In brief: Failing to meet air conditioner demand in summer and heater demand in winter, and being left with products in the warehouse at the end of the season, are common situations in the durable goods sector. For products such as air conditioners, white goods, garden products, and heaters, demand is markedly seasonal and exceeds the factory's normal capacity in peak months. This article compares three ways of meeting the peak (level production, chase demand, hybrid strategy) with a numerical example, and then examines how this decision is made in S&OP. In this article:Three Basic StrategiesNumerical Example: Quarterly PlanWhich Products Should Be Pre-Built?How the Decision Is Made in S&OPEnd-of-Season RiskTechnology and Digitalization: Scenario Planning and Digital TwinImplementation: Checklist Three Basic Strategies There are three basic ways of meeting peak demand. All three answer the same question, namely with which resource and at what cost the peak demand will be met, in a different way. Level Production With level production, the factory runs at a constant rate throughout the year; the surplus produced in low-demand periods is put into stock, and sales are made from this stock in the peak quarters. A stable workforce, a predictable shift pattern, and low unit cost are the strengths of this approach. Its cost is high inventory. In addition to storage and insurance costs, inventory ties up cash and carries obsolescence risk in situations such as model changes or the end of the season. The earlier the pre-build stock is created, the greater the risk of forecast error accumulating as inventory. Chase Demand In the chase demand strategy, production is set equal to demand in every period; inventory stays low, but capacity must be changed continuously. In peak periods, additional shifts, overtime, temporary labor, or subcontracted production are brought in, and these resources are withdrawn when demand falls. Although the strategy minimizes inventory cost, it creates side effects such as hiring, training, quality fluctuation, and dependence on subcontractors. It is applicable when the peak is narrow and short; its cost rises as the peak lengthens. Hybrid Strategy In the hybrid strategy, base production is kept level, limited additional capacity (overtime or an additional shift) is used in the peak quarters, and a certain amount of pre-build stock is created. The balance between costs is the strength of this approach; its cost is that it requires planning discipline. Which products will be pre-built, within what limit overtime will be kept, and when subcontracting will be brought in must be determined in advance. In comparing the three strategies, not only production cost but also inventory carrying cost, overtime premium, subcontracting cost, cash requirement, and service level must be evaluated together. A different cost item stands out in each strategy, and the lowest total-cost option varies with the product family and the length of the season. Numerical Example: Quarterly Plan The example assumes that annual demand is 440 thousand units, distributed across quarters as 70 / 150 / 140 / 80 thousand units. Normal capacity is 110 thousand units per quarter (figures are for illustration purposes). The example compares the two strategies above (level and hybrid) on a quarterly basis. Figures are in thousands of units and are for illustration purposes. Result of Level Production In the level strategy, 110 thousand units are produced per quarter and the plan opens with a starting inventory of 40 thousand units. In the first quarter, since demand is 70 thousand units, ending inventory rises to 80 thousand units. In the second quarter, demand is 150 thousand units; with sales from stock, ending inventory falls to 40 thousand units. In the third quarter, demand of 140 thousand units brings inventory down to 10 thousand units, and in the fourth quarter, against demand of 80 thousand units, inventory rises again to 40 thousand units. The average of ending inventories is 42.5 thousand units. The system does not require overtime; its cost is the inventory carried throughout the year, which reaches 80 thousand units before the peak. Result of the Hybrid Strategy In the hybrid strategy, the plan opens with a starting inventory that is 20 thousand units lower. In the first quarter, 110 thousand units are produced and ending inventory is 60 thousand units. In the peak quarters (second and third quarters), production is raised to 120 thousand units with overtime; ending inventories are 30 and 10 thousand units respectively. In the fourth quarter, since demand falls to 80 thousand units, production is reduced to 90 thousand units and ending inventory closes at 20 thousand units. The average of ending inventories is 30 thousand units; this is about 30% lower than in the level strategy. Result of Chasing Demand If demand were followed exactly, 150 thousand units would have to be produced in the second quarter and 140 thousand units in the third quarter. Since normal capacity is 110 thousand units, this means additional capacity of 40 and 30 thousand units respectively. This quantity cannot be met with overtime; it requires an additional shift, temporary labor, or subcontracting. Inventory stays low, but capacity cost and operational risk increase. The example does not show a single strategy that is valid under all conditions. The advantage of the hybrid strategy emerges when inventory and capacity costs are calculated together: if the sum of the overtime premium and inventory carrying cost is lower than the inventory cost of the level strategy, the hybrid strategy is preferred. The calculation must be made with the actual costs of the durable product family. The real decision in seasonal products concerns the timing of production; this decision is a trade-off among inventory, capacity, and cash. Which Products Should Be Pre-Built? With the most predictable products: Pre-peak stock is built with high-volume core models that have low forecast error.With common components: Where the variant decision can be postponed, semi-finished goods or common components are stocked instead of finished goods.Not with new models: For products approaching a model change or with high demand uncertainty, pre-build stock carries obsolescence risk.Taking channel stocks into account: Pre-season loading of dealers takes place before consumer demand; dealer stocks must be monitored separately. How the Decision Is Made in S&OP This decision is not made in a single monthly meeting; it is spread across the 12-18 month S&OP horizon. In the supply review, level, hybrid, and chase scenarios are prepared together with cost, inventory value, cash requirement, and service level. The management S&OP meeting then approves the pre-build quantity and the limits for overtime and subcontracting. The process does not end when the season starts; as demand deviates from the plan, production for the remaining months and channel allocation are revisited in the monthly cycle. The decision logic of the meetings is explained in detail in the article Which Decision in Which Meeting in the S&OP Cycle?. End-of-Season Risk When the season closes, the remaining stock is either carried over to the next season or cleared with discounts. Both options are costly. For this reason, it is recommended that in the second half of the season production be adjusted according to the actual sales rate rather than the pre-build plan. The end-of-season stock target and discount decisions must also be on the S&OP agenda. Technology and Digitalization: Scenario Planning and Digital Twin Calculating the example above by hand in a table is possible; however, in practice dozens of product families, multiple lines, and changing costs are involved. Manual work usually settles for a few scenarios. Digital planning tools, on the other hand, allow a large number of alternatives to be compared quickly. Enterprise Resource Planning (ERP) is the enterprise transaction system that records orders, inventory, purchasing, production, and finance transactions in a common database; it is the master data and transaction source for planning. It provides sales, inventory, capacity, and cost actuals, and is the source of the cost parameters in the strategy comparison. Advanced Planning and Scheduling (APS) is a planning system based on optimization and heuristic methods that produces feasible plans and schedules by considering capacity, material, and priority constraints at the same time. It calculates the capacity-constrained multi-period plan through optimization and compares the cost of level, chase, and hybrid strategies in the same model. Digital Twin is a virtual model of a factory, line, or supply network that is updated with data; it allows scenarios to be tested without affecting the physical system. It makes it possible to test shift, overtime, subcontracting, and warehouse capacity options on a virtual model of the factory and warehouse network. Artificial Intelligence (AI) and Machine Learning (ML) are methods that learn patterns from data to produce forecasts, classifications, and recommendations; machine learning is the sub-field of artificial intelligence that learns from data. By including weather, campaign, and macroeconomic indicators in the forecast, they determine the likely range of seasonal demand more accurately. Internet of Things (IoT) is the infrastructure in which sensors on machines, vehicles, warehouses, and products transmit data automatically over a network; location, temperature, status, and consumption data are collected without human input. Usage and failure data from connected products in the field serve as a leading indicator of spare parts and renewal demand. Business Intelligence (BI) is the analytics layer that combines data from different sources and presents it to decision makers through dashboards and reports. It provides the dashboard on which inventory build-up and the plan-versus-actual gap are monitored throughout the season. In seasonal products, artificial intelligence contributes most in narrowing the demand range. However, in the medium-term S&OP horizon, how weather conditions and the market will evolve cannot be known with certainty. For this reason, instead of relying on a single forecast, it is recommended to test the optimistic and pessimistic scenarios determined with artificial intelligence separately on the APS or digital twin. This approach puts the stocking decision on a much firmer footing. What the season will bring cannot be known with certainty; but which step will be taken in which scenario can be determined in advance. Implementation: Checklist Build a 12-18 month seasonal demand profile by product family.Clarify the limits and costs of normal capacity, overtime, and subcontracting.Cost the level, hybrid, and chase scenarios with the same template.Determine the product and component list for pre-build according to forecast error and obsolescence risk.Monitor dealer/channel stocks separately throughout the season.In the second half of the season, update production monthly according to the actual sales rate.Calculate the seasonal stocking decision in the planning model with at least two scenarios (e.g., level and hybrid), with a cost comparison. Related Articles Which Decision in Which Meeting in the S&OP Cycle? Calendar, Decision Logic, and ScenariosDemand Planning and S&OP in Fast-Moving Consumer Goods (FMCG): 5 Critical Topics5 Common Mistakes in Safety Stock Calculation and the Correct Approach Frequently Asked Questions Is level production or chasing demand cheaper? There is no single correct answer to this question. When inventory carrying cost is low and demand is predictable, level production stands out. When capacity flexibility is cheap and obsolescence risk is high, the chase strategy is advantageous. In most companies, the lowest total cost is achieved with the hybrid strategy. When should pre-building start? The start date is calculated backward from the peak. The latest date by which the capacity gap at the peak can be closed with idle capacity beforehand is taken as the starting point. Components with long lead times, however, need to be planned even before this date. What should be done if demand comes in below plan in mid-season? In this case, production for the remaining period should be reduced, pre-build-based production should be stopped, and channel allocation should be redone. These decisions are made in the monthly S&OP; where waiting would be costly, holding an interim meeting is recommended. When should subcontracted production be preferred? Subcontracted production should be preferred when the capacity gap lasts for several periods and the product can be produced externally without quality risk. The advantage of subcontracting is that the peak can be met without a permanent capacity investment. How are external data such as weather included in the forecast? Machine learning models can use explanatory variables such as temperature, holidays, and campaigns together. However, in the medium term, weather forecasts retain their uncertainty. External data is most useful in short-term demand sensing and in determining the scenario range. References Wallace, T. F., & Stahl, R. A. (2008). Sales & Operations Planning: The How-To Handbook (3rd ed.). T. F. Wallace & Company.Nahmias, S., & Olsen, T. L. (2015). Production and Operations Analysis (7th ed.). Waveland Press.Chopra, S., & Meindl, P. (2019). Supply Chain Management: Strategy, Planning, and Operation (7th ed.). Pearson. Related Optimum Planlama Trainings handshake Sales and Operations Planning, S&OP/IBP Build the balance of demand and supply with competitive tactics and consensus. Detailed Information factory Production Planning and Detailed Scheduling Better manage orders, capacity-material requirements, and production flow. Detailed Information finance_mode Demand Planning and Sales Forecasting Learn forecasting processes, methods, and analyses through hands-on applications. Detailed Information Institutions We Work With in Durable Goods Some of the institutions to which we provide training and/or consulting services in the durable goods sector are listed below. For all sectors, you can visit our References page. ABB Elektrik, 2011 Agt, 2021 Akplas Plastik, 2010 Aksu Tost, 2021 Alarko-Carrier, 2011, 2012 Alfemo, 2017 Artema, 2016 Bahariye Halı, 2012 Besa, 2012 Bonna, 2021, 2022 Boytaş Mobilya, 2010 BSH Ev Aletleri, 2012, 2019 Chicco, 2012 Copreci, 2015 Cross Jeans, 2017 Çamsan Ordu, 2023 Çilek Mobilya, 2012–2021 Çimstone, 2011 Çokyaşar Holding, 2024 Daikin, 2020 Deceuninck, 2013 E.c.a., 2021, 2023 Elsel, 2016 Erciyes Anadolu Holding, 2021 EssilorLuxottica, 2022, 2023 Family Group, 2014 Foxconn TR, 2017 İstikbal Mobilya, 2012 Kale Musluk, 2013, 2018 Kastamonu Entegre, 2010–2018 Kilim Mobilya, 2013, 2014 Klimasan, 2011 LAV-Gürallar, 2020 Lazzoni Mobilya, 2014, 2016 Mavili Elektronik, 2014, 2017 Neşe Plastik, 2011 Panasonic Electric Works, 2022, 2023 Penti, 2011 Pina, 2015 Rengarenk Mobilya, 2017 Robert Bosch Bursa, 2015 Samet Kalıp, 2010, 2011 Silter, 2014 Tense Elektronik, 2023 Turkuaz Seramik, 2017, 2025 Türk Demirdöküm, 2011 Ukinox, 2016 Vestel, 2024 Vezirköprü, 2019 Vitra, 2016 Yatsan, 2023 Comments “The training was a useful and comprehensive training with the end-to-end integration of the S&OP process into the supply chain, explaining each step and explaining the roles and responsibilities in these steps.” B**** U****, S&OP Manager, Vestel (S&OP, 2024) “It was a training that we enjoyed very much with the knowledge and experience of my teacher İlker, his detailed explanation with examples and the discussion environment together.” Nida Balkancı Sümer, Planning Manager, Vestel (S&OP, 2024) “It was a useful training that will enable me to manage my current business processes more effectively by creating different perspectives. 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