Predictable-demand products, inventory

Build inventory of high-demand or predictable-demand products When most of the products a firm produces have the same peak demand season, the previous approach is not feasible. In such an environment, it is best for the firm to build products that have more predictable demand during the off-season, because there is less to be learned about their demand by waiting. Production of more uncertain items should take place closer to the selling season, when demand is more predictable. Consider a manufacturer of winter jackets that produces jackets both for retail sale and for the Boston police and fire departments. Demand for the Boston police and fire jackets is more predictable these jackets can be made in the off-season and stocked up until winter. The retail jackets demand, however, will likely be better known closer to the time when they are sold, because fashion trends can change quickly. Therefore, the manufacturer should produce the retail jackets close to the peak season, when demand is easier to predict. This strategy helps the supply chain synchronize supply and demand better. 

Figure 6.10 shows the data flow of the software tool BayAPS PP for optimal capacity assignment for given stochastic demands. Transaction data about demand and inventories is typically imported from SAP R/3 as indicated, production capacity master data and side conditions are stored in the software tool. Forecasts can be taken from a forecast tool or from SAP R/3. The output ofthe tool is a list ofpriorities of products and their lot sizes, which are optimal based on the presently available information. Only the next production orders are realized before the computation is repeated, and the subsequently scheduled production is only a prediction. 

The core component of the oil supply chain is the refinery, where the received oil batches are managed to feed the crude distillation units in proportions that give origin to the desired cuts and products. However, the oil supply and the oil products distribution have to answer in agreement to their predicted demands. For this reason, fliere is the need to build decision support tools to manage inventory distribution. This work focuses on the development of a MILP model that describes the oil products distribution through a pipeline that connects one refinery to one tank farm. In order to supply die local market, the model represents the interaction between the pipeline schedule and the internal restrictions at the tank farm. Real world data from CLC (a Portuguese company) validate the model formulation. 

One of the simplest models of demand is to use an estimate of the average demand. This average demand, assuming a constant rate each period, can then be used to understand the effect of production costs or transport costs on inventory levels. Such models are appropriate when we deal with products in situations with predictable demand, that is, low forecast error. In particular, we will focus on the... 

How can it be that we have vast amounts of inventory for which there is no demand Apart from the often unshakable faith in large lot sizes, the main cause resides in the inability of sales to reliably predict future product turnover. This proves the risks inherent in much too long planning horizons. 

The motivation to change from the current system has been low in the past, as the process at Smog Co. is a reliable one, which has worked well for the company. The big three customers, who take three-quarters of sales, tend to order the same things in similar quantities one week in advance of delivery. With a production lead time of three weeks. Smog Co. uses a forecast to schedule production and make sure that finished goods stocks will be available to meet predicted demand. Consistent demand means that forecasts are often close to real demand, so stockouts are rare. In fact the only time this occurred was an incident a couple of years ago, when a key machine went down and a spare part took a long time to source. Current inventory levels now include safety stock to provide cover against a similar problem in the future. 

Automated VMI originated in the late 1980s with department stores in the USA as a solution to manage the difficulties in predicting demand for seasonal clothing. Prior to this manual VMI had been around for many years - particularly in the food industry. Under manual VMI, the manufacturer s salesman took a record of inventory levels and reordered products for delivery to the customer s store, where the manufacturer s representative would restock the shelves. As product variety has increased and life cycles have shortened, manual VMI has been replaced by automated VMI. 

Multiple forecasts for the same line of business within the organization are common (if any planning is even done). The gap between what is planned and what actually happens represents lost profits and lost opportunities. The new paradigm for retail supply chain management begins with an accurate view of customer demand. That demand drives planning for inventory, production, and distribution within some understood error parameters. Consumers will never be completely predictable. At the same time, prediction is bounded by limitations in our statistical and modeling sciences. We can... 

In the earlier section, we modeled demand as a constant rate. Often, however, demand is not very predictable but has a significant amount of randomness. To understand the effect of demand forecast error, we first focus on problems where decisions regarding inventory are made once for an entire period. Examples of products that might require inventory decisions that cover demand over a single period include... 

Consider the foUowing example of a production planning problem (Bowman 1956). Production must be scheduled over the next three quarters where the demands are predicted to be 40, 30, and 60, respectively. In each quarter, items may be produced on a regular shift at a cost of 10 per item. During the regular shift there is a capacity restriction Umiting production to 45 items per quarter. A maximum of 20 items can also be produced during an overtime shift, at a cost of 12 per item. Finally, items can be held in inventory at a cost of 0.50 per unit per month. 

Order patterns requiring a supplier to increase their inventory to be able to meet the demand. The supplier needs to be able to predict and schedule production to best service customers and still control business costs. If a compaity can help in this area with good forecasts, this can be very beneficial for both parties. 

Second, there is the question of how much is needed for production and when is it needed. The development of a request for material needs to be understood completely. How this request is developed can determine whether issues come up in the future. The big difference lies in the whether the request is based on a demand number or a forecast number. A demand-based number reflects what has actually been ordered and is needed to produce the requirement If it is based on a forecast there are no orders that need these materials. The order is being predicted and m not have been received yet The difference is in what will actually be used. If the forecast is off, the material will end up in inventory. 

The synchronized supply chain has been tested repeatedly and it does address the more common problems of the traditional approach. The key is communication from the market. Material and information is released into the system based on the consumption at the primary control point. Every supplier of raw material as well as every producer along the supply chain is linked to that actual demand. Strategically sized and located buffers of inventory are designed to absorb the unpredictable variability, and sufficient protective capacity is planned to maximize the velocity of the product flow. As a result, the waves of demand are avoided and the productivity of the entire system is made much more predictable. As well, the properly synchronized system is more stable and easier to manage. 

Ashley and Orr [8] disallow negative inventories or backlogging, but still find that production smoothing can bring about price smoothing. Under the assumption of deterministic demand and concavity of revenue, they find that price stickiness is realized when demand is foreseen to decrease but not when demand is predicted to increase. 

The earliest known example of a problem that integrate a fixed price decision with inventory policies is that of Kunreuther and Schrage [86]. They consider a problem with demand that is deterministic, a linear function of price, and varying over a season, and they include production set-up costs. Their model does not have lost sales or backlogging, since demand is exactly that predicted by price and time, and there are no production capacity limits. The objective is to determine price, production order period, and production quantities so as to maximize profit. The authors provide a hill-climbing algorithm that provides upper and lower bounds on the price decision. 

How The more predictable and lower-priority products and components can be delivered from inventory with less priority given to speed. Shipments of customised products can be assembled from stocks of standard components and modules within the D-time demanded. 

Using common components across multiple products In this approach, a firm designs common components to be used in multiple products. The total demand of these components is relatively stable, even though each product displays predictable variability. The use of a conunon engine for both lawn mowers and snowblowers allows for engine demand to be relatively stable even though lawn mower and snowblower demand fluctuates over the year. Therefore, the part of the supply chain that produces components can easily synchronize supply with demand, and a relatively low inventory of parts has to be built up. 

In volume-based tailored sourcing, the predictable part of a product s demand is produced at an efficient facility, whereas the uncertain portion is produced at a flexible facility. Benetton provides an example of volume-based tailored sourcing. Benetton required retailers to commit to about 65 percent of their orders about seven months before the start of the sales season. Benetton subcontracted production of this portion without uncertainty to low-cost sources that had long lead times of several months. For the other 35 percent, Benetton allowed retailers to commit orders much closer to or even after the start of the selling season. All uncertainty was concentrated in this portion of the order. Benetton produced this portion of the order in a plant it owned that was very flexible. Production at the Benetton plant was more expensive than production by the subcontractor. However, the plant could produce with a lead time of weeks, whereas subcontractors had a lead time of several months. A combination of the two sources allowed Benetton to reduce its inventories while incurring a high cost of production for only a fraction of its d and. This allowed it to increase profits. 

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