Sales and Operations Planning

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Sales and Operations Planning Process

Sales and operations planning (S&OP) is an aggregate planning process that determines the resource capacity a firm will need to meet its demand over an intermediate time horizon—6 to 12 months in the future.

While the planning horizon varies by industry, within this time frame, it is usually not feasible to increase capacity by building new facilities or purchasing new equipment; however, it is feasible to hire or lay off workers, increase or reduce the workweek, add an extra shift, subcontract out work, use over time, or build up and deplete inventory levels. We use the term aggregate because the plans are developed for product lines or product families, rather than individual products.

An aggregate operations plan might specify how many bicycles are to be produced but would not identify them by color, size, tires, or type of brakes. Resource capacity is also expressed in aggregate terms, typically as labor or machine hours. Labor hours would not be specified by type of labour, nor machine hours by type of machine. And they may be given only for critical processes.

For services, capacity is often limited by space—number of airline seats, number of hotel rooms, and number of hospital beds. Time can also affect capacity. The number of customers who can be served lunch in a restaurant is limited by the number of seats, as well as the number of hours lunch is served. In some overcrowded schools, lunch begins at 10:00 a.m. so that all students can be served by 2:00 p.m.!

There are two objectives to sales and operations planning:

  • To establish a company-wide game plan for allocating resources, and
  • To develop an economic strategy for meeting demand.

The first objective refers to the long-standing battle between the sales and operations functions within a firm. Personnel who are evaluated solely on sales volume tend to make unrealistic sales commitments (either in terms of quantity or timing) that operations are expected to meet, sometimes at an exorbitant price. Operations personnel who are evaluated on keeping manufacturing costs down may refuse to accept orders that require additional financial resources (such as overtime wage rates) or hard-to-meet completion dates.

The job of operations planning is to match forecasted demand with available capacity. If capacity is inadequate, it can usually be expanded, but at a cost. The company needs to determine if the extra cost is worth the increased revenue from the sale and if the sale is consistent with the strategy of the firm.

Thus, the aggregate plan should not be determined by manufacturing personnel alone; rather, it should be agreed on by top management from all the functional areas of the firm—operations, marketing, and finance. Because this is such an important decision, companies engage in a structured, collaborative decision-making process called sales and operations planning (S&OP) outlines the S&OP process.

The sales and operations plan should reflect company policy (such as avoiding layoffs, limiting inventory levels, and maintaining a specified customer service level) and strategic objectives (such as capturing a certain share of the market or achieving targeted levels of quality or profit). Other inputs include financial constraints, demand forecasts (from sales), and capacity constraints (from operations).

Given these inputs, the sales function develops a monthly sales plan. A forecasting model is run to create preliminary demand figures, then adjusted based on input from key customers and sales personnel in the field. The forecast is further adjusted for planned promotions, product introductions, and special offers. Finally, a customer service level is set that specifies the percentage of customer demand that should be satisfied. The sales plan is then shared with the operations function which must convert sales to production requirements as economically as possible.

Operations develops a schedule of production per month by product family that includes the number of workers and other resources needed, and whether the production plan requires overtime or subcontracting. The production plan also shows anticipated inventory levels, backlog (work not yet completed), backorders (work performed later for customers willing to wait for their order), and lost sales (customers whose orders could not be completed or were not accepted).

The sales and operations planning process does not stop here. The two plans must be reconciled. Typically this involves creating an annual plan and updating it with monthly meetings that culminate in executive approval of the final plan. The process is diagrammed. Because of the various factors and viewpoints considered, the sales and operations plan is often referred to as the company’s game plan for the coming year, and deviations from the plan are carefully monitored. Monthly S&OP meetings reconcile differences in supply, demand, and new product plans. An economic strategy for meeting demand can be attained by either adjusting capacity or managing demand.

Strategies for Adjusting Capacity

If demand for a company’s products or services is stable over time, then the resources necessary to meet demand are acquired and maintained over the time horizon of the plan, and minor variations in demand are handled with overtime or under time. Aggregate planning becomes more of a challenge when demand fluctuates over the planning horizon.

For example, seasonal demand patterns can be met by:

  • Producing at a constant rate and using inventory to absorb fluctuations in demand (level production)

  • Hiring and firing workers to match demand (chase demand)

  • Maintaining resources for high-demand levels

  • Increasing or decreasing working hours (overtime and undertime)

  • Subcontracting work to other firms

  • Using part-time workers

  • Providing the service or product at a later time period (backorder)

When one of these alternatives is selected, a company is said to have a pure strategy for meeting demand. When two or more are selected, a company has a mixed strategy.

Level Production

The level production strategy sets production at a fixed rate (usually to meet average demand) and uses inventory to absorb variations in demand. During periods of low demand, overproduction is stored as inventory, to be depleted in periods of high demand. The cost of this strategy is the cost of holding inventory, including the cost of obsolete or perishable items that may have to be discarded.

Chase Demand

The chase-demand strategy, shown in Figure 7.3b, matches the production plan to the demand pattern and absorbs variations in demand by hiring and firing workers. During periods of low demand, production is cut back, and workers are laid off. During periods of high demand, production is increased, and additional workers are hired.

The cost of this strategy is the cost of hiring and firing workers. This approach would not work for industries in which worker skills are scarce or competition for labor is intense, but it can be quite cost-effective during periods of high unemployment or for industries with low-skilled workers.

Chase demand also works in industries where the product is perishable or has restrictive requirements for storage and transport, as is the case with the spirits industry. A variation of chase demand is chase supply. For some industries, the production planning task revolves around the supply of raw materials, not the demand pattern.

Consider Motts, the applesauce manufacturer, whose raw material is available only 40 days during a year. The workforce size at its peak is 1500 workers, but it normally consists of around 350 workers. Almost 10% of the company’s payroll is made up of unemployment benefits—the price of doing business in that particular industry.

Peak Demand

Maintaining resources for peak demand levels ensures high levels of customer service but can be very costly in terms of the investment in extra workers and machines that remain idle during low-demand periods. This strategy is used when superior customer service is important (as for Ritz-Carlton Hotels) or when customers are willing to pay extra for the availability of critical staff or equipment.

Professional services trying to generate more demand may keep staff levels high, defense contractors may be paid to keep extra capacity “available,” child-care facilities may elect to maintain staff levels for continuity when attendance is low, and full-service hospitals may invest in specialized equipment that is rarely used but is critical for the care of a small number of patients.

Overtime and Undertime

Overtime and undertime are common strategies when demand fluctuations are not extreme. A competent staff is maintained, hiring, and firing costs are avoided, and demand is met temporarily without investing in permanent resources. Disadvantages include the premium paid for overtime work, a tired and potentially less efficient workforce, and the possibility that overtime alone may be insufficient to meet peak demand periods.

Undertime can be achieved by working fewer hours during the day or fewer days per week. In addition, vacation time can be scheduled during months of slow demand. For example, furniture manufacturers typically shut down the entire month of July, while shipbuilding goes dormant in December. During the recent recession, 35% of U.S. employers surveyed used unpaid furloughs instead of more layoffs to adjust to decreased demand. Europe routinely uses shorter workweeks and mandatory vacations in economic downturns.


Subcontracting or outsourcing is a feasible alternative if a supplier can reliably meet quality and time requirements. This is a common solution for parts when demand exceeds expectations for the final product. The outsourcing decision requires maintaining strong ties with possible subcontractors and first-hand knowledge of their work.

Disadvantages include reduced profits, loss of control over production, long lead times, and the potential that the subcontractor may become a future competitor. Chase demand also works in industries where the product is perishable or has restrictive requirements for storage and transport, as is the case with the spirits industry.

Part-time Workers

Using part-time workers is feasible for unskilled jobs or in areas with large temporary labor pools (such as students, homemakers, or retirees). Part-time workers are less costly than full-time workers—they receive no healthcare or retirement benefits—and are more flexible—their hours usually vary considerably.

They have been the mainstay of retail, fast-food, and other services for some time and are becoming more accepted in manufacturing and government jobs. Japanese manufacturers traditionally use a large percentage of part-time or temporary workers. Part-time and temporary workers now account for one-third of the U.S. workforce and are expected to increase as companies gingerly enter recovery from the recession.

Backlogs, Backordering, and Lost Sales

Companies that offer customized products and services accept customer orders and fill them at a later date. The accumulation of these orders creates a backlog that grows during periods of high demand and is depleted during periods of low demand. The planned backlog is an important part of the aggregate plan.

For make-to-stock companies, customers who request an item that is temporarily out of stock may have the option of backorder the item. If the customer is unwilling to wait for the back-ordered item, the sale will be lost. Although in general both backorders and lost sales should be avoided, the aggregate plan may include an estimate of both. Backorders are added to the next period’s requirements; lost sales are not.

Strategies for Managing Demand

Aggregate planning can also involve proactive demand management. Strategies for managing demand include:

  • Shifting demand into other periods with incentives, sales promotions, and advertising campaigns;

  • Offering products or services with countercyclical demand patterns; and

  • Partnering with suppliers to reduce information distortion along the supply chain.

Winter coat specials in July, bathing-suit sales in January, early-bird discounts on dinner, lower health club rates mid-mornings, and getaway weekends at hotels during the off-season are all attempts to shift demand into different periods. Electric utilities are especially skilled at off-peak pricing. Promotions can also be used to extend high demand into low-demand seasons.

Holiday gift buying is encouraged earlier each year, and beach resorts plan festivals in September and October to extend the season. Successful demand management depends on accurate forecasts of demand and accurate forecasts of the changes in demand brought about by sales, promotions, and special offers. Disney manages demand in very short time intervals.

For industries with extreme variations in demand, offering products or services with countercyclical demand patterns helps smooth out resource requirements. This approach involves examining the idleness of resources and creating a demand for those resources.

McDonald’s offers breakfast to keep its kitchens busy during the pre-lunch hours, pancake restaurants serve lunch and dinner, heating firms also sell air conditioners, and lawn services remove snow. Amadas Industries, a small U.S. manufacturer of peanut harvesting equipment, does an especially good job of finding countercyclical products to smooth the load on its manufacturing facilities.

The company operates a job shop production system with general-purpose equipment, 50 highly skilled workers, and a talented engineering staff. With these flexible resources, the company can make virtually anything its engineers can design. Inventories of finished goods are limited because of the significant investment in funds and the size of the finished product. Demand for the product is highly seasonal.

Peanut-harvesting equipment is generally purchased on an as-needed basis from August to October, so during the spring and early summer, the company makes bark-scalping equipment for processing mulch and pine nuggets used by landscaping services.

Demand for peanut-harvesting equipment is also affected by the weather each growing season, so during years of extensive drought, the company produces and sells irrigation equipment. The company also decided to market its products internationally with a special eye toward countries whose growing seasons are opposite to that of the United States. Thus, many of its sales are made in China and India during the very months when demand in the United States is low.

Another approach to managing demand recognizes the information distortion caused by ordering goods in batches along a supply chain. Even though a customer may require daily usage of an item, he or she probably does not purchase that item daily. Neither do retail stores restock their shelves continuously.
By the time a replenishment order reaches distributors, wholesalers, manufacturers, and their suppliers, the demand pattern for a product can appear extremely erratic. To control the situation, manufacturers, suppliers, and customers form partnerships in which demand information is shared and orders are placed more continuously.

Quantitative Techniques for Aggregate Planning

One aggregate planning strategy is not always preferable to another. The most effective strategy depends on the demand distribution, competitive position, and cost structure of a firm or product line.

Several quantitative techniques are available to help with the aggregate planning decision. In the sections that follow, we discuss pure and mixed strategies, linear programming, the transportation method, and other quantitative techniques.

Pure Strategies

Solving aggregate planning problems involves formulating strategies for meeting demand, constructing production plans from those strategies, determining the cost and feasibility of each plan, and selecting the lowest-cost plan from among the feasible alternatives. The effectiveness of the aggregate planning process is directly related to management’s understanding of the cost variables involved and the reasonableness of the scenarios tested.

Although chasing demand is the better strategy for Good and Rich from an economic point of view, it may seem unduly harsh on the company’s workforce. An example of a good “fit” between a company’s chase demand strategy and the needs of the workforce is Hershey’s, located in rural Pennsylvania, with a demand and cost structure much like that of Good and Rich.

The location of the manufacturing facility is essential to the effectiveness of the company’s production plan. During the winter, when demand for chocolate is high, the company hires farmers from surrounding areas, who are idle at that time of year.

The farmers are let go during the spring and summer when they are anxious to return to their fields and the demand for chocolate falls. The plan is cost-effective, and the extra help is content with the sporadic hiring and firing practices of the company. General Linear Programming Model Strategies for production planning may be easy to evaluate, but they do not necessarily provide an optimal solution.

Mixed Strategies

Most companies use mixed strategies for production planning. Mixed strategies can incorporate management policies, such as “no more than x% of the workforce can be laid off in one quarter” or “inventory levels cannot exceed x dollars.”

They can also be adapted to the quirks of a company or industry. For example, many industries that experience a slowdown during part of the year may simply shut down manufacturing during the low-demand season and schedule employee vacations during that time.

The Transportation Method

For cases in which the decision to change the size of the workforce has already been made or is prohibited, the transportation method of linear programming can be used to develop an aggregate production plan. This method gathers all the cost information into one matrix and plans production based on the lowest-cost alternatives. A blank transportation tableau with i for inventory, h for holding cost, r for regular production cost, o for overtime, s for subcontracting, and b for backorder.

The capital letters indicate individual capacities or demands. The periods of production, along with the production options, appear in the first column. The periods of use (regardless of when the items are produced) appear across the top row. Cost entries in the period-of-use columns differ by the cost of holding the item in inventory before its use.

Other Quantitative Techniques

Although linear programming models will yield an optimal solution to the aggregate planning problem, there are some limitations. The relationships among variables must be linear, the model must be deterministic, and only one objective is allowed (usually minimizing cost). The linear decision rule, search decision rule, and management coefficients model use different types of cost functions to solve aggregate planning problems.

The linear decision rule (LDR) is an optimizing technique originally developed for aggregate planning in a paint factory. It solves a set of four quadratic equations that describe the major capacity-related costs in the factory: payroll costs, hiring and firing, overtime and undertime, and inventory costs.

The results yield the optimal workforce level and production rate. The search decision rule (SDR) is a pattern search algorithm that tries to find the minimum cost combination of various workforce levels and production rates. Any type of cost function can be used. The search is performed by computer and may involve the evaluation of thousands of possible solutions, but an optimal solution is not guaranteed.

The management coefficients model uses regression analysis to improve the consistency of planning decisions. Techniques like SDR and management coefficients are often embedded in commercial decision support systems or expert systems for aggregate planning. Linear decision rule (LDR) A mathematical technique for aggregate planning. Search decision rule (SDR) A pattern search technique for aggregate planning. Management coefficients model A regression technique for aggregate planning.

Hierarchical Nature of Planning

Planning involves a hierarchy of decisions. By determining a strategy for meeting and managing demand, aggregate planning provides a framework within which shorter-term production and capacity decisions can be made. The levels of production and capacity planning.

In production planning, the next level of detail is a master production schedule, in which weekly (not monthly or quarterly) production plans are specified by individual final product (not product line). At another level of detail, material requirements planning plans the production of the components that go into the final products. Shop floor scheduling schedules the manufacturing operations required to make each component.

In capacity planning, we might develop a resource requirements plan, to verify that a sales and operations plan is doable, and a rough-cut capacity plan as a quick check to see if the master production schedule is feasible. One level down, we would develop a much more detailed capacity requirements plan that matches the factory’s machine and labor resources to the material requirements plan.

Finally, we would use input/output control to monitor the production that takes place at individual machines or work centers. At each level, decisions are made within the parameters set by the higher-level decisions. The process of moving from the aggregate plan to the next level down is called disaggregation.

Collaborative Planning

Collaborative planning is part of the supply chain process of collaborative planning, forecasting, and replenishment (CPFR. In terms of production, CPFR involves selecting the products to be jointly managed, creating a single forecast of customer demand, and synchronizing production across the supply chain. Consensus among partners is reached first on the sales forecast, then on the production plan.

Although the process differs by software vendor, basically each partner has access to an Internet-enabled planning book in which forecasts, customer orders, and production plans are visible for specific items. Partners agree on the level of aggregation to be used.

Events trigger responses by partners. Alerts warn partners of conditions that require special action or changes to the plan. One example of an event that requires collaboration among trading partners is quoting available-to-promise dates for customers.


In the current business environment of outsourcing and build-to-order products, companies must be able to provide the customer with accurate promise dates. Recall that S&OP is the company’s game plan for matching supply and demand. As the time horizon grows shorter and more information becomes available, we develop and execute more detailed plans of action.

For example, we convert the sales and operations plan for product families into a master schedule for individual products based on best estimates of future demand. As customer orders come in consuming the forecast, the remaining quantities are available-to-promise to future customers.

Available-to-promise is the difference between customer orders (CO) and planned production. In the first period of the planning horizon, available-to-promise is calculated by summing the on-hand quantity and planned production, then subtracting customer orders up until the next period of planned production. In subsequent periods, the ATP is simply planned production minus customer orders. No on-hand quantities are used.

However, if customer orders exceed production, units can be taken from the ATP of previous periods. ATP in period 1 = (On-hand quantity + MPS in period 1) − (CO until the next period of planned production) ATP in period n = (MPS in period n) − (CO until the next period of planned production) As companies venture beyond their boundaries to complete customer orders, so available-to-promise inquiries extend beyond a particular plant or distribution center to a network of plants and supplier’s plants worldwide.

ATP may also involve drilling down beyond the end item level to check the availability of critical components. Supply chain and enterprise planning software vendors such as SAP and JDA have available to-promise modules that execute a series of rules when assessing product availability and alert the planner when customer orders exceed or fall short of forecasts. The rules prescribe product substitutions, alternative sources, and allocation procedures.

When the product is not available, the system proposes a capable-to-promise date that is subject to customer approval.

Aggregate Planning for Services

The aggregate planning process is different for services in the following ways:

  • Most services cannot be inventoried: It is impossible to store an airline seat, hotel room, or hair appointment for use later when demand may be higher. When the goods that accompany a service can be inventoried, they typically have a very short life. Newspapers are good for only a day; flowers, at most a week; and cooked hamburgers, only 10 minutes.

  • Demand for services is difficult to predict: Demand variations occur frequently and are often severe. The exponential distribution is commonly used to simulate the erratic demand for services—high demand peaks over short periods with long periods of low demand in between. Customer service levels established by management express the percentage of demand that must be met and, sometimes, how quickly demand must be met. This is an important input to aggregate planning for services.

  • Capacity is also difficult to predict: The variety of services offered and the individualized nature of services make capacity difficult to predict. The “capacity” of a bank teller depends on the number and type of transactions requested by the customer. Units of capacity can also vary.

  • Service capacity must be provided at the appropriate place and time: Many services have branches or outlets widely dispersed over a geographic region. Determining the range of services and staff levels at each location is part of aggregate planning.

  • Labour is usually the most constraining resource for services: This is an advantage in aggregate planning because labor is very flexible. Variations in demand can be handled by hiring temporary workers, using part-time workers, or using overtime. Summer recreation programs and theme parks hire teenagers out of school for the summer. FedEx staffs its peak hours of midnight to 2 a.m. with area college students. McDonald’s, Walmart, and other retail establishments woo senior citizens as reliable part-time workers. Workers can also be cross-trained to perform a variety of jobs and can be called upon as needed. A common example is the sales clerk who also stocks inventory. Less common are police officers who are cross-trained as firefighters and paramedics.

  • Several services have unique aggregate planning problems. Doctors, lawyers, and other professionals have emergency or priority calls for their service that must be meshed with regular appointments. Hotels and airlines routinely overbook their capacity in anticipation of customers who do not show up. Airlines design complex pricing structures for different routes and classes of customers. Planners incorporate these decisions in a process called revenue management.

  • Revenue Management: Revenue management (also called yield management) seeks to maximize profit or yield from time-sensitive products and services. It is used in industries with inflexible and expensive capacity, perishable products or services, segmented markets, advanced sales, and uncertain demand. The types of problems addressed by revenue management include overbooking, partitioning demand into fare classes, and single-order quantities.

Overbooking Services with reservation systems can lose money when customers fail to show up or cancel reservations at the last minute. It is not unusual for no-shows to account for 10% to 30% of an aircraft’s available seats. Thus, hotels, airlines, and restaurants routinely overbook their capacity. Managers who underestimate the number of no-shows must then compensate a customer who has been “bumped” by providing the service free of charge at another time or place.

Fare Classes-Hotels, airlines, stadiums, and theaters typically offer different ticket prices for certain classes of seats or customers. Planners must determine the number of seats or rooms to allocate to these different fare classes. Too many high-priced seats can lose customers, while too few high-priced seats can lower profits. Sabre, a leading technology company in the travel industry, estimates that airlines make more profits from the 3% of customers who are “business travelers” than the rest of customers who have discounted fares.

With the volume of data now available from digital “footprints,” companies can now offer one-to-one marketing deals to online customers, changing prices for each customer based on previous buying habits, or closing a sale for an item left in the shopping cart unpurchased. Congestion prices vary on the newest toll road for the D.C. metro region, where drivers may choose to pay a toll for access to less congested roadways. The price of the toll changes as traffic on the main road increases. Smart vending machines can change prices, too, depending on inventory levels or outside temperature.

Single Order The useful life for products such as newspapers, flowers, baked goods, and seasonal items is so short that in many instances only one order for production can take place. Determining the size of that single order can be difficult. The various types of revenue management problems with cost descriptions.

In each of these problems, the cost of overestimating demand (times the probability that it will occur) must be balanced with the cost of underestimating demand and its probability of occurrence. The optimum probability is where the cost of underestimating demand is equal to or just greater than the cost of overestimating demand. The derivation of the formula is shown below, followed by an example: Cost of underestimating demand ≥ Cost of overestimating demand

P(N ≥ X)Cu ≥ P(N ≥ X) Co

[1- P (N < X)]Cu ≥ P (N < X) Co

P (N < X) ≤ Cu / Cu + Co


Co= cost of overestimating demand or no-shows

Cu= cost of underestimating demand or no-shows

P (N < X)= probability of overestimating demand or no-shows

P (N < X)= probability of overestimating demand or no-shows

P (N ≥ X)= probability of underestimating demand or no-shows

N= number of units demanded or no-shows

X= units ordered or overbooked

Given cost information and distribution of demand or no-shows from past data, we can now match our planning policy with the optimum probability of overestimating demand.

Type of ProblemType of BusinessProbability of Overestimating Demand or No-Shows, P(N < X)Optimal Probability of Demand or No-Show Cu = (Cu + Co)Cost Description
OverbookingHotels, airlines, restaurantsN = number of no-shows
X = number of overbooked rooms or seats
Co = cost of overbooking
Cu = cost of underbooking
Replacement cost Lost profit
Fare ClassesAirlines, cruise ships, passenger trains, extended stay hotelsN = number of fullfare tickets that can be sold
X = seats reserved for fullfare passengers
Co = cost of overestimating full-fare passengers
Cu = cost of underestimating full-fare passengers
Lost full-fare
(Full-fare− discounted fare)
Premium SeatsStadiums, theatersN = number of premium tickets that can be sold
X = seats reserved for premium ticket holders
Co = cost of overestimating premium ticket sales
Cu = cost of underestimating premium ticket sales
Lost regular revenue
(Premium ticket− regular ticket revenue)
Single Order QuantitiesNewspapers, magazines, florists, nurseries, bakeries, sale itemsN = number of items that can be sold
X = number of items ordered
Co = cost of overestimating demand
Cu = cost of underestimating demand
Lost profit
Types of Revenue Management Problems
Article Source
  • Bowman, E. H. “Production Planning by the Transportation Method of Linear Programming.” Journal of Operations Research Society (February 1956), pp. 100–103.

  • Bowman, E. H. “Consistency and Optimality in Managerial Decision Making.” Management Science (January 1963), pp. 310–321.

  • Buffa, E. S., and J. G. Miller. Production-Inventory Systems: Planning and Control, 3rd ed. Homewood, IL: Irwin, 1979.

  • The Linear Decision Rule in Reservoir Management and Design: 1, Development of the Stochastic Model – Revelle – 1969 – Water Resources Research – Wiley Online Library

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