SKUs

The rigorous management of the number of SKUs reinforces this approach structured programs often result in a 20 to 30percent SKU reduction potential and improved focus, and reduce the complexity of the entire supply chain. A further key to success is to hold sales and marketing accountable and incentivized for forecast accuracy, and to keep regular track of it. 

Depending on the requirements of the operation, this system may also include breakdown or aggregation of inventory received into smaller or larger stock keeping units (SKUs) for onward distribution. It may also include interfaces to purchase order systems, labeling applications, automated materials handling systems such as sortation systems, stock location systems, automated storage and retrieval systems. Radio Data Terminals (RDTs), Radio Frequency Identihcation units... 

Weber-Bargioni A, AuwarterW, Klappenberger F, Reichert J, Lefran ois S, Strun-skus T, W5ll C, Schiffrin A, Pennec Y, Barth JV (2008) ChemPhysChem 9 89... 

Type C deficiency results from customers making compromises between their real requirements and existing SKUs (stock keeping units), that is, what is available on the shelf or in the catalogue. Although these compromises are usually not explicit and are difficult to capture, they lead to customer dissatisfaction, reduce wiUingness to make future purchases, and erode the competitiveness of a company. 

Retail supply chains are different than other industry models. Many of the components of the supply chain are the same product sourcing, inbound transportation, processing, location and storage of inventory, outbound transportation, company operations, and information. However, retailers are at the end of the chain, just before the products touch the consumer. As a result, the retailer is at the end of the cumulative efficiencies and deficiencies of aU the chain partners. It may be that retail supply chains are just a bit more complex. Imagine the thousands of vendors, each with their own ideas and operations, aU moving with a thousand different retailers set of unique requirements and multiply this by the 90,000-t different stock keeping units (SKUs) in the typical large discount store. 

Variety More different SKUs and more special customer requirements result in an increase in the variety of tasks performed in the warehouse. 

Adaptability Maintenance storeroom facilities, operations emd personnel must become more adaptable. The pace of the storeroom will continue to increase r uction of lead times, shorter equipment lives, increased inventory turns, more SKUs, and more customer demands require that storeroom adaptability be present to satisfy customers. 

SKU stock-keeping unit, the type identification of a product for purposes of distribution for example, Coca-Cola Classic may be sold in 2-liter bottles packed 6 to a carton, 1-liter bottles packed 12 to a carton, 12-ounce cans packed 24 to a carton, 12-ounce cans arranged as 4 6-can inner packs in a carton, and 12-ounce cans packed 24 to a flat these would aU be different SKUs. 

Order a document from a customer requesting specific SKUs in specific quemtities. 

Next, focus on products handled by the supply chain and verify if the supply process matches product characteristics. If all SKUs are sorted in order of decreasing sales (i.e., from the highest to lowest sales levels) and the cumulative sales are plotted vs. the corresponding ranking of products, the data usually generates a Pareto distribution. Products can thus be divided into three categories A products that represent 20% of the products but 80% of the sales volume, B products that represent 30% of the products and 15% of the sales volume, C products that represent 50% of the products and 5% of the sales volume. 

The supply chain was complex and involved seven countries, two plants, twenty-one distribution centers, ten candidate distribution centers, 5,700 SKUs, and multiple channels of distribution. There was substantial overlap in plants and warehouse locations across both companies, with the US company having six plants and eight warehouses and the European company having four plants and seven warehouses. 

The Home Depot also uses transportation auctions ([34]). The Home Depot was founded in 1978 in Atlanta, GA. Home Depot is the worlds largest home improvement retailer. The 1,000 stores are supplied by thirty-seven distribution centers in forty-five states. The supply chain includes over 7,000 suppliers who provide over 40,000 SKUs to stores and DCs across locations. Over 90% of the products move on trucks. In 1999, the company made 7.1 milUon less-than-truckload shipments and 219,000 full truckload shipments. These were expected to change to 4.7 million less-than-truckload and 877,000 full truckload shipments in 2003. 

As described in [83], the Sears Shoe Distribution Center in Edison, NJ, a 408,000 square foot central distribution facility for footwear, opened in March 1996. The warehouse was planned to support 750 Sears stores nationwide. The warehouse received inbound shipments from over 100 suppliers. The estimated monthly shipment volume was 2.5 million pairs of shoes a month to seven regional DCs. Stores were permitted the flexibility to order inventory one pair at a time. Taking into account seasonal variations, the facility was planned to carry an inventory level of 9,000 active SKUs, but it was flexible enough to accommodate an additional 7,000 SKUs. 

Surveys by major retailers surest that service levels at various stores are around 70%. Thus one out of three customers does not find the item in stock at a store that carries it. Note that apparel SKUs refer to color, style, and size availability, which requires demand estimates of specific sizes at a location. From a supply perspective, if manufacturer deliveries are of a fixed assortment of product across sizes, then it is clear that any deviation from the average size mix can cause stockouts. Thus, stockout reductions may require coordination between a fiexible manufacture of varying sizes (driven by observed store sales) and careful retailer monitoring of inventory levels by size level demand and availability. The additional complication is the impact of fit preferences across customers, ranging from slim fit to loose, baggy fit choices. Such trend effects may add additional complexity to the problem of forecasting demand at the SKU level. 

As described in [18], the main challenges in the auto parts supply chain are (1) intermittent parts consumption with very low turns, (2) enormous disparity across part costs, (3) great variety across models, with hundreds and thousands of SKUs worth billions of dollars in inventory across the supply chain, (4) variable value of delivered service based on the severity of the failure, (5) lack of dealer interest in becoming efficient inventory managers and failure to see the connection between providing good service and selling cars. 

IBM s Optimizer [14] is a system for optimizing spare parts required by installed population of IBM products that exceeds tens of millions. The system tracks 200,000 part numbers and 15 million SKUs. It is used by over 15,000 customer engineers (CEs) that call in part requests. The parts may be delivered to the customer site before or after the CE arrives, the CE may use parts stored on the customer premise, or the CEs may carry a limited number of parts. The system was intended to minimize the overall inventory and transport cost while maintaining the part availability level (PAL) as specified by the service contracts. 

Research done by Kraft foods and AC Nielsen in Brazil market in 2004 with 528 SKUs, 402 stores in Sao Paulo and 185 stores in Rio de Janeiro reported 8.0% OOS. 

Wanke et al. (2010) argue that the perception of logistics systems being complex is confirmed by several authors, but it is not always clear what does it mean. They defined complexity in logistics in terms of quantifiable scales and based on the notion of numerous actors or parts that are interconnected and can be captured by measures such as the company s gross revenue, its number of supphers, active customers, number of employees, number of employees involved in supply chain management, active stock keeping tmits (SKUs), number of distribution centers, orders processed and new product launches per year. 

For data driven forecast, it is suggested to apply statistical forecast models, which will generate good forecast accuracy results, and will also automate the forecasting calculation, saving demand planners time to devote to more complicated and/or variable SKUs. 

In the beverage industry, it can also be seen a huge increase in the number of SKUs commercialized in different markets and geographies, as illustrated in Fig. 4.28. 

Portfolio optimization is an analytical process used to determine the merits of adding, retaining, or deleting items from the product portfolio of a business. It can be summarized as a way to make sure that a company does not keep what they don t need. Portfolio optimization is important as aU SKUs become liabihties to an organization at some point in their lifecycles. The key is to estabhsh repeatable processes to identify when that point is reached and execute plans to capture as... 

Based on demand variability and sales volume, planners understand SKU profile and apply appropriate forecast methods (same as in level 2) for SKUs with low variability, and make to order strategy (pull system) for SKUs and customers with high variability (less than 50% of sales volume). 

Same as in level 4, but in addition more than 80% of the company sales volume is sold using a pull system and only 20% remains using statistical forecast (mainly low variability SKUs). 

Same as in level 4, but in addition Postponement is in place and is incorporated into the manufacturing strategic foundation to increase flexibility and agility and allowing producing increased number of innovative SKUs that will fulfill market demand. 

There is a proven number of SKUs retired from the portfolio based on the set of criteria defined inside the organization. 

There is a formal portfolio optimization process in place and executed on a regular basis (e.g., 2 times a year) to evaluate product portfolio and identify SKUs with underperform sales, lack of clear market strategy or low margin contribution to the company, etc. 

All SKUs identified are evaluated through different areas like Marketing, Sales and Supply chain, and information from Pull customers is essential to understand fit of product to strategic role in the portfolio. 

Strategic Planning, and Finance. The committee reviews the proposed list of SKUs to be retired and make decision of keeping or retiring each product. There is a formal and updated RACI matrix (Responsible, Accountable, Consulted and Informed) that states clear roles and responsibilities for each participant in the process. 

For Statistical Forecast, it is important to define a process to formally analyze and cluster the SKUs sold in different customers and channels based on sales volume and demand variability, in order to apply an approach that combines statistical forecast for SKUs with low variability and actual POS demand information for SKUs with high variability. It is also suggested to implement a root cause analysis to map and understand the reasons of low forecast accuracy by SKU, and then, implement an effective action plan to fix the problems. 

Improve manufacturing efficiency and flexibility to cope with increase of SKUs in the product portfolio. 

---