Introduction
Sugar is ever-present in supermarkets and shops yet, sugarcane is only harvested at only certain periods of the year. Even after harvesting, the sugar cane needs to be processed and then transported to the consumer. To make the sugar available throughout the year, it has to be stored in large quantities after processing before distribution to consumers. Other products like tomatoes are perishable and go bad quickly. Great planning must be done to ensure that the tomatoes are always available whenever a customer goes to the market. If they have to be stored, they must be stored in special conditions and at only specific periods. From the two examples, it is obvious that raw materials and finished products need to be stored and then transported to where consumers are in time (Harland, 56).
A company needs to make use of a competent inventory model for it to be competitive as a business enterprise. Companies involved in manufacturing and supplying enjoy a lot of benefits by running on a linear inventory scope (Wang, 212). The benefits include; minimization of operating capital, rise in sales, and exceptional fulfillment of consumers. Research has shown that if companies can have a well-planned and controlled inventory, they will be successful (Mikkola, 238).
Supervising an inventory is a challenging job especially if the company manufactures thousands of products and is retailed in over 500 points (Larson and Halldorsson, 431). It is even more difficult if these points fall under different echelons. In a multi-echelon global supply system, these points fall in countries far from the country of production. If a company has such a large market, there is a great possibility that some retailers will be at one-time luck products to sell. What usually happens in a multi-echelon network is that products are first shipped to a region’s central storage building or warehouse (Lavassani, 368).
Afterward, the products are then transported to other minor distribution centers and then to the various retail shops where consumers can purchase from. This method has been used by many corporations, whether big or small, to distribute their products. Products distributed by this model range from drugs, beverages, vehicle parts, and furniture (Kaushik, 542). For example, some of the Dell laptops manufactured in China are usually shipped to Nairobi Kenya which is the region’s distribution center; the region being East Africa. Afterward, the laptops are then transported to about 15 distribution centers like Mombasa, Kampala, and Dodoma. Once the laptops are available in the distribution centers, they will be transported to the retail shops where customers will buy one of the laptops (Mentzer, 300).
Without a doubt, there is a great possibility of not realizing an effective network inventory. This may be attributed to the fact that different strategies are needed for each echelon and that one echelon has the capabilities of affecting all the others (Whang, 289).
At each point of the supply chain, there are a set minimum number of specific units that should be ever-present at any given time (Kaminsky,288). Also, there is a couple of question the inventory manager should ask himself; when should the units be replenished? How much of the units are needed at each distribution point? How many distribution points are necessary? Can the inventory model of one channel be used in another?
What would happen if the prices were changed? There are still many more questions to be answered. This goes to show the complexity of the whole system (Wright, 369). There are numerous possibilities of operation and most of them are clouded with numerous uncertainties. The decisions made vary with time, price, location, and culture of the consumers. For a manager to make the right decision, he must have a clear understanding of multi-echelon global systems, which includes the understanding of major pitfalls (Stevens, 45).
Some common pitfalls in Multi-Echelon
One of the common pitfalls occurs when one predicts the demand of a product by the previous demand patterns. This is wrong since the demand for a product is significantly affected by its point in the life cycle, the time of the year, and the changing trends of consumers (Cannella, 69). Some products like video games have very short life cycles compared to television sets. Predicting the sales of a video game depending on its initial sales would be unwise. This decision would lead to the supply network carrying surplus inventory and inevitably raising the cost of the distribution not forgetting numerous backorders (Halldorsson, 121).
Another pitfall is not using the correct replenishing methods. Most of the methods used are just basic; when a product’s level falls to a certain point, it needs to be replenished (Movahedi, 690). As logical as it may sound, this method does not factor in products that are meant for promotions and presentations or the upcoming high sale season. This replenishment method, at times, leads to customer dissatisfaction due to the unavailability of products. Major companies just classify products depending on the time required to replenish them. In a distribution center, for example, they classify products in three groups depending on the period they need to be replenished; namely A, B, and C.
This is how the system works; products in group A are replenished after 2 weeks, group B after 3 weeks, and in group C after 4 weeks. First of all, this method ignores the basics of inventory drivers. They include lead times, unpredictable customers’ desire to purchase, and the consistency or reliability of delivery. Moreover, this type of classification does not truly represent the diversity of supply requirements. The other factor ignored in this methodology is that there is a lack of emergency replenishment methods.
Ignoring the changing cost of a unit as it moves through the supply chain is another major pitfall. For example, shampoo products made in Malaysia are packed into containers to be shipped to Los Angeles harbor where there is a central distribution center (Hyndman, 812). Afterward, they are loaded into trucks in form of pallets and transported to various distribution centers like San Francisco. Then, they are re-packaged in form cases and transported to beauty shops for consumers to buy. After every point, the product is re-packaged leading to a rise in the supply cost. What most companies do is that they ignore the additional cost of the product as they try to optimize the process.
Demand forecast from the central distribution centers and other distribution centers usually presents a very complicated overall demand. This is called the bullwhip effect which forces the manufacturer to guess the demand (Chen, 462). This leads to unwanted consequences in the demand chain. These consequences include unnecessary security supply inventories, inferior projections, elevated expenditures, consumer dissatisfaction, and unproductive use of resources (Haag, 320).
Options available for multi-echelon management
There have been 2 ways to administer predictions and refilling in a multi-echelon global system. These 2 methods are Independent Order Points and Distribution Requirements Planning. The order point method has been known to have significant results when predicting demands and when to replenish a distribution center. This method follows the traditional way on each echelon. Distribution centers are replenished by the regional distribution center, while the regional distribution center is replenished by the manufacturer. When unforeseen circumstances arise, like a lot dry up of sales at once or an unfortunate promotion, this method will prompt orders from other cycles to reduce vanished sales thus reducing the lateness of stocking (Bechtel, 333).
However, this method has a couple of drawbacks if applied in a multi-echelon network. In most organizations, the duty of predicting demands and replenishing the warehouse or a store is usually given to different working groups depending on the number of distribution centers (Ryan, 561). Each group uses computer software to run the distribution with its data. Running of promotions and occasional selling at high season is managed at each distribution center.
This separation of management is perfect for miscommunication and errors to occur. Replenishment of distribution centers is affected by retail store demands, while replenishment of regional distribution centers is affected by distribution center demands and finally, the manufacturer dispatches goods depending on the demand of the regional distribution center (Tayur, 678). When there is miscommunication, there is a huge possibility that more or fewer products will be delivered (A. Cooper, 342).
For example, during Christmas, many companies usually stock the warehouses and stores well in advance but usually have no exact quantity that is needed. This high level of stock prompts the manager not to order more products. When the Christmas season is well underway, the stock level starts to reduce dramatically. By the time the storekeeper decides to replenish, other stores too will be in the same situation.
What follows then is a massive shipment of products from the manufacturer who had little time to process all the requirements of the stores. On the other hand, due to the complexity of the network, the delivery might take longer than expected meaning there will be a period when the customers will not be able to purchase the products. When the products arrive, they will be more or less than ordered. Therefore even after Christmas is over, the stores will be left with a lot of stock to sell.
Even when there is proper communication, synchronizing the orders manually involves a lot of paperwork and can still lead to errors. This is very common for teams delivering a lot of products to many points. Errors arise from typing and analysis (Chockalingam, 351). From the example given earlier on Christmas, inventory managers have to calculate well in advance the total amount of products that are expected to sell. Then, these products are stocked in the stores. When this huge demand for products arises, they have to calculate the demand for each area, each warehouse, and each store. At this time they have very limited time to deliver, this situation will most likely lead to errors in calculations since everything is done in a hurry.
The Distribution Requirement Planning (DRP) method usually tries to tackle the problems found in Independent Order Points. This method tries to join the requirements of a store and a warehouse so that they appear as one unit. Both groups can now concentrate on consumers’ needs rather than the other team’s conduct. The data becomes more precise by eliminating a lot of paperwork. The entire distribution network increases its efficiency if it is treated as a unit. Unlike in the Independent Order Points model where each warehouse and store requirements are treated independently, this method combines the needs of a warehouse and a store and fulfills them at once. Inventory drivers such as lead time and demand are predicted for all stores and warehouses together (Williamson, 111).
The DRP method also has its disadvantages too. This is mainly attributed to the fact there is no detailed information on each warehouse and store. Therefore, predictions of demand are usually inaccurate. Since there is a round-up of inventory parameters, such as lead times, special deliveries are not possible. Special deliveries are done when there is a special event for promotion or presentation.
Also, the particular inventory managers in the warehouses lose the ability to control and manage the activities in their perimeters (D. Lambert, 411). For example, it would be very difficult for a warehouse to clear its stock if it needs to undergo a maintenance exercise; the ability to be flexible becomes crippled. The major problem though is the inaccuracies in predicting demand, especially for slow-mover goods.
For example, the average number of new BMWs sold in a showroom in Johannesburg is about two in a week. Remember there is not much space for keeping many cars. However, the number of sales per week can rise from 2 to 7 in a very unpredictable manner (Selwyn, 764). When applying the DRP method where the demand for the whole country has been computed as a whole, it means there is a big possibility for that showroom to have no BMWs for about 4 weeks. Likewise, there might be no sales of BMWs for weeks and that would mean there would be an accumulation of cars at the end of the fourth week. Therefore, combining the demand of an area can result in consumer dissatisfaction (Zhang, 321). So is there another way?
Synchronized Order Points
Both the Independent Order Point method and Distribution Requirements Planning have some drawbacks (Disney, 389). The DRP generalizes the whole operation and makes the store manager powerless while the Independent Order Point method has great opportunities for miscommunication. A new approach has been developed that combines the best of both methods to come up with a more effective methodology. It is called the Synchronized Order Points and is used to manage inventories. SOP is as flexible as the Independent Order Point model and can synchronize the inventory parameters (Macneil, 254).
In this model, the observed demand trends in stores are averaged to predict the demand in the warehouse. This approach is better than IOP in the sense that it averages the store demand rather than the warehouse demand. Shopping trends take weeks to be realized in the distribution centers but with the SOP model, they are realized sooner (Chambers, 732). Furthermore, the average demands of the stores are more accurate especially after there has been a low sale.
The demand of a distribution center that has been analyzed from the previous trends tends to predict customer’s preference unlike using predictions from the shipment history as in IOP. The goal of inventory is to be able to predict the quantity and time of shipment, but not the customer demands. Therefore the synchronized order point method goes a step further (Lambert, 453).
During Christmas and other special occasions, inventory managers expect consumer demand to rise. They get ready for the season by changing the predictions to receive more orders than usual. Also, the preparation involves servicing the warehouse to achieve the optimum capacity of the warehouse (Lee, 329). This is very crucial as we have seen that the consumer demand may rise unexpectedly in which more space would be needed. The warehouse must embrace itself for any volume of products that it may be required to handle. Some of the products that experience occasional demands are snow shovels, world cup merchandise, gift wrappers, and many more (Drucker, 321).
In such a situation, the SOP method applies mathematics to add the demands of every store to achieve an average that would be used to replenish the warehouse. What makes it different from the DRP is that time-shifting is included in the calculation (MacDuffie, 422). To give an example, the valentine’s cards that are usually sold on the 14th of February had been in a warehouse for about a month. Therefore, the demand peak for the warehouse is usually a month earlier than the consumer’s peak. This is very important if the consumer has to get the card on the 14th of February.
The time-shifting approach in the SOP method is also applied in other time-bound events like the launching of new products or publicizing of a product. Before each of these events, synchronization is done by adding up all the profiles of the stores that are served by the same distribution center and then time-shifted. This is done to get the actual time when the warehouse should be replenished and reduce backorder (J. Mentzer, 295).
Logic dictates that the supply and demand should be balanced; this is the approach the DRP method takes down the supply chain. Whenever there is a lot of stock in a warehouse due to underselling after an overestimation of projected sales, the warehouse just ceases to order more until all the stock reaches replenishment point (Ketchen, 190). Since the Synchronized Order Point model does not employ storage demand predictions, it utilizes a dissimilar method in dealing with stock quantity inventories.
A multiple-echelon refilling resolution via the SOP model shifts the distribution center record left over to take care of inventory upset in the proceeding shops. The distribution center inventory is amplified in the case of shops being overloaded leading to no orders as anticipated. This will result in holdup thereby lessening retailer demands (Cooper, 57).
The reverse is also applicable; if the shops have run out of supplies, they are bound to replenish more than usual. The distribution center inventory status is reduced to hasten the retailer’s demands (Bean, 332). The sophisticated mathematics necessary to establish the exact quantity of stock in the distribution center is the major advantage of SOP over IOP. Since the shop’s excess stocks run out with time, it makes the SOP model is the best model to be used when it comes to the development of computer inventory applications (Hines, 208).
Conclusion
Synchronizing retail shops with distribution centers via a clever analysis together with time-shifting, helps organizations to concentrate on their customer’s needs. Top organizations are now using the SOP model to match their demands with supplies. Noteworthy profits are obtainable for organizations ready to embrace SOP in multi-echelon circumstances (William, 444). Synchronizing central distribution centers, distribution centers, and retail stores increases the profit margin and lays the foundation for efficient supplying models.
Works Cited
Bean, Michael. Bullwhips and Beer: Why Supply Chain Management is so Difficult. Chicago: Ganeshan, 2006. Print.
Bechtel, Jennifer. Supply chain strategies: Customer driven and customer-focused. Oxford: Kern and Willcocks, 2000. Print.
Cannella, Sandy. On the bullwhip avoidance phase: supply chain collaboration and order smoothing. Toronto: Ganeshan, 2010. Print.
Chambers, Christine. Supply Chain Management Research and Production and Operations Management. Atlanta: Mcgraw Hill, 2006. Print.
Chen, Yen. Quantifying the Bullwhip Effect in a Simple Supply Chain. New Orleans: Simchi, 2000. Print.
Chockalingam, Mark. Tracking and Measurement of Demand Forecast Accuracy and Implications for Safety stock Strategies. Miami: Oxford, 2001. Print.
Cooper, Alice. Supply Chain Management: More Than a New Name for Logistics. Chicago: Turnbull, 2004. Print.
Cooper, Vincent. Purchasing and Supply Management. Seattle: Adventure Press, 2008. Print.
Disney, Michael S. On the bullwhip and inventory variance produced by an ordering policy. Chicago: Chicago, 2003. Print.
Drucker, Eva. Operations Management. Washington DC: Skyline Press, 2002. Print.
Haag, Susan. Management Information Systems For The Information Age. Toronto: McGraw Hill Ryerson, 2006. Print.
Halldorsson, Victor. Inter-organizational theories behind Supply Chain Management. Toronto: Verlag, 2003. Print.
Harland, Charles. Supply Chain Management. Chicago: Macmillan, 2004. Print.
Hines, Timothy. Supply chain strategies: Customer driven and customer-focused. Oxford: Oxford, 1999. Print.
Hyndman, Raymond J. Another look at measures of forecast accuracy. New York: Harvard Press, 2005. Print.
Kaminsky, Reter. Designing and Managing the Supply Chain. Chicago: Mcgraw Hill, 2007. Print.
Kaushik, Dust K. Industrial Marketing Management. Oxford: Oxford, 2000. Print.
Ketchen, George Jr. Bridging organization theory and supply chain management: The case of best value supply chains. Atlanta: Elsevier, 2006. Print.
Lambert, Dennis. Logistics Management. Chicago: Hemila, 2005. Print.
Lambert, Douglas. Supply Chain Management: Processes. Berlin: Sturtgatt, 1998. Print.
Larson, Dorothy P, and Alfred Halldorsson. Logistics versus supply chain management. Seattle: Mcgraw Hill, 2004. Print.
Lavassani, Kevin. Developments in Theories of Supply Chain Management. New Jersy: Movahedi, 2009. Print.
Lee, Hau. The Bullwhip Effect in Supply Chain. Boston: Sloan, 2005. Print.
MacDuffie, Allan. Management and Supply Chain Dynamics. Miami: Woodly, 2004. Print.
Macneil, Ann. Sustainability Index and Supply Chain Green Standards. Miami: Doug Page, 1997. Print.
Mason-Jones, Rachel. Coping with Uncertainty: Reducing “Bullwhip” Behaviour in Global Supply Chains. Atlanta: Towill, 2000. Print.
Mentzer, John. Supply Chain Management Process Standards. London: Falcon Press, 2006. Print.
Mentzer, Joseph. Defining Supply Chain Management. New York: Oxford, 2007. Print.
Mentzer, Tommy D. Defining Supply Chain Management. New York: Mcgraw Hill, 2001. Print.
Mikkola, Job. Complementary theories to supply chain management. New York: Bowersox, 2007. Print.
Movahedi, Ben. Marketplace Enabled Supply Chain. Toronto: Up Town, 2009. Print.
Ryan, Donn. The Impact of Exponential Smoothing Forecasts on the Bullwhip Effect. Atlanta: Simchi, 2000. Print.
Selwyn, Bentder. Conceptualizing the ‘Bullwhip Effect’ in global commodity chains. Chicago: Tempelmeier, 2008. Print.
Stevens, Johnny. Customer-driven. New York: Hewitt, 2000. Print.
Tayur, Ronald. Supply Chain Management. Manilla: Padmanabhan, 2010. Print.
Wang, Hu. Production and Operations Management. Beijing: Wang, 2006. Print.
Whang, Sang. Information distortion in a supply chain. Tokyo: Padmanabhan, 1997. Print.
William, Perreault D. Basic Marketing: A Marketing Strategy Planning Approach. New York: McGraw-Hill, 2008. Print.
Williamson, Fred. Logistics. Salt Lake City: Doug Page, 2003. Print.
Wright, Forrester Jay. Industrial Dynamics. Boston: MIT Press, 2007. Print.
Zhang, Judy. Supply chain strategies: Customer driven and customer-focused. Miami: Houlihan, 2009. Print.