Introduction
ACME Corporation is a multinational corporation that designs and sells voice assistant devices that are compatible to use in IoT devices. These devices are commonly used in the production of other larger goods. Therefore, the primary clients of the company are businesses and other manufacturers. The purpose of this project is to propose a supply chain solution that will increase the company’s responsiveness to customers in terms of faster delivery times. One possible solution is to switch to decentralized inventory storage and distribution. Regional distribution centers will have to be opened, but it is impossible to accomplish this task with currently available data.
The information on the U.S. population will be critical when making decisions about the exact locations of prospective regional distribution centers. The U.S. Census Bureau (2019) provides this data, and it is published online. The company’s ERP system also has information that is vital in the context of a new supply chain strategy. It contains detailed, although sometimes fragmented, data on sales, operating costs, current list of SKUs, and sales forecast (ACME Corporation, 2019). The primary objective of the Supply Chain Analysis team is to provide a set of recommendations that could be used to implement the new distribution model adequately.
The team will spend time to yield a correct interpretation of the ERP report, and build several supply chain models, and test their efficacy using the available sales data. The resilience of supply networks is critical, but it significantly impacts the operation costs (Purvis, Spall, Naim, & Spiegler, 2016). Therefore, the team should also consider the amount of expenditure that will be required to implement the new strategy. This report will cover the current sales data, analyze what regions and states have the most demand, and what data is needed to develop an adequate supply chain solution.
Management Information Systems Supporting Sales
Because the company will pursue to decentralize its inventory management and distribution model and spread its operation across many regional facilities, it is vital to understand where our products are mostly demanded. Such a strategy would allow the company to increase its responsiveness to the majority of its customers while keeping shipping costs at a low level. For example, if the company decides to open a distribution center in a region that accounts for 20% of the total sales, it will cover 20% of its clients with a single facility. Not only is it a cost-effective approach, but it also increases customer satisfaction levels.
Sales data shows the quantity of each product that should be delivered in order to meet the demand. Sales forecast, for instance, provides a certain level of insight on where regional facilities should be established. It will also help decrease the delivery times and enhance the supply chain management processes, because the required amount of goods will already be at regional distribution centers. However, relying solely on estimates is an inferior idea; therefore, more data is needed to receive a complete picture of the current market.
The current ERP system report is far from complete because it lacks sales information by region and by state. This data is necessary to provide accurate estimates of potential supply chain models’ effectiveness. Sales information that is filtered by geographical zones will show where the demand is higher and steadier, and where the market is more fluctuant. By combining the desired sales data and population estimates from the U.S. Census Bureau, it will be possible to make more accurate sales forecasts and build a supply chain network according to those estimates. Current sales history shows only the quantity of orders per day per product and profit margin. While it is possible to see which of the products is most demanded, it is of little use when developing a new supply chain network.
Logistics – Purchasing
For the company to have an adequate supply channel, the management should strive to increase the efficiency of logistics operations. Therefore, this project, in terms of logistics, should propose a strategy where the time starting from manufacturing to delivery to a customer is minimized. The team should also consider how costly it would be to process each order. However, improvements are not possible without understanding the current state of processes and how well each part of the system is working.
The company should concentrate on its flagship voice assistant device with 4 gigabytes of memory. The data on order history shows that this product has the second-highest number of ordered units, which is 13,971. The average processing cost for orders of this device is 180.88 dollars, which is below the total average. Lead times are also lower than the overall average – 39 vs. 39.5, which means that this model is faster to process and spends less time in our inventory system.
While the version with 1 gigabyte of memory has a higher number of ordered units, the flagship has a higher purchase cost. In aggregate, the current situation shows that operations in the supply chain of the flagship device are more efficient than for other products. The company must seek improvements in processing other items in the product line because some units that cost less to purchase are more expensive to process.
More detailed information would be beneficial when making design decisions about logistics operations. Particularly, the report lacks specific information about order processing costs – what comprises this price, and how it can be lowered. With more detailed data, the team will be able to try to propose different solutions to bring that cost down. It may also affect the lead times – the faster the processing, the quicker the delivery.
References
ACME Corporation. (2019). ERP system report.
Purvis, L., Spall, S., Naim, M., & Spiegler, V. (2016). Developing a resilient supply chain strategy during ‘boom’ and ‘bust’. Production Planning & Control, 27(7), 579-590.
The U.S. Census Bureau. (2019). Population and housing unit estimates datasets. Web.