You are the director of supply chain planning over at PlushToysInc Corp. and you are tasked with implementing an aggregate planning strategy.
Your CEO meets with you and says it’s time to roll out the newest stuffed animals for the holiday season.
The company recently completed a sales forecast totaling 7,000 units to satisfy customer demand.
You have two options:
Option 1 – make 6,000 stuffed animals with a marketing budget of $600,000. This makes you work under demand and with less inherent risk.
Option 2 – make 7,000 stuffed animals to meet the forecast with a marketing budget of $700,000.
Look at both options and apply principles from chapter 10 to explain which methods will work best to carry out your directives and why.
over 350 words, you have 12 hours to finish it, please know this is a discussion, and i have provide the file of
Paul A. Souders/Corbis
Chapter Outline
Introduction
10.1 S&OP in the Planning Cycle
10.2 Major Approaches to S&OP
10.3 Organizing for and Implementing S&OP
10.4 Services Considerations
10.5 Linking S&OP throughout the Supply Chain
10.6 Applying Optimization Modeling to S&OP
Chapter Summary
Chapter ObjeCtives
By the end of this chapter, you will be able to:
· Distinguish among strategic planning, tactical planning, and detailed planning and control, and describe why sales and operations planning (S&OP) is important to an organization and its supply chain partners.
· Describe the differences between top-down and bottom-up S&OP and discuss the strengths and weaknesses of level, chase, and mixed production strategies.
· Discuss the organizational issues that arise when firms decide to incorporate S&OP into their efforts.
· Describe the managerial considerations surrounding the use of S&OP in a service environment.
· Examine how S&OP can be used to coordinate activities up and down the supply chain.
· Apply optimization modeling techniques to the S&OP process.
294
Chapter 10 • SaleS and OperatiOnS planning (aggregate planning) 295
Covolo Diving gear, Part 1
JUNE 1, 2017—The senior staff members for Covolo Div-ing Gear were sitting down for their semiannual plan-ning meeting. As it had been too often before, the mood
was tense.
“This is nuts,” complained David Griffin, the vice pres-ident of manufacturing. “Last January, marketing sat here and presented us with a sales forecast of 30,000 gauge sets each month, so that’s what I planned on producing. But by March, they’d upped it to 33,000. How can I be expected to keep a smooth-running manufacturing organization under
these conditions? I can’t handle another six months like the last.”
“We do the best we can, but it’s hard to develop a dead-on forecast, especially for more than a few months in ad-vance,” countered Patricia Rodriguez, the vice president of marketing. “Besides, manufacturing was able to increase production to only 31,000, even though we both finally agreed on the higher number. Why is that?”
“I’ll tell you why,” said Jack Nelson, head of purchasing. “Each gauge set we make requires special parts that come from Germany, and our German suppliers just can’t in-crease their shipments to us without some notice. Next time you guys plan on changing production levels, why don’t you include me in on the conversation?”
At this point, Gina Covolo, the CEO, spoke up: “Ok, folks, settle down. We work for the same company, remem-ber? I’ve been reading up on something called sales and operations planning. If I understand it right, it will help us coordinate our efforts better than we have in the past. We will have to meet more often, probably monthly, but if we do it right, we will all be working from the same sales fore-cast, and we will know exactly what each of our areas has to do to execute the plan. I think that’s a whole lot better than getting angry at one another.” And to the accompanying groans of everyone in the room, Gina added: “Who knows, we might even be able to keep our heads above water.”
Sales and operations planning (S&Op)
A process to develop tactical plans by integrating market-ing plans for new and existing products with the manage-ment of the supply chain. The process brings together all the plans for the business into one integrated set of plans. Also called aggregate planning.
aggregate planning
See sales and operations planning (S&OP).
Throughout this book, we have emphasized how critical it is to coordinate operations and sup-ply chain decisions with other functional areas and the firm’s supply chain partners. This theme appears in our discussions of strategy, new product development, capacity planning, and pro-cess choice, to name a few. This chapter takes the concept of cross-functional and interfirm co-ordination a step further, through a process known as sales and operations planning (S&OP) (sometimes called aggregate planning ). Adapting from the APICS definition, we define sales and operations planning as a process to develop tactical plans by integrating marketing plans for new and existing products with the management of the supply chain. The process brings together all the plans for the business (sales, marketing, development, manufacturing, sourcing, and financial) into one integrated set of plans. It is performed at least once a month and is reviewed by management at an aggregate (product family) level.1
We start by describing how S&OP fits into an organization’s planning scheme. We then present several methods for generating and selecting plans and for implementing the S&OP pro-cess in an organization.
10.1 S&oP in the Planning CyCle
Strategic planning
Planning that takes place at the highest levels of the firm, addressing needs that might not arise for years into the future.
In most organizations, planning actually takes place at several levels, each covering a certain period of time into the future (Figure 10.1). Strategic planning takes place at the highest levels of the firm; it addresses needs that might not arise for years into the future.
1J. H. Blackstone, ed., APICS Dictionary, 14th ed. (Chicago, IL: APICS, 2014).
296 PART IV • Planning and Controlling Operations and Supply Chains
Tactical planning
Planning that covers a shorter period, usually 12 to 24 months out, although the planning horizon may be lon-ger in industries with very long lead times (e.g., engineer-to-order firms).
Detailed planning and control
Planning that covers time peri-ods ranging from weeks down to just a few hours out.
Tactical planning covers a shorter period, usually 12 to 24 months out, although the planning horizon may be longer in industries with very long lead times (e.g., engineer-to-order firms). Tactical planning is typically more detailed, but it is constrained by the longer-term strategic decisions. For example, managers responsible for tactical planning might be able to adjust over-all inventory or workforce levels, but only within the constraints imposed by strategic decisions such as the size of the facilities and types of processes used.
Detailed planning and control covers time periods ranging from weeks down to just a few hours out. Because the planning horizon is so short, managers who do detailed planning and control usually have few, if any, options for adjusting capacity levels. Rather, they must try to make the best use of available capacity in order to get as much work done as possible.
The three approaches differ in (1) the time frame covered, (2) the level of planning detail required, and (3) the degree of flexibility managers have to change capacity. See Figure 10.1. Strategic planning has the longest time horizon, has the least amount of specific infor-mation (after all, we are planning for years out), and affords managers the greatest degree of flexibility to change capacity. Detailed planning and control is just the opposite: Planning can cover daily or even hourly activity, and the relatively short time horizons leave managers with few, if any, options for changing capacity. Tactical planning fills the gap between these extremes.
S&OP is aimed squarely at helping businesses develop superior tactical plans. Specifically:
· S&OP indicates how the organization will use its tactical capacity resources to meet expected customer demand. Examples of tactical capacity resources in-clude the size of the workforce, inventory, number of shifts, and even availability of subcontractors.
· S&OP strikes a balance between the various needs and constraints of the supply chain partners. For example, S&OP must consider not only customer demand but also the capabilities of all suppliers, production facilities, and logistics service providers that work together to provide the product or service. The result is a plan that is not only fea-sible but also balances costs, delivery, quality, and flexibility.
· S&OP serves as a coordinating mechanism for the various supply chain partners. At the end of the S&OP process, there should be a shared agreement about what each of the affected partners—marketing operations, and finance, as well as key suppliers and transportation providers—needs to do to make the plan a reality. Good S&OP makes it very clear what everyone should—and should not—do. This shared agreement allows the different parties to make more detailed decisions with the confidence that their ef-forts will be consistent with those of other partners.
· S&OP expresses the business’s plans in terms that everyone can understand.
Finance personnel typically think of business activity in terms of cash flows, financial ratios, and other measures of profitability. Marketing managers concentrate on sales levels and market segments, while operations and supply chain managers tend to focus more on the activities associated with the particular products or services being pro-duced. As we shall see, S&OP makes a deliberate effort to express the resulting plans in a format that is easy for all partners to understand and incorporate into their detailed planning efforts.
| Detailed planning and control | Tactical planning | Strategic planning | |
| • Limited ability to adjust capacity | • Workforce, inventory, subcontracting, | • “Bricks and mortar” and major | |
| • Detailed planning (day to day, | and logistics decisions | process choice decisions | |
| hour to hour) | • Planning numbers somewhat | • Planning done at a very high level | |
| • Lowest risk | “aggregated” (month by month) | (quarterly or yearly) | |
| • Moderate risk | • High risk | ||
| Now | Months out | Years out | |
| Days/weeks out |
Figure 10.1 Different Levels of Planning
CHAPTER 10 • Sales and Operations Planning (Aggregate Planning) 297
Top-down planning
An approach to S&OP in which a single, aggregated sales forecast drives the planning process. For top-down planning to work, the mix of products or services must be essentially the same from one time period to the next or the products or services to be provided must have very similar resource requirements.
Bottom-up planning
An approach to S&OP that is used when the product/service mix is unstable and resource re-quirements vary greatly across the offerings. Under such condi-tions, managers will need to estimate the requirements for each set of products or services separately and then add them up to get an overall picture of the resource requirements.
Planning values
Values that decision makers use to translate a sales forecast into resource requirements and to determine the feasibility and costs of alternative sales and operations plans.
There are two major approaches to S&OP: top-down planning and bottom-up planning. Figure 10.2 summarizes the criteria organizations must consider when choosing between the two.
The simplest approach is top-down planning. Here, a single, aggregated sales forecast drives the planning process. For top- down planning to work, the mix of products or services must be essentially the same from one time period to the next or the various products or services must have very similar resource requirements to one another. The key assumption under top-down planning is that managers can make accurate tactical plans based on the overall forecast and then divide the resources across individual products or services later on, during the detailed planning and control stage.
Bottom-up planning is used when the product/service mix is unstable and resource re-quirements vary greatly across the offerings. Under such conditions, an overall sales forecast is not very helpful in determining resource requirements. Instead, managers will need to estimate the requirements for each set of products or services separately and then add them up to get an overall picture of the resource requirements.
Regardless of the approach used, managers will need planning values to carry out the anal-ysis. Planning values are values, based on analysis or historical data, that decision makers use to translate a sales forecast into resource requirements and to determine the feasibility and costs of alternative sales and operations plans. Example 10.1 shows one method of developing planning values when the product mix is stable.
Are resource needs similar across the various products or services offered?
OR
Is the mix of products or services the same from one period to the next?
Yes No
Top-down planning
Alternative production strategies:
· Level
· Chase
· Mixed
Bottom-up planning
Alternative production strategies:
· Level
· Chase
· Mixed
Figure 10.2 Determining the Appropriate Approach to S&OP
Example 10.1
Calculating Planning
Values for Ernie’s
Electrical
Ernie’s Electrical performs three services: cable TV installations, satellite TV installations, and digital subscriber line (DSL) installations. Table 10.1 shows Ernie’s service mix, as well as the labor hours and supply costs associated with each type of installation.
Table 10.1 Service Mix at Ernie’s Electrical
| Labor Hours Per | Supply Costs Per | ||
| Service Description | Service Mix | Installation | Installation |
| Cable TV installation | 40% | 2 | $15 |
| Satellite TV installation | 40% | 3 | $90 |
| DSL installation | 20% | 4 | $155 |
298 part iv • planning and COntrOlling OperatiOnS and Supply ChainS
Ernie’s service mix is the same from one month to the next. As a result, the company can use a single set of planning values, based on the weighted averages of labor hours and supply costs:
Estimated labor hours per installation:
40% * 2 hours + 40% * 3 hours + 20% * 4 hours = 2.8 hours
Estimated supply costs per installation:
40% * +15 + 40% * +90 + 20% * +155 = +73
Ernie expects total installations for the next three months to be 150, 175, and 200, respectively. With this sales forecast and the planning values above, Ernie can quickly esti-mate labor hours and supply costs for each month (Table 10.2).
Table 10.2 Estimated Resource Requirements at Ernie’s Electrical
| SaleS FOreCaSt | laBOr hOurS (2.8 per | Supply COStS ($73 per | |
| MOnth | (inStallatiOnS) | inStallatiOn) | inStallatiOn) |
| Month 1 | 150 | 420 | $10,950 |
| Month 2 | 175 | 490 | $12,775 |
| Month 3 | 200 | 560 | $14,600 |
The process for generating a top-down plan consists of three steps:
1. Develop the aggregate sales forecast and planning values. Top-down planning starts with the aggregate sales forecast. The planning values are used in the next two steps to help management translate the sales forecast into resource requirements and determine the feasibility and costs of alternative S&OP strategies.
2. Translate the sales forecast into resource requirements. The goal of this second step is to move the analysis from “sales” numbers to the “operations and supply chain” numbers needed for tactical planning. Some typical resources include labor hours, equipment hours, and material dollars, to name a few.
3. Generate alternative production plans. In this step, management determines the feasibil-ity and costs for various production plans. We will describe three particular approaches— level production, chase production, and mixed production—in more detail later.
We illustrate top-down planning through a series of examples for a fictional manufacturer, Pennington Cabinets.
example 10.2
Developing the aggregate Sales Forecast and Planning values for Pennington Cabinets
| Jeff Greenberg/Alamy |
CHAPTER 10 • Sales and Operations Planning (Aggregate Planning) 299
Pennington Cabinets is a manufacturer of several different lines of kitchen and bathroom cabinets that are sold through major home improvement retailers. Pennington’s marketing vice president has come up with the following combined sales forecast for the next 12 months:
| Sales Forecast | |
| Month | (Cabinet Sets) |
| January | 750 |
| February | 760 |
| March | 800 |
| April | 800 |
| May | 820 |
| June | 840 |
| July | 910 |
| August | 910 |
| September | 910 |
| October | 880 |
| November | 860 |
| December | 840 |
Under top -down planning, managers base the planning process on aggregated sales figures, such as those shown here. For example, January’s forecast value of 750 reflects total expected demand across Pennington’s entire line of cabinets. The primary advantage of top-down planning is that it allows managers to see the relationships among overall demand, production, and inventory levels. There will be plenty of time to do detailed planning and control later on.
In addition to the sales forecast, Pennington has also developed the planning values shown in Table 10.3.
Table 10.3 Planning Values for Pennington Cabinets
| Cabinet Set Planning Values | |
| Regular production cost: | $2,000 per cabinet set |
| Overtime production cost: | $2,062 per cabinet set |
| Average monthly inventory holding cost: | $40 per cabinet set, per month |
| Average labor hours per cabinet set: | 20 hours |
| Production Planning Values | |
| Maximum regular production per month: | 848 cabinet sets |
| Allowable overtime production per month: | 1/10 of regular production |
| Workforce Planning Values | |
| Hours worked per month per employee: | 160 hours |
| Estimated cost to hire a worker: | $1,750 |
| Estimated cost to lay off a worker: | $1,500 |
Planning values such as these are often developed from company records, detailed analysis, and managerial experience. “Average labor hours per cabinet,” for example, might be derived by looking at past production results, while “Maximum regular production per month” might be based on a detailed analysis of manufacturing capacity (see Chapter 6). In contrast, the human resources (HR) manager might use data on recruiting, interviewing, and training costs to develop estimates of hiring and layoff costs.
The sales forecast shows an expected peak from July through September. As stated in the planning values, Pennington can produce up to 848 cabinet sets a month, using regu-lar production time. Figure 10.3 graphs the expected sales level against maximum regular production per month.
300 PART IV • Planning and Controlling Operations and Supply Chains
| Cabinet sets |
950
900
850
800
750
| 700 | Sales forecast | ||||||||||||||||||||
| Capacity | |||||||||||||||||||||
| 650 | |||||||||||||||||||||
| 600 | |||||||||||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 11 12 | ||||||||||||
| Month |
Figure 10.3 Graphing Expected Sales Levels versus Capacity
The implication of Figure 10.3 is clear: Pennington won’t be able to meet expected demand in the peak months with just regular production.
| Example 10.3 | |||||||||
| The next step for Pennington is to translate the sales forecast into resource requirements. | |||||||||
| Translating the Sales | |||||||||
| The key resource Pennington is concerned about is labor, although other resources could | |||||||||
| Forecast into Resource | be examined, depending on the needs of the firm. Translating sales into labor hours and, | ||||||||
| Requirements at | ultimately, workers needed allows Pennington to see how demand drives resource require- | ||||||||
| Pennington Cabinets | ments. Table 10.4 shows the start of this process. | ||||||||
| Table 10.4 Translating Sales into Resource Requirements at Pennington Cabinets | |||||||||
| Sales | Sales (In | Sales (In | |||||||
| Month | Forecast | Labor Hours) | Workers) | ||||||
| January | 750 | 15,000 | 93.75 | ||||||
| February | 760 | 15,200 | 95.00 | ||||||
| March | 800 | 16,000 | 100.00 | ||||||
| April | 800 | 16,000 | 100.00 | ||||||
| May | 820 | 16,400 | 102.50 | ||||||
| June | 840 | 16,800 | 105.00 | ||||||
| July | 910 | 18,200 | 113.75 | ||||||
| August | 910 | 18,200 | 113.75 | ||||||
| September | 910 | 18,200 | 113.75 | ||||||
| October | 880 | 17,600 | 110.00 | ||||||
| November | 860 | 17,200 | 107.50 | ||||||
| December | 840 | 16,800 | 105.00 | ||||||
Level production plan
A sales and operations plan in which production is held constant and inventory is used to absorb differences between production and the sales forecast.
To illustrate, April’s demand represents (20 hours per cabinet) * (800 cabinets) = 16,000 labor hours. If every worker works 160 hours a month, this is the equivalent of (16,000 labor hours)/(160 hours) = 100 workers.
Level, Chase, and Mixed Production Plans
Once a firm has translated the sales forecast into resource requirements, the next step is to generate alternative production plans. Three common approaches are level production, chase production, and mixed production plans. The fundamental difference among the three is how production and inventory levels are allowed to vary.
Under a level production plan, production is held constant, and inventory is used to ab-sorb differences between production and the sales forecast. This approach is best suited to an
| CHAPTER 10 • Sales and Operations Planning (Aggregate Planning) 301 | |
| Chase production plan | environment in which changing the production level is very difficult or extremely costly (e.g., an |
| A sales and operations plan in | oil refinery) and the cost of holding inventory is relatively low. |
| which production is changed | A chase production plan is just the opposite. Here production is changed in each time period |
| in each time period to match | |
| to match the sales forecast in each time period. The result is that production “chases” demand. This | |
| the sales forecast. | |
| Mixed production plan | approach is best suited to environments in which holding inventory is extremely expensive or im- |
| possible (as with services) or the costs of changing production levels are relatively low. | |
| A sales and operations plan that | |
| A mixed production plan falls between these two extremes. Specifically, a mixed | |
| varies both production and | |
| production plan will vary both production and inventory levels, in an effort to develop the | |
| inventory levels in an effort to | |
| develop the most effective plan. | most effective plan. |
| Example 10.4 | ||||||||||||||
| After translating the sales forecast into resource requirements (Table 10.4), Pennington | ||||||||||||||
| Generating a Level | ||||||||||||||
| management decides to begin their analysis by generating a level production plan. Penning- | ||||||||||||||
| Production Plan for | ton starts off January with 100 workers and 100 cabinet sets in inventory, and it wants to | |||||||||||||
| Pennington Cabinets | end the planning cycle with these numbers. Table 10.5 shows a completed level production | |||||||||||||
| plan for Pennington Cabinets. Following is a discussion of the highlights of this plan. | ||||||||||||||
| Table 10.5 Level Production Plan for Pennington Cabinets | ||||||||||||||
| Sales | Regular | Allowable | Overtime | Inventory/ | ||||||||||
| Sales | (In Labor | Sales (In | Actual | Pro- | Overtime | Pro- | Back | |||||||
| Month | Forecast | Hours) | Workers) Workers duction Production | duction | Hirings Layoffs | Orders | ||||||||
| 100.00 | 100.00 | |||||||||||||
| January | 750 | 15,000 | 93.75 | 105.00 | 840.00 | 84.00 | 0 | 5.00 | 0.00 | 190.00 | ||||
| February | 760 | 15,200 | 95.00 | 105.00 | 840.00 | 84.00 | 0 | 0.00 | 0.00 | 270.00 | ||||
| March | 800 | 16,000 | 100.00 | 105.00 | 840.00 | 84.00 | 0 | 0.00 | 0.00 | 310.00 | ||||
| April | 800 | 16,000 | 100.00 | 105.00 | 840.00 | 84.00 | 0 | 0.00 | 0.00 | 350.00 | ||||
| May | 820 | 16,400 | 102.50 | 105.00 | 840.00 | 84.00 | 0 | 0.00 | 0.00 | 370.00 | ||||
| June | 840 | 16,800 | 105.00 | 105.00 | 840.00 | 84.00 | 0 | 0.00 | 0.00 | 370.00 | ||||
| July | 910 | 18,200 | 113.75 | 105.00 | 840.00 | 84.00 | 0 | 0.00 | 0.00 | 300.00 | ||||
| August | 910 | 18,200 | 113.75 | 105.00 | 840.00 | 84.00 | 0 | 0.00 | 0.00 | 230.00 | ||||
| September | 910 | 18,200 | 113.75 | 105.00 | 840.00 | 84.00 | 0 | 0.00 | 0.00 | 160.00 | ||||
| October | 880 | 17,600 | 110.00 | 105.00 | 840.00 | 84.00 | 0 | 0.00 | 0.00 | 120.00 | ||||
| November | 860 | 17,200 | 107.50 | 105.00 | 840.00 | 84.00 | 0 | 0.00 | 0.00 | 100.00 | ||||
| December | 840 | 16,800 | 105.00 | 105.00 | 840.00 | 84.00 | 0 | 0.00 | 0.00 | 100.00 | ||||
| 0 | 5 | |||||||||||||
| Totals: | 10,080 | 10,080 | 0 | 5 | 5 | 2,870 | ||||||||
Actual Workers and Regular Production
Under the level production plan, the actual workforce is held constant, at 105. Why 105? Because 105 represents the average workforce required over the 12-month planning hori-zon. By maintaining a workforce of 105 workers, Pennington produces:
105(160 hours per month/20 hours per set) = 840 sets per month
or:
(840 sets per month)(12 months) = 10,080 cabinet sets for the year
You may have noticed that this production total matches sales for the entire year. The difference, of course, is in the timing of the production and the sales: Inventory builds up when sales are less than the production level and drains down when sales outstrip produc-tion. Finally, with 105 workers, Pennington comes close to reaching the regular production maximum of 848 cabinet sets per month but doesn’t exceed it.
302 PART IV • Planning and Controlling Operations and Supply Chains
Hirings and Layoffs
Whenever the workforce level changes, Pennington must hire or release workers. This occurs at two different times in the level production plan. In January, Pennington hires 5 workers to bring the workforce up to 105 from the initial level of 100. To bring the work-force back down to its starting level, Pennington lays off 5 workers at the end of December. While this may seem unrealistic, doing so (at least for calculation purposes) ensures Pen-nington that it will be able to compare alternative plans under the same beginning and ending conditions.
Inventory Levels
The ending inventory level in any month is calculated as follows:
| EIt = EIt – 1 + RPt + OPt – St | (10.1) | |
| where: | = ending inventory for time period t | |
| EIt | ||
| RPt | = regular production in time period t | |
| OPt | = overtime production in time period t | |
| St | = sales in time period t |
For January, the ending inventory is:
EIJanuary = EIDecember + RPJanuary + OPJanuary – SJanuary
= 100 + 840 + 0 – 750 = 190 cabinet sets
Likewise, the ending inventory for February is:
EIFebruary = EIJanuary + RPFebruary + OPFebruary – SFebruary
= 190 + 840 + 0 – 760 = 270 cabinet sets
As expected, the level production plan builds up inventory from January through May (when production exceeds sales) and then drains it down during the peak months of July through December. But look at the ending inventory levels for each month: They are all greater than zero, suggesting that Pennington is holding more cabinet sets than it needs to meet the forecast. This may seem wasteful at first glance. But remem-ber that Pennington is developing a plan based on forecasted sales. The extra inventory protects Pennington if actual sales turn out to be higher than the forecast. Otherwise, Pennington might not be able to meet all the demand, resulting in back orders or even lost sales.
The Cost of the Plan
Of course, Pennington has no way of knowing at this point whether a level production plan is the best plan or not. To do so, management will need some way to compare competing plans. Management starts this process by calculating the costs of the level production plan, using the planning values in Table 10.3:
| Regular Production Costs | |
| 10,080 cabinet sets * ($2,000) = | $20,160,000 |
| Hiring and Layoff Costs | |
| 5 hirings * ($1,750) + 5 layoffs * ($1,500) = | $16,250 |
| Inventory Holding Costs | |
| 2,870 cabinet sets * ($40) = | $114,800 |
| Total: | $20,291,050 |
| CHAPTER 10 • Sales and Operations Planning (Aggregate Planning) 303 | ||||||||||||||||
| Example 10.5 | Table 10.6 shows a chase production plan for Pennington Cabinets. Notice that the first | |||||||||||||||
| Generating a Chase | ||||||||||||||||
| four columns are identical to those for the level production plan (Table 10.5). However, | ||||||||||||||||
| Production Plan for | results for the remaining columns are quite different: | |||||||||||||||
| Pennington Cabinets | • Actual workforce production and overtime production vary so that total production | |||||||||||||||
| essentially matches sales for each month. Because total production “chases” sales, in- | ||||||||||||||||
| ventory never builds up, as it did under the level production plan. In fact, it never gets | ||||||||||||||||
| higher than 106 cabinet sets. | ||||||||||||||||
| • From July through November, monthly sales are higher than the maximum regular | ||||||||||||||||
| production level of 848. Under the chase approach, Pennington will need to make up | ||||||||||||||||
| the difference through overtime production. | ||||||||||||||||
| • While the chase production plan keeps inventory levels low, it results in more hirings | ||||||||||||||||
| and layoffs and in overtime production costs. | ||||||||||||||||
| • Because Pennington can’t hire fractional workers, the company can’t always exactly | ||||||||||||||||
| match production to sales. In this example, Pennington ends up with slightly more | ||||||||||||||||
| cabinet sets in inventory at the end of the planning period (106 versus 100). Still, this | ||||||||||||||||
| is close enough to compare with other plans. | ||||||||||||||||
| Table 10.6 Chase Production Plan for Pennington Cabinets | ||||||||||||||||
| Sales | Sales | Regular | Allowable | Overtime | Inventory/ | |||||||||||
| Sales | (In Labor | (In | Actual | Pro- | Overtime | Pro- | Back | |||||||||
| Month | Forecast | Hours) | Workers) Workers duction Production | duction | Hirings Layoffs | Orders | ||||||||||
| 100.00 | 100.00 | |||||||||||||||
| January | 750 | 15,000 | 93.75 | 94.00 | 752.00 | 75.20 | 0 | 0.00 | 6.00 | 102.00 | ||||||
| February | 760 | 15,200 | 95.00 | 95.00 | 760.00 | 76.00 | 0 | 1.00 | 0.00 | 102.00 | ||||||
| March | 800 | 16,000 | 100.00 | 100.00 | 800.00 | 80.00 | 0 | 5.00 | 0.00 | 102.00 | ||||||
| April | 800 | 16,000 | 100.00 | 100.00 | 800.00 | 80.00 | 0 | 0.00 | 0.00 | 102.00 | ||||||
| May | 820 | 16,400 | 102.50 | 103.00 | 824.00 | 82.40 | 0 | 3.00 | 0.00 | 106.00 | ||||||
| June | 840 | 16,800 | 105.00 | 105.00 | 840.00 | 84.00 | 0 | 2.00 | 0.00 | 106.00 | ||||||
| July | 910 | 18,200 | 113.75 | 106.00 | 848.00 | 84.80 | 62 | 1.00 | 0.00 | 106.00 | ||||||
| August | 910 | 18,200 | 113.75 | 106.00 | 848.00 | 84.80 | 62 | 0.00 | 0.00 | 106.00 | ||||||
| September | 910 | 18,200 | 113.75 | 106.00 | 848.00 | 84.80 | 62 | 0.00 | 0.00 | 106.00 | ||||||
| October | 880 | 17,600 | 110.00 | 106.00 | 848.00 | 84.80 | 32 | 0.00 | 0.00 | 106.00 | ||||||
| November | 860 | 17,200 | 107.50 | 106.00 | 848.00 | 84.80 | 12 | 0.00 | 0.00 | 106.00 | ||||||
| December | 840 | 16,800 | 105.00 | 105.00 | 840.00 | 84.00 | 0 | 0.00 | 1.00 | 106.00 | ||||||
| 0 | 5 | |||||||||||||||
| Totals: | 10,080 | 9,856 | 230 | 12 | 12 | 1,256 | ||||||||||
| The cost calculations for the chase production plan follow. In this case, 9,856 cabi- | ||||||||||||||||
| net sets were produced through regular production, and the remaining 230 were produced | ||||||||||||||||
| using overtime: | ||||||||||||||||
| | ||||||||||||||||
| Regular Production Costs | ||||||||||||||||
| 9,856 cabinet sets * ($2,000) = | $19,712,000 | |||||||||||||||
| Overtime Production Costs | ||||||||||||||||
| 230 cabinet sets * ($2,062) = | $474,260 | |||||||||||||||
| Hiring and Layoff Costs | ||||||||||||||||
| 12 hirings * ($1,750) + 12 layoffs * ($1,500) = | $39,000 | |||||||||||||||
| Inventory Holding Costs | ||||||||||||||||
| 1,256 cabinet sets * ($40) = | $50,240 | |||||||||||||||
| Total: | $20,275,500 | |||||||||||||||
304 PART IV • Planning and Controlling Operations and Supply Chains
Example 10.6
Generating a Mixed
Production Plan for
Pennington Cabinets
In the real world, the best plan will probably be something other than a level or chase plan. A mixed production plan varies both production and inventory levels in an effort to develop the best plan. Because there are many different ways to do this, the number of potential mixed plans is essentially limitless.
Suppose Pennington’s workers have strong reservations about working overtime dur-ing the summer months, a chief requirement under the chase plan. The mixed production plan shown in Table 10.7 limits overtime to just 12 cabinet sets per month in October and November. This is just one example of the type of qualitative issues a management team must consider when developing a sales and operations plan.
Table 10.7 Mixed Production Plan for Pennington Cabinets
| Sales (In | Regular | Allowable | Overtime | ||||||||
| Sales | Labor | Sales (In | Actual | Pro- | Overtime | Pro- | Inventory/ | ||||
| Month | Forecast | Hours) | Workers) Workers duction Production | duction | Hirings Layoffs Back Orders | ||||||
| 100.00 | 100.00 | ||||||||||
| January | 750 | 15,000 | 93.75 | 100.00 | 800.00 | 80.00 | 0 | 0.00 | 0.00 | 150.00 | |
| February | 760 | 15,200 | 95.00 | 100.00 | 800.00 | 80.00 | 0 | 0.00 | 0.00 | 190.00 | |
| March | 800 | 16,000 | 100.00 | 103.00 | 824.00 | 82.40 | 0 | 3.00 | 0.00 | 214.00 | |
| April | 800 | 16,000 | 100.00 | 106.00 | 848.00 | 84.80 | 0 | 3.00 | 0.00 | 262.00 | |
| May | 820 | 16,400 | 102.50 | 106.00 | 848.00 | 84.80 | 0 | 0.00 | 0.00 | 290.00 | |
| June | 840 | 16,800 | 105.00 | 106.00 | 848.00 | 84.80 | 0 | 0.00 | 0.00 | 298.00 | |
| July | 910 | 18,200 | 113.75 | 106.00 | 848.00 | 84.80 | 0 | 0.00 | 0.00 | 236.00 | |
| August | 910 | 18,200 | 113.75 | 106.00 | 848.00 | 84.80 | 0 | 0.00 | 0.00 | 174.00 | |
| September | 910 | 18,200 | 113.75 | 106.00 | 848.00 | 84.80 | 0 | 0.00 | 0.00 | 112.00 | |
| October | 880 | 17,600 | 110.00 | 106.00 | 848.00 | 84.80 | 12 | 0.00 | 0.00 | 92.00 | |
| November | 860 | 17,200 | 107.50 | 106.00 | 848.00 | 84.80 | 12 | 0.00 | 0.00 | 92.00 | |
| December | 840 | 16,800 | 105.00 | 106.00 | 848.00 | 84.80 | 0 | 0.00 | 0.00 | 100.00 | |
| 0 | 6 | ||||||||||
| Totals: | 10,080 | 10,056 | 24 | 6 | 6 | 2,210 | |||||
The cost of the mixed production strategy is:
| Regular Production Costs | |
| 10,056 cabinet sets * ($2,000) = | $20,112,000 |
| Overtime Production Costs | |
| 24 cabinet sets * ($2,062) = | $49,488 |
| Hiring and Layoff Costs | |
| 6 hirings * ($1,750) + 6 layoffs * ($1,500) = | $19,500 |
| Inventory Holding Costs | |
| 2,210 cabinet sets * ($40) = | $88,400 |
| Total: | $20,269,388 |
Top-down planning works well in situations where planners can use a single set of planning val-ues to estimate resource requirements and costs. But what happens when this is not the case?
As we noted earlier, bottom-up planning is used when the products or services have different resource requirements and the mix is unstable from one period to the next. The steps for gen-erating a bottom-up plan are similar to those for creating a top-down plan. The main difference is that the resource requirements must be evaluated individually for each product or service and then added up across all products or services to get a picture of overall requirements.
| CHAPTER 10 • Sales and Operations Planning (Aggregate Planning) 305 | ||||||
| Example 10.7 | Philips Toys produces a summer toy line and a winter toy line. Machine and labor require- | |||||
| Bottom-Up Planning | ||||||
| ments for each product line are given in Table 10.8. | ||||||
| at Philips Toys | Table 10.8 Machine and Labor Requirements for Philips Toys | |||||
| Product Line | Machine Hours/Unit | Labor Hours/Unit | ||||
| Summer toys | 0.75 | 0.25 | ||||
| Winter toys | 0.85 | 2.00 | ||||
Both product lines have fairly similar machine hour requirements. However, they dif-fer greatly with regard to labor requirements; products in the winter line need, on average, eight times as much labor as products in the summer line.
The difference in labor requirements becomes important when the product mix changes. Look at the data in Table 10.9. Even though the aggregate forecast across both product lines is 700 units each month, the product mix changes as Philips moves into and then out of the summer season. The impact on resource requirements can be seen in the labor hours needed each month.
Table 10.9 Forecasted Demand and Resulting Resource Needs for Philips Toys
| Forecast | |||||
| Summer | Winter | Aggregate | Machine | Labor | |
| Month | Line | Line | Forecast | Hours | Hours |
| January | 0 | 700 | 700 | 595 | 1,400 |
| February | 100 | 600 | 700 | 585 | 1,225 |
| March | 500 | 200 | 700 | 545 | 525 |
| April | 700 | 0 | 700 | 525 | 175 |
| May | 700 | 0 | 700 | 525 | 175 |
| June | 700 | 0 | 700 | 525 | 175 |
| July | 700 | 0 | 700 | 525 | 175 |
| August | 500 | 200 | 700 | 545 | 525 |
| September | 400 | 300 | 700 | 555 | 700 |
| October | 200 | 500 | 700 | 575 | 1,050 |
| November | 0 | 700 | 700 | 595 | 1,400 |
| December | 0 | 700 | 700 | 595 | 1,400 |
Load profile
A display of future capacity requirements based on released and/or planned orders over a given span of time.
Figure 10.4 graphs the projected machine hours and labor hours shown in Table 10.9. Such graphs are often referred to as load profiles. A load profile is a display of future ca-pacity requirements based on released and/or planned orders over a given span of time.2 As the load profiles suggest, machine hour requirements are fairly constant throughout the year. This is because both product lines have similar machine time requirements. In contrast, the load profile for labor dips dramatically in the summer months, reflecting the lower labor requirements associated with the summer product line.
| 1,500 | |||||||||||||||||
| Machine hours | |||||||||||||||||
| Hoursrequired | 1,000 | Labor hours | |||||||||||||||
| 500 | |||||||||||||||||
| 0 | |||||||||||||||||
| 1 | 3 | 5 | 7 | 9 | 11 | ||||||||||||
Month
Figure 10.4 Load Profiles at Philips Toys
2Ibid.
306 PART IV • Planning and Controlling Operations and Supply Chains
To develop a sales and operations plan, Philips will need to maintain a separate set of planning values for each product line it produces and then total up the requirements. The rest of the planning process will be very similar to top-down planning. Philips will probably have to choose between adjusting the workforce to avoid excess labor costs in the summer months and finding some way to smooth the labor requirements, perhaps by making more winter toys in the summer months. This, however, will drive up inventory levels.
| Cash Flow Analysis
|
|||||||||||
| One of the key benefits of S&OP is that it expresses business plans in a common language that | |||||||||||
| all partners can understand. Consider for instance the finance area. Among its many responsi- | |||||||||||
| Net cash flow | bilities, finance is charged with making sure that the business has the cash it needs to carry out | ||||||||||
| the sales and operations plan and that any excess cash is put to good use. Finance personnel are | |||||||||||
| The net flow of dollars into or | |||||||||||
| therefore very interested in assessing the net cash flow for any production plan. Net cash flow is | |||||||||||
| out of a business over some | |||||||||||
| time period. | defined as the net flow of dollars into or out of a business over some time period: | ||||||||||
| Net cash flow = cash inflows – cash outflows | (10.2) | ||||||||||
| Example 10.8 | Pennington sells each cabinet set for $2,800, on average. Management has already deter- | ||||||||||
| Cash Flow Analysis at | |||||||||||
| mined that the regular production cost for a cabinet set is $2,000, the overtime production | |||||||||||
| Pennington Cabinets | cost is $2,062, and the monthly holding cost per cabinet set is $40 (Table 10.3). | ||||||||||
| Now suppose that Pennington incurs these revenues and expenses in the month in | |||||||||||
| which they occur. That is, each sale of a cabinet set generates a cash inflow of $2,800, and | |||||||||||
| each cabinet set produced and each cabinet set held in inventory generate cash outflows of | |||||||||||
| $2,000 ($2,062 if overtime is used) and $40, respectively. Table 10.10 shows a simplified | |||||||||||
| cash flow analysis for the mixed production plan in Table 10.7. | |||||||||||
| Table 10.10 Cash Flow Analysis for Pennington Cabinets, Mixed Production Plan | |||||||||||
| Inventory/ | |||||||||||
| Sales | Regular | Overtime | Back | Cash | Cash | Net | Cumulative | ||||
| Month | Forecast | Production | Production | Orders | Inflows | Outflows | Flow | Net Flow | |||
| January | 750 | 800 | 0 | 150 | 2,100,000 | 1,606,000 | 494,000 | 494,000 | |||
| February | 760 | 800 | 0 | 190 | 2,128,000 | 1,607,600 | 520,400 | 1,014,400 | |||
| March | 800 | 824 | 0 | 214 | 2,240,000 | 1,656,560 | 583,440 | 1,597,840 | |||
| April | 800 | 848 | 0 | 262 | 2,240,000 | 1,706,480 | 533,520 | 2,131,360 | |||
| May | 820 | 848 | 0 | 290 | 2,296,000 | 1,707,600 | 588,400 | 2,719,760 | |||
| June | 840 | 848 | 0 | 298 | 2,352,000 | 1,707,920 | 644,080 | 3,363,840 | |||
| July | 910 | 848 | 0 | 236 | 2,548,000 | 1,705,440 | 842,560 | 4,206,400 | |||
| August | 910 | 848 | 0 | 174 | 2,548,000 | 1,702,960 | 845,040 | 5,051,440 | |||
| September | 910 | 848 | 0 | 112 | 2,548,000 | 1,700,480 | 847,520 | 5,898,960 | |||
| October | 880 | 848 | 12 | 92 | 2,464,000 | 1,724,424 | 739,576 | 6,638,536 | |||
| November | 860 | 848 | 12 | 92 | 2,408,000 | 1,724,424 | 683,576 | 7,322,112 | |||
| December | 840 | 848 | 0 | 100 | 2,352,000 | 1,700,000 | 652,000 | 7,974,112 | |||
To illustrate, the net cash flow calculation for January is:
Net cash flow = cash inflows – cash outflows
= Sales revenues – regular production costs – overtime production costs
· inventory holding costs
· +2,800(750 cabinet sets) – +2,000(800 cabinet sets)
· +2,062(0 cabinet sets) – +40(150 cabinet sets)
· +2,100,000 – +1,600,000 – +0 – +6,000 = +494,000
CHAPTER 10 • Sales and Operations Planning (Aggregate Planning) 307
Applying the same logic, the net cash flow in February is $520,400. Finally, Pennington can calculate the cumulative net cash flow through February as +494,000 + +520,000 = +1,014,400. The cash flow analysis expresses the sales and oper-ations plan in terms that are meaningful to financial managers. In this case, the cabinet set business is expected to generate anywhere from around $500,000 to $850,000 in positive cash flow each month and nearly $8 million over the course of the year. These additional funds can be used to cover other expenses, retire debt, or perhaps support additional busi-ness investments.
Now let’s consider an alternative scenario, shown in Table 10.11. Everything, including total sales for the 12-month planning period, is the same as before, except now the timing of the forecasted sales has changed. Specifically, sales in the first half of the year are much lower than previously, while sales increase dramatically in the last half.
The new sales pattern results in negative net cash flows for the first three months of the year and a cumulative negative net cash flow that does not disappear until May. In this case, finance will need to find the funds necessary to support this particular plan, or the company will need to develop an alternative sales and operations plan that is not as burdensome. Finally, you may have noticed that the net cash flow at the end of the year is lower than before ($7,766,512 versus $7,974,112). This difference is due to the fact that the second plan results in higher inventory holding costs. Figure 10.5 compares the cash flow results for Tables 10.10 and 10.11.
| 9,000,000 | ||||||||||||||
| 8,000,000 | ||||||||||||||
| ows | 7,000,000 | |||||||||||||
| 6,000,000 | ||||||||||||||
| cash | ||||||||||||||
| 5,000,000 | ||||||||||||||
| net | 4,000,000 | |||||||||||||
| Cumulative | 3,000,000 | |||||||||||||
| 2,000,000 | ||||||||||||||
| 1,000,000 | ||||||||||||||
| 0 | ||||||||||||||
| –1,000,000 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
| Month |
Figure 10.5 Cumulative Net Cash Flows under Two Different Sales Scenarios
Table 10.10
Table 10.11
Table 10.11 Cash Flow Analysis for Pennington Cabinets, Different Sales Pattern
| Sales | Regular | Overtime | Inventory/ | Cash | Cash | Net | Cumulative | |
| Month | Forecast | Production | Production | Back Orders | in Flows | Outflows | Flow | Net Flow |
| January | 500 | 800 | 0 | 400 | 1,400,000 | 1,616,000 | (216,000) | (216,000) |
| February | 520 | 800 | 0 | 680 | 1,456,000 | 1,627,200 | (171,200) | (387,200) |
| March | 550 | 824 | 0 | 954 | 1,540,000 | 1,686,160 | (146,160) | (533,360) |
| April | 700 | 848 | 0 | 1,102 | 1,960,000 | 1,740,080 | 219,920 | (313,440) |
| May | 880 | 848 | 0 | 1,070 | 2,464,000 | 1,738,800 | 725,200 | 411,760 |
| June | 960 | 848 | 0 | 958 | 2,688,000 | 1,734,320 | 953,680 | 1,365,440 |
| July | 1,040 | 848 | 0 | 766 | 2,912,000 | 1,726,640 | 1,185,360 | 2,550,800 |
| August | 1,040 | 848 | 0 | 574 | 2,912,000 | 1,718,960 | 1,193,040 | 3,743,840 |
| September | 1,040 | 848 | 0 | 382 | 2,912,000 | 1,711,280 | 1,200,720 | 4,944,560 |
| October | 980 | 848 | 12 | 262 | 2,744,000 | 1,731,224 | 1,012,776 | 5,957,336 |
| November | 970 | 848 | 12 | 152 | 2,716,000 | 1,726,824 | 989,176 | 6,946,512 |
| December | 900 | 848 | 0 | 100 | 2,520,000 | 1,700,000 | 820,000 | 7,766,512 |
308 PART IV • Planning and Controlling Operations and Supply Chains
10.3 Organizing for and Implementing S&OP
Figure 10.6
Fine-Tuning the Sales
and Operations Plan
We have spent a fair amount of time describing the basic calculations associated with S&OP. But S&OP is more than just “running the numbers.” Richard Ling put it best when he stated, “S&OP is a people process supported by information.”3 In this section, we address some critical organi-zational questions associated with S&OP:
· How do we choose between alternative plans?
· How often should S&OP be done?
· How do we implement S&OP in our business environment?
Choosing between Alternative Plans
Coming up with a suitable sales and operations plan is an iterative process. An organization may have to change a plan several times before coming up with a plan that is acceptable to all parties. This fine- tuning often means that decision makers will need to make trade-offs. Figure 10.6 illustrates this idea.
A classic example is the trade-off between inventory and customer service. Suppose that after reviewing a plan, finance wants to reduce inventory levels further to bring down costs. Marketing might raise concerns that this could potentially hurt customer service. All parties would have to come to some agreement concerning the right balance between the two compet-ing objectives—cost and customer service. As another example, operations might want market-ing to use pricing and promotion to smooth out peaks and valleys in demand. S&OP could be used to see if the cost of these pricing and promotion efforts is more than offset by improve-ments in production and inventory costs.
In choosing a sales and operations plan, managers must consider all aspects of a plan, not just costs. For example:
· What impact will the plan have on supply chain partners such as key suppliers and transportation providers? This could be particularly important if production levels vary considerably from one period to the next.
· What are cash flows like? Some plans may be profitable at the end of the planning cycle but still include periods in which cash expenses exceed revenues. We discussed earlier how cash flow analysis can be used to evaluate such plans.
· Do the supply chain partners and the firm itself have the space needed to hold any planned inventories?
· Does the plan contain significant changes in the workforce? If so, what would be the impact on workforce satisfaction and productivity? Could the HR department handle the additional workload?
· How flexible is the plan? That is, how easy or difficult would it be to modify the plan as conditions warrant?
| Generate sales | |
| and operations | |
| plan. | |
| Negotiate and | |
| make trade-offs | |
| Is the plan | |
| acceptable to | No |
| all participants? |
Yes
Implement plan
3R. Ling, “For True Enterprise Integration, Turn First to SOP,” APICS—The Performance Advantage 10, no. 3 (March 2000): 40–45.
CHAPTER 10 • Sales and Operations Planning (Aggregate Planning) 309
This is just a small sample of the kinds of questions that need to be addressed, but it raises a key point: Sales and operations plans help managers make decisions. They do not make the deci-sions for managers.
| Example 10.9 | ||||||
| Table 10.12 summarizes the costs, strengths, and weaknesses of the three alternative pro- | ||||||
| Selecting a Plan at | ||||||
| duction strategies we developed for Pennington Cabinets in Examples 10.4 through 10.6. | ||||||
| Pennington Cabinets | For practical purposes, the costs are too close for us to say that one plan is clearly cheap- | |||||
| est. After all, these are plans based on forecasts and planning values that represent, at best, | ||||||
| rough estimates of resource requirements and costs. We can almost guarantee that actual | ||||||
| results will be different. | ||||||
| Table 10.12 Summary of Alternative Plans at Pennington Cabinet | ||||||
| Level Plan | Chase Plan | Mixed Plan | ||||
| Regular production costs | $20,160,000 | $19,712,000 | $20,112,000 | |||
| Overtime production costs | 0 | $474,260 | $49,488 | |||
| Hiring and layoff costs | $16,250 | $39,000 | $19,500 | |||
| Inventory costs | $114,800 | $50,240 | $88,400 | |||
| Total costs | $20,291,050 | $20,275,500 | $20,269,388 | |||
| Key factors | Flat production | Minimal inventory. | Reasonably | |||
| level. Inventory | Significant overtime | stable production. | ||||
| levels grow | required in | Inventory levels | ||||
| as high as | peak months. | grow, but not as | ||||
| 370 cabinet | high as under a | |||||
| sets. | pure level approach. | |||||
| Some overtime | ||||||
| required. | ||||||
To choose a plan, then, Pennington will need to consider other factors. The level plan has the advantage of consistency because the same amount is made each and every month. This eases production planning and allows for workforce stability for Pennington and its partners. Furthermore, it allows Pennington to avoid expensive overtime—but at the cost of holding additional inventory. The build-up of inventory under the level plan does pose a risk: What if actual demand takes a sharp downturn later in the year? If this happens, Pennington will have to cut production drastically or risk being stuck with expensive un-wanted inventory.
The chase plan is just the opposite. Inventory levels never rise much above 100 (the starting level), but production levels vary anywhere from 752 in January to 910 in each month of the third quarter. Such instability in production and workforce levels may have unanticipated consequences.
The mixed plan strikes a balance between these extremes. Inventory levels increase over the slower months, but not as drastically as under the level approach. Similarly, the mixed plan uses overtime production, but not to the same extent as the chase plan. Based on these results, Pennington management might select a plan, or even develop another mixed plan in order to derive an even better solution.
Rolling planning horizon
A planning approach in which an organization updates its sales and operations plan regularly, such as on a monthly or quarterly basis.
Sales and operations plans must be updated on a regular basis to remain current. Most firms do this by establishing a rolling planning horizon , which requires them to update the sales and operations plan regularly, usually on a monthly basis. For example, suppose that it is now the beginning of October, and Pennington has just completed the sales and operations plan for Janu-ary through December of next year. (Note: October, November, and December are so close in time that they fall under detailed planning and control, discussed in Chapter 12.) One month later at the beginning of November, Pennington’s planning team might come together and revisit the plan, rolling it forward one month and planning for February through January. Figure 10.7
310 PART IV • Planning and Controlling Operations and Supply Chains
Figure 10.7
Updating the Sales and
Operations Plan
| Oct. | Sales and operations plan, | ||
| January–December | |||
| Nov. | |||
| Sales and operations plan, | |||
| February–January |
illustrates the idea. By establishing a rolling planning horizon, a firm can fine-tune its sales and operations plan as new information becomes available.
Implementing S&OP in an Organization
We have already discussed the steps involved in generating a sales and operations plan. But before these steps can occur, managers have to commit themselves to the S&OP process. Furthermore, managers have to realize that excellent S&OP is an organizational skill that can take months, or even years, to develop. Ling describes the implementation of S&OP as a three-phase process:4
· Developing the foundation;
· Integrating and streamlining the process; and
· Gaining a competitive advantage.
Developing the Foundation. In the first phase of implementing S&OP, companies build the managerial support and infrastructure needed to make S&OP a success. Key steps include edu-cating all participants about the benefits of S&OP, identifying the appropriate product or service families to plan around, and establishing the information systems needed to provide accurate planning values. Ling stresses the point that even though this phase typically takes six to nine months, many companies never progress further because “they expect the process to work im-mediately and don’t establish the right quality and timing of information.”5
Integrating and Streamlining the Process. In the second phase of implementing S&OP, S&OP becomes part of the organization’s normal planning activities. Managers become accustomed to updating the plan on a regular basis, and more importantly, they use the planning results to guide key demand and resource decisions. The sales and operations plan becomes a focal point for cross-functional coordination. Managers also look for ways to improve the S&OP process further. As Ling puts it, “Because implementing a process like this may not yield the right structure and organi-zation on the first attempt, some restructuring and streamlining usually occurs at this point.”6
Gaining a Competitive Advantage. In the final phase of implementing S&OP, a few companies reach the point where their S&OP process actually becomes a source of competitive advantage— a core competency, if you will. Companies know they have reached this last phase when:7
· There is a well-integrated demand planning process, including the use of forecasting models;
· Continuous improvement is planned and monitored as an integral part of the S&OP process;
· Capital equipment planning can be triggered at any time; and
· What-if analyses are a way of life, and the S&OP database is networked to provide ready access to S&OP data.
The last two points deserve further discussion. Capital equipment decisions typically fall under the auspices of strategic planning. Yet S&OP can give managers an “early warning” when changes in long-term capacity are needed. Pennington’s sales and operations plans (Examples 10.4 through 10.6) all show that demand is bumping up against the company’s capacity limits. Top management can use this information to start planning for additional investments in manufacturing capacity.
Finally, most organizations that perform S&OP for any length of time end up developing relatively sophisticated databases and decision tools to support their efforts. These tools, in turn,
4Ibid.
5Ibid.
6Ibid.
7Ibid.
Chapter 10 • SaleS and OperatiOnS planning (aggregate planning) 311
often give managers greater power to perform what- if analyses, in which the sales forecasts or even the planning values themselves can be varied to see how the plan reacts. The result is even more robust sales and operations plans.
In many ways, S&OP is even more critical in a service environment than it is in manufacturing. Service outputs cannot be built ahead of time and stored in inventory. An empty airline seat or an unused hour of a service technician’s time is lost forever. For this reason, service capacity must be closely matched to demand in every period. The effect is to limit most services to following some form of a chase production plan.
That said, services have many options for aligning resources with demand. These options fall into two camps:
· Making sales match capacity, and
· Making capacity (typically the workforce) match sales.
yield management
An approach that services commonly use with highly perishable “products,” in which prices are regularly adjusted to maximize total profit.
Firms have long used pricing and promotion to bring sales in line with production capacity. Yield management is an approach that services commonly use with highly perishable “products,” such as airline seats and hotel rooms. These services have a real incentive to make sure every unit of capacity—whether it is an airline seat on the next flight or a hotel room for tonight— contributes to the firm’s bottom line.
Put simply, the goal of yield management is to maximize total profit, where:
Total profit = (average profit per service unit sold)(number of service units sold)
Here’s how it works. When demand levels are lower than expected, yield management systems boost demand by lowering the price, but only if the expected result is an increase in total profit. Conversely, when demand is higher than expected, prices are raised, but only if the expected result is higher total profit.
The idea seems pretty straightforward, but what makes yield management distinctive is the level of sophistication involved. The airline and hotel industries, in particular, have complex
| Age Fotostock |
Services with highly perishable “products,” such as a ski resort, often vary the price of their services to smooth out demand and maximize profits.
312 part iv • planning and COntrOlling OperatiOnS and Supply ChainS
| Caro/AlamyFotograficzna |
| Hechtenberg/AgencjaClaudia |
By selling furniture unassembled, IKEA is able to offload part of the manufacturing task to the consumer, thereby holding costs down.
| yield management systems that regularly and automatically adjust the price of their services for | |||
| unbooked capacity in an effort to maximize total profit. If you have ever booked a hotel room or | |||
| made a plane reservation, only to have the price for new reservations change two days later, you | |||
| have seen yield management in action. | |||
| tiered workforce | |||
| A strategy used to vary work- | Making Capacity Match Sales
|
||
| force levels, in which additional | |||
| full-time or part-time employ- | We have already seen how overtime can be used to vary capacity. Another example is to use | ||
| ees are hired during peak de- | |||
| mand periods, while a smaller | a tiered workforce. For example, some service organizations hire additional full-time or part- | ||
| permanent staff is maintained | time employees during peak demand periods, while maintaining a smaller permanent staff year- | ||
| year-round. | |||
| round. This is common in the retailing, hospitality, and agricultural industries. | |||
| Offloading | Other services use offloading to shift part of the work to the customer. Examples include | ||
| A strategy for reducing and | companies that have customers deliver and assemble their own furniture (e.g., IKEA) and handle | ||
| smoothing out workforce | |||
| their own financial transactions online. This reduces overall workforce requirements for the ser- | |||
| requirements that involves | |||
| having customers perform part | vice firm, and it also helps to smooth out workforce requirements. This is because the customer | ||
| of the work themselves. | acts like a part-time employee, showing up just when the demand occurs. | ||
| example 10.10 | It takes Adam’s Carpet Cleaning Service an average of four hours to clean the carpets in a | ||
| Service offloading | |||
| home. This includes three hours of actual cleaning time plus one hour to move the furni- | |||
| at adam’s Carpet | ture out of the way and then back into position. Adam’s is considering modifying its ser- | ||
| Cleaning Service | vice so that the customer takes responsibility for moving the furniture, in effect offloading | ||
| 25% of the workload. The impact on Adam’s labor hours can be seen in Table 10.13. |
CHAPTER 10 • Sales and Operations Planning (Aggregate Planning) 313
Table 10.13 Impact of Customer Offloading at Adam’s Carpet Cleaning Service
| Labor Hours Needed | |||
| No Offload to | 25% Offloaded to | ||
| Month | Forecast | Customer | Customer |
| 1 | 60 | 240 | 180 |
| 2 | 55 | 220 | 165 |
| 3 | 50 | 200 | 150 |
| 4 | 50 | 200 | 150 |
| 5 | 30 | 120 | 90 |
| 6 | 30 | 120 | 90 |
| 7 | 25 | 100 | 75 |
| 8 | 30 | 120 | 90 |
| 9 | 40 | 160 | 120 |
| 10 | 40 | 160 | 120 |
| 11 | 45 | 180 | 135 |
| 12 | 55 | 220 | 165 |
| Average: | 170 | 127.5 | |
| Lowest: | 100 | 75 | |
| Highest: | 240 | 180 | |
| Difference: | 140 | 105 | |
Average monthly labor requirements drop by 25%, and the absolute difference between the highest and lowest months drops by 25%. Of course, Adam’s would need to balance the potential cost savings against the lowered revenues associated with the new service—after all, the customer can’t be expected to work for free.
10.5 Linking S&OP Throughout the Supply Chain
Earlier, we noted that the S&OP process should consider not only the impact on various parties within the firm but also the impact on outside parties—the firm’s supply chain partners. It makes little sense, for example, to try to implement a plan that cannot be supported by key suppliers or service providers who move or store the goods. This represents the potential downside of not considering supply chain partners when developing a plan.
But there is an upside to linking the S&OP process with supply chain partners. For one thing, coordinating plans across the supply chain can help firms do a better job of improving overall supply chain performance, particularly in the area of cost. Pennington, for example, might discover that suppliers are willing to give the company substantial price discounts if Pennington stabilizes its orders for material—something easier to achieve under a level production plan.
Second, linking plans can help eliminate uncertainty, thereby improving synchronization between supply chain partners. For instance, once Pennington decides on a sales and operations plan, its supply chain partners can use the information to plan their own activities. By tying their plans to Pennington’s, key suppliers can avoid “guessing” what demand will be. Even better, Pennington might try to establish linkages with its downstream partners—that is, its customers— in an effort to get even more accurate sales forecasts. If you’ve read Chapter 9, you might be say-ing to yourself, “That sounds a lot like what collaborative planning, forecasting, and replenish-ment (CPFR) hopes to accomplish,” and you’d be right. In fact, the logical ties between CPFR and S&OP are so strong that a leading industry group has put together a road map that describes how businesses can integrate these efforts.8
8Voluntary Interindustry Commerce Solutions (VICS), Linking CPFR and S&OP: A Roadmap to Integrated Business Planning, 2010, www.gs1us.org/DesktopModules/Bring2mind/DMX/Download.aspx?Command=Core_Download& EntryId=1375&PortalId=0&TabId=785.
314 PART IV • Planning and Controlling Operations and Supply Chains
Figure 10.8
S&OP
Linking S&OP Up and (customer)
Down the Supply Chain
S&OP
Pennington
S&OP (1st-tier supplier)
S&OP (2nd-tier supplier)
Of course, the information can flow downstream as well as upstream. If, for example, a key supplier increases its capacity, such information would be useful for Pennington’s S&OP effort. This linking of S&OP throughout the supply chain is shown in Figure 10.8. Sharing of plans already takes place in many industries, with the results being greater coordination, improved productivity, and fewer disruptions in the flow of goods and services through the supply chain.
10.6 Applying Optimization Modeling to S&OP
| Optimization model | In Chapter 8, we introduced optimization models. As you will recall, optimization models are |
| A class of mathematical models | a class of mathematical models used when the user seeks to optimize some objective function |
| used when the user seeks to op- | subject to some constraints. An objective function is a quantitative function that we hope to |
| timize some objective function | |
| optimize (e.g., we might want to maximize profits or minimize costs). Constraints are quantifi- | |
| subject to some constraints. | |
| able conditions that place limitations on the set of possible solutions (demand that must be met, | |
| Objective function | |
| limits on materials or equipment time, etc.). A solution is acceptable only if it does not break any | |
| A quantitative function that | |
| of the constraints. | |
| an optimization model seeks | |
| to optimize (i.e., maximize or | In order for optimization modeling to work, the user must be able to state in mathemati- |
| minimize). | cal terms both the objective function and the constraints. Once the user is able to do this, special |
| Constraint | modeling algorithms can be used to generate solutions. |
| A quantifiable condition that | S&OP is ideally suited to such analyses. In particular, managers may be interested in un- |
| places limitations on the set of | derstanding what pattern of resource decisions—labor, inventory, machine time, and so on—will |
| possible solutions. The solution | |
| result in the lowest total cost while still meeting the sales forecast. In Example 10.11, we show | |
| to an optimization model is | |
| how Microsoft Excel’s Solver function can be used to apply optimization modeling to S&OP. | |
| acceptable only if it does not | |
| break any of the constraints. |
| Example 10.11 | ||||||
| Bob Irons Industries manufactures and sells DNA testing equipment for use in cancer clinics | ||||||
| S&OP Optimization | ||||||
| around the globe. Bob, the owner and CEO, has developed a spreadsheet (Figure 10.9) to help | ||||||
| Modeling at Bob Irons | calculate the costs associated with various sales and operations plans. | |||||
| Industries | It’s worth taking a few minutes to see how Bob’s spreadsheet works. The cells that con- | |||||
| tain the planning values are highlighted, as are the columns for the sales forecast, hirings, | ||||||
| and layoffs, indicating that Bob can change these cells. The remaining numbers are all cal- | ||||||
| culated values. | ||||||
| To illustrate, the calculations for January are as follows: | ||||||
| Sales (in labor hours) = B15 * D3 = 500 units * 20 hours per unit | ||||||
| = 10,000 labor hours | ||||||
| C15 | 10,000 labor hours | |||||
| Sales (in worker hours) = | = | = 62.5 workers | ||||
| D4 | 160 hours per worker |
CHAPTER 10 • Sales and Operations Planning (Aggregate Planning) 315
| 1 | A | B | C | D | E | F | G | H | I | ||||||||
| S&OP Spreadsheet | |||||||||||||||||
| 2 | |||||||||||||||||
| 3 | Labor hrs. per unit: | 20 | |||||||||||||||
| 4 | Worker hrs. per month: | 160 | |||||||||||||||
| 5 | Beginning & ending workforce: | 100 | |||||||||||||||
| 6 | Beginning & ending inventory: | 100 | |||||||||||||||
| 7 | Total plan cost | ||||||||||||||||
| 8 | Production | cost per unit: | $550.00 | $6,600,000 | |||||||||||||
| 9 | Hiring cost: | $300.00 | $7,500 | ||||||||||||||
| 10 | Layoff cost: | $200.00 | $5,000 | ||||||||||||||
| 11 | Holding cost per unit per month: | $4.00 | $54,800 | ||||||||||||||
| 12 | $6,667,300 | Grand total | |||||||||||||||
| Ending | |||||||||||||||||
| Sales | Sales (in | Sales (in | Actual | Actual | Hirings | Layoffs | Inventory/ | ||||||||||
| 13 | Month | Forecast | labor hrs.) | workers) | Workers | Production | Back Orders | ||||||||||
| 14 | 100 | 100 | |||||||||||||||
| 15 | January | 500500 | 10,000 | 62.5 | 125.00 | 1,000.00 | 25.00 | 0.00 | 600.00 | ||||||||
| 16 | February | 600 | 12,000 | 75 | 125.00 | 1,000.00 | 0.00 | 0.00 | 1,000.00 | ||||||||
| 17 | March | 700 | 14,000 | 87.5 | 125.00 | 1,000.00 | 0.00 | 0.00 | 1,300.00 | ||||||||
| 18 | April | 800 | 16,000 | 100 | 125.00 | 1,000.00 | 0.00 | 0.00 | 1,500.00 | ||||||||
| 19 | May | 900 | 18,000 | 112.5 | 125.00 | 1,000.00 | 0.00 | 0.00 | 1,600.00 | ||||||||
| 20 | June | 1,000 | 20,000 | 125 | 125.00 | 1,000.00 | 0.00 | 0.00 | 1,600.00 | ||||||||
| 21 | July | 1,000 | 20,000 | 125 | 125.00 | 1,000.00 | 0.00 | 0.00 | 1,600.00 | ||||||||
| 22 | August | 1,100 | 22,000 | 137.5 | 125.00 | 1,000.00 | 0.00 | 0.00 | 1,500.00 | ||||||||
| 23 | September | 1,200 | 24,000 | 150 | 125.00 | 1,000.00 | 0.00 | 0.00 | 1,300.00 | ||||||||
| 24 | October | 1,300 | 26,000 | 162.5 | 125.00 | 1,000.00 | 0.00 | 0.00 | 1,000.00 | ||||||||
| 25 | November | 1,400 | 28,000 | 175 | 125.00 | 1,000.00 | 0.00 | 0.00 | 600.00 | ||||||||
| 26 | December | 1,500 | 30,000 | 187.5 | 125.00 | 1,000.00 | 0.00 | 0.00 | 100.00 | ||||||||
| 27 | 0.00 | 25.00 | |||||||||||||||
| 28 | Totals: | 12,000 | 12,000.00 | 25.00 | 25.00 | 13,700.00 | |||||||||||
| 29 | Average = | 125 | |||||||||||||||
| Figure 10.9 S&OP Spreadsheet for Bob Irons Industries (Level Plan) | |||||||||||||||||
| Actual workers = E14 + G15 – H15 = 100 beginning workers + 25 hires – 0 layoffs | |||||||||||||||||
| = 125 workers | |||||||||||||||||
| Actual production = | E15*D4 = | 125 workers * 160 hours per month | = 1,000 units | ||||||||||||||
| D3 | 20 hours per unit |
Ending inventory = I14 + F15 – B15 = 100 + 1,000 – 500 = 600 units
The plan shown in Figure 10.9 is, in fact, a level production plan with a total cost of $6,667,300. Looking at the plan, Bob wonders if he can do better. As an alternative, Bob updates the spreadsheet to show a chase plan. The results are shown in Figure 10.10.
The results surprise Bob: The total cost for the chase plan is exactly the same as the cost for the level plan. He wonders if there is indeed a better solution that meets all of the constraints.
Bob decides to use the Solver function of Excel to find the lowest-cost solution. To start the process, Bob takes a few moments to identify the objective function, decision vari-ables, and constraints for the optimization model and to match them up to his spreadsheet (Table 10.14).
As Table 10.14 indicates, Bob will need to set up the Solver function to minimize total costs (cell F12) by changing the hiring and layoff values (cells G15–H26). At the same time, the cells containing the ending inventory values must stay at or above 0 for the first 11 months (cells I15–I25), and at or above 100 in the past month (cell I26).
Furthermore, Bob wants to make sure that none of the hiring or layoff numbers (cells G15–H26) is negative. This may seem like a strange requirement, but unless Bob does this, the model will try to reduce costs forever by endlessly offsetting a negative hire with a negative layoff, each iteration of which would “save” +300 + +200 = +500.
316 PART IV • Planning and Controlling Operations and Supply Chains
| 1 | A | B | C | D | E | F | G | H | I | ||||
| S&OP Spreadsheet | |||||||||||||
| 2 | |||||||||||||
| 3 | Labor hrs. per unit: | 20 | |||||||||||
| 4 | Worker hrs. per month: | 160 | |||||||||||
| 5 | Beginning & ending workforce: | 100 | |||||||||||
| 6 | Beginning & ending inventory: | 100 | |||||||||||
| 7 | Total plan cost | ||||||||||||
| 8 | Production | cost per unit: | $550.00 | $6,600,000 | |||||||||
| 9 | Hiring cost: | $300.00 | $37,500 | ||||||||||
| 10 | Layoff cost: | $200.00 | $25,000 | ||||||||||
| 11 | Holding cost per unit per month: | $4.00 | $4,800 | ||||||||||
| 12 | $6,667,300 | Grand total | |||||||||||
| Ending | |||||||||||||
| Sales | Sales (in | Sales (in | Actual | Actual | Hirings | Layoffs | Inventory/ | ||||||
| 13 | Month | Forecast | labor hrs.) | workers) | Workers | Production | Back Orders | ||||||
| 14 | 100 | 100 | |||||||||||
| 15 | January | 500 | 10,000 | 62.5 | 62.50 | 500.00 | 0.00 | 37.50 | 100.00 | ||||
| 16 | February | 600 | 12,000 | 75 | 75.00 | 600.00 | 12.50 | 0.00 | 100.00 | ||||
| 17 | March | 700 | 14,000 | 87.5 | 87.50 | 700.00 | 12.50 | 0.00 | 100.00 | ||||
| 18 | April | 800 | 16,000 | 100 | 100.00 | 800.00 | 12.50 | 0.00 | 100.00 | ||||
| 19 | May | 900 | 18,000 | 112.5 | 112.50 | 900.00 | 12.50 | 0.00 | 100.00 | ||||
| 20 | June | 1,000 | 20,000 | 125 | 125.00 | 1,000.00 | 12.50 | 0.00 | 100.00 | ||||
| 21 | July | 1,000 | 20,000 | 125 | 125.00 | 1,000.00 | 0.00 | 0.00 | 100.00 | ||||
| 22 | August | 1,100 | 22,000 | 137.5 | 137.50 | 1,100.00 | 12.50 | 0.00 | 100.00 | ||||
| 23 | September | 1,200 | 24,000 | 150 | 150.00 | 1,200.00 | 12.50 | 0.00 | 100.00 | ||||
| 24 | October | 1,300 | 26,000 | 162.5 | 162.50 | 1,300.00 | 12.50 | 0.00 | 100.00 | ||||
| 25 | November | 1,400 | 28,000 | 175 | 175.00 | 1,400.00 | 12.50 | 0.00 | 100.00 | ||||
| 26 | December | 1,500 | 30,000 | 187.5 | 187.50 | 1,500.00 | 12.50 | 0.00 | 100.00 | ||||
| 27 | 0.00 | 87.50 | |||||||||||
| 28 | Totals: | 12,000 | 12,000.00 | 125.00 | 125.00 | 1,200.00 | |||||||
| 29 | Average = | 125 |
Figure 10.10 S&OP Spreadsheet for Bob Irons Industries (Chase Plan)
Table 10.14 Description of the Optimization Problem for Bob Irons Industries
| Description | Cell Reference |
| Objective function: | |
| Minimize total production, hiring, layoff, and inventory costs | F12 |
| By changing the following decision variables: | |
| Hiring and layoffs | G15:H26 |
| Subject to the following constraints: | |
| Inventory in the last period must be at least 100 units | I26 Ú 100 |
| Inventory cannot go below zero (i.e., the sales forecast | I15:I25 Ú 0 |
| must be met) | |
| Hiring and layoff values cannot be negative | G15:H26 Ú 0 |
Figure 10.11 shows the lowest-cost solution, as identified by Solver. The open dialog box illustrates how the problem stated in Table 10.14 was encoded into Solver. The new plan is roughly $18,000 cheaper than either the level or the chase approach. The suggested solution is to keep the workforce at around 92 workers for the first six months and then bump it up to around 158 workers for the past six months. Under this plan, the inventory level falls to zero only once (at the end of June).
CHAPTER 10 • SaleS and OperatiOnS planning (aggregate planning) 317
Figure 10.11 Solver-Generated Optimal Solution for Bob Irons Industries
Microsoft® and Windows® are registered trademarks of the Microsoft Corporation in the U.S.A. and other countries. This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation. Reproduced by permission.
Before making a final decision, Bob has to consider other factors as well. The Solver solution contains fractional workers—will it still work for whole numbers? If so, will Bob be able to hire and train nearly 67 workers in July? Does the company have enough space to store up to 500 units? Is the savings worth the added complexity? Solver can help Bob identify ways to lower costs, but the final decision is Bob’s, not the spreadsheet’s.
S&OP fills the gap between long-term strategic planning and short-term planning and control. Through S&OP, firms can not only plan and coordinate efforts in their own functional areas—operations, marketing, finance, human resources, and so on—but also effec-tively communicate to other members of the supply chain what they expect to accomplish over the intermediate time horizon.
In this chapter, we described several approaches to S&OP and demonstrated the power of the technique. We discussed when and where top-down versus bottom- up planning can be used and showed three basic approaches to S&OP: level, chase, and mixed production.
We also touched on some of the qualitative issues sur-rounding S&OP: How do we select a plan? How can we use S&OP to foster agreement and cooperation among the various parties? How can we organize for S&OP?
We also argued for increased sharing of S&OP information across the supply chain. As information technologies become more sophisticated and organizations put more emphasis on the supply chain, we can expect to see more and more sharing of S&OP between supply chain partners. Finally, we ended the chapter with a discussion of how optimization modeling tech-niques can be applied to the S&OP process.
Ending inventory level (page 302):
| EIt = EIt – 1 + RPt + OPt – St | (10.1) | |
| where: | = ending inventory for time period t | |
| EIt | ||
| RPt | = regular production in time period t | |
| OPt | = overtime production in time period t | |
| St | = sales in time period t |
| Net cash flow (page 306): | |
| Net cash flow = cash inflows – cash outflows | (10.2) |
Aggregate planning 295 Bottom-up planning 297 Chase production plan 301 Constraint 314
Detailed planning and control 296 Level production plan 300
Load profile 305
Mixed production plan 301 Net cash flow 306 Objective function 314 Offloading 312 Optimization model 314 Planning values 297
Rolling planning horizon 309
Sales and operations planning (S&OP) 295
Strategic planning 295 Tactical planning 296 Tiered workforce 312 Top-down planning 297 Yield management 311
P r o b l e m Hua Ng Exporters
Hua Ng Exporters makes commercial exercise equipment that is sold primarily in Europe and the United States. Hua Ng’s two major product lines are stair steppers and elliptical machines. Resource requirements for both product lines, as well as six-month forecasts, are as follows:
| Labor Hours | Fabrication | Assembly Line | |||
| Product Line | Per Unit | Hours Per Unit | Hours Per Unit | ||
| Stair steppers | 2.5 | 0.8 | 0.15 | ||
| Ellipticals | 1.0 | 1.8 | 0.20 | ||
| Sales Forecast | |||||
| Month | Stair | Ellipticals | |||
| 1 | 560 | 400 | |||
| 2 | 560 | 400 | |||
| 3 | 545 | 415 | |||
| 4 | 525 | 435 | |||
| 5 | 525 | 435 | |||
| 6 | 525 | 435 | |||
Assuming that Hua Ng follows a chase production plan, develop load profiles for the next six months for labor, fabrication, and assembly line hours. Interpret the results.
Solution
The first step is to translate the sales forecasts for the two product lines into resource require-ments. This will require us to calculate and then combine the resource needs for both product
lines. Table 10.15 shows the results.
To illustrate how we arrived at these results, we calculated the total labor hours for month 1 as follows:
(560 stair steppers)(2.5 hours) + (400 ellipticals)(1 hour) = 1,400 + 400 = 1,800 hours
The remaining numbers are calculated in a similar fashion. Figure 10.12 shows the load profiles for the three resources.
CHAPTER 10 • Sales and Operations Planning (Aggregate Planning) 319
Table 10.15 Resource Requirements at Hua Ng Exporters
| Sales Forecast | Total Labor | Total | Total |
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