John Deere’s Complexity Costs

John-Deere constitutes a brand name for Deere & Company which is an American organization that manufactures construction, forestry, and agricultural equipment. The company also manufactures drivetrains, diesel engines that drive heavy machinery, and lawn mowing equipment (Saarinen, 2017). Furthermore, the company also offers financial services and other associated activities. John-Deere is a worldwide company and is characterized by three dissimilar segments. The segments comprise of: financial services, construction and forestry equipment, and agriculture and turf equipment (Drohomeretski et al. 2012). The construction and forestry equipment and the agricultural and turf equipment segments major in the sale to residential and commercial consumers. From these two segments, the company has emerged among the largest manufacturers of equipment globally. John-Deere leads in the provision of unconventional products and services to the consumers whose work relates to land from harvesting, to building, to cultivating or enriching in an effort to feed the ever-increasing, worldwide consumer appetite.

In the past few years, John-Deere has experienced a drop in the total sales which has been estimated at 36 per cent (Drohomeretski et al. 2012). As a result, the company has experienced an incessant debility in the total revenue. The main apparent issue causing the reduction in proceeds being the poor farming society. As such, according to Drohomeretski et al. (2012), the drop in revenues has had a toll on the agriculture and turf segment of the company. In this context, the current paper evaluates the current business operations in the agriculture and turf segment of the company to identify gaps of performance in relation to production, costs, and the delivery of products. Based on the evaluation, a customer migration model is described as a means of benefiting product line optimization and the means of mitigating the performance gap created by complexity costs through the described technology.

Evaluation of Current Business Operations

The Agriculture and Turf Segment Evaluation

The agriculture segment of the John-Deere Corporation manufactures and distributes a complete line of equipment and associated parts which include small, medium and large utility tractors, combines, loaders, sugar cane and cotton harvesters, corn pickers together with application and seeding machinery (Moran, 2017). On the other hand, the turf segment manufactures and distributes utility and turf machinery including push and riding lawn care equipment, golf course equipment, and vehicles as well as an extensive line of related value-added products comprising of cohesive agricultural managing technology systems together with other external power products. As part of this segment, the company has implemented a global operating model to assist in the growth of the organization as wells as increasing the company’s competitive edge in the market. The model separates the target markets into four distinct customer categories that are specifically tailored to develop an understanding with the consumers resulting in client service that is typical of the company (Mark, Lemon, & Vandenbosch, 2014). Furthermore, the model includes a separation of the company’s equipment operations into five distinct categories which include crop harvesting, turf and utility, hay and forage, crop care, and tractors. Thus, the agriculture and turf segment provides extra products and services as a way of achieving better customer satisfaction.

Considering the equipment manufactured under the agriculture and turf segment, and the company being a main player in the machinery market, it is apparent that Deere sustains multiple production lines. Each of the production lines may contain a number of disparate product variants. According to Deisinger et al (2000), variants are developed through the selection for every feature available on an equipment – for instance, the type of engine, axle, or transmission – and one of the several potential options – like 200 or 300 engine horsepower. However, all options are not compatible while those that form a feasible combination are referred to as a configuration. Sustaining numerous configurations reduces the revenues by increasing what has been termed complexity costs (Helpman, Melitz, & Rubinstein, 2016).  Such costs, are beyond the costs of carrying inventory for every configuration, capture elements such reduced efficiency in manufacturing, recurrent line changeovers, and the general overhead resulting from documentation maintenance and configuration support. Yunes et al (2007) define complexity costs as the expenses at an organization and its accompanying suppliers that are indirect and result from variation in components. In the case of John-Deere, complexity costs include the costs of documenting, designing, and testing component variants. As such, complexity costs constitute the performance gap in relation to costs, production, and delivery of John-Deere products.

John-Deere’s Complexity Cost Technologic Intervention

Reduction of complexity in the product line constitutes a common objective among corporations. For instance, a company such as Unilever has employed a product logic structure aimed at simplifying its worldwide home and personal product collection (Buckley & Casson, 2016). Often, the drive behind product line reduction is mainly the reduction of costs. Product line complication has long been tied to an increase in costs (Buckley & Casson, 2016). Therefore, in this section, operational and marketing methodology and technologic tools are described as means of reducing John-Deere’s complexity costs in the agriculture and turf segment through a focus on product line configurations as well as sustaining high levels of customer service and thus increasing revenue. One relevant intervention to the complexity cost is an algorithm described by Yunes et al (2007) whose primary element is the model of customer migration. The algorithm quantifies the behaviour of the company’s consumers in the context that a customer may require a particular configuration. However, if the customer’s preferred configuration is not available, he/she may migrate to an alternative configuration that is almost similar to the first choice (Fugate, Mentzer, & Stank, 2010). The customer migration model requires customer segmentation, actual sales, and part-price utilities from the company to model each consumer and individually identifying a group of suitable configurations that are listed in a descending preference order forming the migration list. When the configuration at the top of the customer’s list is not available, he/she may opt to buy the next configuration available in the list (Fugate, Mentzer, & Stank, 2010). Furthermore, if all the configurations are not available, the customer may defect to competitors.

Employing the migration list of each customer, along with profits and expenses for all possible configurations a mixed-integer program (MIP) may be developed to maximize John-Deere’s revenues with a particular product line (Fugate, Mentzer, & Stank, 2010). The application of such an algorithm provides an overall methodology for determining the least profitable configurations that may be candidates for elimination; the recommendations on the means through which John-Deere can focus on particular profitable product lines; and the identification of the advanced drivers of efficiency in product lines.

Overview of Solution

The proposed intervention to the complexity costs faced by the agriculture and turf segment comprises three major steps. The first step includes building and costing out feasible configurations, the second includes the development of a customer migration list, and the third optimization of product lines by means of a MIP (Fugate, Mentzer, & Stank, 2010). Before the selection of the configurations to be included in a particular product line, it is important to understand the configurations that can be feasibly developed as well as their accompanying unit expenses and revenues. Producing such data is not an easy task considering the fact that John-Deere’s machines may have more than twenty features and a number of options within every feature (Yunes et al., 2007). Ideally, this could develop a myriad of potential configurations. However, in practice numerous mishmashes of options may not be physically practical – for instance, a particular transmission may not be used with a particular axle. Subsequently, each product line possesses a distinctive set of rules for a configuration that dictate the manner in which the options of a line may be combined (Fugate, Mentzer, & Stank, 2010). When the price and the total expenses are known for a particular configuration, it is then possible to compute its contribution to the company’s profits.

 To create a cost out and establish the profits emanating from all the feasible configurations a programming language can be employed. According to Yunes et al (2007), for such a task, a programming language with an intrinsic expressive ability and quick search mechanism is vital. A good example of such includes a constraint-programming (CP) language (Grossi, Romei, & Turini, 2017). Following the addition of all the essential constraints to the CP algorithm, the model should provide all practicable configurations together with their prices. In modelling the behaviour of the customers, a representative construct of how consumers assess different configuration options that resonates with the MIP formulation is needed (Yunes et al., 2007). To achieve this, a model of a customer migration list can be employed which may be considered an individual classification of the configurations by the consumer. Therefore, based on the company’s marketing information, a customer migration list can be developed. After the generation of every feasible configuration, or the solution of the complexity cost problem, and the calculation of the customer migration list, the outcomes are combined in a MIP with the aim of achieving the highest profits possible.

To accomplish the aforementioned model, it is supposed that if customers are provided with the whole selection of John-Deere agriculture and turf products, they would form a mental list ordered according to their preference. Such preference would be based on the distinct options possessed by each configuration. If money is not a determining factor for the customer, then he/she would buy the configuration occupying the top of their mental list if it is available. If the configuration is too expensive or unavailable then he/she would move to the next in the list up to the point where he/she finds an affordable and available equipment. The model also implies that in an instance where too many of the customer’s choices are either not available or surpass their budget, he/she would leave John-Deere without purchasing (Kerzner & Kerzner, 2017). As such, this idea constitutes the construct in capturing the behavior of the customers.

Solution Implementation

To implement the customer migration model, a cross-functional group of workers at the company responsible for its implementation may be formed. Such may include representative members from the company’s corporate analysis team, product marketing team, the order-fulfilment team, dealer council, and sales leadership. Taking insights from the results of the model, the representative may make decisions to encourage customers to buy from the company. Such decisions may include providing the customers with discounts to steer them in the direction of reduced configuration sets. Reduced configuration sets may be considered the core of production lines. Although offering greater discounts to customers will increase migration, it will lead to an increase in costs (Yunes et al., 2007). Therefore, there should be a tradeoff to regulate the amount of effort required in customer inducement to initiate migration. Further, specific targets for migration need to be established for every two lines of production for the number of consumers migrating together with the number of discounts to be provided.

Conclusion

Many of the problems faced by John-Deere relate to product line optimization. Despite the fact that a lot of literature covers product line optimization, industrial applications remain scarce. One of the main opportunity established in the evaluation of John-Deere’s agriculture and turf segment is that of complexity costs. As an intervention, a customer migration model has been proposed to establish feasible configurations along with their costs and profits. The proposed model can be applied in solving the disparate variants of the corporation’s basic model. As a result, the model helps in the identification of the configurations constituting the fundamentals of any relevant solution necessary in developing optimum portfolios.  Furthermore, the model provides insights into the issue of optimization of product lines particularly in relation to the effects of various choices of modelling for consumer heterogeneity and flexibility. Through approximation of the benefits accruing from the flexibility of the customers, the model helps in steering the consumers in the direction of smaller sets of configuration increasing profits as a result.

References

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