A review of marketing—operations interface models: From co-existence to coordination and collaboration International Journal of Production Economics, Christopher Tang. Download PDF. A short summary of this paper. A review of marketing—operations interface models: From co-existence to coordination and collaboration. Due to their inherent roles and responsibilities, coordination and collaborations between marketing and operations areas can be difficult in practice.
Also, by examining some missing gaps, we discuss some topics for potential future research. This research work is partially supported by the Edward Carter Endowment Fund.
However, when facing fierce global competition, firms need to develop and launch new products and services quickly. The marketing group can use various marketing mechanisms promotion and pricing to set or change customer expectations quickly, but the operations group may find it challenging to meet these expectations because its production plans i.
What to produce? How much to produce? To compete successful in a dynamic market, each firm needs to manage the conflict between marketing and operations. Up till the early 80s, most firms were operating according to functional areas, each of which focused on its own performance measures.
For example, marketing research was focusing on how pricing or promotion affects brand choice or sales e. The decline of sales in the manufacturing sectors in the 80s motivated practitioners and researchers to identify the underlying causes and develop effective competitive strategies in the late 80s. These innovative ideas have motivated firms to develop different strategies to differentiate their products and services.
Table 1 highlights the role of marketing and operations as well as different measures of differentiation. Use Marketing Use Operations Customer service level. Without a well defined marketing plan to establish the right expectation for the right customers, a well executive operations plan is insufficient Hill Without coordinating with the operations area, an excellent marketing plan for offering products and services that meet customer needs can result in disappointment. For example, Boeing promised to develop its new Dreamliner aircraft that creates excellent value.
However, without coordinating its marketing plan with its supply chain operations, Boeing is facing major delays Tang and Zimmerman Ultimately, marketing and operations coordination is essential for a firm to establish and deliver customer expectations.
By analyzing a stylized model, Hess and Lucas showed that a firm can improve its profit by allocating its resources between marketing research for new product and production planning. First, given the customer demand function, the retailer determines her optimal retail price and order quantity over time. The former article used empirical data to establish the linkages between marketing priorities price, quality, features, product variety, etc.
First, based on the survey response provided by executives, Hausman et al. Third, Kulp et al. This also explains why most companies are organized in this manner. Figure 1 depicts a traditional planning process between marketing and operations so that each area focuses on its own performance measure.
Because marketing does not have direct control of cost, the objective or performance measure for the marketing area is either market share or sales.
As marketing is focusing on revenue or demand generation and as operations is focusing cost or waste reduction, each functional area can make its own decisions in a disjoint manner. In general, a plan is usually developed through an iterative negotiation process among different functional groups, each of which has its own performance measures.
It is natural to expect that many firms now encourage marketing and operations groups to exchange information and consult each other when developing a coordinated plan. In general, a well coordinated marketing and operations plan would reduce the conflicts between marketing and operations. Fisher and Raman presented a model for coordinating the responsive supply operations and the dynamic market demand. Their model has been shown to be effective in making supply meet demand at Obermeyer Fisher et al.
To anticipate and respond to market dynamics better, a firm may need to go beyond coordination by having the marketing and operations groups to jointly develop a plan.
As articulated in Donohue , a joint performance measure between marketing and operations is needed to develop a collaborative plan. For example, Sogomonian and Tang presented a model for examining the benefit of integrating promotion and production plan.
To capture the promotional effect on demand found in the marketing literature Guadagni and Little , they assumed that the demand in each period is a decreasing function of the time elapsed since the last promotion.
By solving a deterministic dynamic program, they showed that a firm can increase his profit significantly by integrating its promotion and production plan in a collaborative manner. Essentially, to facilitate the collaboration between marketing and operations, one needs to change the planning process from the one depicted in Figure 1 to the one depicted in Figure 2. At the same time, it would allow the operations group to meet the customer expectations and deliver the values as promised.
Ultimately, by having both groups to develop a joint marketing and operations plan, the company should focus on maximizing its profit subject to meeting customer expectations and delivering the promised values. Although one may argue that a company can perform even better if the marketing and operations groups are merged into one so that they are completely integrated. Although this makes sense in theory, it may not be t practical because of different roles and responsibilities of these two groups.
In addition, complete integration of these groups would create cultural and personality conflicts due to different backgrounds, experiences, expertise, personality, and cultures Crittenden et al.
This may explain why few companies merge the marketing and operations into a single entity. For example, some models deal with the interactions between customer selection and the company internal capability, other models may examine the interactions between promotion and the company internal capability, and some models may investigate the interactions among product selection, competition, and the company internal capability.
The remainder of this paper is organized as follows. In the next section, we review briefly about some common demand models and some common measures for supply capabilities. We present some recent operations management models that incorporate certain strategic consumer behavior in Section 5.
In Section 6, we end this paper with a discussion of future research. As such, we apologize for any omission that is due to our negligence. As articulated in Lilien et al. For the sake of simplicity, let us consider a situation in which there are 2 competing products offered by the same firm or by 2 competing firms.
The reader is referred to Kok et al. Raju et al. Kok et al. Each consumer i derives utility Uij from purchasing product j, where Uij is a deterministic function that depends on various marketing and operations factors such as product attributes functionality, price, quality and service attributes convenience, delivery. The multiple attributes of each product is represented by a vector xj and each consumer i has her own ideal vector yi.
Consider the following situation. Both products have a single attribute: 3 These models are based on a deterministic utility function examined by Hotelling The reader is referred to Lancaster for an extensive review of location choice models.
The reader is referred to Anderson et al. Instead of a definitive purchasing decision imposed by the rational choice model, the choice probability in the multinomial logit model enables researchers to use historical purchasing behavior of each consumer to estimate the value of the parameters associated with the utility function and to estimate the demand for each product Guadagni and Little , Anderson et al.
More recently, Chong et al. By using panel data collected from 5 supermarkets, they showed how a category manager can use their model to reconfigure a brand in order to achieve a higher brand share. In additional to rational choice model, Su introduced the notion of consumer inertia that captures the!
However, the firm can expand its capacity as the demand increases over time. The reader is referred to Luss for a comprehensive review of capacity expansion models.
In addition, a firm may be able to acquire additional capacity using new technology that is more cost efficient Gaimon See Yano and Lee for a comprehensive review about product planning with yield uncertainty.
When producing multiple products or serving multiple classes of customers, the effective capacity of a process depends on the flexibility of the process. A completely flexible process such as flexible manufacturing system or a flexible product designed or produced using the postponement concept would enable a firm to change its production from one product to another without incurring significant setup time or setup cost.
The reader is referred to Buzacott and Yao and van Hoek for comprehensive reviews about models that examine the notion of flexible manufacturing and the concept of postponement, respectively. Given the production capacity, the actual physical output for meeting customer demand depends heavily on the production plan how much to produce?
The reader is referred to Mula et al. Also, depending on the switchover cost or time, a firm may decide to produce a batch of one product before switching over to produce a different product Graves Then in Section 4. As such, competing firms are under tremendous pressure to develop, produce and sell new products quickly. In Section 4. Finally, we examine the issue of joint product pricing and production planning in Section 4. Consider a manufacturer who produces and sells a single product at unit price p in a single market over a single selling season.
The unit product cost is c, the salvage value for each unit of unsold item at the end of the season is s, and the backorder cost for each unit of unmet demand is b. In the traditional newsvendor problem, the manufacturer needs to decide on the capacity Q that maximizes his expected profit. The reader is referred to Khouja for an extensive review of various extensions of the newsvendor problem developed between and In the operations research literature, the probability distribution of the product demand D is usually assumed to be given exogenously by the marketing department.
This basic intuition has motivated researchers to develop several customer or market selection models that can be described as follows. First, Taaffe et al. The market selection decision can be captured by a binary decision variable yi that equals 1 is market i is chosen and equals 0, otherwise.
Second, as discussed in Section 3. In addition, they extended their analysis to the case when the set of potential markets is partially known. Kalknaci and Whang extended the single period model developed by Taaffee et al. The reader is referred to Lee et al. In this setting, Donohue presented a queueing model that would enable the manufacturer to reduce the congestion in the plant by having the marketing to accept customer orders only with certain operating characteristics measured in terms of the mean and the variance of the service time.
In the same vein, Hall et al. This form of marketing campaign would certainly increase market share, but it is unclear if the service providers can keep their promises. Unless the firm incorporates the changes in customer demand when deciding on its service capacity, customer disappointment is likely to occur.
Consider a service facility that has N parallel service counters, each of which has a service rate Y. Each counter has its own waiting line and customers can switch from one line to another at any point in time. In this case, the firm failed to deliver its promise when the number of customers in the system exceeds NK.
By considering the case when customer arrivals follow a Poisson process and when the service time is exponentially distributed, So and Tang determined px K ; i. In the second model, So and Song considered the case in which the marketing department launches a campaign that guarantees the service time is less than t by charging each customer p.
The service rate is given as Y. We now describe some models that examined the issue of guaranteed customer delivery time under competition. First, So presented a model that deal with N competing service providers who compete in terms of price and guaranteed delivery time. In their model, the retail price is fixed market price , but each firm j selects his guaranteed delivery time tj. Li and Lee considered a different duopolistic model in which the retail prices and service rates of firm j and firm k are given as pj, Yj and pk, Yk, respectively.
Upon arrival, it is assumed that each arriving customer has information about the actual number of customers waiting at firm j and firm k. They show that the firm with a higher service rate always enjoys a price premium by charging a higher price.
The reader is referred to Upasani and Uzsoy for a comprehensive review of models that focused on delivery time competition. To compete in a dynamic marketplace, shortening the new product development cycle time can be a competitive weapon Clark and Fujimoto To obtain market growth, many firms introduce many new products frequently. The reader is referred to Krishnan and Ulrich for an excellent review about research work that focused on the product development planning process within a firm that ranges from product concept development, product design, to project management.
As more new products become available, many old products could become obsolete, and hence, they should be phased out. Consequently, we have witnessed shorter product life cycles in many industries such as personal computers, cellular phones, electronics, various types of toys, etc. Therefore, when managing new product development, a firm needs to examine the following strategic issues: 1 When should the firm launch its new product?
We now review models that deal with some of these strategic issues. Cohen et al. The planning process is divided into two phases: product development and marketing. The quality of the new product Q1 TD, TP and the new product development cost C TD, TP depend on two decision variables that captures the time or effort associated with two product development stages: 1 the time to design the new product TD; and 2 the time for prototyping and testing TP.
By treating the quality as the utility associated with each product, they used the attraction model as described in Section 3. Also, the above program captures the tradeoff between the profit associated with the old and the profit associated of the new product. As a follow on study, Cohen et al. The models developed by Cohen et al. Motivated by the competitive dynamics of two motion picture studios, Krider and Weinberg presented a competitive game model that is intended to examine the timing for each firm to introduce its movie in equilibrium.
When managing the product rollover process i. However, as articulated in Billington et al. Lim and Tang developed a model for determining the time at which the new product is introduced Tn as well as the time at which the old product is eliminated To over a planning horizon [0, T]. They used the exogenous demand model as described in Section 3. By formulating each strategy as an optimization problem, they determined the optimal timing for introducing new products and for phasing out old products.
In many instances, the online channel is an attractive option for manufacturers and retailers to market and sell their products due to its easy access and low entry cost. Selling through multiple channels can certain increase revenue, but it can increase cost as well.
However, there are occasions in which the online selling price is the same Huang and Swaminathan or slightly higher than the traditional channel Cattani et al. Cattani et al. The product is identical across channels, but customers expend different effort to purchase in each channel.
Using this demand function, they presented a Stackelberg game in which the manufacturer acts as the leader who sets the wholesale price and his own retail price for his own sales channel and the traditional retailer acts as the follower who sets her own retail price.
In the case where the manufacturer is committed to match the price set by the traditional retailer, they showed that both the manufacturer and the retailer would prefer pricing strategy 3. In contrast, if the effort in the two channels is relatively comparable, then as he sets prices in the direct channel, the manufacturer has a great incentive to undercut prices in the traditional channel. Motivated by different pricing strategies between the traditional channel and the online channel, Huang and Swaminathan developed a deterministic model to compare different pricing strategies for the case when a firm sells its products through two channels.
By using the exogenous demand model as described in Section 3. Clearly, the collaborative pricing strategy dominates other pricing strategies; however, under certain conditions, they showed that the optimal profit derived from the coordinated pricing strategy is close to the optimal profit obtained from the collaborative pricing strategy.
However, there is a recent trend that certain new products are sold through exclusive channels. For any given retail price under each arrangement, they used the exogenous demand model as described in Section 3. In addition, they identified conditions under which the manufacturer should sell its new products through an exclusive channel. By taking this additional utility into consideration, Chong et al. This result is consistent with earlier empirical studies conducted by Kekre and Srinivasan and Bayus and Putsis Besides higher market share, brands with broader product lines can charge a higher selling price.
The reader is referred to Ho and Tang and Ramdas for details. Despite the effort in extending product lines, many firms failed to improve their profits mainly because they did not account for various hidden costs additional production and administrative costs when planning their line extensions Quelch and Kenny To incorporate the issue of production capacity, production technology and product assorting and pricing issues, Yano and Dobson provided a review of deterministic product assortment problems that can be formulated as integer programming problems.
As companies extend their product lines, researchers developed models to examine the following questions: 1 What are the benefits and costs associated with product line extension? We now review some models that examine these questions.
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