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Managing the Seed-Corn Supply Chain

at Syngenta
Philip C. Jones • Greg Kegler • Timothy J. Lowe • Rodney D. Traub
Syngenta Seeds, Inc., 7500 Olson Memorial Highway, Golden Valley, Minnesota 55427
College of Business Administration, North Dakota State University, Fargo, North Dakota 58105 • ﬁ • •

Each year, Syngenta Seeds, Inc. produces over 50 seed-corn hybrids and the following year
markets over 100 hybrids under the NK brand name. The fact that growing seed corn is a
biological process dependent upon local weather and insect conditions during the growing
season complicates production planning. In addition, customers’ experiences with a particular
hybrid during a given year strongly inﬂuence demand for that hybrid during the next year.
To help mitigate some of these yield and demand uncertainties, Syngenta (and other seed
companies as well) take advantage of a second growing season for seed corn in South America,
which occurs after many of the yield uncertainties and some of the demand uncertainties have
been resolved or reduced. To better manage this production-planning process, Syngenta and
the University of Iowa developed and implemented a second-chance production-planning
model. A trial of the model showed that using it to plan 2000 production would have increased
margins by approximately $5 million. Today, Syngenta uses this model to plan production for
those varieties that account for 80 percent of total sales volume.
(Inventory: production, uncertainty. Industries: agriculture, food.)

E ach spring, farmers (consumers of seed corn) de-

cide how to allocate their land among a variety of
possible crops, one of which is corn, at least in many
choices particular farmers make are, therefore, highly
dependent upon their locations.
In addition, farmers’ decisions may be heavily inﬂu-
areas including the Midwest. After deciding how enced by their experience during the previous growing
much of their land to plant in corn, farmers must de- season. Suppose, for example, a farmer happened to
cide which hybrid(s) to purchase and plant. There are choose a particular hybrid intended for a cooler, less
literally hundreds of different hybrids produced either humid climate. If the weather happened to be abnor-
by one of eight ﬁrms (including Syngenta) that account mally hot and humid in that growing season, the
for approximately 73 percent of the total United States farmer would likely have a much lower yield than he
market of approximately $2.3 billion or by one of the or she expected and hence would be less inclined to
over 300 smaller regional ﬁrms that account for the purchase that particular hybrid again. Conversely, if
remaining 27 percent. These hybrids differ in their re- the hybrid chosen happened to be optimized for the
sistance to certain diseases and insects as well as their growing conditions as they actually occurred, the sit-
performance under different soil and climatic condi- uation would be reversed. If the hybrid were new and
tions. Certain hybrids, for example, are optimized for hence not thoroughly tested, very few growers would
the shorter, cooler northern corn belt while others are be inclined to make large plantings. Instead they
optimized for the longer, hotter southern corn belt. The would tend to make small test plantings to evaluate

Interfaces, 䉷 2003 INFORMS 0092-2102/03/3301/0080$05.00

Vol. 33, No. 1, January–February 2003, pp. 80–90 1526-551X electronic ISSN

the hybrid’s performance under their local growing —Average yields for 2001 North American produc-
conditions. tion of seed corn,
Because seed corn cannot be produced instantane- —Average yields for 2001 South American produc-
ously but instead must be produced over a long sum- tion of seed corn, and
mer growing season, Syngenta and other seed com- —Demand during 2002.
panies must rely on their inventories of seed corn Much of the variability of seed-corn demand in any
produced in previous growing seasons to ﬁll farmers’ year is due to the variability of experiences with par-
demands for the current growing season. The produc- ticular hybrids during the previous growing season.
tion of hybrid seed corn can be brieﬂy described as When it plans 2001 production, Syngenta knows about
follows. A hybrid is the genetic cross of two genetically those experiences (which affect demand during 2001)
different parent inbred plants. To produce the genetic for the year 2000. Thus, for the 2001 planning process,
cross (or hybrid) that will be sold as seed corn, the seed demand during 2001 may be regarded as far more cer-
company or its contractor grows these two parent in- tain than the demand that will exist during 2002 (Fig-
breds in the same ﬁeld in alternating rows. As the ure 1). During this ﬁrst phase of the planning process,
plants mature, the tassels from one of the parents prior to spring planting, Syngenta determines, for each
(called the female plant) are removed in a labor- hybrid, how much acreage to plant for the 2001 North
intensive “detasseling” operation, thereby insuring American production period and makes a contingent
that the only pollen available to pollinate the female 2001 production plan for South America.
In the second phase of the planning process later in
plant must come from the other parent (called the male
the year, it updates and ﬁnalizes the production plan
plant). The resulting corn that matures on the female
for South America. At this point, Syngenta knows the
plant is, therefore, a genetic cross of its two parents.
ﬁnal 2001 demand and the average yields from North
Once corn from the female matures, the seed company
American production; the only signiﬁcant uncertain-
picks it and transports it to processing plants where it
ties remaining are the average yields from any planned
is dried, sorted, treated with antifungal or other coat-
South American production and the demand during
ings, bagged, and stored in anticipation of the upcom-
the 2002 sales period.
ing selling season.
Inputs to the ﬁrst-stage planning process include
Thus, production to meet the demand for seed corn
—Information about on-hand inventories of seed
for the 2002 growing season actually occurs in 2001 (or
earlier) during one of two growing seasons: the North —Projected demand during 2001,
American growing season in which seed-corn parent —The distributions of yield in both North and South
stock is planted in the spring and harvested in late America,
summer or the South American growing season which —The distribution of demand during the year 2002,
is offset by approximately six months. —The selling price of seed corn, and
Syngenta plans for 2001 seed-corn production prior —The costs of both North and South American
to the time when it actually knows the ﬁnal demands production.
for 2001 seed corn. But, when it plans 2001 production, In the planning process, planners decide about how
Syngenta knows the following with a fair degree of much acreage to devote to producing each variety of
certainty: seed corn in both North and South America.
—Inventory on hand to meet 2001 demands, In recent years, competition from other ﬁrms and
—Production costs for North American production, research leading to new proprietary genetics have
and combined to shorten product life cycles. As a result,
—Production costs for South American production. each year fewer hybrids have a long, stable demand
What it does not know with certainty are the history that would make forecasting demand easy. In-
following: stead, more hybrids are either beginning their life cy-
—Demand during 2001, cles with little certainty regarding their demand or

Vol. 33, No. 1, January–February 2003 81

Figure 1: The seed-corn planning process accounts for a second chance at production in South America.

ending their life cycles with predictable but declining Iowa researchers contacted Syngenta to see if the com-
demand. Because of the shortened product life cycles, pany had any interest in pursuing a joint research ef-
production planning has become more crucial to the fort aimed at better understanding and modeling the
success of the company and simultaneously more seed-corn planning process. Syngenta agreed to pro-
challenging. vide information on the seed-corn industry, its crop
growing practices, and speciﬁc data regarding demand
distributions, yield distributions, seed-corn prices, and
Modeling production costs. In return for providing this infor-
The impetus for studying and modeling the seed-corn mation and for serving as a beta test site, Syngenta
planning process came from a conversation between obtained the right to use any resulting software and
one of the University of Iowa researchers and a student models for its own production planning.
in the evening MBA program. That evening’s class had The university team modeled the seed-corn plan-
included a discussion of the single-period news- ning process as a two-stage (corresponding to North
vendor model in which there is a single chance to order
or produce a product to meet a subsequent random
demand (Figure 2).
The instructor mentioned planning seed-corn pro-
duction as a possible application for such a model, suit-
ably modiﬁed to incorporate the fact that production
yield is a random variable. After class a student who
worked for one of the largest seed-corn producers in
the world mentioned that planning for seed corn was
actually a more complicated process. During the en-
suing conversation, the student explained that the pro-
cess was complicated primarily because South Amer-
ican production, offset by approximately six months,
provides the company with a second chance to pro-
duce seed corn for sale in the next marketing season. Figure 2: The news-vendor planning process considers only one chance
After some initial modeling efforts, the University of to procure product.

82 Vol. 33, No. 1, January–February 2003

American and South American planting decisions) dy- carried in inventory for a limited number of years be-
namic programming problem, the objective of which cause seed that is too old has a very low germination
is to maximize expected gross margin. Jones et al. rate. We estimated the demand distribution from his-
(2002) illustrate the value (increase in expected margin) torical data. To do this, we obtained data records that
of two production opportunities versus one. Figure 1 provided 207 observations of forecasted demand and
is a graphical representation of the model developed actual demand for different hybrids. First, we normal-
with one exception; a certain demand equal to the ex- ized the data by dividing, in each case, the actual de-
pected demand replaces the ﬁrst random demand. The mand by the forecasted demand, providing us with a
primary reason for this change is that the ﬁrst stage of
the planning process takes place just before spring de-
Syngenta estimates that using the
mand occurs, and by that time, most of the original
uncertainty regarding that demand has been resolved. model will increase margins by
As a result, we found that incorporating the spring several million dollars.
demand as a random variable rather than as a certain
demand complicated the model and enlarged its data total of 207 ratios. We then estimated a distribution of
requirements without providing any signiﬁcant these ratios (the normalized demand distribution) by
beneﬁts. constructing a histogram from the 207 ratios. This his-
To be very speciﬁc, the model requires the following togram shows, for example, what percentage of the
pieces of information: time actual demand was between 90 percent and 100
—The sales price per unit (unit ⳱ 80,000 kernels) of percent of forecasted demand. To obtain the actual de-
seed corn, mand distribution used in the model, we then multi-
—The shortage cost per unit of seed corn, plied forecasted demand, a datum, by the distribution
—The salvage value per unit of unsold seed corn, of ratios (the normalized demand distribution). Be-
—The cost per unit of processing and shipping seed cause we used linear programming to model the prob-
corn (for both North and South America), lem, we used discrete approximations for both de-
—The cost per acre of planting, managing, and har- mand and yield distributions. We chose all the data
vesting seed corn (for both North and South America),
used in constructing the normalized demand distri-
—The probability distribution for next year’s de-
bution for years and for hybrids for which actual in-
mand for seed corn, and
ventory was left on hand after the sales period. This is
—The probability distribution of seed corn yield
important because otherwise we would not have been
(based on units per acre for both North and South
able to say with certainty what actual demand was—
had ending inventory been zero, all we could have said
Most of these data items are quite straightforward
is that demand exceeded supply.
and were obtained by examining historical ﬁnancial,
accounting, and production data. Three of these items, In applying the model, the analyst ﬁrst runs it prior
however, required some effort: shortage cost, salvage to spring planting using the best estimates of demand
value, and demand distribution. Determining the ap- and yield distributions available at that time. The out-
propriate shortage cost required input from the ﬁ- puts for each hybrid variety from this initial applica-
nance, accounting, and marketing groups at Syngenta. tion are recommendations on
After much discussion and analysis, we approximated —How many acres to plant for the North American
shortage cost as the lost proﬁt from two years’ worth growing season, and
of sales. Thus, if the proﬁt per unit sold is $x, the short- —For each possible value of North American yield,
age cost is $2x. Leftover seed corn can be stored and how many acres should be planted for the South
used to meet demand the following year. Salvage American growing season.
value, therefore, is closely approximated by the ex- At the end of the North American growing season
pected cost of producing seed corn less the cost of stor- just prior to planting in South America, the analyst also
ing it until the next year. Seed corn, however, can be runs a simpliﬁed single-growing-season version of the

Vol. 33, No. 1, January–February 2003 83

model. At the time of this second run, Syngenta knows Because formal modeling procedures, at least those
North American yield and, based on information ac- based on optimization methods, were new to Syn-
cumulated during the current growing season, can up- genta’s production planning process, Syngenta in-
date estimates of South American yield and next year’s sisted on validating the model and its results before
demand prior to running the model. The output from using it in practice. To do this, Syngenta selected four
this second model run is a recommendation on how hybrid varieties that company representatives believed
many acres to plant in South America. to represent the range of typical varieties and for which
The objective for both stages in the dynamic pro- detailed information was available regarding
gramming recursion is to maximize expected gross —Production costs,
margin: expected revenue from seed-corn sales less ex- —Yield estimates at the time production decisions
pected costs of production, holding, and shortage. The were made,
objective function is either a sum or an integral of con- —Demand estimates at the time production deci-
cave functions, depending upon whether or not the sions were made,
probability distributions are discrete or continuous. As
—Actual (realized) yields, and
a result, the objective function itself is concave, so the
—Actual (realized) product demands.
model is well posed. However, we pose the problem
In the study, we ran the model for each of the four
as a linear program and solve it using the What’s Best!
hybrids in each of the two years of the study (Tables 1
add-on to Microsoft Excel.
and 2). The idea was to compare what Syngenta actu-
Our model treats each hybrid independently of oth-
ally did to what would have happened if it had used
ers. Although one might suspect that some production
constraint (land availability, for example) would link
the different hybrids, this is not the case. Syngenta has One major beneﬁt is the reduction in
enough opportunities to contract out the production of forecasting bias.
seed-corn to outside producers that availability of land
and availability of other production inputs are not the model and followed its recommendations with no
constraints. modiﬁcation. For each model run, we used the yield
distributions and demand distribution that Syngenta
could have used at the time it made the acreage deci-
sions. Yield distributions and cost data were different
Syngenta’s original production planning process was
for North America and South America. Once the
iterative: First, the marketing group collected estimates
model-computed planting acreages were available, we
of next year’s sales from its sales force and used them
made the assumption that realized yields for the model
to develop an aggregate demand forecast. Typically,
scenario would have been the same as the yields that
senior managers imposed production constraints and
were actually observed. It should be noted that Syn-
ﬁnancial constraints that precluded producing every-
genta recorded forecasted demand to the nearest 1,000
thing Marketing wanted. To resolve the differences,
marketing representatives and their counterparts from units and recorded sales to the nearest 100 units while
Production, Finance, and Accounting usually held recording acres, actual production, and inventories to
many meetings in which they negotiated to arrive at a the nearest unit. The results adopt the same reporting
yearly production plan. Typically, they regarded convention (Tables 1 and 2).
South American production only as a reactionary res- To go into this in more detail, we will consider Hy-
cue event to help overcome a shortfall resulting from brid A for year 1 of the study period. The seed com-
an unexpectedly poor yield in North America. They pany forecasted a demand of 67,000 units in year 1 for
drew up the typical North American production plan, this particular hybrid. It expected a yield, based on
therefore, under the assumption that North American prior harvest data, of 41.2 units per acre and actually
production would have to cover demand. planted 1,507 acres in North America (in the summer

84 Vol. 33, No. 1, January–February 2003

Hybrid A Hybrid B Hybrid C Hybrid D

Actual Model Actual Model Actual Model Actual Model

Year 1 Year 1
Initial inventory 28,678 28,678 121,614 121,614 Initial inventory 0 0 16,717 16,717
Forecasted demand 67,000 67,000 275,000 275,000 Forecasted demand 43,000 43,000 43,000 43,000
Acres planted 1,507/0 1,844/0 4,827/1,009 4,264/0 Acres planted 780/0 587/0 2,528/320 1,396/0
N.A./S.A. N.A./S.A.
Production 69,322 84,824 339,386 271,198 Production 28,392 21,372 145,283 72,464
Sales 72,000 72,000 396,000 392,812 Open-market purchase 31,000 8,807 0 0
Inventory carryover 26,000 41,502 65,000 0 Sales 26,600 26,600 95,000 89,181
Margin $3,836,480 $3,197,713 $25,119,572 $28,410,563 Inventory carryover 32,792 3,580 67,000 0
Margin ⳮ$295,848 $876,607 $3,364,508 $5,822,088
Year 2
Initial inventory 26,000 41,502 65,000 0 Year 2
Forecasted demand 164,000 164,000 409,000 409,000 Initial inventory 32,792 3,580 67,000 0
Acres planted 4,697/0 3,687/0 9,992/0 8,465/0 Forecasted demand 33,000 33,000 116,000 116,000
N.A./S.A. Acres planted 0/0 749/0 1,900/0 2,967/0
Production 232,502 182,492 656,474 556,136 N.A./S.A.
Actual sales 146,000 146,000 229,000 229,000 Production 0 32,220 85,880 134,128
Inventory carryover 103,000 77,994 492,474 327,135 Sales 22,000 22,000 47,600 47,600
Margin $4,930,685 $6,650,799 $391,190 $4,257,127 Inventory carryover 10,792 13,800 105,280 86,528
Margin $1,486,872 $459,723 $648,680 ⳮ$365,994
Table 1: This table shows the model results versus the actual results for
Hybrids A and B. Although the model does not outperform decisions ac- Table 2: This table shows the model results versus the actual results for
tually taken in every case, using the model would have improved margins Hybrids C and D. Although the model does not outperform decisions ac-
over the two-year period by approximately 12 percent for Hybrid A and by tually taken in every case, using the model would have improved margins
28 percent for Hybrid B. The entries for inventory carryover are the number over the two-year period by approximately 12 percent for Hybrid C and by
of units after sales carried into the next year. Entries in bold represent 36 percent for Hybrid D.
higher margin outcomes.

prior to the year 1 sales period) and 0 acres in South demand) by the normalized distribution. Using the
America. The actual planting decision of 1,507 acres, model, the production plan for North America was
when multiplied by the expected yield of 41.2 units per 1,844 acres. If the company had planted this many
acre, results in an expected production of 62,088 units. acres and had obtained the same yield of 46 units per
When added to initial inventory of 28,678 units, the acre, the total production would have been 84,824
expected total supply would have been 90,766 units. units. Because the actual North American yield of 46
The ﬁrm planned for overproduction because the costs units per acre was much larger than the expected yield
of shortages are much larger than the costs of overpro- of 41.2 units per acre, the model’s second-period pro-
duction (because excess inventory can be carried over duction plan, computed after period 1 yield is known,
for sale the next year). The company has a ﬁnancial called for 0 acres in South America. Combined with
incentive to weight its decisions towards avoiding carryover, using the model’s production plan would
shortages rather than avoiding excess inventory. Its ac- have given Syngenta a total supply of approximately
tual yield was 46 units per acre for a total production 113,502 units. Because actual demand was 72,000 units,
of 69,322 units. Its total supply (including inventory the company actually carried over 26,000 units to the
carried over) was 98,000 units. next year. Using the model’s production plan would
For the model run for this problem, we used a yield have led to a carryover of about 41,502 units.
distribution that ranged from 31.2 to 51.2 units per In both cases, supply was sufﬁcient to meet demand,
acre, with an expected yield of 41.2. We generated the so revenue was the same. To determine margin, we
demand distribution by multiplying 67,000 (forecasted subtracted planting and harvesting costs as well as

Vol. 33, No. 1, January–February 2003 85

carryover costs from revenues. In this case, the model’s Combined with carryover, the yield based on using the
production plan incurred extra planting and harvest- model’s production plan would have given Syngenta
ing costs as well as extra inventory carrying costs, so a total supply of 392,812 units. Because actual demand
the year 1 actual margin realized by the company was was 396,000, Syngenta actually carried over 65,000
greater than what it would have earned had it used the units to the next year. Had it used the model’s pro-
model. duction plan, Syngenta would have had a shortage of
Continuing on to year 2 with the same hybrid, the 3,188 units.
company forecasted a demand of 164,000 units, To determine margins, we subtracted planting and
planted 4,697 acres in North America and eventually harvesting costs and carryover and shortage costs from
sold 146,000 units. The model’s production plan called revenues. In this case, the model’s production plan in-
for 3,687 acres to be planted in North America. The curred lower planting and harvesting costs and lower
model called for lower second-year acreage partly be- inventory carrying costs than the company had actu-
cause the carryover from year 1 would have been ally incurred, so the year 1 actual margin realized by
larger using the ﬁrst period acreage it speciﬁed. Had the company was substantially less than what it would
the company used the model’s suggested acreage de- have earned had it used the model.
cision in year 1, its year 1 margin would have been Continuing on to year 2 for Hybrid B, the company
lower than it actually obtained. By using the model in forecasted a demand of 409,000 units, planted 9,992
year 1 and year 2 for this hybrid, however, it would acres in North America (no South American acres), and
have obtained an overall (over the two-year interval)
eventually sold 229,000 units, leaving an inventory
margin increase of approximately 12 percent.
carryover of 492,474 units. The model’s production
For Hybrid B, the seed company forecasted a de-
plan called for 8,465 acres to be planted in North
mand of 275,000 units in year 1. It expected a yield,
America, which would have led to a production level
based on prior harvest data, of 59.6 units per acre in
of 556,136 units and an inventory carryover of 327,135
North America and 45 units per acre in South America.
units. In summary, using the model’s suggested acre-
The company actually planted 4,827 acres in North
age decision in year 1 instead of the company’s actual
America and 1,009 acres in South America. Its actual
decision would have led to an increase (relative to
yield was 63.6 units per acre in North America and 32.1
what the ﬁrm actually realized) in year 1 margin of
units per acre in South America for a total production
about $3,000,000. The additional improvement that
of 339,386 units. Its total supply (including inventory
carried over) to face year 1 demand was 461,000 units. would have occurred in year 2 for this hybrid would
For the model run for this problem, we used a yield have given it an overall (over the two-year interval)
distribution that ranged from 39.6 to 79.6 units per margin increase of about $7 million.
acre, with an expected yield of 59.6 for North America The actual results versus the model results for Hy-
(the corresponding numbers for South America were brids C and D are documented in Table 2. As with
23, 69, and 46 respectively). We generated the demand Hybrids A and B, the model does not always outper-
distribution by multiplying 275,000 (expected de- form decisions actually taken, but on an aggregate ba-
mand) times the normalized demand distribution. Us- sis it would have improved performance. In fact, ag-
ing the model, the production plan for North America gregating results for the four hybrids over the two-year
was 4,264 acres. If the company had planted this many study period shows that margins would have im-
acres, using the realized 63.6 units per acre ﬁgure, the proved by more than 24 percent while inventory carry-
total production would have been 271,198 units. Be- over would have been reduced by 27 percent.
cause the actual North American yield of 63.6 units per These data, limited though they are, suggest that us-
acre was larger than the expected yield of 59.6 units ing the model would indeed produce production plans
per acre and there was a substantial carryover from quite different from those the company actually
the previous year, the model’s South American pro- adopted. On average, the model’s production plans
duction plan called for zero acres in South America. produce less inventory carryover and greater margin

86 Vol. 33, No. 1, January–February 2003

than those actually adopted. Also, the model tended Based on historical data, we developed different de-
to plant a smaller acreage than Syngenta actually did. mand distributions for the hybrids in each of these
Although the results of this experiment appeared three classes, using the modeling procedure described
promising, the production plans the model recom- earlier.
mended were quite different from those Syngenta pro- Currently, the vice president of supply management
duced by using its current production-planning pro- runs the model with input from marketing, produc-
cess. The senior managers decided that the model tion, and inventory managers. A team of managers
would have to prove itself further before they would from these three areas decides whether to follow the
adopt it as part of the planning process. To test the model outputs. Each year, Syngenta modiﬁes certain
model further, they decided to use the model to de- model parameters (yield distributions, demand fore-
velop an independent production plan in parallel to casts, and normalized demand distributions) to ac-
their ongoing processes for planning production for count for the most recent information. It takes several
2000 to produce seed corn to sell in 2001. Syngenta ﬁrst weeks to accumulate the required model inputs, but
developed production plans for 18 of its top hybrids actually running the model and analyzing its output
using its normal procedures. These production plans takes only one to two days. It performs ﬁrst runs of
were the ones actually implemented in 2000. the year (two-period model) in late February or early
Afterwards, we ran the model on the same 18 hy- March to allow adequate time for production contract-
brids using the same demand, yield, and cost data that ing. It performs second runs of the model (one-period
model) in late August or early September to conﬁrm
had been inputs to the normal procedures. Syngenta
the South American production decisions.
kept track of actual production yields and actual year
2001 seed-corn demands for these 18 hybrids. In June
2001, after sales results for 2001 were ﬁnalized, it could Impact
therefore compare what actually happened with what
In developing and implementing the model, we clearly
would have happened if it had followed the model’s
demonstrated the existence of systematic bias in the
recommendations. This side-by-side comparison
demand forecasts. Speciﬁcally, we found that the his-
showed that, by using the model and implementing its
torical demand forecasts Syngenta had produced using
recommendations, Syngenta would have planted
the traditional aggregation or roll-up methods over-
fewer acres, would have had less inventory to carry
estimated demand 73 percent of the time. By using the
over, and could have increased its margins by approx- model to analyze various realistic scenarios, we found
imately $5 million on these 18 hybrids. that eliminating the bias in forecasting procedures
The ﬁnal results of the 2000 production-planning ex- could produce substantial beneﬁts. This has spurred
periment were not known until June 2001, well after Syngenta to thoroughly review and modify its fore-
the 2001 production plan had to be implemented, but casting process. For 2002, it organized a team dedi-
preliminary results from the experiment had indicated cated solely to forecasting and inventory management
margin improvements in the same range as actually with the goal of reducing the inventory-to-sales ratio.
occurred. As a consequence, Syngenta regarded the Using the model has changed the way Syngenta
2000 production planning experiment as a successful thinks about and values a second chance at production
test of the model and decided to use the model begin- in South America. Before we developed the model, it
ning in 2001 to help plan production of hybrids in three saw South American production merely as a high cost
classes: tool for adjusting inventory. Now, it sees the second-
—Top selling hybrids comprising 80 percent of its chance opportunity in South America as a viable
total sales volume, inventory-management tool and plans for it, making it
—New hybrids with high demand uncertainty, and an integrated event rather than a reactionary rescue
—Late life cycle hybrids with established but declin- event. Even when Syngenta does not use South Amer-
ing demand. ican production, the fact that it is an available option

Vol. 33, No. 1, January–February 2003 87

enables it to reduce the acreage in North America it in 1998–1999 and only 59 percent of the time in 2000–
devotes to seed-corn production. As a result, Syngenta 2001 (Figure 3). The ideal number is 50 percent.
has been able to reduce its working capital while still Syngenta’s business is changing. Customers are in-
meeting customer demands for seed corn. creasingly demanding a wider variety of multiple seed
A key example of Syngenta’s change of thinking is treatments (fungicide, pesticide, and so forth) on mul-
that it now contracts for South American production in tiple hybrids. Research on genetically modiﬁed organ-
advance. As a result, Syngenta has been able to choose isms (GMOs) has led to new varieties that resist dep-
better growers at reduced prices, allowing it to better redation from the dreaded European corn borer,
predict yield, to reduce production costs, and to come tolerate applications of Roundup herbicide, and toler-
close to its inventory goals. ate corn root worm. These new genetic varieties and
Since implementing the model, Syngenta has ana- combinations and the growing number of possible
lyzed industry benchmarks to investigate how its lead-
ing competitors use the second-chance production op-
portunity in South America. It found that on average,
seed-corn companies sell only 60 percent of the seed
corn they produce in South America each year, storing
the remaining 40 percent until the next year. By using
the model, Syngenta has improved its use rate of seed
grown in South America to 80 percent. Since produc-
ing seed corn in South America is a costly option, Syn-
genta believes that improving the South American use
rate is a key indicator.
The ﬁnal results for the production plans Syngenta
developed and implemented during the calendar year
2001 will not be available until late spring or early
summer of 2002 when it has its ﬁnal sales ﬁgures. Syn-
genta estimates, however, that using the model to help Figure 3a: An analysis of actual demand versus forecast for seed corn
plan 2001 production will increase margins by several using 1998 and 1999 data revealed that 73 percent of the time, actual
million dollars. demand fell short of forecast.

Senior managers in Syngenta have stated that, al-

though the margin improvements are very beneﬁcial,
the major beneﬁts of the model and its implementation
lie elsewhere. Speciﬁcally, senior managers think the
major beneﬁts are
—The different thought process driving improve-
ments in forecasting demand that have reduced the
systematic bias in demand forecasts,
—The opportunity to reduce working capital while
still meeting customer needs, and
—The recognition that using modeling helps them
to be proactive in developing planning tools in a
changing business environment.
One major beneﬁt is the reduction in forecasting
bias. In comparing the normalized demand distribu- Figure 3b: After experience with the model, Syngenta found that actual
tions for 1998–1999 and 2000–2001, we found that the demand (2000–2001 seed-corn data) fell short of forecast only 59 percent
forecasts overestimated demand 73 percent of the time of the time.

88 Vol. 33, No. 1, January–February 2003

seed coatings imply an explosion in the number of end

products. This growth in the number of end products
will increase demand uncertainty at the stock-keeping-
unit (SKU) level. Syngenta cannot delay customization
(via GMO type) until it has resolved demand uncer-
tainties because it must produce the seed corn it sells
for one growing season in a previous growing season.
Although theoretically it can delay customization (via
seed-coating type), doing so would necessitate making
huge investments in treating equipment to be able to
treat seeds rapidly enough to provide an acceptably
short lead time to customers.
To meet such customer demands in a fairly ﬂat sales
market without making unacceptably high invest-
ments in working capital, Syngenta will need better
planning processes than it has previously used. We are
working to develop tools to help plan production in
this increasingly challenging environment. Syngenta
also needs production-planning tools for other seed
products, such as soybeans. We are trying to develop
production-planning models for these products as
well. According to Ed Shonsey, president of Syngenta
Seeds, North America,
The efforts associated with developing the model and the use
of the derived analysis tool have already paid huge beneﬁts
to our company. They have changed the way we think about
uncertainty and risk and have forced us to rethink the way
we do business. I am convinced that the use of the model and
similar decision-support tools will assist us in being success-
ful in the future.

Executive summaries of Edelman award papers are presented here. The complete article was
published in the INFORMS journal Interfaces [2003, 33:1, 80-90]. Full text is available by
subscription at

Vol. 33, No. 1, January–February 2003 89