Sales and Distribution Management - Assignment Example

Introduction Christmas is the busiest season on calendar. There is no doubt that most importers have contracted for the distribution of their goods several weeks earlier. In fact, it can even be assumed that distributors across the UK are stretched thin in their efforts to fulfil their existing contractual obligations. The tasks of ensuring that our sales team sell our cargo to wholesalers/retailers and that we find a distributor who will deliver our goods to the said retailers/wholesalers in time for Christmas is going to be a difficult undertaking. It is, however, not an impossible undertaking. To solve the solve both the sales and distribution problem the company finds itself confronted with, it is important to objectively outline the situation. The situation, as it currently stands, is that our company had contracted with a sales/distribution company to both sell and distribute 3,000 tones of our toys across the United Kingdom in time for Christmas. The goods have been loaded onto the Emma Maersk and will arrive in the United Kingdom on the due date. The problem lies in the sales and distribution of the unloaded cargo to stores across the UK, given that the company which had originally been contracted to handle this process has gone out of business. As we have exactly three weeks to resolve this problem, the imperatives of immediately deciding upon a model for the effective and efficient sales and distribution of the unloaded cargo are inarguable. There are two steps to the resolution of the above-described problem. The first step involves motivating and managing a sales team to sell the goods within the specified time-frame and the second involves distributing the goods within that same time-frame. Our ability to do satisfy the first depends on our understanding of team behaviour and the ways and means by which to motivate sales teams. As for the second, the resolution of the distribution problem lies in the implementation of a modified version of the Just-In-Time paradigm. 2 Sales Team Management Models Researchers who study general team characteristics often examine and reference the models of Gladstein (1984), Hackman (1987), and Campion et al. (1996). These three key models are discussed below. 2.1 Gladstein's model Gladstein's (1984) model categorized the variables and labeled them as either input, process, or output (Figure 1). Gladstein defines inputs as contributions from individual, group, and organization for group effectiveness. Gladstein categorized the inputs into two levels: group and organizational. Process refers to the activities of decision-making and output refers to the outcomes of the team activities. Group task moderates the relationship between group process and group effectiveness. Group level inputs and organizational level inputs directly affect group effectiveness. Both levels of inputs indirectly affect the group process. Gladstein's model defines team effectiveness based on the performance of the team and the satisfaction of the team members. Figure 1 General Model of Group Behavior: Constructs and Measured Variables. From "Groups in context: a model of task group effectiveness," by D. Gladstein, 1984, Administrative Science Quarterly, 29, p. 502. 2.2 Hackman's model Hackman (1987) created a practical framework for team studies (Figure 2). Hackman's model (1987), like Gladstein's model (1984) uses an "input-process-output" framework for analyzing group behavior and performance. Hackman's model consists of six major variables: organizational context, group design, group synergy, process, group task, and group effectiveness. In Hackman's model, organizational context, refers to the reward system, the education system, and the information system, all of which support the work of the team. Organizational context, along with group design and group synergy, influences how well team members are able to apply their skill and knowledge to the team task. The model defines group synergy as interactions between members, which increases group progress and decreases progress losses (Hackman, 1987). These variables, along with group tasks, determine group effectiveness. In Hackman's model, the significance and variety of the group tasks are important. In addition, the appropriate skills of the team members are important to the design of the work team (Hackman, 1987). In his model, task output (the ability of team members to work together and the satisfaction of those team members) contributes to team effectiveness. Figure 2. An Overview of the Normative Model of Group Effectiveness. From "The Design of Work Teams," by R. Hackman, 1987, In Jay W. Lorsch (Ed.), Handbook of Organizational Behavior, 331. Englewood Cliffs: Prentice Hall. 2.3 Campion, Medsker, and Higgs' model Campion et al. (1993) developed a model (Figure 3) of team characteristics and team effectiveness. Their model is based on the studies of Gladstein (1984); Hackman (1987); and Guzzo and Shea (1992). Guzzo and Shea (1992) reviewed previous studies regarding task performance of teams in organizations, according to several schools of thought: socio-technical theory; interaction process; group development; group composition and goals; contextual influences on performance; and inter-group relations. Guzzo and Shea (1992) reviewed previous studies, which focused on conceptual and theoretical paradigms. Guzzo and Shea (1992) developed a theoretical prediction for the correlations between group variables and group performance. Campion, Medsker, and Higgs (1993) developed five common themes, all of which relate to team effectiveness. The five themes in their model were: job design, interdependence, composition, context, and process. Each team theme is associated with a unique set of team characteristics. The authors also identified team effectiveness criteria. These criteria are team productivity, employee satisfaction, and manager judgments of effectiveness (Campion et al., 1993). Campion et al. (1996) replicated and expanded on their previous study Campion et al. (1993). Their 1996 study eliminated two team characteristics (task identity and preference for group work), based on their 1993 study results. The two variables were not highly-related to team effectiveness (Campion et al., 1993) and were not relevant to their second study (Campion et al., 1996). They measured team effectiveness based on manager judgment of team effectiveness, employee satisfaction, and productivity (performance appraisals). Figure 3. Themes and Characteristics Related to Workgroup Effectiveness. From "Relations between work team characteristics and effectiveness: A replication and extension," by Campion, Papper, & Medsker, 1996, Personnel Psychology, 49, p. 431. In each of the models, team characteristics notably overlap each other. However, the authors differ in emphases and definitions of team characteristics and effectiveness. The following table compares similarities and differences among the three models. This table summary explains how the models from Gladstein (1984) and Hackman (1987) related to the Campion et al.'s model (1993, 1996). As the table indicates, Campion et al.'s 1996 model strongly relates to the other two models (Gladstein, 1984 and Hackman, 1987). The three models share the same variable categories: group design, organizational context, process, and effectiveness. Within the group design criteria, tasks are common variables that appear in each of the three models. Within the effectiveness criteria, satisfaction also appeared in each model. 2.4 Model Selection As may be determined from the above review of the three team models, each has its unique advantages. Therefore, rather than select a particular model, we will manage our sales team and incentivise them through the utilization of the tools proposed by each of these models. Effectiveness and efficiency will be ensured through the precise articulation of this particular task's requirements and the means and ways through which they may be able to satisfy this task. Added to that, team goals will be clearly articulated, and team members will have a direct input in the articulation of both objectives and the strategy for their attainment. Involving team members in this process will ensure their engagement and involvement in the task at hand. Furthermore, satisfaction will be guaranteed through efficient, effective and constructive communication, on the one hand, and financial rewards, on the other. In other words, by borrowing from all of the models outlined in the preceding, the sales team can be efficiently and effectively managed towards the realization of the said goal. Assuming the success of the sales team, the second problem is the distribution and delivery of the goods to their various destinations. 3 Distribution and Delivery Just-in-time (JIT) production systems have zero inventory systems and no buffer. The processing time variation and machine breakdowns cause disturbance in the production line. Therefore, the system efficiency drops. Intermediate buffers increase the efficiency of line. Sarker and Fitzsimmons (1989) studied the effects of above variability on the performance of push and pull systems. Golhar and Sarker (1992) addressed the perception that participation in just-in-time (JIT) delivery system is economically disadvantageous for suppliers. In just-in-time (JIT) system, the supplier has to coordinate his production with the buyer's demand so as to maintain zero inventory, but, in reality the supplier ends up with carrying large inventories to deliver limited shipments. An iterative solution is proposed. Generalized total cost inventory model is a piecewise convex function. Jamal and Sarker (1993) extended this problem and estimated the batch size from the lower bound concept of the just-in-time (JIT) delivery amount. They developed an efficient algorithm to calculate the optimal or near optimal batch sizes for both manufacturing and raw material ordering policies. Sarker and Parija (1994) developed a general cost model based on Golhar and Sarker's (1992) problem which considered both supplier and buyer to determine optimal ordering policy for the raw material and manufacturing batch size to minimize the total cost. They also considered that, at the end of the delivery, a few finished goods are left over which is less than the shipment amount. They solved the problem in semi-closed form and found that total cost function is piecewise convex. Sarker and Khan (1999) proposed an ordering policy for raw materials to meet the requirements of a production facility that must deliver finished goods according to customers' demand at a fixed point of time. They considered: (a) a finite production rate environment using raw materials from outside supplier, (b) only the product lot sizing and their associated raw material supply quantities, (c) a supply policy where the product was delivered in equal quantities at a fixed interval, and, (d) the products were supplied after processing the entire lot and quality certification of the products. They evaluated relationships between production batch size, raw material quantity and delivery patterns. Few years later, Khan and Sarker (2002) developed another model for a manufacturing system which procures raw material from the suppliers in a lot and processes them as finished products. They estimated production batch sizes for a just-in-time (JIT) delivery system and incorporated a just-in-time (JIT) raw material supply system. Also, they assumed that the production system must deliver finished products demanded by outside buyers at fixed interval points in time. In view of all previous research, Golhar and Sarker (1992) studied an operational policy for a lean supply chain system consisting of a manufacturer, multiple suppliers and multiple buyers. They dealt with three interrelated problems in supply chain. They are (a) single supplier and single buyer with fixed delivery size, (b) multiple suppliers and multiple buyers with individual delivery schedule and (c) time dependent delivery quantity with trend demand. They formulated these problems as mixed integer, nonlinear programming problems with discrete, non-convex objective functions and constraints. Golhar and Sarker (1992) also developed a closed-form heuristic which provided near optimal solutions and tight lower bounds. Proceeding from the above stated, it is evident that the primary concept of a just-in-time (JIT) production system can be stated as 'producing and/or supplying the products only the right items in the right quantities at the right time.' Several manufacturing facilities, which previously carried large finished goods inventories to meet customers' demand, implemented the just-in-time (JIT) delivery system. Due to the implementation, the lot sizes are reduced and the frequency of delivery is scheduled for customers' demand. The direct impact of the just-in-time (JIT) system is to reduce inventory carrying costs by the customers. Conversely, the manufacturer should acquire accurate data of the customers' demand and maintain an optimum schedule to coordinate the distribution system. In order to coordinate the production with customers' demand in fixed time intervals, and to synchronize the ordering of raw materials with production schedules, both raw materials and finished items inventories should be maintained at an economic level to reduce the total cost of the system. The present scenario is reminiscent of the JIT model outlined in the preceding. The goods have been manufactured and their point of sale has been defined. Similar to the JIT philosophy wherein the manufacture of good proceeds following demand and is limited by the size of the demand, the cargo has been manufactured and shipped to the UK in response to retailers' orders and demands. The only question which needs to be settled here, therefore, is how to get the cargo from the port to the retailers. This problem can best be approached through the exploitation of the JIT delivery model. As per that model, depending on the size of the shipment and the number of retailers to which it must be delivered, versus the time-frame within which delivery must be concluded, either one or several distributors are chosen. Within the context of a lean (JIT) supply chain, two issues must be decided upon at this point, with these decisions effectively determining whether or not the goods will reach the retailers on the agreed upon date. The first is the number of suppliers and the second is selection of the distributor(s). The strategy for approaching both of these issues will be reviewed in the following sections. 3.1 Distributor Selection The question of distributor selection bears upon both the identity of the distributor and the number of distributors ultimately selected. As pertains to distributor selection, the academic literature on the topic indicates that the associate decision-making process is both complex and complicated because multiple criteria must often be considered (Weber et al., 1994). Agreeing, Dickson (1966) identified 23 distributor selection criteria, as based on both a review of the theoretical literature and the results of a questionnaire sent to sales and marketing managers. These 23 criteria primarily relate to the distributor's reputation and work record, as determined through the number and size of contracts successfully fulfilled every year, versus the number and size of the unsuccessful contracts; the number of breakdowns the distributor's delivery equipment suffers per year and their equipment maintenance record (Dickson, 1966). The criteria highlighted in the above can prove highly effective in the selection of a distributor(s) who will deliver the cargo to the retailers on time. A review of the number of contracts the distributor successfully fulfils versus those which he fails to will serve as a very good indicator of the distributor's reliability. More importantly, a review of the size of the contracts which were successfully satisfied versus those which were not will guide us towards the determination of whether or not the distributor can handle the entire cargo and, if not, what percentage of the 3,000 tonnes he can successfully deliver on time. In other words, it is imperative that the criteria outlined in the above be used for distributor selection because, through its implementation, the company will maximise its ability to successfully resolve the current problem and ensure that the goods are delivered within three weeks. It may be argued, however, that implementation is unrealistic because the distributor selection process, as outlined in the above, is time-consuming. It necessitates that we, a company located in China, gather the relevant data on distributors located in the United Kingdom and analyse that data for the sole purpose of compiling a list of preferred distributors, following from which we will contact them to see whether or not we can arrive at an agreement. The process, in other words, is both complicated and time-consuming. This last can be used to support arguments against the expenditure of time and effort in the distributor selection process but it must not. The reason is simple. If we were not to carefully select distributors according to the criteria outlined in the above, not only may our goods not be delivered on time but we may find ourselves in the same situation we are currently in. One need only point to the fact that had we applied the said criteria and gathered the requisite information, we would not have contracted with a distributor who was on the verge of declaring bankruptcy. Our past experience, therefore, underscores the imperatives of distributor selection according to the said criteria, irrespective of the effort involved (although not of the time). 3.2 Distribution Model Our past experience combined with our current situation further determines the imperatives of contracting with more than one distributor. Pinedo (2002) argues that the optimal approach to on-scheduled JIT distribution and delivery is the division of the cargo amongst more than one distributor. The delivery and distribution cost may be higher but the benefits are much more so. Quite simply stated, contracting with on distributor places a company at the mercy of whatever circumstances that distributor may confront, whether employee strikes, equipment breakdown, or driver problems. Not only are these problems extremely common but any one of them impinges upon the ability of the distributor to satisfy his contractual obligations towards all of his customers (Pinedo, 2002). The commonality of these problems and the fact that any distributor (although to varying degrees) is vulnerable to them, underscores the imperatives of our managing the associate risk through the division of our cargo amongst more than one distributor. Division will, at the very least, ensure that part of our cargo is delivered, thereby offsetting the possibility of bankruptcy which we are currently trying to stave off. Vidal and Goetschalckx (1997) identify and discuss a second important benefit to the division of product delivery amongst more than one distributor. Within the context of Just-in-Time delivery where time is an integral factor, given that the manufacture, shipping and delivery of goods occurs in response to specified demand within a particular time-frame, simultaneous/parallel delivery is the preferred strategy for ensuring that the products in question reach retailers on time. As per this delivery model, the retailers/warehouses to which the products are to be delivered are grouped according to region and their geographic proximity to one another. Retailers/warehouses which fall within the same geographic perimeter, are assigned to one distributor and those which fall within another, are assigned to a second. Delivery and distribution then proceed simultaneously, significantly reducing the time required to deliver to various retailers is diverse locations (Vidal and Goetschalckx, 1997). In response to the above argued, one can very well counter with the statement that any sizeable distributor has the equipment and the capacity required to deliver to more than one location simultaneously. Kaminsky and Simchi-Levi (2003) maintain that the single distributor model is infinitely more cost-effective than the multiple-distributor model. That is true because the delivery of small lots is more expensive per unit than the delivery of large lots. However, should we select only one distributor we would not be reducing our risks. Added to that, given the season and the time constraints we currently face, the possibility of our finding one large distributor who would be able to deliver our 3,000 tonne cargo to different parts of the country and to different retailers, might be slim. Accordingly, as Johnson (2001) contends, within the context of large cargo which has to be delivered within a clearly limited, and relatively short, time-frame, it is preferable to select more than one small to medium-sized distributor. Nevertheless, even though the selection of more than one distributor appears preferable given our current situation, this decision must be founded upon a determination of which delivery and distribution model will best be able to affect a balance between cost and delivery and distribution timeliness. An informed decision must be based on knowledge of the literature on distribution problems and the optimal strategy for resolving them within the context of a model which balances between cost and timeliness. 4 Proposed Model/Problem Solution There is a huge body of literature on the production-distribution problems, models, networks, or systems. As pointed out in the survey by Chen (2004), many existing models study strategic or tactical levels of decisions, and very few have addressed integrated decisions at the detailed scheduling level (Vidal and Goetschalckx, 1997; Jayaraman and Pirkul, 2001; Shen et al., 2003). As per this literature, production and distribution are intrinsically related components of the same equation; some of this literature holds that the majority of resources should go into production, while others into distribution is of equal importance (Vidal and Goetschalckx, 1997; Jayaraman and Pirkul, 2001; Shen et al., 2003). Given that the literature which highlights the importance of delivery and distribution focuses on time-based performance and the importance of timely distribution and delivery scheduling, this is the literature that shall enter into the design of our solution/model. Sarmiento and Nagi (1999) maintain that distribution and delivery activities are as important as are production and manufacturing activities. Johnson (2001) holds that this is especially true in relation to toys (and perishable goods) which have to be distributed for sale during specified and limited seasons, such as Christmas. The value of the good, in this instance, toys, is immediately linked to the season, with the implication being that if they are not distributed in time for seasonal sales, the goods in question will be devalued and, indeed, would hardly be sold in anything near than the projected/estimated volumes (Johnson, 2001). Consequently, models which highlight the timeliness of delivery and distribution and recognize it to be as important as the manufacturing process are relevant to the case at hand. In determining the strategy for delivery and distribution, the literature proposes the imperatives of considering a number of factors. These are cost of delivery and distribution as a percentage of the total cost of the cargo; the cost of non-delivery or tardy delivery to the company; and the total size of the cargo versus the delivery capacity of any single distribution company (Vidal and Goetschalckx, 1997; Jayaraman and Pirkul, 2001; Shen et al., 2003). Proceeding with an emphasis on the fact that were delivery not to occur on time, the cargo be virtually worthless and our company could become bankrupt, it is more than evident that timely delivery is the primary objective here. Certainly, cost is an important factor but, needless to say, we are in a situation where we can afford to allow cost to cut into our projected profit margins, not only to save the company from bankruptcy but to secure the United Kingdom as a market for our toys. In order to ensure the satisfaction of the above-stated objective and resolve the problem at hand, we will begin by compiling a list of reputable small to medium sized distributors in the United Kingdom and collect the relevant data on each. Following the selection of the distributors who are most likely able to satisfy our urgent delivery and distribution requirements, most likely ones who are involved in JIT delivery, we will initiate contact and begin negotiations. In our negotiations, the sales team must realise two things. The first is that we are going to have to pay a higher than expected cost because of the timing and urgency and second that the future of our company is contingent upon the satisfactory fulfilment of our obligations towards our UK customers/retailers. Therefore, while cost is a factor it is not the primary decisive factor. Further to the above and in recollection of the earlier presented argument for the division of the cargo, it is necessary that we divide the delivery between more than one distributor. Doing so will contribute to our risk management strategy and maximise the chances for the successful delivery of at least part of our cargo, thereby staving of the potential of bankruptcy we are currently facing. In other words, as per the proposed solution, more than one distributor will be selected in accordance with the distributor selection criteria earlier outlined and the primary objective is not the assurance of our profit margin as much as it is the assurance of the delivery of our cargo to its various destinations. 5 Conclusion As is evidenced from the academic literature reviewed in the above, and the accompanying argument, all of sales, delivery and distribution within the three week period available to us is possible. Success is contingent upon our approaching the distribution problem from within the framework of a JIT model and applying the associate distributor selection criteria and on implementing team management models vis--vis the sales team. Following from that, and given the imperatives of our managing the associate risks, it is necessary that we divide delivery amongst more than one distributor. Indeed, should we follow the aforementioned and above argued strategy and realistically accept that we may have to cut into our projected profit margin, we can satisfy the delivery deadline. 6 References Campion, M., Medsker, G., & Higgs, A. (1993). Relations between work group characteristics and effectiveness: Implications for designing effective work groups. Personnel Psychology, 46, 823-850. Campion, M., Papper, E., & Medsker, G. (1996). Relations between work team characteristics and effectiveness: A replication and extension. Personnel Psychology, 49, 429-452. Dickson, G.W. (1966) An analysis of vendor selection systems and decisions.' Journal of Purchasing, 2(1), 5-17. Gladstein, D. (1984). Groups in context: A model of task group effectiveness. Administrative Science Quarterly, 29, 499-517. Golhar, D. Y. and Sarker, B. R. (1992) Economic manufacturing quantity in a just in time delivery system," International Journal of Production Research, 30(5), 961-972. 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