Aggregate planning is the medium-term process through which an organization decides how much capacity, labor, inventory, overtime, subcontracting, and backlog it will use to meet expected demand over the coming months (ASCM, 2019; Jacobs & Chase, 2024). It occupies the space between long-term strategic capacity decisions and short-term scheduling. Managers are not yet deciding which individual job will run on a machine at 10:00 a.m. next Tuesday; instead, they are deciding whether the organization should maintain a stable workforce, build seasonal inventory, expand overtime, use temporary labor, subcontract production, shift demand, or combine these options across a planning horizon.
The central problem is therefore one of coordination under uncertainty. Demand forecasts are imperfect, labor and equipment cannot always be changed instantly, inventory ties up cash, and customer service deteriorates when capacity is insufficient. Recent reviews of aggregate production planning show that contemporary models increasingly incorporate uncertainty, sustainability, workforce effects, and disruption risk rather than minimizing a single short-term cost function (Leong, Wong, & Anjomshoae, 2024; Raoufi et al., 2025). A chase strategy remains one important option, but it should be evaluated against level and mixed strategies rather than treated as the default solution.
The Planning Problem Is a Balance of Costs and Constraints
An aggregate plan begins with a demand forecast by period and a realistic picture of available capacity. Capacity should represent effective output after maintenance, changeovers, learning, absenteeism, quality losses, and normal operating constraints rather than a theoretical engineering maximum. Relevant inputs normally include regular labor capacity, overtime limits, hiring and training lead time, supplier capacity, storage, subcontracting, service targets, and any legal or contractual restrictions on workforce adjustment.
Costs then translate alternatives into comparable consequences. Regular production, overtime, hiring, layoffs, inventory holding, backorders, lost sales, subcontracting, expedited transport, idle time, and training can all matter. Some consequences are difficult to express accurately in money. Repeated layoffs can damage morale and institutional knowledge; excessive overtime can increase fatigue and quality problems; subcontracting can transfer work to suppliers with weaker labor or environmental controls. A technically optimal spreadsheet solution may therefore be strategically poor if it treats human or service consequences as zero-cost.
The planning objective should also reflect the operating system. Manufacturers can often use inventory as a buffer between stable production and variable demand. Airlines, hotels, hospitals, call centres, restaurants, and other services cannot store yesterday’s unused capacity. These organizations often need flexible labor, reservation systems, pricing, appointment scheduling, or other demand-management tools. The same planning logic applies, but the feasible buffer changes.
Level and Chase Strategies Represent Opposite Capacity Philosophies
A level strategy maintains relatively stable workforce or output even when demand changes. When production exceeds demand, inventory accumulates; when demand exceeds production, inventory is consumed or orders may wait. The main advantage is stability. Employees develop proficiency, suppliers face more predictable requirements, and the organization avoids repeated hiring and separation costs. The disadvantage is that the firm must absorb demand variability through inventory, idle capacity, backlog, or waiting time.
A chase strategy adjusts capacity or output to follow demand more closely. Firms may hire and release workers, change shifts, use overtime, schedule temporary employees, subcontract, or modify working hours. Inventory can remain lower because output rises and falls with expected demand. Chase is attractive where output cannot be stored and labor or capacity can be changed quickly. It can be costly where skills are scarce, training is lengthy, quality depends on experience, or workers bear substantial income volatility.
The distinction should not be confused with order-fulfillment strategy. Make-to-stock, make-to-order, assemble-to-order, and engineer-to-order describe when customer demand becomes connected to a specific product or job. A make-to-order business can still maintain a stable workforce and quote longer lead times during a peak, while a make-to-stock manufacturer can vary overtime aggressively to chase seasonal demand. Fulfillment position and aggregate capacity policy answer different questions.
| Planning choice | Primary buffer | Typical strength | Typical risk |
|---|---|---|---|
| Level | Inventory, backlog, or unused capacity | Workforce and production stability | Holding cost, obsolescence, waiting, or stockout |
| Chase | Flexible labor and capacity | Low finished-goods inventory and close demand matching | Hiring cost, instability, fatigue, quality variation |
| Mixed | Several buffers used selectively | Balances conflicting costs and risks | Requires stronger coordination and planning discipline |
Most real organizations use a mixed strategy, often combining inventory and production-capacity decisions across the planning horizon (Silver et al., 2016). A manufacturer may retain a core workforce, build moderate inventory before a predictable peak, use overtime during the highest-demand month, and subcontract only when internal capacity reaches a defined threshold. A hotel may use stable permanent staffing, seasonal workers, dynamic pricing, and minimum-stay restrictions. Mixed planning is often superior because no single adjustment mechanism remains cheap or safe across the entire range of demand.
A Worked Example Shows Why Timing Matters
Assume that a small manufacturer expects demand of 900, 1,100, 1,400, and 1,000 units over four months. Regular capacity is 1,050 units each month. Beginning inventory is 200 units, management wants at least 100 units of ending inventory after month four, and overtime can provide up to 250 units in any month. Regular production is cheaper than overtime, but carrying inventory has a monthly cost.
If the company uses level regular production of 1,050 units per month, month one begins with 200 units, produces 1,050, ships 900, and ends with 350. Month two then ends with 300 units after producing another 1,050 and meeting 1,100 units of demand. In month three, 300 units of opening inventory plus 1,050 of regular production provides only 1,350 units against demand of 1,400. The company therefore needs at least 50 units of overtime, subcontracting, or backlog merely to avoid a stockout. Because it also wants 100 units of ending inventory after month four, the complete plan has to consider later requirements rather than solve each month in isolation.
A mixed plan could schedule additional overtime in month three, preserve sufficient inventory for the final period, and avoid workforce changes entirely. The exact least-cost solution depends on the overtime rate, inventory holding cost, required ending stock, and whether backlog is permitted. This is why aggregate planning should be expressed through a period-by-period balance equation:
Ending inventoryt = beginning inventoryt + regular productiont + overtimet + subcontractingt − demandt + backlog carried into or out of the period as defined by the model.
Producing the entire horizon’s forecast at the beginning is not what “aggregate planning” means. Doing so could require capacity the organization does not possess and create unnecessary storage, working-capital, deterioration, and obsolescence costs. The objective is to coordinate timing and capacity, not to buy or manufacture everything in bulk.
Modern Aggregate Planning Extends Beyond a Simple Cost-Minimization Exercise
Traditional models commonly use linear or mixed-integer programming to minimize total planning cost subject to workforce, inventory, capacity, and demand-balance constraints. These methods remain useful, but current research increasingly models uncertainty explicitly. Leong et al. (2024) found that stochastic and fuzzy approaches are common in recent aggregate-planning literature because demand, production, supply, and costs are rarely known with certainty. Raoufi et al. (2025) similarly identify uncertainty and sustainability as major directions in contemporary APP research.
A rolling horizon is therefore preferable to treating a 12- or 18-month plan as fixed. The organization can freeze decisions that are already committed, revise later periods as new demand information appears, and add another period at the far end of the horizon. This approach respects the fact that a forecast for next month is generally more actionable than a forecast for next year.
Aggregate planning also fits naturally within sales and operations planning (S&OP) or integrated business planning. Sales, marketing, finance, operations, procurement, and human resources need one reconciled set of assumptions. A promotion that marketing launches without capacity confirmation can create missed deliveries; an operations plan that builds inventory without financial review can absorb cash unnecessarily; a staffing change that ignores human-resource lead times may be impossible to execute. Cross-functional planning converts a forecast into an organizational commitment.
Demand management can be part of the solution as well. Seasonal pricing, promotions during low-demand periods, reservations, appointments, lead-time communication, complementary products, and customer incentives can shift some demand instead of forcing all adjustment onto capacity. This is especially important in services. However, demand management should remain transparent and should not simply ration essential services away from customers who cannot pay peak prices.
A Chase Strategy Is Appropriate Only When Capacity Can Move Safely and Economically
The decision to chase demand should be based on adjustment economics, not on the superficial appeal of low inventory. Chase is relatively attractive when labor can be scheduled flexibly, the skills required are readily available, training is short, output cannot be stored, demand variation is predictable enough to staff against, and quality remains stable as workforce levels change. It is less attractive where technical skills take months to build, knowledge loss is costly, labor protections limit rapid adjustment, safety suffers under overtime, or customer service depends heavily on experienced employees.
Managers should therefore evaluate several performance dimensions at the same time: total cost, service level, forecast error, inventory turns, capacity utilization, overtime, employee turnover, quality, backlog, supplier performance, and schedule stability. Maximizing one metric can damage another. Extremely high utilization may create queues; extremely low inventory may produce frequent stockouts; low labor cost may coexist with high turnover and poor quality.
The broader lesson is that aggregate planning is not simply a choice between “level” and “chase.” It is a structured method for deciding where the organization wants variability to reside. A firm can absorb volatility in inventory, workforce hours, customer waiting, supplier capacity, price, backlog, or combinations of these mechanisms. The best plan is the one that meets demand at an acceptable total economic and social cost while remaining feasible under uncertainty.
References
ASCM. (2019). Principles on Demand: Aggregate Operations Planning.
Jacobs, F. R., & Chase, R. B. (2024). Operations and Supply Chain Management. McGraw Hill.
Leong, W. Y., Wong, K. Y., & Anjomshoae, A. (2024). A systematic literature review of aggregate production planning: Social and economic perspectives. Journal of Industrial Engineering and Management.
Raoufi, K., Tajasob, P., Mirzapour Al-e-Hashem, S. M. J., et al. (2025). Challenges, opportunities, and future research directions of aggregate production planning: A state-of-the-art analysis of sustainability, uncertainty, and case studies. Journal of Cleaner Production.
Stevenson, W. J. (2021). Operations Management. McGraw Hill.
Silver, E. A., Pyke, D. F., & Thomas, D. J. (2016). Inventory and Production Management in Supply Chains. CRC Press.
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