Delivering a product to market typically involves a complex and delicate synthesis of raw materials, supply chains, technology and resources, all balanced and timed, to provide just the right amount of product to customers, precisely when they need it. Manufacturing forecasts determine the choreography that guides the production dance, protecting the process from the whims of unexpected market forces, such as economic shifts or changes in customer preferences, that could have drastic consequences for companies. If manufacturers can’t consistently anticipate these shifts and swiftly respond by adjusting production, they face the prospect of producing too much or too little inventory, either of which can wreak serious consequences, not only for the bottom line but for brand reputation and customer satisfaction, as well.
What Is Manufacturing Forecasting?
No one can predict the future with certainty, but that hasn’t stopped manufacturers from trying. Manufacturing forecasting is essentially the science of trying to anticipate demand for products — a critical process that can help manufacturers make more accurate decisions about how much of their products to create, when to create them and where to deliver them.
Accurate manufacturing forecasting lays the foundation for a wide range of planning and decision-making activities for manufacturers. The ability to predict product demand has implications across the entire organization, from production planning and inventory forecasting to resource requirements and purchasing.
Manufacturing forecasting is a truly collaborative effort. Many companies have dedicated forecasting analysts and planners who must understand past trends; dissect and interpret data about sales, marketing and suppliers; and apply strategic foresight. As a result, these analysts work closely with supply chain managers, production teams, and sales and marketing resources to build precise forecasts that guide everything from manufacturing schedules and inventory management to product positioning and production planning. Accurate forecasts also fuel decisions for budget planning, capital investments in equipment and raw material procurement, among others.
- Manufacturing forecasting is the process of estimating demand for products, based on a host of internal and external factors.
- Accurate demand forecasting enables manufacturers to align resources, machinery, supply chains and delivery to optimize efficiency and budgeting.
- Failing to properly estimate demand for products can result in overproduction or underproduction, both of which incur significant financial consequences.
- Many manufacturers use a combination of demand forecasting methods to ensure the greatest accuracy.
- Technology, such as advanced analytics tools and enterprise resource planning (ERP) systems that use artificial intelligence (AI) and machine learning, can help manufacturers develop faster, more accurate insights for forecasting.
Manufacturing Forecasting Explained
Success in any business ultimately comes down to the ability to anticipate market shifts and trends, and then build winning strategies that leverage that knowledge. In the absence of a crystal ball revealing all the answers, companies must rely on data to build business forecasts that can help them reduce risk by anticipating market demand. This step, in turn, aids them in optimizing resource allocation, ensuring smoother operations and enhancing customer satisfaction. There are several methods for forecasting demand that use a combination of data-driven analytics and expert feedback meant to anticipate multiple scenarios that could impact customer demand. In addition, manufacturers can choose from a long list of sophisticated forecasting models developed by experts who apply mathematical and statistical formulas to estimate demand. Recent technology advances have also made the process of gathering data and applying forecasting methods easier for manufacturers.
A lot rides on accurate manufacturing forecasts because inaccuracy creates consequences. Excess inventory, for example, will restrict available funds and resources, while a lack of inventory can result in stockouts, missed sales opportunities and disappointed customers. Either scenario can negatively impact profit. Aligning production with demand minimizes the risk of overproduction and leads to greater efficiencies, such as keeping the cost of resources in line with the budget and eliminating waste. It also leads to more efficient inventory management, which, in turn, reduces the carrying costs associated with holding unused inventory in warehouses. An overall understanding of market trends allows companies to adapt to changes, pounce on market opportunities and navigate potential threats.
The first step in building a manufacturing forecast is to understand the broad categories of data that should be compiled to build a comprehensive overview of potential market conditions. Depending on their products, manufacturers will likely prioritize various data sources differently, but the following data types typically form the foundation of a well-rounded demand forecast.
Historical Sales Trends
One of the first steps in any forecasting process is to analyze historical data to discover patterns and trends that can drive more accurate decisions about future activities. In manufacturing forecasting, data about sales volumes and growth rates can serve as a powerful tool for predicting future product demand. Segmenting sales data can build even greater insights. For example, sales by product category, region, customer segment, channel or promotional activity can help predict future demand. It’s important to remember, however, that historical patterns don’t guarantee future performance and are just one piece of the manufacturing forecasting puzzle.
Estimating the future availability of raw materials or components from suppliers is a vital component of building accurate production schedules, managing inventory and delivering products. Important supplier data includes lead times and raw material availability. Supplier forecasts fuel decisions about the markets in which manufacturers sell, the distribution channels they use and how they deliver products to customers. For example, if a supplier forecast indicates a future shortage of a particular component, a company might decide to prioritize placing products in the market that don’t rely on that component.
It’s crucial that companies use consistent data throughout the supply chain to ensure that forecasts are comparable and reliable. For example, if a company uses a certain method to calculate lead times from suppliers, it should use the same method across all suppliers for consistency.
For many manufacturers, demand for their products can ebb and flow during specific times of the year. Demand for children’s toys, for example, often skyrockets during the holiday season, while swimsuit sales peak during the summer. Supply for some product components might also vary seasonally, which impacts a manufacturer’s ability to meet demand. Of course, fluctuations aren’t always tied to the weather. They can also include events like the back-to-school season or unique industry activities.
Effectively forecasting seasonal changes enables manufacturers to adjust production schedules and inventories accordingly. Manufacturers should be careful not to rely too heavily on recent data, however. The farther back companies can go to analyze patterns, the more accurate the predictions. They should also incorporate feedback from sales teams, retail associates and local representatives to build a clearer picture of emerging trends.
Many internal and external factors can throw the best-laid forecast plans into disarray if not properly accounted for. For example, limited machine capacity or a shortage of raw materials can impair a manufacturer’s ability to produce enough product to meet demand. Intangible market factors, such as regulations or labor union agreements, can have a similar impact. Assessing business constraints enables manufacturers to take a proactive approach to aligning their operations with realistic expectations. It ensures adequate resource allocation and highlights potential bottlenecks before they create significant problems.
Some manufacturers make the mistake of thinking business constraints are static — yet, whether because of a breakthrough in technology, a change in regulations or an unexpected change in supplier relationships, they are anything but. Manufacturers should engage with suppliers to understand constraints and regularly communicate with regulators about evolving market standards.
One of the most important external factors in a manufacturing forecast is an assessment of overall economic performance in the markets where a company conducts business. For manufacturers, integrating economic data into manufacturing forecasts is the equivalent of having an umbrella in the event of an unexpected storm. Economic forecasts aim to predict the impact of future economic conditions by assessing market factors, such as interest rates, inflation, unemployment rates and consumer spending trends.
Economic factors can affect companies differently. For example, an economic recession often restricts disposable income. As a result, a luxury manufacturer might see sales decrease, while low-cost manufacturers might see business increase. Building economic forecasts involves reviewing government-released economic indicators, insights from institutions like the U.S. Federal Reserve, and regional and industry-specific economic data. In fact, many of the most valuable insights derived from an economic forecast can come from reviewing industrial and regional economic data. Manufacturers should also consider subscribing to specialized economic forecasting services.
Demand Forecasting Methods in Manufacturing
Once data is collected, manufacturers must determine which demand forecasting method best suits their needs. There are two types of forecasting: quantitative forecasting, which relies on historical data, such as sales history; and qualitative forecasting, which incorporates feedback from external experts and customers. Within those two broad categories, the following four methods of demand forecasting incorporate various degrees of quantitative and qualitative data. Which method makes the most sense depends on each company and the nature of its products. New companies, for example, don’t have historical data, which prevents them from using some methods.
The push system of demand forecasting refers to building production estimates based on anticipated demand. Push systems rely on accurate forecasting to guide decisions about what to push through the supply chain before confirmed orders are received. For example, a bakery using the push system would use information about past demand and seasonal trends to determine how many cupcakes to make each morning, aiming to sell all of its inventory by midday. For manufacturers, push systems help streamline production schedules and optimize inventory management. It also results in meeting customer demands promptly.
Push systems have disadvantages, though. Because they rely on predictions, overestimation can lead to excess inventory, which ties up funds. Underestimation, on the other hand, can lead to stockouts, which means missed sales opportunities and, potentially, a loss of customer trust.
The unpredictability of demand forecasting makes it critical to revisit and refine forecasts. Interdepartmental communication is also essential. Communicating with sales and marketing teams about upcoming promotions or shifts in market dynamics, for example, can help manufacturers adjust forecasts for greater accuracy.
A sales-driven forecast is founded on historical sales data. A simple example is a manufacturer of custom motorcycles that analyzes sales of various models over the past year to predict demand for the next six months. Sales-driven forecasts are grounded in realistic numbers, such as recent sales trends, customer purchasing behavior and seasonal fluctuations. Because they’re based on recent history, sales-driven forecasts give manufacturers more tangible, actionable data for decision-making based on actual market dynamics.
Relying exclusively on recent sales data, however, has its drawbacks. External factors, such as economic disruptions or the entrance of a new competitor, can render sales-driven forecasts susceptible to inaccuracies. In addition, historical sales data might not accurately gauge the potential for new products or market expansions. For those reasons, manufacturers would be wise to blend historical sales data with good ol’ market intuition, including feedback from sales teams about market dynamics.
Where push systems and sales-driven forecasts use estimations of demand to drive manufacturing forecasts, production-driven forecasts are based on a company’s capacity to build products. In addition to estimating production capacity, production-driven forecasts also factor in capabilities and historical production data. For the previous example of the bakery, it’s the equivalent of basing a forecast for making cupcakes on the amount of ingredients, ovens and bakers available, as well as analyzing how many cupcakes were made daily over the previous six months.
Production-driven forecasts offer several advantages. First, companies can optimize production capacity to ensure that they use just the right number of resources available for production. Because production-driven forecasts are reality-based, companies commit only to what they can produce, not what the market demands. As a result, manufacturers can avoid overpromising or under-delivering. On the other hand, production-driven forecasts offer an inward-facing view, which can cause companies to lose sight of shifts in demand or customer needs. To avoid that possibility and to align more tightly with shifting markets and avoid surprises, manufacturers should consider tempering their production-driven forecasts with feedback from sales teams and customers.
The pull system method of demand forecasting is the most accurate way of forecasting demand because it’s based on the “pull” of actual orders rather than estimations of demand or production capacity. For our bakery, it’s the equivalent of making cupcakes only after a customer orders one. Because production is aligned with specific customer commitments, the risks of overproduction or under-delivery are vastly reduced, compared to other demand forecasting methods. Pull systems are a real-time, reactive strategy in which there is essentially no forecast at all. Everything is made to order, which helps companies avoid waste and respond immediately to shifting customer needs.
That said, pull systems have significant drawbacks. First, not all products are suited to this method. In the bakery example, customers would have to wait roughly 30 minutes for their orders — not a recipe for customer satisfaction. It also leaves manufacturers vulnerable in the event of a supply chain disruption or surge in demand that puts a strain on resources. Pull-system strategies require close communication throughout the supply chain, as well as robust feedback channels with distributors and customers. To minimize the disadvantages of a pull system, companies should consider adopting a hybrid demand forecasting strategy that blends reactive pull strategies with some of the forward-thinking elements of the other methods discussed above.
How Do Manufacturers Forecast Demand?
With so much riding on accurate demand forecasts, it’s clear why most manufacturers spend considerable time and effort trying to be as thorough as possible. That often means using multiple methods to gauge demand, incorporating both quantitative and qualitative factors to get the broadest possible view of the market. For most companies, advanced analytics tools also play a pivotal role. The following steps can help organize the manufacturing forecasting process.
Collect Relevant Data
When it comes to manufacturing forecasting, everything begins with data. The more data, the better, including everything from internal data, such as historical sales and pending orders, to external data, such as market trends, seasonal reports and macroeconomic indicators.
The responsibility for collecting all this data often falls on a combination of supply chain professionals, sales teams and data analysts. The process begins with understanding the objectives for the forecast and then identifying data sources. Enterprise resource planning (ERP) systems can play a huge role in this process, particularly those that integrate real-time financial, sales, supply chain and production data in a single database. This smooths data collection and helps avoid a common mistake: overreliance on a single data source, which leads to a myopic view. Other pitfalls are not updating data frequently — rendering even the most sophisticated forecasting models obsolete — and not looking beyond the immediate industry. For example, a bicycle manufacturer should analyze global climate trends to understand potential shifts in bicycle demand.
Clean the Data
If data is the lifeblood of accurate manufacturing forecasting, then clean, consistent, error-free data is the heart. The process of eliminating data errors, inconsistencies and duplications is essential to enhance the accuracy of analytical modeling and forecasting.
To ensure clean data, data analysts and IT specialists pore over datasets, relying on technology, such as databases, ERP systems and specific data-cleansing tools, to spot and rectify inconsistencies. It’s a critical task because data errors can lead to costly misjudgments in inventory management or production scaling, for example. And the process becomes all the more complex as data volumes increase. If data is housed in multiple disconnected systems, for example, it’s likely that redundant, outdated information exists in different, incompatible formats, making it challenging to get an accurate picture for forecasting.
That’s why it’s critical for manufacturers to consider several means of ensuring clean data for forecasting. Integrated systems for financial management, supply chain management, customer relationship management and production can not only prevent data errors that result from manual consolidation, but they also offer the ability to generate real-time analytics. Another option is to use outside consultants, who can bring data cleansing technology and industry expertise to the table.
Use Advanced Analytics Tools
The days of using spreadsheets to analyze data for forecasting are long gone. Today’s advanced analytics tools offer a competitive edge by delving deeper into data, using technologies like machine learning, predictive modeling and statistical algorithms to uncover trends and project future demand. Indeed, advanced analytics tools can be game-changing in the demand forecasting process. When fed clean data, they can offer specific, actionable insights to help optimize inventory, reduce waste, predict equipment maintenance schedules and adjust production timelines to meet demand.
The results can be transformative, but advanced analytics tools pose challenges, too. For starters, some analytics tools can be complex to use, requiring a steep learning curve and specific resources, such as data scientists to fine-tune algorithms and analysts to interpret results. Many features of advanced analytics tools, however, have been incorporated into broader systems, such as ERPs, that don’t require the same level of expertise. It’s also important that manufacturers understand that advanced analytics aren’t clairvoyant. Sudden market changes or unexpected global events can wreak havoc with even the best predictive models. That’s why it’s critical to balance quantitative analytics, which rely on historical data like sales volume, with qualitative analytics that capture subjective feedback from experts and customers. Another caveat: Introduce analytics tools gradually, using pilot projects, to gauge performance and employee comfort.
Apply the Forecasting Model
Once data has been gathered and analyzed, it’s time to apply a forecasting model that uses quantitative and qualitative data to transform insights into actionable strategies. Forecasting models are specific techniques that manufacturers can apply to their data to build forecasts. There are many different models that incorporate various mathematical and statistical techniques to arrive at their forecasts, each with strengths, weaknesses and ideal use cases. The selection of a forecasting model hinges on the needs of each company and the expectations for each forecast.
Examples of various quantitative forecast models include:
- Time-series models, which rely on historical patterns to predict future outcomes.
- Causal models, which look for relationships between products and events, such as advertising spend.
Examples of qualitative forecast models include:
- The Delphi method, developed by Rand Corp. in the 1950s, which relies on feedback from panels of experts.
- Market research, which uses surveys and questionnaires to understand customer preferences and demand.
Selecting a forecasting model can be challenging. Using a single model can often be limiting over time. Manufacturers would be wise to consider a blend of models, both quantitative and qualitative, and then weigh the results to generate a balanced forecast.
Validate the Model
Forecasting models are precision instruments, meaning they need to be tuned to the specific data of each manufacturer for accuracy. The process of validating a forecasting model involves assessing it against new, unseen data, and refining it based on its performance to ensure accuracy and reliability. Each company’s data has unique attributes, patterns and anomalies. To be sure that the model is appropriate for use, manufacturers can feed historical data into the model and ask it to predict outcomes for past periods, then compare the model’s results with the actual outcomes. If there are discrepancies, the model can be fine-tuned by adjusting assumptions and parameters in the model’s calculations. This can produce accurate results, giving manufacturers confidence that it can produce accurate future forecasts.
Forecasting models aren’t foolproof, however. For example, they may not be able to account for factors like economic shifts, changing consumer preferences or global events, like a pandemic. Forecast models can also be “overfitted,” meaning that they can be designed with too much complexity, resulting in inaccurate forecasts. Applying multiple models can help account for unforeseen variables. Regularly updating models and reassessing parameters guarantees that they will remain attuned to evolving market dynamics.
Automate Future Data Analysis
Automation is transforming business processes, and data analysis and forecasting are not exempt. Software can automatically analyze vast amounts of data in real time without continuous manual input. Refining forecasts based on incoming information can dramatically transform efficiency and accuracy. For example, automation makes it possible for manufacturers to dynamically add real-time data to their forecasts, such as data from retailers and online sales. As a result, not only do companies reduce the amount of time needed to compile data, but their forecasts are inherently more reflective of up-to-date market trends.
Making automation like this possible often requires integrating advanced data analysis tools with legacy infrastructure, which can pose technology challenges. It also requires high volumes of data to build better predictions, which can add “noise” to forecast models, distorting predictions and requiring frequent tuning. Companies should invest in training so staff becomes familiar with advanced data analysis tools and the principles that drive them. Companies may also consider dipping their toes into automation first, perhaps by automating a single forecast for a product or region. It can make processes more manageable, while providing learning opportunities.
Manufacturing Forecasting Challenges
Until experts develop a foolproof method for predicting the future, manufacturing forecasts will always leave room for improvement. From external factors, such as surprising shifts in consumer preferences and unexpected weather patterns, to internal challenges, such as maintaining clean data, here are seven top challenges manufacturers face when trying to forecast demand.
Even the best forecasting methods and models are imperfect because, frankly, life is unpredictable. Consider a toy manufacturer whose product suddenly becomes the “it” toy of the holiday season, leading to an unexpected spike in demand that causes stockouts and lost sales opportunities. These unpredictable fluctuations in customer demand, known as demand volatility, require both adept maneuvering and forward thinking to ensure smooth sailing.
Though it’s still impossible to consistently predict human behavior, manufacturers have options when it comes to mitigating the effects of demand volatility. For example, switching from quarterly to monthly forecasts can help manufacturers stay on top of unexpected shifts in demand. Using systems that track sales data in real time is another way to keep a finger on the pulse of the customer. Another option is to build a flexible supply chain, with multiple options to quickly obtain components of a product in the event of a sudden surge.
Technology can also be a huge help. Advanced analytics systems, particularly those using machine learning, can quickly process information from multiple sources, such as sales data, economic indicators and social media sentiment, to deliver deep, real-time insights into demand patterns. Scenario planning — essentially running forecast models that anticipate multiple high- and low-demand situations — can also prepare manufacturers for demand volatility.
Seasonality in manufacturing forecasting refers to the largely expected peaks and valleys in customer demand during the year; the ability to plan for these shifts is key. Seasonality often coincides with weather. For example, a swimwear manufacturer will see predictably high sales during the summer months. But seasonality doesn’t always relate to weather. For example, sales of soccer merchandise peak during the World Cup.
Knowing that summer follows spring, however, doesn’t guarantee that a manufacturer will be prepared for seasonal changes. Summer swimwear sales will increase, but fashion trends may ultimately determine which styles are winners and losers. As a result, manufacturers are left in a precarious position, needing to place bets on seasonal trends by initiating production well in advance of a seasonal event. Miscalculations can result in either a surplus of inventory that needs to be sold at a discount or stockouts that lead to lost sales and frustrated customers.
Preparing for seasonal fluctuations begins with harnessing historical data to identify patterns and trends, and then combining it with feedback from retailers and distributors to understand market sentiments and retail promotion strategies that can make forecasts more accurate.
Inaccurate Historical Data
Even the world’s most accurate forecasting tool can’t overcome inaccurate data. For example, if a car parts manufacturer recalls a faulty product but doesn’t accurately account for the recall in its sales systems, sales data for the period will be inflated. Often, such errors are the result of human error, although technological glitches can also skew data. For instance, poorly built integrations between systems can lead to duplicated data.
It’s not difficult to see how such errors can foil manufacturing forecasts, leading to overproduction and underproduction. To avoid inaccurate forecasts due to data errors, manufacturers should first look inward by building strong data governance policies to ensure quality, as well as implementing regular training on protocols. In addition, companies should regularly audit historical data and cross-reference it with other reliable sources, such as retailer data. Third-party audits may also be worth any additional cost. AI can also provide critical help by automatically identifying patterns in data and flagging data anomalies before they’re used in forecasts.
External Factors Affecting Demand
Manufacturers may feel tempted to rely solely on internal data, such as historical sales patterns, to gauge future demand, but external factors are just as important. Everything from global economic trends to geopolitical events and technological breakthroughs can influence buying patterns. For example, car manufacturers that accurately foresaw — and planned for — how the rise in climate awareness would lead to increased electric vehicle sales are now in an enviable market position.
But gauging external factors is tricky business because so much lies beyond a manufacturer’s control. The key is to recognize the implications of external factors before competitors do, so manufacturers can adjust forecasts accordingly or be first to market with relevant products.
Mitigating external factors begins with research. For example, a tech manufacturer that regularly reviews global adoption rates for emerging technologies is more likely to be prepared for the latest tech trend. Manufacturers also need to keep their ears to the ground by maintaining open communication with suppliers, distributors and even competitors to get a sense of shifting market trends and potential disruptions, as well as looking for advanced forecasting technology that incorporates external data sources for a broader view of the market.
Demand fragmentation refers to an increasingly common challenge manufacturers face today: consumer demand for an ever-growing set of variations in their products. For example, a milk producer sees high demand, but its customers now want healthier options beyond whole milk, such as soy, almond, oat and lactose-free. This creates a complex dilemma for manufacturers that now need to account for each subcategory of their products. If the milk manufacturer fails to forecast demand for soy and almond milk, for example, it’ll likely find itself with a costly surplus of whole milk that will soon pass its expiration date.
Advanced forecasting tools that integrate real-time market data to build predictive analytics can help manufacturers respond quickly to demand fragmentation. Manufacturers can also use frequent customer surveys to gain valuable insights into shifting preferences, derived directly from customers. Some manufacturers have begun using agile manufacturing techniques that emphasize creating smaller batches of products, enabling those manufacturers to gauge market response and adjust production accordingly.
Limited Supply Chain Visibility
For any complex product, such as a car, manufacturers depend on an intricate ecosystem of suppliers to provide the necessary components to build it. Any disruptions in obtaining components can have drastic consequences when it comes to delivering products on time and on budget. Without supply chain visibility, for example, a car manufacturer might be unaware of a delay in steel shipments from a supplier in another country. As a result, production ends up being halted and forecast shipments to a key customer are missed. Not only is the customer upset, but sales are lost and costs of production may need to increase.
New technologies are helping manufacturers build greater supply chain visibility. Internet of Things (IoT) devices and sensors, for example, provide manufacturers with live updates throughout their supply chains, enabling them to adjust forecasts and schedules based on accurate, live data. Strong communication with suppliers is also a necessity. One often overlooked way to build supplier communication is to establish a supply chain visibility task force comprised of employees from logistics, procurement and production. The task force would regularly monitor the supply chain for potential risks and coordinate with suppliers and partners.
Forecasting Errors and Bias
Manufacturing forecasts are, ultimately, no more or less than educated guesses. As such, they’re susceptible to errors and bias. Forecasting errors happen when the amount of products sold differs significantly — positively or negatively — from a company’s predictions for demand, leaving companies holding on to excess inventory or not having enough to meet demand. Errors can arise for several reasons. Duplicated data, due to poor integration between systems, can skew forecasts, for example. Unexpected external events, such as unseasonably warm weather, also can make forecasts for some manufacturers meaningless.
Bias, on the other hand, is a more consistent pattern of either overestimating or underestimating demand. Consistent overestimates of demand, for example, could reflect an overconfident bias from enthusiastic sales teams.
One way for manufacturers to overcome errors and bias is to use multiple qualitative and quantitative forecasting methods. Data-driven quantitative models can mitigate an optimistic bias from sales teams’ qualitative feedback. It’s also important for companies to recognize that manufacturing forecasts aren’t written in stone. Revisiting and refining forecasts, in light of new data, enables manufacturers to adjust predictions.
Benefits of Forecasting in Manufacturing
Demand forecasts are the guiding force that drives successful manufacturing cost management, with implications for resources, manufacturing accounting, capital investments, supply chain management and retail commitments. Once manufacturers build demand forecasts, planning and budgeting can take shape, enabling companies to more effectively and efficiently manage processes, inventories, resources and suppliers. Accurate forecasts ultimately give manufacturers a competitive edge by enabling more agile responses to market shifts. Here are the most important benefits of manufacturing forecasting.
Improved Production Planning
When manufacturers can predict demand for their products accurately and consistently, the production planning process becomes a well-oiled machine. Armed with up-to-date information about fluctuations in customer preferences, manufacturers can align supplies, equipment and manpower to adjust in lockstep with the market’s ebbs and flows. When demand spikes, supplies are already ordered and inventory is immediately available to maximize every sales opportunity. Similarly, when manufacturers can anticipate declines in sales, no excess inventory needs to be sold at a discount. Improved production planning to meet market demand eliminates waste and maximizes resources in cost-efficient ways. It also boosts customer satisfaction.
The best way for manufacturers to keep production lines humming with efficiency is to continuously refine forecasting models. Using both quantitative and qualitative models can also increase forecasting accuracy to make production planning more efficient. Technologies, like forecasting tools with AI, can also analyze high volumes of data quickly to build more agile forecasts.
Efficient Resource Allocation
Efficient manufacturing requires a careful balance of raw materials, manpower, machinery and financial resources to maximize output and minimize costs. Accurate demand forecasting gives manufacturers a clear idea of what resources will be needed and where, so they can avoid overstocking or understocking, both of which come with financial implications. If a bicycle manufacturer, for example, accurately forecasts a surge in demand for mountain bikes, it can align resources to meet that demand, rather than building racing bikes that no one wants. Not only does efficient resource allocation from manufacturing forecasts save money, it also allows manufacturers to stay ahead of competitors by deftly adapting to market shifts.
The goal of gauging product demand is to ensure that manufacturers aren’t caught producing too much or too little inventory, both of which can lead to significant financial implications. When manufacturers carry too much inventory because demand didn’t meet forecasts, expected revenue sits on shelves in their warehouses, tying up capital. In addition, they’ll likely have to eventually discount that inventory to clear it. It also means a good portion of the cost to build those products, such as the cost of supplies and labor, was wasted. When manufacturers fail to anticipate demand for their products, on the other hand, they lose out on sales because they don’t have inventory to sell. Not only does it cost money, but it strains relationships with retailers and customers.
Using the quantitative and qualitative methods described earlier to blend historical data, current market trends and predictive analytics, manufacturers can give themselves the best opportunity to align demand with production, keeping inventories lean while satisfying customer demand.
Enhanced Supply Chain Management
Most products are like recipes, comprised of component pieces that need to be combined to create a finished offering for customers. In many cases, manufacturers must source some or all those components from other companies, collectively known as a supply chain. This complex engine requires constant attention to guarantee that component parts flow in perfect balance and timing to allow manufacturers to keep their pipelines chugging along smoothly.
Manufacturing forecasting is the process of predicting what products need to be built, when and where. Accurate forecasts create the blueprint manufacturers need to coordinate with suppliers and ensure that they have just enough raw materials to meet all the inevitable ebbs and flows of customer demand. As a result, products move swiftly and smoothly from production to happy consumers. The costs of storing excess inventory or rushing orders to meet unexpected demand are minimized.
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The more thorough the forecasting process, the less manufacturers leave to chance. Consider the steps in a comprehensive manufacturing forecast: reviewing sales histories, monitoring economic indicators and global sales trends, applying multiple forecasting methods and advanced tools, speaking with suppliers and customers about recent trends and even checking weather reports. With each element of a forecast, manufacturers prepare themselves for multiple outcomes. Risk mitigation, because of thorough manufacturing forecasting, protects a company’s bottom line by ensuring optimal resource allocation and enabling companies to build contingency plans to keep product pipelines moving. For example, with a thorough forecast, a shortage of raw materials from a supplier doesn’t have to spell doom because the manufacturer has already arranged alternate suppliers to pick up the slack.
If business is like a game of chess, manufacturing forecasting is the process of trying to think one move ahead of your opponent. The ability to anticipate market demand and trends that competitors don’t see can turn fledgling companies into powerhouses. For example, although electric vehicles have existed since the late 1800s, Tesla’s ability to align production of its cars with skyrocketing demand from consumers more than 100 years later catapulted the company from nothing to being one of the world’s most highly valuated companies.
With the competitive advantage of consistently accurate forecasts, manufacturers can reap the benefits of increased revenue and an elevated brand. On the other hand, without the benefit of accurate forecasting, gaining competitive advantage is as likely as winning the Indianapolis 500 while wearing a blindfold. With no ability to anticipate market shifts, companies will rapidly pay the price of too much or too little inventory, unstable supply chains and products that are out of touch with consumer preferences.
In boxing, agility refers to the ability to move quickly in response to an opponent’s punches. It’s not much different in the business world, where manufacturers are defined by their ability to use quick thinking and decisive action to weather inevitable storms. Manufacturing forecasting is the cornerstone of agile thinking, allowing manufacturers to swiftly reallocate resources, ramp up production and modify marketing strategies to capitalize on trends.
When done well, manufacturing forecasting allows manufacturers to meet demand while also preparing them for potential supply chain disruptions that could bring assembly lines to a screeching halt. For example, a key element of building manufacturing forecasts should be the inclusion of qualitative data from suppliers, not only about market trends but also about their own production challenges. By understanding those challenges, manufacturers can anticipate potential supply chain disruptions and proactively prepare contingency plans with alternate suppliers to seamlessly plug any holes in the process. Another important tip: Leveraging technology that provides a real-time view of integrated data from sales, production and supply chain sources fuels smarter, quicker decisions to adapt to market changes.
Reduction in Bullwhip Effect
The “bullwhip effect” takes its name from the motion of a whip as it moves from handle to tip. In the manufacturing context, it refers to how minor fluctuations in demand from retailers can result in significant swings in orders and inventory throughout the supply chain. For example, a retailer might see an increase in umbrella sales during a week of rainy weather. To be on the safe side, the retailer doubles its usual order with its wholesale supplier of umbrellas. The wholesaler sees the increased order and, also wanting to be prepared, doubles its usual order with the manufacturer, as well. Suddenly, the manufacturer sees a steep increase in demand and increases production. But if skies soon clear and demand normalizes, the manufacturer could be sitting on a lot of unused inventory.
Manufacturing forecasting can better prepare manufacturers for the bullwhip effect. For example, using historical data and predictive analytics, the umbrella manufacturer might determine that the spike in demand is a short-term anomaly. As a result, it increases production modestly. Inventory levels are optimized, and the manufacturer avoids the cost of overproduction.
Streamline Your Manufacturing Forecasts With NetSuite
NetSuite ERP can play a pivotal role in overcoming many of the most challenging obstacles to successful manufacturing forecasting. Many companies struggle to gather enough companywide data to improve forecasting accuracy because information often resides in multiple disconnected systems that make it difficult to get a single, consistent view of sales and production data. NetSuite ERP’s natively integrated components for finance, sales, supply chain management and production, however, create a powerful foundation of rich, clean, consolidated real-time data from across the organization to fuel better predictions.
For example, if a manufacturer runs a successful online promotion, NetSuite automatically recognizes a spike in direct sales in NetSuite CRM. Because NetSuite CRM is integrated with NetSuite’s planning and scheduling module, the production team sees the spike and adjusts manufacturing quotas in response. Integration with NetSuite Supply Chain Management means the system automatically orders new component parts to meet the new production schedule. And, of course, all transactions are automatically captured and recorded in real time in NetSuite Cloud Accounting Software, which carefully tracks the manufacturer’s cash position, streamlines accounts payable and receivable processes, and manages the life cycle of fixed assets.
In addition, manufacturers can leverage their data with NetSuite Demand Planning, which predicts future inventory needs based on historical demand, seasonality, open opportunities, sales forecasts and other metrics. It determines when to reorder items to enhance supply chain planning and ensure that the right items are on hand to fill orders. The demand planning module also uses state-of-the-art algorithms to analyze past sales data, identify patterns and forecast future demand. NetSuite’s AI and machine learning features also mean the system continually learns from past forecasting performance to refine future predictions.
Yogi Berra once said: “If you don’t know where you’re going, you might end up someplace else.” In typical Yogi fashion, it’s hard to know just what the New York Yankees’ Hall of Famer was trying to say, but it aptly describes the importance of manufacturing forecasting. Accurate demand forecasts allow manufacturers to stay on track with production, while keeping one step ahead of market shifts and competitors. Forecasts aren’t guarantees, but manufacturers that combine quantitative data with qualitative market feedback and advanced analytics tools give themselves the greatest chance to end up exactly where they’re supposed to be.
Manufacturing Forecasting FAQs
What are the two types of manufacturing forecasting?
Broadly speaking, there are two categories of manufacturing forecasts. Quantitative forecasting relies on historical data, such as sales history and historical economic data. Qualitative forecasting incorporates feedback from external sources, such as economic experts, suppliers, retailers and customers. The most accurate manufacturing forecasts often use a combination of quantitative and qualitative data to estimate demand.
What are the four manufacturing forecasting methods?
There are four general categories of forecasting methods. Push forecasting methods align production estimates with quantitative and qualitative estimates of demand. Pull forecasting methods gauge demand, based on specific orders from customers. Sales-driven forecasting methods use historical sales data to predict future demand trends. Production-driven forecasting methods estimate output according to a manufacturer’s capacity to build products. Within each method are several specific forecasting models, which include detailed mathematical formulas and strategies that can be applied to forecasts. These models are often developed and modified by mathematicians and other experts.
Why is forecasting important in the manufacturing industry?
In an ideal world, manufacturers would know beforehand exactly how many products their customers will purchase every year. Armed with that information, they’d never produce any more or less product than necessary. Unfortunately, no one can predict the future with certainty. Manufacturing forecasting, however, helps companies predict demand to avoid creating too much or too little inventory.
What is the role of forecasting in production management?
Demand forecasts are the primary means by which manufacturers estimate how much product to build. Demand forecasts have a ripple effect throughout the organization, with important implications for profitability. Estimates for production influence how many employees companies hire, how much machinery they purchase and how it’s deployed, how many components they require to build products and how much they commit to deliver to retailers.