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INTRODUCTION

It was a beautiful, sunny day in mid-October 2017 in St. Louis. Chad Murphy, Mark Roberts, Jane Fitzpatrick and Cheryl Stone were sitting around a conference table in the small conference room with the apropos name of “Create” at Building D of Monsanto’s Creve Coeur headquarters. The fall foliage added an extra element of beauty to the Monsanto campus, but the team could barely notice the natural beauty outside their window. They were in that room for over ten hours, and the task on the table was far from being resolved. It was once again time to get into the “product portfolio” and “inventory/production planning” task for the fiscal year 2017/18. The team had been specifically assigned to look into an application area with which a few of their existing products had had performance issues (mostly yield related) relative to recent competitive offerings. Performance shortcomings could be substantially improved by new hybrid seeds currently in the development pipeline. Their demand forecasts were also less accurate than usual, and the low commodity prices were not helping the overall sales. The team had been working exhaustively on this problem for the past two weeks, and they had narrowed down the product portfolio selection choice to five new hybrids (see Exhibit 8) and ten existing products (see Exhibit 10) at different product life cycle stages. Their task was to suggest the right product portfolio and the planned production of each of them for 2017, keeping in mind that their target of new product introduction was around 20% of existing products. Mark and Jane had earned their graduate degrees from a renowned Midwestern business school with strong general management, finance and supply chain management specialties. Their first jobs after graduate school were at Monsanto, where they had worked for the last three years. Chad and Cheryl were Monsanto veterans, with over fifteen years of experience and cross-functional expertise in production, sourcing, finance and product development. They had seen such team tasks and decisions before, but they were first to admit they were not getting any easier. They were looking forward to a more analytical and data-driven approach from their younger colleagues, and wanted to get them grounded on the practical realities and organizational constraints inherent to the task.

Mark and Jane had sat through the same supply chain management course during their graduate program. Their funny professor with a Greek accent loved to lecture on product rollover strategy success, which seemed particularly relevant to the current task. They were skimming through their course notes and were reading them aloud:

“…For a product rollover to be successful, the entire supply chain has to be effectively

managed to accommodate the rollover challenges. On the one hand, new products have to be introduced at an appropriate time to meet the needs of the market. At the same time, the pipeline must be carefully emptied out of old products. Within the supply chain, new products affect the demand for existing products, and in the case of new product stock-outs, some of the existing products may substitute for them. However, a new product introduction may often cause

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obsolescence of inventories of some of the existing products, especially the ones in the later stages of their product life cycles.

In product rollover situations, the introduction of new products and the elimination of old

ones have to be considered simultaneously. At a simple conceptual level, there are two different strategies to look at: solo rollover and dual rollover. Solo rollover is when the old product is phased out when the new product is introduced. In dual rollover, both the current and the new product overlap for a while. The product co-existence complicates demand forecasting, substitution structure for products when stock-outs occur, and pricing of products later in their lifecycle. It also increases the risk of obsolescence of carried-over product inventories from previous seasons…” Mark and Jane were now part of the task force to make a decision on the product portfolio, and to plan and execute production and inventory plans for seed corn hybrids. It was not a course homework exercise anymore, but a real-life, high-stakes project with all the complexities and risks of an uncertain business environment. Cheryl interjected in the product rollover discussion by reminding them of a recent example of a similar decision:

“Do you remember the Canola situation we faced last year in Canada?” In one of our Canola applications we faced some grain quality issues with our existing product, COP-1. We identified a new product, CNP-2. Both of these products were up against a competitor’s product, CCP.” Cheryl moved quickly to grab a marker and started writing on the whiteboard. “The relative comparison data looked something like this”:1,2

Product Yield

Advantage (bushels/acre)3

Attractiveness of New Product Market Trait

Price COGS

COP-1 +3 Below Average $660 $200 CNP-2 +1 Strong ? $200 CCP +2 Average $710 $195 (estimate)

We already had other products, OG-1,2, serving the same market at different lifecycle stages.

To the best of my recollection:

1 Canola is developed through conventional plant breeding from rapeseed. The root “rape” comes from the Latin “rapum” meaning “turnip.” The word canola stands for “Can(ada) + o(il) + l(ow) + a(cid).” 2 Canola’s lead times and production planning challenges are similar to those of corn. 3 Numbers indicate increases (+) or decreases (-) over the current base benchmark for farm yield in bushels/acre.

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Product Price Life Cycle

OG-1 $670 Mature OG-2 $600 Decline COP-1 $660 Growth CNP-2 $720 (proposed) Launch

“Why did you think $720 was a good price?” Mark interrupted. What was the expected cannibalization that the introduction of the CNP-2 would cause for existing products, and especially COP-1? Did we have any reasonable market estimates on forecasted new product market demand and cannibalization impact to existing products?”

Cheryl responded excitedly, “Mark, you are asking the right questions. From what I recall, at that time we thought the market was going to grow to around 200k bags4. We were around a 10% market share then, or producing around 20k bags, but we felt the strength of the market trait of the new product was enhancing the brand and let us target closer to 15% of the market share. As you know, our ‘utilization’ definition of ‘actual sales/sales forecast’ is how we describe our forecast accuracy, and for these products we had seen variations from 80% to 120%. My best guess would have been 80%. We thought it would probably cannibalize sales for the COP-1 at a 40% level. The risk of obsolescence of any product that is mature and beyond stage is around 70-80%. And what I wrote on the board as $720 is what I remember the team had proposed. I cannot recall the justification for it. But there is always a lot of hand waiving and assuming, as it is hard to have a demand curve for a new product with serious performance uncertainties. Any thoughts or questions on any of that?” Jane jumped into the conversation. “Now I fully understand the dilemma, and how it relates to the product rollover concepts of our courses. In a solo rollover, we would bring in CNP-1 and kill OG-1, and hope we would be able to compete effectively with CCP. Dual rollover would allow us to delay the phasing out of OG-1 (and later, COP-1), but for an uncertain amount of time. We would still have to decide which of the OGs to keep in the portfolio, and for how long we should keep COP-1. We are always operating under a production budget constraint, and we assume our bag production budget would increase from 20k to 30k bags.” Mark grabbed his laptop and fired up Excel. “Well, we need a spreadsheet even for this simple example. We could optimize life cycle profitability by launching CNP-1 and managing the product portfolio over the next 4-5 years. But we should also account for budget constraints, the timing of the phasing out of older products as their obsolescence rates increase, and we need

4 “k” stands for “1,000”

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to account for the fact that we probably have 10-20% leftover inventory of each product from the previous season. Does that sound right, Chad? Chad smiled in an encouraging way. “I’d love to take a look at your spreadsheet for this problem. We will need to scale it up for the crop seed portfolio we are looking at. But now we definitely need to call it a day. With the Cardinals not making it to the the playoffs, Dallas Keuchel of the Houston Astros pitching against Los Angeles Dodgers ace Clayton Kershaw is all I can think about this evening. It will be a fin game! See you tomorrow guys, bright and early!”

MONSANTO COMPANY

Monsanto is a Fortune 500 sustainable agriculture business headquartered in St. Louis, Missouri, USA. It delivers agricultural products that support farmers all around the world in three primary lines of business—Agricultural and Vegetable Seeds, Plant Biotechnology Traits, and Crop Protection Solutions. It has 404 facilities in 66 countries, and globally has 21,183 employees. In the U.S. it has 146 facilities in 33 states, and has 10,277 employees. Financial highlights of the company (for 2015) can be found in Exhibit 1.

John F. Queeny founded Monsanto in 1901, using his wife’s maiden name. The company’s first product was saccharine (an artificial sweetener discovered in 1879). By the early 1990s, Monsanto grew to become the third largest U.S. chemical company and ranked in the top 150 of the Fortune 500 list. By the mid-1990s, Monsanto pursued a strategy of transforming from a chemical company to a life sciences company. As a life sciences company Monsanto is committed to finding solutions to the growing global needs for food and health.

In 1997, Monsanto’s chemical businesses were spun off and two new companies were formed: a life sciences company called Monsanto with $5 billion in sales, and a chemical company, Solutia, with $3 billion in sales. The new Monsanto continued assembling resources and building capabilities for its life sciences strategy and went through a series of investments, acquisitions, and partnerships. Exhibit 2 shows some of the more recent history of Monsanto’s transformation and its more relevant acquisitions. The spin-off was followed by substantial investments in agricultural biotechnology. The biotechnology process alters the genetic structure of a plant by physically inserting genes with desired characteristics. Inserting traits into plants gives the plant specific characteristics such as protection from a certain insect, a specific color or taste, or increased yield.

Through its more recent acquisitions of Climate Corp. and Agradis, Inc., Monsanto positioned itself for a data sciences and analytics strategy in precision agriculture and a strongly digitized agribusiness supply chain. The current Monsanto is a company working together with farmers and other stakeholders to support sustainable agriculture by bringing a broad range of

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solutions to help nourish a rapidly growing world. The company accomplishes this through offering leading seed brands in crops like corn, cotton, oilseeds, fruits, and vegetables. It also produces leading in-the-seed trait technologies for farmers to help them protect their yield, improve on-farm efficiency, and reduce farm costs.

MONSANTO’S GLOBAL SUPPLY CHAIN

There are 8,948 employees working across Monsanto’s global supply chain. Monsanto’s global operations encompass six regions, 47 countries, and 199 locations. More detailed information on the structure of Monsanto’s global operations can be found in Exhibit 3.

In a recent transformation of the supply chain organization, Monsanto put forward a new organizational structure which allows for breaking down functional silos, creating cross-functional cooperation between technology teams, manufacturing operations, and commercial business; and emphasizes a global-thinking approach. At the same time, the company fully transformed itself from a product-based company (mostly around seeds and traits), to an agribusiness service and solutions company (specializing in improved yield services and crop protection control solutions), and is currently positioning itself for an emerging data science and analytics-driven customized solutions approach. These company transformations further challenge the structure of the supply chain organization and its role. See Exhibit 4 for more information.

Monsanto’s new global supply chain organization has four distinct strategic focus areas: Plan, Source, Make, and Deliver. Refer to Exhibit 5 for relevant information.

1. Planning

The planning department determines the correct timing, location, and quantity for making products to meet customer expectations. The long-term goal of the planning department is to employ an Integrated Planning Process for Monsanto’s global supply chain.

2. Procurement (Source)

The procurement department secures the products and services that Monsanto needs to operate. The long-term goal of the procurement department is to optimize Monsanto’s global strategic sourcing organization to drive value to the business.

3. Production (Make)

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The production department produces, packages, and stocks Monsanto’s products to ensure that customers get what they need and want. The long-term goal of the production department is to be a reliable producer in quality, cost, volume, dependability, and consistency for the products Monsanto provides.

4. Delivery/Customer Care

The delivery/customer care department oversees the ordering, fulfillment, delivery, and billing process for customers. The long-term goal of the department is to deliver a best-in-class customer care model to be utilized company-wide.

HYBRID CORN SEED SUPPLY CHAIN PLANNING CHALLENGES A hybrid is the genetic cross of two genetically different parent inbred plants. To produce the genetic cross that will be sold as seed corn, Monsanto grows these two inbreeds in the same field. There are normally four female rows and one male row used to make each hybrid. As the plants mature, the tassels from one of the parents (called the “female” plant) are removed in a labor-intensive operation, thus ensuring the only pollen available to pollinate the female plant must come from the other parent (called the “male” plant). The resulting corn that matures on the female plant is a genetic cross of its two parents. Once corn from the female matures, the seed company picks it and transports it to processing plants where it is dried, sorted, treated with protective coatings, bagged, and stored in anticipation of the upcoming selling season. Because seed corn cannot be produced instantaneously, but rather gets produced over a long summer growing season, Monsanto must rely on their inventories of seed corn produced in previous growing seasons to fill farmers’ demands for the current growing season. Thus, production to meet the North American demand for seed corn for the 2018 growing season actually occurs in 2017 (or earlier) during the North American growing season. For this growing season, seed corn parent stock is planted in the spring and harvested in late summer. Planning Complexity

Monsanto’s seed corn supply chain has many factors that contribute to its complexity and

affect its overall performance. This often manifests itself in high inventories and/or an inadequate service level to their dealers and farmers. Some of the key factors contributing to Monsanto’s seed supply chain complexity are:

• High dependability on weather conditions and commodity prices • Variable production yield

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• Demand forecast uncertainty • Long lead times from forecast to product availability • Large number of stock-keeping-units (SKUs) due to product variety, quality, coating

treatment options, etc. In the U.S., the planting season for corn typically starts around April. Due to weather and rain conditions, as well as geographical specifications, this start may range from late March to mid-May. Farmers carefully take care of their crops with fertilizers and/or chemicals for about four months and harvest their corn in the August-September time frame, assuming everything goes as planned. Then farmers sell their crops and start to prepare for the following year’s crop plan. In 2015, corn production was 13.7 billion bushels in the U.S., with yields averaging about 169.3 bushels per acre (bu/ac).

Currently, there are long planning lead times for corn in the U.S. (12-18 months). Earlier in the planning cycle, the planning team gathers the information on seed stock, which are the parental seeds for the commercial hybrid production based on available inventory (produced one year in advance). The planning team also plans for the parent seed production to generate seeds to be used in the next year of production for commercial hybrid corn. So the planning cycle encompasses the current year’s (year Y) parental seed production based on estimated commercial hybrid corn sales for the next year (year Y+1). As the corn production is an annual cycle, the production of parental seed has to happen one year in advance of sales to allow for the production of the hybrid in the following year. Monsanto’s U.S. commercial corn hybrid seed portfolio has a high variety of hybrids in corn (238 products in FY2015) with a 15-25% new product launch each year. Many factors impact demand forecast:

1. Commodity price is an important factor for farmers to consider. Whenever corn commodity price drops or increases, it always poses a risk on farmers’ decisions. Farmers compare prices and profit margins for substitute crops (e.g., corn vs. soybeans).

2. Weather is another important factor for farmers. Sometimes, even though farmers want to grow corn, consistent rain or temperatures might delay their planting, thus forcing them to change crops.

3. Corn yield performance is the third most important factor in a farmer’s decision-making process. If a current years’ seed performance is not good (exceptional environmental conditions may cause lower yield), farmers may choose to switch to other hybrids, or even other brands. In the end, a long planning lead time for meeting Y+1 demand would add even more risk into the forecast.

After the planning team outlines the items which are forecastable for Y+1, the sales team starts to prepare their Y+1 demand forecast for each hybrid in different relative maturity and

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treatment. After three months, these numbers get refined and communicated to Monsanto’s customers in order to improve the quality of demand forecasts. Around April, all teams involved in seed manufacturing work together and finalize the Y+1 production plan, and then planting starts. After four months, when corn is ready to be harvested, the planning team makes updated demand forecasts and needed production plan adjustments with the manufacturing team for alignment with the latest demand forecast. After harvesting, corn goes through a series of performance improving (coating, etc.) and packaging processes and quality checks before the selling season. Some aggregate production plan information on Monsanto and its competitors (based on available data for 2014 and 2015) can be found in Exhibit 6. The seed corn inventories for the vast portfolio of hybrids have been a major challenge for Monsanto’s supply chain planning team. The presence of significant production yield and demand uncertainties necessitate the use of excessive safety stocks. Safety stocks are means of balancing the financial risk of excess, unsold inventory with the financial risk of lost sales. The typical safety stock models that Monsanto uses require the following pieces of information:

• Sales price per unit • Shortage cost per unit of seed corn • Salvage value per unit of seed corn • Cost per unit of processing and shipping costs to the regional farmer markets • Cost per acre of planting, managing, and harvesting seed corn • Probability distribution for next year’s demand for seed corn • Probability distribution of seed corn yield (units per acre)

Most of these are quite straightforward and can be obtained by examining historical, financial, accounting, and production data. Shortage cost is typically approximated as list profit from two years’ worth of sales. Thus, if the profit per unit sold is $x, the shortage cost per unit is $2x. Since leftover seed corn can be stored and used to meet demand for the following year, the salvage value is approximated by the expected cost of producing seed corn less the cost of storing it for a year. Exhibit 7 provides some data on historically observed demand forecast and production variability of high and low volume hybrids. The impact of estimation error on both demand forecast and production yield is serious, complicating supply chain planning and relevant costs, and will enter prominently into the discussion on the planning of new product introduction later in this case.

PLANNING CHALLENGES FOR SEED CORN SUPPLY CHAINS

Another planning challenge for Monsanto’s supply chains, especially for seed corn, is to plan a vast portfolio of hybrid seeds, taking full advantage of the available global production capacity. Growing seed corn is a biological process heavily dependent on local weather and

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insect conditions during the growing season. Customers’ experiences with a particular hybrid during a given year strongly influence demand for that hybrid for the next year. Each spring, farmers decide how to allocate their land devoted to corn among a variety of corn hybrids. There are hundreds of different hybrids produced by agribusiness firms, with many of them coming from Monsanto. Corn hybrids differ in their resistance to certain diseases and insects, as well as their performance under different soil and climactic conditions. Certain hybrids, for example, are optimized for the shorter, cooler northern corn belt, while others are optimized for the larger, hotter southern corn belt.

Experiences of the previous growing season often influence planting decisions of the next season. For example, a farmer who chose a particular hybrid intended for a cooler, less humid climate might end up with lower-than-expected yield if the realized weather in the growing season was hot and humid. As a result, the farmer may be less inclined to plant this hybrid the next year. For completely new hybrids that have not been thoroughly tested before, growers often avoid large planting, and instead prefer to make small test plantings to evaluate the hybrid’s performance under the local growing conditions.

To provide a more concrete example, production to meet the demand for seed corn for the

2018 growing season actually occurs in 2017. In North America, seed corn parent stock is planted in the spring of 2017 and harvested in late summer 2017.

When Monsanto plans 2017 seed corn production (in March or April of 2017), it knows

or has estimates for the following:

• Inventory on hand to meet 2017 seed corn demand • Production costs for North American production

However, Monsanto has incomplete and/or inaccurate information and uses oft-unreliable forecasts for:

• Exact demand for 2017 (forecasted demand distribution) • Average yield for 2016 North American production of seed corn (estimate) • Demand for 2018 (forecasted demand distribution)

Much of the variability of seed corn demand in any year is due to the variability of experiences with particular hybrids during the previous growing season. When it plans 2017 production, Monsanto knows the farmers’ experiences for 2016, which affect demand forecasts for 2017. Thus, for the 2018 demand planning purposes, in early spring 2017 the demand forecast for 2017 is more accurate than the demand forecast for 2018.

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During the first phase of the planning process, prior to Spring 2017 planting, Monsanto determines for each hybrid (using a portfolio of over 200 hybrids) how much acreage to plant for the 2017 North American production period.

For the upcoming planting season, Monsanto has to determine how much safety stock should be produced (not only of existing products [hybrid seeds], but also new hybrid seeds they are planning to introduce) in order to ensure there is enough supply to support sales in the next fiscal year. The key factors used to calculate safety stock for each hybrid are:

A. Financials: COGS and gross profit per hybrid. B. Substitution rate: percentage of time that a farmer would buy a different

Monsanto product to replace one that was out of stock; this reduces the financial impact from being short on a product.

C. Obsolescence rate: percentage of product remaining at the end of each season that is discarded due to either quality or marketing loss (i.e., no demand for the product in the following year).

D. Variability: historical analysis shows that variability for hybrids with smaller forecasts have higher variability rates for both production and demand. A normal distribution is used (mean and standard deviation) to describe both production and demand variability. The production and demand variabilities are combined using a statistical formula to create a combined variability.

a. Production variability: uncertainty surrounding the useable yield (passes quality) that will be achieved from a growing season

b. Demand variability: measure of the difference between actual sales and forecasted volume

Based on analysis of historical data and the current portfolio characteristics, the task force

reached consensus on using the product life cycle stage of each hybrid to assign substitution and obsolescence rates according to the table below.

Some suggested numbers for combined variability (production and demand) are provided below.

Lifecycle Obsolescence Substitution Success Rate

Launch 15.0% 50% Growth 5.0% 40% Mature 20.0% 70% Decline 50.0% 80%

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Variability Volume Cut Combined Variability

Less than or equal to 100,000 56% Between 100,000 and 1,000,000 35%

Monsanto’s production planning process is reasonably iterative. First, the marketing group collects estimates of next year’s sales from its sales force and uses them to develop demand forecasts. Then, other senior managers impose production constraints and other financial constraints to curtail production of some of the proposed sales force demand forecasts. To resolve the differences, marketing representatives and their counterparts from production, finance, and accounting usually hold many meetings prior to arriving at an agreed production plan. For most cases, the North American production plan was made under the assumption that North American production would cover the region’s demand.

MONSANTO PRODUCT DEVELOPMENT STRATEGY FOR HYBRID SEEEDS

As in many other industries, Monsanto is witnessing shorter and shorter product life cycles for its hybrid seeds. Short product life cycles increase the frequency of product rollovers, the introduction of new hybrids, and the eventual displacement of old ones. However, what makes the supply chain planning of new product introduction challenging is that new and old hybrids have overlapping life cycles, may substitute for each other, drive faster obsolescence of existing hybrids, and complicate the demand forecasting and inventory management of the whole hybrid portfolio. At any point in time, the Monsanto hybrid seed corn product portfolio contains well over 200 products (currently there are 238 products). The typical product life cycle (PLC) is 4-5 years. To maintain a steady flow of products in the portfolio over the PLC, around 20% of new products have to be introduced every year (the actual percentage of new products has varied in the 15-25% range the last few years). Developing new hybrid seed corn products in most of the cases implies altering the genetic structure of a plant by physically inserting genes with desired characteristics. This means inserting traits into plants to give the plant specific characteristics, such as protection from environmental factors in certain environments or increased yield for certain farm fields. Trait integration is a risk-fraught process that involves introducing a new gene into a conventional variety to produce an improved or novel variant of that variety. Unfortunately, the performance data on the pre-integrated variety is not adequate information to predict the post-integrated variant’s performance. There might be multiple dimensions of performance, but to keep things simple this case study will only focus on the yield performance of the variant in the

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field in harvested bushels per acre (bu/ac). After trait integration, the seed corn product is almost ready for commercial launch, but there is substantial uncertainty regarding its actual field performance. Performance risk of introduced products in different farm environments is a major factor contributing to the inability to accurately forecast the seed corn product demand. Given the biological limitations of corn seed production (only 1-2 planting periods per year), it is extremely difficult to accurately map all possible environmental interactions (weather, soil conditions, insects, etc.) back to the genetic makeup of a given hybrid. There is a limitation on the number of experiments that can be run, further complicated by management time and financial resources. As a result, it is hard to know for what exact environment the proposed hybrid will perform the best, or at least better than existing products. Hypothetically, Monsanto is considering, after careful market assessment of existing gaps in meeting market needs, the introduction of hybrid seed corn among five candidate new seeds: NH1, NH2, NH3, NH4 and NH5. Based on available time and possible experimentation opportunities, the company has some average performance data (yield in bu/avg). The actual performance of any of the above hybrids is heavily influenced by environmental risk of the actual environment in which they will be used. Typical environmental risks are:

A. Biotic – insects, weeds, disease agents, etc. B. Abiotic – either weather dependent (rain, wind, sun, hail, etc.) or agronomic (fertilizer

used, plant population, row spacing, crop protection, etc.) Each one of the above environmental risks heavily interacts with the others, thus seriously limiting the availability of accurate data of performance of any hybrid product during the pre-commercialization phases. All the company has in this phase is average performance data from a limited number of experiments and a range of possible outcomes in the actual field. Difficulties of accurately predicting the performance of a product being considered for commercialization are compounded when it comes time to associate a value/price for that product. This is further compounded by the fact that the value of the expected performance/yield increase will be at least 18 months into the future. After the selection process, a given set of products are planned for the commercial launch. The product advancement and planning process requires the following information and actions:

1. Forecast the desired volume needed

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2. Begin developing the supply (plant what was forecasted) 3. Assign a price for the product

From our new product development team, we have gathered their best estimates on the above

hybrids, and they are presented in Exhibit 8. For the purposes of planning the product portfolio of fiscal year 2018/19, we know available inventories for seed sales of 2018 and are forecasting sales for 2019 (with forecasts already in place for 2018). Even though the development team has suggested a price point, the final pricing decision is still open. The new product development and production planning timeline will look as follows:

18 Month Lead Time FY 17/18 FY 18/19

1. Select new class of products Known production of new class 2. 2019 forecast of mix (new and old) Known value of new class 3. Field data on demand Farmer demand of new class 4. Market development trialing Marketing of new class 5. Price new class Substitution of new class

In an effort to start selling as early as possible in any season, Monsanto plants its products well before it knows the actual field performance data and the actual availability of supply. After the company has decided what will be planted, but before receiving the final marketing trials performance data, Monsanto announces prices of all products, new and old. As a result of the practice, forecasted demand data used for planting purposes is treated as independent of price, and the forecasted volume is based on highly variable performance data. Furthermore, at the point that demand forecast is developed, there is limited information on new competitor products and their performance data. Due to the long lead times and the need to manage the overall product portfolio, Monsanto attempts to generate demand forecasts for the entire PLC for each new product, often with an optimistic and pessimistic scenario serving as envelopes of the possible demand realizations (see Exhibit 9). It is expected for any new product introduced that its sales and effects on the existing product line will be projected in at least two extreme scenarios (optimistic [90th percentile of sales, and the product having an extra year in its life cycle] or pessimistic [30th percentile of sales, and with one less year in its life cycle]) on top of their “expected/ most likely” scenario over the whole life cycle. Overall life cycle profitability and return on investment (ROI) criteria are applied to select new products. All of the information on the existing products that will be affected by the introduction of the new hybrids (NH1 to NH5) is summarized in Exhibit 10. Pricing of the new products is a major and risky undertaking in the early stages of the PLC. The implications of overpricing and underpricing a product are significant. Underpricing a product that performs in the field much better than its expected average performance based on early trials will result in strong demand that the supply chain may fail to serve. On the other

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hand, overpricing a product could easily lead to failure of the product launch. It is common for 15-20% of new products launched to fail to meet expected value targets and result in very low acceptance by farmers and low overall demand. Exhibit 11 offers a simple explanation of the pricing approach used for some of the new products. From earlier products it is rather easy to estimate marginal cost for new products. However, what is challenging to estimate is the value generated for farmers using it (Exhibit 12 explains the usual estimation approach for farmer value). But assuming such an estimate has been made, based on a planned planted seed quantity, and using sales force and dealer inputs, Monsanto converges to a target price at which it expects to sell the targeted quantity. In some cases, the target price is backtracked from financial targets on gross profits for the new product. Such targets, combined with limitations on available acreage and inventoried quantity of parent seeds to produce any given hybrid, end up determining targeted quantities and prices for the new products. The estimation of a full demand curve for the new products at the time of launch is a theoretical notion of implausible applicability in the low accuracy performance and demand forecast environment for hybrid products. The presence of aggressive competition introducing many new products to the same application space, and with limited information on the competitor’s product performance and prices, make the demand curve estimation and optimized pricing of new products an extremely challenging task. Exhibit 12 attempts to explain the uncertainties behind estimating the value generated by new products with a major risk factor: their actual yield performance as compared to their average estimated performances on post-trait integration data. In the provided example in the exhibit, the new hybrid (NH-A) is an improved variant for hybrid (EH-B), an application that farmers currently use. The effect is to estimate the expected value for farmers per bushel of seed corn used when deciding to plant hybrid A versus hybrid B. According to the examples, not only does the hybrid improve yields by 10 bushels on average, but it also saves cost or increases prices for the farmer by $4 per bushel, for a total of $40 of additional value per planted acre. Monsanto usually targets to appropriate 1/3 of this generated value, while leaving 2/3 to the farmers and other stakeholders in the sustainable agriculture supply chain. In their example, this will imply pricing $13 per unit higher than hybrid B. According to past data, Monsanto fails to accurately estimate this “win-win” value share target 15-20% of the time, with the inaccuracy driven by both field performance of the product and market acceptance of it.

EPILOGUE Jane and Mark were congregating around the coffee pot in the common area outside the conference room. It was the last week of October and Halloween was coming up soon. Making it an extra scary week was the looming deadline for submitting the final task force recommendation on the product portfolio and seed production plan assignment. Chad and

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Cheryl had already outlined the questions they felt had to be answered in the report, using their precious experiences from similar assignments:

1. How many new hybrids should be considered for launch? How do we decide which of the new hybrids to launch when we take into account their life cycle evolution?

2. How do we decide on proposed prices for the new hybrids? What factors should such a decision be based on?

3. How many of the existing products should be produced in 2018? And looking over the next few years, which of the existing products should be phased out, and when?

4. What is the recommended product portfolio for fiscal year 2018/19 and what quantity should be produced of each product? Constraints to be considered are that the product portfolio should support current market share, capture a reasonable slice of an assumed 10% future market growth rate, and the recommended production plan cannot exceed the current production budget by more than 10%.

5. Outline a decision framework on answering question 4, with a corresponding spreadsheet tool to support such a decision. Apply your decision framework and support tool to give specific answers:

5.1 First, for the Canola decision situation Cheryl originally presented (see pages 2 and 3);

5.2 Second, for the actual assigned task on the corn seed portfolio problem the team is facing in October 2017.

6. How might Monsanto modify its supply chain, forecasting and new product strategy to improve its performance? What improvements would yield the greatest reduction in risk or improvement in financial performance?

Chad and Cheryl joined Jane and Mark at the water cooler in the corner of the room.

Chad was talking, as usual, about baseball. Cheryl was from Houston, and was excited about her Astros making it to the Series. Chad was from Los Angeles and loved his Dodgers. Chad made a passing remark about how even Justin Verlander of the Astros had proved to be “human” and hittable in the previous game. Jane commented that she felt like their Monsanto team was going into the bottom of the ninth inning, and the game was tied, but they would finally deliver even if it meant they will go eleven innings like last night’s game the Astros won in extra innings. Mark was baseball agnostic and loudly wondered if their spreadsheets and analyses thus far had what it would take to deliver under pressure in this make-or-break assignment. Jane remarked in baseball talk that “the closers of last night were not exactly what she was hoping for.” “It’s closing time, my friend,” Chad commented, “last out, and full count. Our last strike better be coming.” Cheryl, in a calming tone, advised, “Just one more day, one more game. Let’s do our best!”

16

Exhibit 1

MONSANTO COMPANY

2015 Financial Highlights

17

Exhibit 2

MONSANTO COMPANY

Monsanto’s Recent History

18

Exhibit 3

MONSANTO COMPANY

Overview of Monsanto’s Global Supply Chain

19

Exhibit 4

MONSANTO COMPANY

Evolution from Products to Solutions

20

Exhibit 5

MONSANTO COMPANY

Reorganized Supply Chain Structure and its Four Focus Areas

21

Exhibit 6

MONSANTO COMPANY

Projected Acres for Seed Growing in 2014-2015 for Monsanto and Competitors

Seed Brand Groups

2014

Projected Acres

2015

Projected Acres Asgrow/DEKALB* 23,825,948 22,844,866

Channel* 3,725,051 4,057,531

MON Regional Brands* 3,410,577 3,160,238

DUPONT PIONEER 32,440,517 30,977,222

Syngenta 5,248,075 4,962,617

Dow 5,049,039 4,246,640

All others 1,005,098 1,121,476

Corn States** 16,174,531 16,804,409

* Prominent Monsanto corn and soybean brand(s) ** Direct partners with Monsanto; they license both Germ and Trait from Monsanto’s pipeline

22

Exhibit 7

MONSANTO COMPANY

Safety Stocks for Hybrids

Sales Forecast and Production Variability: Significant Impact on Safety Stock

• Lower volume hybrids = High variability = High safety stock

• Higher volume hybrids = Low variability = Low safety stock

• Reducing variability reduces safety stock

0%

50%

100%

150%

200%

250%

300%

350%

Actu

al N

et S

ales

/ Net

Sal

es F

orec

ast

Net Sales Forecast Volume

Net Sales Forecast Accuracy vs. Net Sales Forecast Volume

0%

50%

100%

150%

200%

250%

300%

350%

400%

Actu

al P

rodu

ctio

n/Pr

oduc

tion

Plan

Production Plan Volume

Production Accuracy vs. Production Plan Volume

Each point represents data for a hybrid in a singleyear from history

100% : Actual units produced = Production Target100% : Forecast = Actual

23

Exhibit 8

MONSANTO COMPANY

New Products

New Hybrids Yield Advantage (Bu/Ac)

Gross Profit Y+1 Forecasts (bags)

NH-1 +4.5 145 110k NH-2 +2.0 155 220k NH-3 +0.6 170 60k NH-4 -- 140 390k NH-5 -1.5 165 85k

Notes:

a) Potential Competitor New Product Launch: PCNP-1, with yield advantage +2.5 (bu/ac) at an estimated price of $270

b) For all newly-launched products, it is expected they can substitute up to 40% of products in growth and up to 70% of products in mature stages. The risk of obsolescence for newly-launched products is less than 15%.

c) Yield advantage numbers are over a base benchmark, which is 170 bu/ac. (+) indicates increase and (-) indicates decrease.

24

Exhibit 9

MONSANTO COMPANY

PLC Forecast for Hybrid NH-1

• Forecast generated at Y-1 for Y0 and Y1, and potentially the whole PLC • Hybrid H5 is a substitute for current product EH-1, which is expected to be phased out by

Y2

SALES

TIME P1 is phased out

Optimistic Forecast

Pessimistic Forecast

Range of

Forecast Error

Y-1

Y0

Y1

Y2

Y3

Y4

Y5

25

Exhibit 105

MONSANTO COMPANY

Task Force Data for Production Planning Analysis of Existing 10 Seed Corn Hybrids serving similar applications as the new hybrids (NH-1 to NH-5), and the competing product (PCNP-1)

Product Lifecycle Y+1 Forecast

(bags)

Gross Profit ($)

Obsolescence Substitution Rate

Yield Advantage

(bu/ac) EH-1 Mature 55k 160 20% 70% --

EH-2 Decline 60k 95 50% 80% --

EH-3 Growth 250k 175 5% 40% +1

EH-4 Mature 140k 170 20% 70% --

EH-5 Mature 300k 160 20% 70% --

EH-6 New (1 year) 80k 110 5% < 10% +2.5

EH-7 New (1 year) 180k 155 5% < 10% +1.5

EH-8 Growth 500k 175 10% 40% +2

EH-9 Decline 150k 150 15% 40% --

EH-10 Growth 85k 120 5% 40% -.5

5 Estimated COGS for all products in the $70-90 range, with the most recent products at higher COGS numbers

26

Exhibit 11

MONSANTO COMPANY

Pricing and Gross Profit (GP) Consideration for Hybrids

Quantity

GPPrice/Unit

QNow

PNow

GPTargetPTarget

GPNow

QTarget

GP

Seed Demand Curve

(GPNow*QNow ) (GPTarget*QTarget)

Total Gross Profit

Marginal Cost

Value Price

Our Premium

Feasible Price Range

Com

petit

ive

Pres

sure

Mar

ketin

gEf

forts

Max

27

Exhibit 12

MONSANTO COMPANY

An Approach to Value Pricing of New Hybrids