Demand customer, data

The customer demand data is given in Table 6.5. Data regarding supplier capacities, lead-time, and product quality are given in Tables 6.6 through 6.8. 

The customer demand data is generated from periodic customer orders using a polynomial regression function based on the case company historical data with a random variable added that follows the uniform distribution to represent the uncertainty. 

A comparison of the company s strategy and parameterised and non-parameterised strategies in the experiment can he found in Chapters 8, 9 and 10. Eq. (6.1) represents the periodic customer orders from a polynomial regression function calculated based on case company historical data, in which r(t) represents the percentage of total customer orders generated from the domestic market. The regression coefficients came from the customer demand data. Eq. 

In order to compare the company s strategy and parameterised and non-parameterised strategies in chapters 8-10, Eq. (6.6) represents the periodic international customer orders from a polynomial regression function obtained based on case company historical data, in which r(t) represents the percentages of total customer orders generated from the domestic market. The regression coefficients came from the customer demand data. Eq. (6.7) represents the international customer order with quantity uncertainty. Eq. (6.8) represents the part of international customer order that is released on time at period t, where ai(t) is a random variable to represent the incompleteness of customer order release. Eq. (6.9) represents the part of international customer order that is released with delay at period t. Eq. (6.10) represents the amount of international customer orders that the manufacturer actually receives at period t, which is the sum of on-time released customer orders, D t), generated at the period in advance of the required customer order information lead time, ld t), and the sum of previously delayed released customer orders, which reached the manufacturer at period t. It should... 

Since the early 1970 s there has been a growing belief that chemical measurements must not only be done correctly, but that data, the product of the measurement process, must be seen to be accurate, precise, and reliable. Analytical data have become another manufactured product and like all manufactured products, the customers demand that Quality Assurance (QA) must be built in. 

When designing a work cell, you need two key pieces of information—the customer demand rate and the time the process takes. With this data, you can determine takt time—the rate at which you must produce in order to keep up with demand. 

In our example, RayRay gathers this data by visiting other beauty salons to see how many customers come in during a given period. He does this several times, on different days of the week, and during different times of the day to determine customer demand rate. To determine process cycle time, RayRay times several customers as they go through each step in the process (customer check-in, shampoo, hair cut, and payment). 

If your product or service is new, you may not have previous customer demand or time data. In this case, you can derive the data from a pilot study or prototype. Or, you can gather data from similar processes, or even from your competitors (if they ll let you). 

For instance, consider the logistics network design model discussed previously. A DSS is often used to assist in optimizing the number of warehouses required as well as their size and customer allocation to each warehouse. The DSS uses information about the distribution system to calculate the various costs related to the site selection and customer allocation. The data required for this problem involve the manufacturers, warehouses, and customers and the transportation between them. Since this is a long-term planning tool, yearly demand data and costs are typically used, but sometimes the user may need to determine how to account for seasonality. In addition, in order for this kind of DSS to be utilized successfully, the user needs to break the products into product families... 

Statistical analysis Sometimes asking questions is not sufficient. In this case, statistical techniques can sometimes be used to determine trends and patterns in the data. For example, often statistical data such as the average inventory in a warehouse, the average number of stops and length of a route, and the variability of customer demand can be useful to decision makers. 

For example, consider a simulation model of a production line. As the computer runs the model, a series of decisions is made. How long does a job take on machine one On machine two Does machine three break while job four is being processed on it As the model runs, statistical data (utihzation rates, completion times, etc.) are collected and analyzed. Since this is a random model, each time the model is run, the results may be different. Statistical techniques are used to determine the average outcome of the model as well as the variability of this outcome. Also, varying different input parameters allows different models and decisions to be compared. For example, different distribution systems can be compared utilizing the same simulated customer demand. Simulation is often a useful tool for understanding very complex systems that are difficult to analyze analytically. 

As Tom Rodak (Commerx, Inc.) reported in today s time-constrained workplace, you can spend a great deal of valuable time trying to find the information you need to make product design decisions. Unfortunately, not many have the luxury of time. Unforgiving deadlines and customer demands make the ability to find information quickly a necessity. Over the past few years, the Internet has rapidly evolved as an ideal tool for locating this needed data. However, with the incredible vastness of the Internet, knowing where to go is key to success. 

The fashion retail business is characterized by short product life cycles, volatile customer demands and tremendous product varieties. Most fashion items are of strong seasonality. Uncertain customer demands in a frequently changing market envirorunent and nitmerous explanatory variables that influence fashion sales caitse an increase in irregularity or randomicity of sales data. Such distinct characteristics increase the complexity of sales forecasting in the fashion retail industry. For most fashion products, market demand is rmcertain until the selling season has started. When the actual demand deviates from the forecast, fashion retailers may not have time to respond to changes. Stock outages may occur for certain styles or sizes of fashion products and thus affect the profitability for fashion retailers. 

The historic definition, as defined by the first and second generation of supply chain pioneers, is limited and applicable to only stages 1 and 2 of the supply chain maturity model in Chapter 1 (Figure 1.3). The basis of the design of these traditional processes is the premise that an underlying pattern in historical customer shipment data can be identified using statistics. Any additional unexplained patterns could be simply addressed as randomness, or an unexplainable variation. These same processes assumed that the patterns (demand signals)— in this case, only trend/cycle and/or seasonality— will continue into the future. 

Limited or no access to demand data from customers, and company does not provide demand data to its suppliers. 

There are limited internal customer POS data available from key customers through EDI (Electronic Data Interchange), but the information is not formally integrated with the demand planning process. (POS information is only used for other activities like category management). 

In addition to collecting information in advance of a product launch, via conjoint analysis or other survey methods, it is possible to make use of postlaunch demand data to further understand customer preferences. The Internet, by allowing many options to be displayed and choices to be recorded, greatly facilitates this process. Since allowing customers to choose some features of the product to fit their needs best requires dramatic changes of the traditional design process, we will discuss this topic in the next section. 

Supply chain visibility— According to Blackhurst et al. (2005) supply chain visibility refers to the sharing of information in real-time across the supply chain stages and among their partners. The net effect of visibility is to make the supply chain more responsive, increase availability and reduce inventory risk. For example, Dell shares customer demand information with its suppliers so that they can maintain proper inventory of needed parts. Wal-Mart shares points-of-sales (PoS) data with its suppliers so that they can forecast and plan their replenishment strategies. 

In order to get a better customer demand forecast result, we need to use two or more prediction methods for customer demand forecasts. Then the forecast results are combined according to weight value for every single result obtained by different prediction methods. By doing so maybe we can fully use obtained data information, thereby to improve forecast accuracy and get better prediction results. 

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