As summer approaches, demand for air conditioners, lighter clothing, and refreshing beverages rises significantly. Clothing retailers introduce breezy summer collections in late spring, while beverage companies promote ice creams, iced teas and tropical fruit juices to match the season. Thus, purchase patterns shift away from products favoured during the winter months. This cyclical shift in consumer purchasing behaviour, known as seasonality, refers to predictable demand fluctuations that, while varying in magnitude, are certain to occur. Seasonality can be categorised into technical and man-made seasonality. Technical seasonality arises from natural factors, such as weather, which drive demand for products like woollen clothing in winter or air conditioners in summer. Man-made seasonality tends to be shorter compared to technical seasonality and stems from events like festivals or promotions, such as Diwali or Black Friday, which trigger spending spikes.  <h2>Why is it difficult to manage seasonality?</h2>Demand fluctuations exceeding capacity: Managing seasonality, regardless of its type, is challenging because demand fluctuations are expected, but their magnitudes are much larger than those during non-season periods. For example, certain products, like air conditioners/refrigerators, can experience sales surges of more than 20% during peak periods, such as summer or Diwali. This demand surge is often well beyond the company's production capacity for the same period, due to capacity limitations, lead times, and supply chain constraints.<figure class="image"><img style="aspect-ratio:3989/3050;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745586381.png" width="3989" height="3050"></figure>Inaccurate forecasts: Since manufacturing all required items during the sales season itself is often impractical, companies have no choice but to forecast expected sales and produce goods in advance to ensure availability when demand peaks. However, demand is influenced by external factors such as consumer preferences, economic conditions, competitive actions, and unforeseen events. So, no forecasting method can perfectly predict demand fluctuations, making it impossible to estimate exactly how much to manufacture. The inherent inaccuracy of forecasts can be aggravated by supply chain and other conflicts:Poor data accuracy: In addition to using POS data, using outdated, incomplete, or incorrect data can further undermine forecast reliability. Without accurate data, predictions may not reflect real demand. Limited communication and data sharing: Limited communication and data sharing among supply chain stakeholders can result in fragmented or inconsistent information. When departments or partners aren’t aligned, forecasting worsens. Differing departmental goals and alignment: Departments often have differing goals, which can lead to disagreements in the forecasting process. Misalignment between sales, marketing, and operations, for instance, can create conflicting priorities and deteriorating forecasts. Accounting for in-season fluctuations: Forecasting challenges are not limited to initial predictions. As the season unfolds, it becomes increasingly difficult to incorporate fluctuations into demand forecasts. For example, a milder winter reduces the need for heavy jackets, while a harsher winter increases the demand for insulated clothing. Navigating a season can be akin to steering through ever-changing seas. <h2>Impact of inaccurate forecasts</h2>Inaccurate forecasting and the difficulty of real-time corrections, given the lead times involved, make it difficult for companies to plan production, inventory, and supply chain operations effectively. This significantly impacts companies in various ways: Rushed production: To meet seasonal demand, companies often push for overtime or expedite production, leading to increased labour and operational costs and potential compromises in quality. Supply chain bottlenecks: Sourcing additional raw materials (RMs) or components on short notice can be difficult or expensive. These supply chain challenges may cause delays, preventing companies from meeting demand on time. Stockouts and lost sales: Despite efforts to scale up production, the company may still fall short of meeting full demand, leading to stockouts, missed sales, and dissatisfied customers during a crucial revenue window. In a last-minute scramble to mitigate these, companies often resort to costly expediting measures such as inter-store or inter-depot transfers. When demand goes unmet despite best efforts, frustrated customers are forced to look elsewhere. For example, if a store runs out of a popular smartphone model, shoppers may turn to competitors, not only causing immediate revenue loss but also risking long-term customer attrition. The potential for future sales is also jeopardised.<figure class="image"><img style="aspect-ratio:3989/1876;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745586306.png" width="3989" height="1876"></figure>Excess stock: In trying to meet potential demand, often with an optimistic outlook, companies may overproduce, resulting in surplus inventory once the season ends. This excess stock ties up working capital and storage space, placing financial strain on the business.<figure class="image"><img style="aspect-ratio:3996/1876;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745586257.png" width="3996" height="1876"></figure>Margin erosion: In addition to costly expediting measures to prevent stockouts, businesses often resort to heavy discounting to liquidate excess inventory. This also leads to margin erosion. This is because while this approach helps clear stock, it comes at the expense of profitability and reduces full-price sell-through rates, ultimately impacting future sales strategies and brand positioning.<figure class="image"><img style="aspect-ratio:3988/2641;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745586195.png" width="3988" height="2641"></figure><h2>Accurate forecast: An oxymoron</h2>Since the problem is inaccurate forecasting, working towards an accurate forecast, possibly Artificial Intelligence-enabled, may seem like the ideal solution.  But the question arises: ‘Can forecasts ever truly be accurate?’ Currently, despite significant investments in advanced tools, forecasts remain far from perfectly reliable. However, steps can be taken to minimise inaccuracies. To achieve this, we can leverage the inherent characteristics of forecasts to enhance supply chain reliability.<h5> </h5><h2>Characteristics of forecasts</h2>1. Forecast variability: Typically, companies rely on POS data to create forecasts. However, forecast accuracy is poorest at the point of sale but improves as we move upstream toward production. Aggregated forecasts, which compile data from multiple sources, are typically more reliable, as these data points tend to balance out individual variations, resulting in a more stable and predictable overall demand pattern.<figure class="image"><img style="aspect-ratio:3996/1969;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745586101.png" width="3996" height="1969"></figure>2. Reliability of forecasts by SKU types: Although slow-moving SKUs (tail products) are challenging to forecast accurately due to their unpredictable demand, fast-moving items offer more reliable forecasts thanks to their consistent sales patterns. 3. Impact of Replenishment Lead Times (RLTs): Accurate forecasting is often more challenging over longer horizons. Shorter lead times or horizons, however, generally yield more precise forecasts because they depend on recent, reliable data, reducing uncertainties. Longer lead times require assumptions about various external factors, such as competitor activities and market trends, which heightens the risk of forecast errors due to the increasing uncertainty over time. By leveraging these forecast characteristics, we can minimise the impact of forecast inaccuracies and build a supply chain that ensures product availability despite seasonal fluctuations.  <h2>Key solution elements</h2>Build agility: Creating such an efficient supply chain means that, firstly, there must be a way to reduce RLTs, or the speed at which items are restocked, so that the forecast horizon can be shortened. Moreover, when lead times are reduced, the minimum inventory to be held at various nodes in the network for each SKU will decrease, enabling the supply chain to stock a wider range of items with the same working capital. This will improve the availability of items without burdening any part of the supply chain with additional investment. Setting inventory limits: The first step is to set inventory limits for each SKU at various stocking points across different geographies. Shipments can be halted when these limits are breached. This enables early detection of demand or market changes, preventing overstocking. At the same time, production and replenishment can prioritise high-risk SKUs to avoid stockouts. Dynamically adjusting these inventory limits to reflect changing demand patterns ensures the supply chain remains efficient, responsive, and aligned with market needs, maintaining product availability at every point. Pull-based inventory movement: When, in addition to the above step, a ‘pull-based’ system is adopted in the supply chain to respond to daily consumption triggers, it minimises the lag between plants and real-time demand signals. Furthermore, by maintaining most inventory at aggregate levels, such as plant warehouses, the company ensures that most of the stock is positioned where demand variability is lowest while also effectively decoupling supply lead time from replenishment time. This system enables the company to respond with agility to daily demand across diverse geographies while keeping overall stock levels minimal. Season build-up: Despite supply chain agility, forecast-based inventory build-up for seasonal demand is inevitable and therefore inherently prone to errors. So, proactive measures in the Sales & Operations Planning (S&OP) must be implemented to minimise the impact of forecast uncertainty.<figure class="image"><img style="aspect-ratio:4001/1999;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745585994.png" width="4001" height="1999"></figure>How much to make: Firstly, as part of the S&OP process, companies should leverage aggregated forecasts to capture overall demand trends. This approach is more effective than POS-based forecasting or sales-driven inventory positioning, which often leads to stock imbalances. While demand will naturally fluctuate across SKUs and timeframes, combining aggregated forecasting with inventory held at aggregated levels and replenishment based on actual consumption helps optimise stock distribution across the network, reducing variability and enhancing supply chain stability. When to start making: It is also logical to schedule the production of forecasted inventory as close to the season as possible. This approach allows for better sales estimates (shorter forecast horizon) while minimising the tying up of working capital for longer than necessary. But it should also allow for a time buffer of at least one RLT to catch up if uncertainty arises. Let’s take a simple example to see how this can work. Let’s say a plant has a rated capacity of 1,000 air conditioners per month, with typical consumption of 700 units, leaving spare capacity of 300 units. The company can plan accordingly. Suppose peak demand in April is expected to reach 1,600 units. In that case, the plant can produce the additional 600 units required for the season by March (assuming one month as the RLT), utilising its spare capacity to manufacture 300 units per month in the two months leading up to March. The plant’s inventory limits on what it can make can be increased slowly. This will allow the supply chain to react smoothly and avoid RM unavailability (due to inadequate sourcing time) or production urgencies.<figure class="image"><img style="aspect-ratio:3998/1485;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745585939.png" width="3998" height="1485"></figure>Which SKUs to make: To reduce inventory risk while ensuring availability after the build-up, it is also helpful to classify SKUs into – runners (fast movers; top 80% of sales), repeaters and laggards (slow movers; lowest 10% sales), and create the season build-up strategy based on that. Since it’s known that the runners will continue to sell well even after the season, there is minimal risk if they are built up in excess. So, extending the earlier example, if a company is manufacturing many types of air conditioners, it can confidently manufacture the runner air conditioners using spare capacity. This means the company will have to make very few of these items during the season, and the entire available capacity can be dedicated to the repeaters and laggards based on actual market consumption. Ramp down: At the end of the season, inventory limits - temporarily exceeded to meet peak demand- must be reinstated. To achieve this, production ramp-down should begin one RLT before the season's expected close. Just as ramp-up was managed through gradual increases, ramp-down should be executed through controlled reductions to prevent supply chain disruptions. This approach ensures a smooth transition back to pre-season operational levels, maintaining stability and efficiency. Automated decision making: All the decisions have to ensure the supply chain responds and adapts to daily consumption signals (both before and during the season), as well as the steps required for seasonal build-up and ramp-down, can be automated. This automation ensures meticulous execution, preventing delays in sourcing RM and manufacturing. <h2>Conclusion</h2>Poor seasonal management can lead to substantial financial losses. Companies typically face margin erosion of 10%-40%, depending on the category and the extent of inventory mismatch due to excess stock liquidations, and a 5%-30% revenue loss from stockouts during peak demand periods. Adopting a pull-based supply chain with inventory limits that dynamically adjust based on demand sensing not only mitigates the inherent inaccuracies of traditional forecasting but also aligns supply chain operations with actual market conditions, leading to improved availability and reduced working capital requirements. Furthermore, by implementing the suggested processes for seasonal build-up and ramp-down, companies can better navigate large, known demand fluctuations with greater agility, maintain excellent product availability, and capitalise on market opportunities more effectively, ultimately enhancing profitability.

Key Takeaways

Facing inventory challenges during peak seasons? Discover practical, proven strategies to manage seasonal demand, improve forecast accuracy, and optimise supply chains for better availability, lower costs, and higher profitability.

Managing inventory through seasonal fluctuations

As summer approaches, demand for air conditioners, lighter clothing, and refreshing beverages rises significantly. Clothing retailers introduce breezy summer collections in late spring, while beverage companies promote ice creams, iced teas and tropical fruit juices to match the season. Thus, purchase patterns shift away from products favoured during the winter months. This cyclical shift in consumer purchasing behaviour, known as seasonality, refers to predictable demand fluctuations that, while varying in magnitude, are certain to occur. Seasonality can be categorised into technical and man-made seasonality. Technical seasonality arises from natural factors, such as weather, which drive demand for products like woollen clothing in winter or air conditioners in summer. Man-made seasonality tends to be shorter compared to technical seasonality and stems from events like festivals or promotions, such as Diwali or Black Friday, which trigger spending spikes.  <h2>Why is it difficult to manage seasonality?</h2>Demand fluctuations exceeding capacity: Managing seasonality, regardless of its type, is challenging because demand fluctuations are expected, but their magnitudes are much larger than those during non-season periods. For example, certain products, like air conditioners/refrigerators, can experience sales surges of more than 20% during peak periods, such as summer or Diwali. This demand surge is often well beyond the company's production capacity for the same period, due to capacity limitations, lead times, and supply chain constraints.<figure class="image"><img style="aspect-ratio:3989/3050;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745586381.png" alt="Vector Consulting Group | Article | Managing Inventory| Infographic showing examples of technical and man-made seasonal sales spikes in India for items like umbrellas, air conditioners, sweets, beauty brands, and jewellery." width="3989" height="3050"></figure>Inaccurate forecasts: Since manufacturing all required items during the sales season itself is often impractical, companies have no choice but to forecast expected sales and produce goods in advance to ensure availability when demand peaks. However, demand is influenced by external factors such as consumer preferences, economic conditions, competitive actions, and unforeseen events. So, no forecasting method can perfectly predict demand fluctuations, making it impossible to estimate exactly how much to manufacture. The inherent inaccuracy of forecasts can be aggravated by supply chain and other conflicts:Poor data accuracy: In addition to using POS data, using outdated, incomplete, or incorrect data can further undermine forecast reliability. Without accurate data, predictions may not reflect real demand. Limited communication and data sharing: Limited communication and data sharing among supply chain stakeholders can result in fragmented or inconsistent information. When departments or partners aren’t aligned, forecasting worsens. Differing departmental goals and alignment: Departments often have differing goals, which can lead to disagreements in the forecasting process. Misalignment between sales, marketing, and operations, for instance, can create conflicting priorities and deteriorating forecasts. Accounting for in-season fluctuations: Forecasting challenges are not limited to initial predictions. As the season unfolds, it becomes increasingly difficult to incorporate fluctuations into demand forecasts. For example, a milder winter reduces the need for heavy jackets, while a harsher winter increases the demand for insulated clothing. Navigating a season can be akin to steering through ever-changing seas. <h2>Impact of inaccurate forecasts</h2>Inaccurate forecasting and the difficulty of real-time corrections, given the lead times involved, make it difficult for companies to plan production, inventory, and supply chain operations effectively. This significantly impacts companies in various ways: Rushed production: To meet seasonal demand, companies often push for overtime or expedite production, leading to increased labour and operational costs and potential compromises in quality. Supply chain bottlenecks: Sourcing additional raw materials (RMs) or components on short notice can be difficult or expensive. These supply chain challenges may cause delays, preventing companies from meeting demand on time. Stockouts and lost sales: Despite efforts to scale up production, the company may still fall short of meeting full demand, leading to stockouts, missed sales, and dissatisfied customers during a crucial revenue window. In a last-minute scramble to mitigate these, companies often resort to costly expediting measures such as inter-store or inter-depot transfers. When demand goes unmet despite best efforts, frustrated customers are forced to look elsewhere. For example, if a store runs out of a popular smartphone model, shoppers may turn to competitors, not only causing immediate revenue loss but also risking long-term customer attrition. The potential for future sales is also jeopardised.<figure class="image"><img style="aspect-ratio:3989/1876;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745586306.png" alt="Vector Consulting Group | Article | Managing Inventory| Variation of stocked-out scenarios across smartphones, FMCG, and chocolates highlighting customer reactions, lost sales, and market share impacts caused by product unavailability." width="3989" height="1876"></figure>Excess stock: In trying to meet potential demand, often with an optimistic outlook, companies may overproduce, resulting in surplus inventory once the season ends. This excess stock ties up working capital and storage space, placing financial strain on the business.<figure class="image"><img style="aspect-ratio:3996/1876;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745586257.png" alt="Vector Consulting Group | Article | Managing Inventory| Variation of excess stock levels and their business impact across industries, including luxury fashion, FMCG, and home inverter batteries, highlighting unsold inventory, discounting, and inventory holding challenges." width="3996" height="1876"></figure>Margin erosion: In addition to costly expediting measures to prevent stockouts, businesses often resort to heavy discounting to liquidate excess inventory. This also leads to margin erosion. This is because while this approach helps clear stock, it comes at the expense of profitability and reduces full-price sell-through rates, ultimately impacting future sales strategies and brand positioning.<figure class="image"><img style="aspect-ratio:3988/2641;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745586195.png" alt="Vector Consulting Group | Article | Managing Inventory |Comparison chart titled 'Typical discounts offered and consequent margin erosion' across the Fashion, Consumer Electronics, Passenger Cars, and FMCG sectors, detailing the percentages and reasons behind seasonal markdown losses." width="3988" height="2641"></figure><h2>Accurate forecast: An oxymoron</h2>Since the problem is inaccurate forecasting, working towards an accurate forecast, possibly Artificial Intelligence-enabled, may seem like the ideal solution.  But the question arises: ‘Can forecasts ever truly be accurate?’ Currently, despite significant investments in advanced tools, forecasts remain far from perfectly reliable. However, steps can be taken to minimise inaccuracies. To achieve this, we can leverage the inherent characteristics of forecasts to enhance supply chain reliability.<h5> </h5><h2>Characteristics of forecasts</h2>1. Forecast variability: Typically, companies rely on POS data to create forecasts. However, forecast accuracy is poorest at the point of sale but improves as we move upstream toward production. Aggregated forecasts, which compile data from multiple sources, are typically more reliable, as these data points tend to balance out individual variations, resulting in a more stable and predictable overall demand pattern.<figure class="image"><img style="aspect-ratio:3996/1969;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745586101.png" alt="Vector Consulting Group | Article | Managing Inventory |Line graph showing that in typical push-based distribution channels, forecast accuracy decreases while inventory levels increase as you move from Plant to Depot to Dealer." width="3996" height="1969"></figure>2. Reliability of forecasts by SKU types: Although slow-moving SKUs (tail products) are challenging to forecast accurately due to their unpredictable demand, fast-moving items offer more reliable forecasts thanks to their consistent sales patterns. 3. Impact of Replenishment Lead Times (RLTs): Accurate forecasting is often more challenging over longer horizons. Shorter lead times or horizons, however, generally yield more precise forecasts because they depend on recent, reliable data, reducing uncertainties. Longer lead times require assumptions about various external factors, such as competitor activities and market trends, which heightens the risk of forecast errors due to the increasing uncertainty over time. By leveraging these forecast characteristics, we can minimise the impact of forecast inaccuracies and build a supply chain that ensures product availability despite seasonal fluctuations.  <h2>Key solution elements</h2>Build agility: Creating such an efficient supply chain means that, firstly, there must be a way to reduce RLTs, or the speed at which items are restocked, so that the forecast horizon can be shortened. Moreover, when lead times are reduced, the minimum inventory to be held at various nodes in the network for each SKU will decrease, enabling the supply chain to stock a wider range of items with the same working capital. This will improve the availability of items without burdening any part of the supply chain with additional investment. Setting inventory limits: The first step is to set inventory limits for each SKU at various stocking points across different geographies. Shipments can be halted when these limits are breached. This enables early detection of demand or market changes, preventing overstocking. At the same time, production and replenishment can prioritise high-risk SKUs to avoid stockouts. Dynamically adjusting these inventory limits to reflect changing demand patterns ensures the supply chain remains efficient, responsive, and aligned with market needs, maintaining product availability at every point. Pull-based inventory movement: When, in addition to the above step, a ‘pull-based’ system is adopted in the supply chain to respond to daily consumption triggers, it minimises the lag between plants and real-time demand signals. Furthermore, by maintaining most inventory at aggregate levels, such as plant warehouses, the company ensures that most of the stock is positioned where demand variability is lowest while also effectively decoupling supply lead time from replenishment time. This system enables the company to respond with agility to daily demand across diverse geographies while keeping overall stock levels minimal. Season build-up: Despite supply chain agility, forecast-based inventory build-up for seasonal demand is inevitable and therefore inherently prone to errors. So, proactive measures in the Sales & Operations Planning (S&OP) must be implemented to minimise the impact of forecast uncertainty.<figure class="image"><img style="aspect-ratio:4001/1999;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745585994.png" alt="Vector Consulting Group | Article | Managing Inventory | Chart contrasting pre-season and in-season demand against capacity, highlighting pre-season build-up needed to cover peak season shortfall." width="4001" height="1999"></figure>How much to make: Firstly, as part of the S&OP process, companies should leverage aggregated forecasts to capture overall demand trends. This approach is more effective than POS-based forecasting or sales-driven inventory positioning, which often leads to stock imbalances. While demand will naturally fluctuate across SKUs and timeframes, combining aggregated forecasting with inventory held at aggregated levels and replenishment based on actual consumption helps optimise stock distribution across the network, reducing variability and enhancing supply chain stability. When to start making: It is also logical to schedule the production of forecasted inventory as close to the season as possible. This approach allows for better sales estimates (shorter forecast horizon) while minimising the tying up of working capital for longer than necessary. But it should also allow for a time buffer of at least one RLT to catch up if uncertainty arises. Let’s take a simple example to see how this can work. Let’s say a plant has a rated capacity of 1,000 air conditioners per month, with typical consumption of 700 units, leaving spare capacity of 300 units. The company can plan accordingly. Suppose peak demand in April is expected to reach 1,600 units. In that case, the plant can produce the additional 600 units required for the season by March (assuming one month as the RLT), utilising its spare capacity to manufacture 300 units per month in the two months leading up to March. The plant’s inventory limits on what it can make can be increased slowly. This will allow the supply chain to react smoothly and avoid RM unavailability (due to inadequate sourcing time) or production urgencies.<figure class="image"><img style="aspect-ratio:3998/1485;" src="https://web-assets.vectorconsulting.in/storage/editor_uploads//image_1745585939.png" alt="Vector Consulting Group | Article | Managing Inventory |Graph illustrating the timeline for seasonal inventory management, highlighting the ramp-up period before the season and the ramp-down period before the season ends, using a one Replenishment Lead Time (1RLT) buffer." width="3998" height="1485"></figure>Which SKUs to make: To reduce inventory risk while ensuring availability after the build-up, it is also helpful to classify SKUs into – runners (fast movers; top 80% of sales), repeaters and laggards (slow movers; lowest 10% sales), and create the season build-up strategy based on that. Since it’s known that the runners will continue to sell well even after the season, there is minimal risk if they are built up in excess. So, extending the earlier example, if a company is manufacturing many types of air conditioners, it can confidently manufacture the runner air conditioners using spare capacity. This means the company will have to make very few of these items during the season, and the entire available capacity can be dedicated to the repeaters and laggards based on actual market consumption. Ramp down: At the end of the season, inventory limits - temporarily exceeded to meet peak demand- must be reinstated. To achieve this, production ramp-down should begin one RLT before the season's expected close. Just as ramp-up was managed through gradual increases, ramp-down should be executed through controlled reductions to prevent supply chain disruptions. This approach ensures a smooth transition back to pre-season operational levels, maintaining stability and efficiency. Automated decision making: All the decisions have to ensure the supply chain responds and adapts to daily consumption signals (both before and during the season), as well as the steps required for seasonal build-up and ramp-down, can be automated. This automation ensures meticulous execution, preventing delays in sourcing RM and manufacturing. <h2>Conclusion</h2>Poor seasonal management can lead to substantial financial losses. Companies typically face margin erosion of 10%-40%, depending on the category and the extent of inventory mismatch due to excess stock liquidations, and a 5%-30% revenue loss from stockouts during peak demand periods. Adopting a pull-based supply chain with inventory limits that dynamically adjust based on demand sensing not only mitigates the inherent inaccuracies of traditional forecasting but also aligns supply chain operations with actual market conditions, leading to improved availability and reduced working capital requirements. Furthermore, by implementing the suggested processes for seasonal build-up and ramp-down, companies can better navigate large, known demand fluctuations with greater agility, maintain excellent product availability, and capitalise on market opportunities more effectively, ultimately enhancing profitability.<o:p></o:p>

<h5>Managing inventory through seasonal fluctuations</h5>

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Struggling with seasonal demand spikes? Learn how to transform inventory management with agile, pull-based strategies from Vector Consulting Group.