Inventory management example

Dell, for example (Fig. 22.1), became one of the biggest and the most valuable PC producers during the 90s while there are obvious differences between the PC and chemical businesses, approaches like product-customer segmentation or online inventory management can definitely be successfully borrowed. 

The main achievement of the proposed model is to provide a detailed tank farm inventory management, looking into the sets of tanks of each product (rotation scheme). As future work it is proposed to improve the tanks cycle formulation and develop a set of examples to test the behavior of the Disaggregated Tanks Formulation. Additionally, it is proposed to develop a decomposition strategy to link subsequent time horizons. 

Many examples of inventory management software and systems exist with capabilities to translate across different part identification numbers and actively track inventories. The Army should not have to engage in basic design and development of software. Conversely, not all existing applications are appropriate or effective for this particular situ-... 

Decisions can be made at random points in time when specific events take place. In the revenue management example, the decision maker may decide on prices at the random points in time when customer requests are received and may decide whether to order and how much to order at the random points in time when the inventory changes. 

In the revenue management example, the decisions involve how much of the product to order, as well as how to set the price. Thus, decision a = (q, r) denotes that quantity q is ordered tmd that the price is set at r. Suppose the supplier requires that an integer amount between a and b be ordered at a time. Also suppose that the state s denotes the current inventory, and that the inventory may not exceed capacity Q at any time. Then the order quantity may be no more than Q — s. Also suppose that the price can be set to be any retd number between r, and r. Then the set of feasible decisions is d(i) = a, a + 1, a + 2,, min <2 s. b X [r, rj]. 

In the revenue management example, suppose unsatisfied demand is back-ordered and that an inventory cost/shortage penalty of h s) is incurred when the inventory level is s at the beginning of the time period. Then f(s, (q, r ), s ) = r (s + q — s ) — h s) with s s s -H q. Thus,... 

All inventory management systems answer two questions How much to order, and when to order. The rules established to answer these two questions must be continuously reviewed and revised as appropriate. As demonstrated using Little s Law earlier in the chapter, the answer to these questions depends on other characteristics in the system. For example, as the flow time of an item becomes shorter, the system will need less inventory, so the two questions of when and how much need to be answered again. 

Starting on the path to the proactive system offers many opportunities for short-term improvement. There may be ways to integrate, permanently or temporarily, the widget fulfillment process without automation. For example, in the case above, we combined the functions of inventory management and purchasing into the role of the planner buyer. 

Throughout this book we have described the barriers to more effechve supply chains. We have also described many of the tools and techniques that address this barrier. For example. Section 3-7.2 describes a eollabo-ration effort called Collaborative Planning Forecasting and Replenishment, CPFR for short. CPFR requires a business relationship between partners and has taken root in the retail industry. Collaboration strives to better match demand and supply, improve inventory management practiees, and capitalize on new systems through sharing. CPFR is primarily a link between retailers and their manufacturer suppliers. However, it is expected that the CPFR concept will expand to other industries. 

As an example, consider a finished goods item produced by some firm, let us say a cell phone. To the producing firm, the cell phone itself is an independenf-demand item. Its component parts—such as the circuit board, the keypad, the plastic housing, the battery, etc.,— are dependent-demand items. The demand for these items is driven by the production schedule for the cell phones, which is solely under the control of the company that produces the phones. The company s management chooses the production plan in response to projected consumer demand for these phones. Thus, inventory management for dependent-demand items is largely an issue of ensuring that component parts are available in sufficient quantities to execute the production plan. 

As indicated by Bozarth (2005), another idea embedded in his framework is that the stationary demand approaches map to pull systems, whereas the non-stationary approaches map to push systems. Although definitions tend to vary—see Hopp and Spearman (2004) for an excellent discussion—pull systems are those that execute replenishments only in response to actual demand, while push systems drive replenishments from the schedule of projected future demands, which, consistent with Bozarth s framework, will vary over time. Hopp and Spearman (2004) also point out that kanban, or "card"—controlled production systems (see, e.g., Vollmann et al., 2005, for descriptions of several examples of this), are only one, specific implementation of what they more generally describe as constant-WIP systems (where WIP stands for "work-in-process," or inventory that is not yet fully converted from raw materials into finished goods). Again, since our focus will be on independent-demand inventory management systems, the reader is referred to the Hopp and Spearman article for further details regarding such dependent-demand systems. 

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