecasting at the Planning BOM level, so you can capture key patterns and incorporate business knowledge at the levels that matter most. Furthermore, you can analyze and deploy optimal safety stock levels at any level of your BOM.

Leveraging Epicor Kinetic’s Planning BOM capabilities alongside Smart IP&O’s advanced forecasting and inventory optimization features ensures that you can meet demand efficiently and accurately, regardless of the complexity of your product configurations. This synergy not only enhances forecast accuracy but also strengthens overall operational efficiency, helping you stay ahead in a competitive market.

For some of our customers, weather has a significant influence on demand. Extreme short-term weather events like fires, droughts, hot spells, and so forth can have a significant near-term influence on demand.

There are two ways to factor weather into a demand forecast: indirectly and directly. The indirect route is easier using the scenario-based approach of Smart Demand Planner. The direct approach requires a tailored special project requiring additional data and hand-crafted modeling.

Indirect Accounting for Weather

The standard model built into Smart Demand Planner (SDP) accommodates weather effects in four ways:

1. If the world is steadily getting warmer/colder/drier/wetter in ways that impact your sales, SDP detects these trends automatically and incorporates them into the demand scenarios it generates.
2. If your business has a regular rhythm in which certain days of the week or certain months of the year have consistently higher or lower-than-average demand, SDP also automatically detects this seasonality and incorporates it into its demand scenarios.
3. Often it is the cussed randomness of weather that interferes with forecast accuracy. We often refer to this effect as “noise”. Noise is a catch-all term that incorporates all kinds of random trouble. Besides weather, a geopolitical flareup, the surprise failure of a regional bank, or a ship getting stuck in the Suez Canal can and have added surprises to product demand. SDP assesses the volatility of demand and reproduces it in its demand scenarios.
4. Management overrides. Most of the time, customers let SDP churn away to automatically generate tens of thousands of demand scenarios. But if users feel the need to touch up specific forecasts using their insider knowledge, SDP can make that happen through management overrides.

Direct Accounting for Weather

Sometimes a user will be able to articulate subject matter expertise linking factors outside their company (such as interest rates or raw materials costs or technology trends) to their own aggregate sales. In these situations, Smart Software can arrange for one-off special projects that provide alternative (“causal”) models to supplement our standard statistical forecasting models. Contact your Smart Software representative to discuss a possible causal modeling project.

Meanwhile, don’t forget your umbrella.

​In a highly configurable manufacturing environment, forecasting finished goods can become a complex and daunting task. The number of possible finished products will skyrocket when many components are interchangeable. A traditional MRP would force us to forecast every single finished product which can be unrealistic or even impossible. Several leading ERP solutions introduce the concept of the “Planning BOM”, which allows the use of forecasts at a higher level in the manufacturing process. In this article, we will discuss this functionality in ERP, and how you can take advantage of it with Smart Inventory Planning and Optimization (Smart IP&O) to get ahead of your demand in the face of this complexity.

Why Would I Need a Planning BOM?

Traditionally, each finished product or SKU would have a rigidly defined bill of materials. If we stock that product and want to plan around forecasted demand, we would forecast demand for those products and then feed MRP to blow this forecasted demand from the finished good level down to its components via the BOM.

Many companies, however, offer highly configurable products where customers can select options on the product they are buying. As an example, recall the last time you bought a personal computer. You chose a brand and model, but from there, you were likely presented with options: what speed of CPU do you want? How much RAM do you want? What kind of hard drive and how much space? If that business wants to have these computers ready and available to ship to you in a reasonable time, suddenly they are no longer just anticipating demand for that model—they must forecast that model for every type of CPU, for all quantities of RAM, for all types of hard drive, and all possible combinations of those as well! For some manufacturers, these configurations can compound to hundreds or thousands of possible finished good permutations.

There may be so many possible customizations that the demand at the finished product level is completely unforecastable in a traditional sense. Thousands of those computers may sell every year, but for each possible configuration, the demand may be extremely low and sporadic—perhaps certain combinations sell once and never again.

This often forces these companies to plan reorder points and safety stock levels mostly at the component level, while largely reacting to firm demand at the finished good level via MRP. While this is a valid approach, it lacks a systematic way to leverage forecasts that may account for anticipated future activity such as promotions, upcoming projects, or sales opportunities. Forecasting at the “configured” level is effectively impossible, and trying to weave in these forecast assumptions at the component level isn’t feasible either.

Planning BOM Explained

This is where Planning BOMs come in. Perhaps the sales team is working a big b2b opportunity for that model, or there’s a planned promotion for Cyber Monday. While trying to work in those assumptions for every possible configuration isn’t realistic, doing it at the model level is totally doable—and tremendously valuable.

The Planning BOM can use a forecast at a higher level and then blow demand down based on predefined proportions for its possible components. For example, the computer manufacturer may know that most people opt for 16GB of RAM, and far fewer opt for the upgrades to 32 or 64. The planning BOM allows the organization to (for example) blow 60% of the demand down to the 16GB option, 30% to the 32GB option, and 10% to the 64GB option. They could do the same for CPUs, hard drives, or any other customizations available.  

The business can now focus their forecast at this model level, leaving the Planning BOM to figure out the component mix. Clearly, defining these proportions requires some thought, but Planning BOMs effectively allow businesses to forecast what would otherwise be unforecastable.

The Importance of a Good Forecast

Of course, we still need a good forecast to load into an ERP system. As explained in this article, while ERP  can import a forecast, it often cannot generate one and when it does it tends to require a great deal of hard to use configurations that don’t often get revisited resulting in inaccurate forecasts.  It is therefore up to the business to come up with their own sets of forecasts, often manually produced in Excel. Forecasting manually generally presents a number of challenges, including but not limited to:

• The inability to identify demand patterns like seasonality or trend
• Overreliance on customer or sales forecasts
• Lack of accuracy or performance tracking

No matter how well configured the MRP is with your carefully considered Planning BOMs, a poor forecast means poor MRP output and mistrust in the system—garbage in, garbage out. Continuing along with the “computer company” example, without a systematic way of capturing key demand patterns and/or domain knowledge in the forecast, MRP can never see it.

Extend ERP  with Smart IP&O

Smart IP&O is designed to extend your ERP system with a number of integrated demand planning and inventory optimization solutions. For example, it can generate statistical forecasts automatically for large numbers of items, allows for intuitive forecast adjustments, tracks forecast accuracy, and ultimately allows you to generate true consensus-based forecasts to better anticipate the needs of your customers.

Thanks to highly flexible product hierarchies, Smart IP&O is perfectly suited to forecasting at the Planning BOM level so you can capture key patterns and incorporate business knowledge at the levels that matter most. Furthermore you can analyze and deploy optimal safety stock levels at any level of your BOM.