Posted: May 25th, 2022
Operations and Quality Management
“Research forecasting and demand. As a regional manager of 27 Burger Queens, you are thinking about expanding the number of outlets in your area. What types of forecasts would you want to create in order to support your decision?”
Of the many types of forecasts that would need to be created to deliver accurate location analysis and expansion plans, the most critical of all are geo-economic analysis of potentially high growth areas that would not cannibalize the sales of existing Burger Queen restaurants. Location-based and impact assessment programs would need to be created for each of the specific locations being considered to ensure other restaurants’ sales and the potential business of other franchisees is not negatively impacted by the decision to expand (Leung, 2003). Location and impact assessments would need to take into account the composition of the target market in the immediate radius of the potential sites by socio-economic, demographic, psychographic and existing brand loyalties as well. All of these analyses could be completed using data dining and advanced analytics processes and procedures to ensure orthogonality of each location relative to another and consistency of selection criteria being used (Prewitt, 2007).
With econometric and customer segmentation data, both simple and gravitational methods for trade area analysis next need to be completed. Using a Geographic Information System (GIS) to integrate together data sets of population size, demographic composition, per capita incomes, discretionary income and an assessment of local competition . The manager for Burger Queen could have an excellent idea of where each store location needs to be based. Using GIS data to further differentiate by open retail locations could also give the manager greater insight into how best to geographically position the potential Burger Queen locations for greatest competitive advantage against the competition as well (Prewitt, 2007). In addition to accomplishing these tasks from an analytics standpoint, the GIS system could also tell the manager were competitors are the strongest, meaning they are areas that are unassailable in terms of market development (Leung, 2003). For example of there is a specific area of the city or region that is highly loyal to Subway or McDonald’s, the GIS systems could quickly show that data, indicating high concentrations of very brand-loyal customers. This would make launching a store in any of these locations extremely difficult.
Finally the location of the store needs to cater to how residents of that area prefer to purchase fast food. For many, the drive-thru window is their choice given the time savings and the opportunity to continue having conversations on their cell phones in their cars. For many quick service restaurants (QSR) the drive-thru window is responsible for the majority of revenue and consistently ranks high as a customer preferred way of purchasing (Meng, Tepanon, Uysal, 2008).
Interpolating a series of statistical datasets then using them to complete a multi-tiered regression analysis is one of the approaches the manager could take. Building a statistically valid model to interpolate overall market penetration by Standard Metropolitan Statistical Area (SMSA) is a first step, yet the model would need to be extrapolated across a broader range of Census data to ensure statistical veracity, statistical stability and accuracy (Prewitt, 2007a). Of the three dominant approaches for managing this level of statistical modeling, regression, the Gravity Model and Reilly’s Law are all considered comparable in terms of their statistical levels of accuracy and performance (Prewitt, 2007a).
The decision of which model to use is also predicated on the type of store, its target market, potential competitors and the potential for incremental new growth from the given region. In addition the geographic and demographic “shape” of the market can be modeled statistically and simulated through the use of statistical programs SAS and SPSS (Prewitt, 2007a). The reliance on purely demographic data however has shown to have several problems. Using psychographic, customer loyalty and repurchase data can deliver exponentially greater increases in accuracy and reliability of statistical modeling however in SMSA regions (Prewitt, 2007a).
“Research inventory management. Explain, based on your research, how planning inventory is both an art and a science. In addition, suppose you have been given the task of reducing inventory in your company, without negatively impacting customer service. What actions might you be able to take to accomplish this task?”
The costliest and most volatile resource for any business is the inventory it carries and attempts to sell through its direct and indirect sales. For high technology manufacturers, the inventory they carry can age within weeks, rending millions of dollars in obsolete products in a matter of months. One preemptive move by a competitor can lead to entire warehouses of product being completely unusable in the electronic components industry for example. These industry examples are provided to illustrate just how volatile and capricious the factors are that impact inventory, with customer demands and preferences being the most disruptive of all due to imperfect forecasting techniques (Kurawarwala, Hirofumi, 1996).
Given the lack of precision surrounding inventory management, it is very much both an art and a science. The science of supply chain collaboration and forecasting, demand forecasting and pricing (Kurawarwala, Hirofumi, 1996) and the art of staying in step with new, disruptive technologies (Giannoccaro, Pontrandolfo, 2002) make inventory management very difficult to optimize to perfection. Instead, many companies concentrate on seeking to minimize cost, process and time drains, in reality many must settle for a constraint-based approach to excelling at a handful of metrics and not all of them at once. Optimizing to a specific series of constraints for managing inventory, including inventory turns, inventory value measured in sunk costs and potential sales, and ability of a company to support advanced functions including Vendor Managed Inventory (VMI) differentiate companies within industries and across the spectrum of inventory management best practices (Giannoccaro, Pontrandolfo, 2002).
In order to reduce inventory without impacting customer service, several strategies can be used. If given this challenge within a manufacturer, the first strategy would be to work on perfecting demand management and sales forecasting to trim back excess inventory and reduce excess inventory. There is always room for improvement in sales and demand forecasting, especially if there are resellers and an indirect channel involved. Putting in a real-time forecasting and demand management system could trim up to 30% of inventory management carrying costs within a year or less (Chung, Willemain, OKeefe, 2000). Second, the focus would shift to better managing existing inventory practices and offloading carrying costs and risks by using Vendor Managed Inventory (VMI) techniques and programs (Kurawarwala, Hirofumi, 1996). Using this approach to trim costs would be highly effective in that inventory would only be brought in-house when needed for fulfillment or production. Third, the use of supply chain analytics and metrics to measure how continual process improvements can be made. With all of these strategies, in-warehouse efficiency could be increased in addition to responsive to each selling and service channel.
Second Student — 200 Words
Inventory management is by far the most challenging aspect of running a manufacturing enterprise. The use of technologies to streamline and augment processes throughout distribution centers continues to shift away from manual, bar code driven approaches to electronic technology (Mathaba, Dlodlo, Smith, Adigun, 2011). Inventory management is for the most part an art where experience can be supplement with technologies including Radio Frequency identification (RFID) that can track any item, at any location in a warehouse, on a 24/7 basis (Mathaba, Dlodlo, Smith, Adigun, 2011). Managing inventories to be more responsive to customer needs starts by setting the best possible expectations based on the current process performance, followed by a continual refining of how well connections are made and sustained with selling organizations. With a strong alliance and sharing of communication, there can be a better level of performance achieved with the many tasks of managing inventories. The costs of managing inventory can also be minimized by having a more accurate information flow between every member of a company’s ecosystem (Mathaba, Dlodlo, Smith, Adigun, 2011). For many companies, they seek out analytics and business intelligence to drive out the uncertainty of managing inventory, making the responsiveness to customers a top priority.
Third Student — 200 Words
The quantification of inventory management is more attuned to demand management best practices, less on internal cost and pricing analysis (Wang, Yan, 2009). The balancing of the unquantifiable and highly quantified and monetized aspects of inventory management will always be at odds each other, hence the difficulty in reducing inventory management to a series of equations. Yet this is what many companies continually aspire to, despite the unquantifiable aspects of tacit and implicit knowedlge that is part of running any inventory management program. To optimize the level of inventory management from a customers service perspective, first realistic expectations need to be defined. Once these expectations have been defined, the tighter synchronization and coordination at the product or program level within an enterprise needs to take place in order for inventory to match customer demand. To reduce inventory management costs and errors, enterprise must form a strong, galvanizing connection with suppliers, buyers, internal production, and customers most of all. In conclusion, inventory management will always be part art and science, as tacit & implicit knowledge is needed to provide greater insight into the analysis generated fro enterprise software applications specifically designed to streamline inventory management. The inherent unquantifiable aspects of demand management will also make inventory management a continually difficult, complex problem enterprises must confront daily to stay profitable and grow.
Forecasting and Demand References
Leung, S. (2003, Oct 01). Where’s the beef? A glutted market leaves food chains hungry for sites; finding spots for new outlets takes heaps of research and an eye for details; hint: Move next to Wal-Mart. Wall Street Journal, pp. a.1-a.1.
Meng, F., Tepanon, Y., & Uysal, M. (2008). Measuring tourist satisfaction by attribute and motivation: The case of a nature-based resort. Journal of Vacation Marketing, 14(1), 41-56.
Prewitt, M. (2007). Customer data mining aids in quests for lucrative new sites. Nations Restaurant News, 41(18), 1-1,53. http://nrn.com/article/customer-data-mining-aids-quests-lucrative-new-sites
Prewitt, M. (2007a). Site analysis tools do new encroachment-aversion duty. Nations Restaurant News, 41(10), 1-1,6.
Inventory Management References
Chung, Q.B., Willemain, T.R., & OKeefe,, R.M. (2000). Influence of model management systems on decision making: Empirical evidence and implications. The Journal of the Operational Research Society, 51(8), 936-948.
Giannoccaro, I., & Pontrandolfo, P. (2002). Inventory management in supply chains: A reinforcement learning approach. International Journal of Production Economics, 78(2), 153-161. Link: http://ideas.repec.org/a/eee/proeco/v78y2002i2p153-161.html
Kurawarwala, a.A., & Hirofumi, M. (1996). Forecasting and inventory management of short life-cycle products. Operations Research, 44(1), 131-131.
Mathaba, S., Dlodlo, N., Smith, a., & Adigun, M. (2011). The use of RFID and web 2.0 technologies to improve inventory management in south african enterprises. Electronic Journal of Information Systems Evaluation, 14(2), 228-241. Link:
Wang, H., & Yan, H. (2009). Inventory management for customers with alternative lead times. Production and Operations Management, 18(6), 705-720.
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