Forecasting demand and inventory management using Bayesian time series
T.A. Spedding University of Greenwich, Chatham Maritime, Kent, UK K.K. Chan Nanyang Technological University, Singapore
Batch production, Demand, Forecasting, Inventory management, Bayesian statistics, Time series
Keywords
Introduction
A typical scenario in a manufacturing company in Singapore is one in which all the strategic decisions, including forecasting of future demand, are provided by an overseas office. The forecast model provided by the overseas office is often inaccurate because the forecasting is performed before the actual production schedule and it is based on marketing survey results and historical data from an overseas research team. This
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Bayesian dynamics time series and forecasting techniques can be used to solve inventory problems because Bayesian inference statistics has the analogue idea that posterior knowledge (actual sales demand) can be derived from prior knowledge (such as the manager's experience) and the likelihood (the similar or expected trend) of the product demand (Box and Tioa, 1973; Jeffreys, 1961; Lee, 1988; Press, 1989). In many real life forecasting problems (for example when previous demand data are not available for newly launched products), there is little or no useful information
This work was carried out while the author was Associate Professor in the School of Mechanical and Production Engineering at Nanyang Technical University in Singapore.
Integrated Manufacturing Systems 11/5 [2000] 331±339 # MCB University Press [ISSN 0957-6061]
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T.A. Spedding and K.K. Chan Forecasting demand and inventory management using Bayesian time series Integrated Manufacturing Systems 11/5 [2000] 331±339
available at the time when the initial forecast is required. Hence, the early forecast must be based largely on subjective considerations (such as the manager's experience and the general demand of a similar or comparable product). As the latest information (actual sales demand) becomes available, the forecasting model is modified with the subjective estimation in the presence of the actual data. This
Forecasts are extensively used to support business decisions and direct the work of operations managers. The two major types of forecasts are qualitative and quantitative. Within each of these types are multiple methods and models. Qualitative forecasts are based upon subjective data. Quantitative forecasts are derived from objective data. Both methods are not suitable for all situations and circumstances. Each has inherent strengths and weaknesses. The forecaster must understand the strengths and shortcomings of each method and choose appropriately. One example of forecasting is the United States Marine Corps use of forecasting techniques, both qualitative and quantitative, to predict ammunition requirements.
Forecasting should include the use of both quantitative and qualitative approaches to forecast demand for its products.
The current demand forecasting method is based on qualitative techniques more than quantitative ones. If the forecast is not accurate, the company would carry both inventory and stock out costs. It might lose customers due to shortage of supply or carry additional holding costs due to excess production. If the actual demand doesn’t match the forecast ones, and the forecast was too high, this will result in high inventories, obsolescence, asset disposals, and increased carrying costs. When a forecast is too low, the customer resorts to a competitive product or retailer. A supplier could lose both sales and shelf space at that retail location forever if their predictions continue to be inaccurate. The tolerance level of the average consumer
* Forecasting is an impartial strategic ingredient that will ensure apt base for reputable planning. Our forecast is always the first step in developing plans in running the business along with our future plans of growth strategies. With this tool, we are able to anticipate our sales within reason that then can allow for us to control our costs in conjunction with inventory which will then help us to enhance our customer service. Sales forecasting is a vital strategic tactic in our company’s methodology.
Planning and Forecasting is a vital function of management especially as it is related to inventory management. Planning has four processes associated with it. They are establishing goals, formulating strategies, implementing the plan and evaluating its success. The planning process of inventory will assist the organization choose the correct inventory system resulting in reduced costs and increased efficiency. For any business, having large amounts of inventory could prove to be expensive. In most company’s the management team will forecast sales on a monthly basis in order to keep enough inventories to fill customer orders in a timely fashion but not have an overflow of stock. There are various types of
In an article published on the Harvard Business Review website we see that “a factory needs to be flexible and respond to customer orders quickly” (Upton 1995). As the global market for products is growing factories need to be able to keep up with the demand of products in many more countries than they had in years past. Managers have to make decisions on whether or not to expand a factory or build a new factory and the decision between the two have large differences in cost. Forecasting can help managers decide if they should expand or not expand. Capacity does not need to be crowded because that can lead to decrease in quality of products and can cause underproduction. Capacity decisions are one of the toughest decisions manager have to
| "Dynamic models, usually computer-based, that allow the forecaster to make assumptions about the internal variables and external environment in the model" is a definition for which of the following forecasting methodologies?
The manufacturer in China independently performs sales forecasting as well as the permission to produce product intended to meet the predicted demand (University of Phoenix, 2006). This plant purchases
M&L Manufacturing Company is an example of a company that could benefit from forecasting. In the past the company has made an educated guess to determine necessary production for
But even this is not possible in case of a new product or innovation. A forecast of sales, demand, cash, requirements and several such business valuables are extremely essential for a business in order to be able to appropriately plan and conduct its operations in an effective and efficient manner. Yet, forecasts cannot be made accurately as there are several factors and changes in the current environment that leads to variations in forecasts and impacts or causes a manager to make changes in the forecasts.
Coca-Cola is a multi-national corporation that has its headquartered in Georgia, Atlanta. The company involves itself in the business of beverage production. Subsidiary plants are located all over the world i.e. Asia, Africa, Europe, Australia and South America. Since the company has many branches and deals with a whole lot of inventory, there is a need to act proactively and timely so that there are no delays in logistics. Projections of inventory, i.e. inventory forecasting, are often done in the operations and supply chain management so that overproduction and inefficiencies are avoided. Coca-Cola also uses historic data to find out the direction of future trends (Butler, 2015). The process is used by the company to determine near accurate allocation of budgets for future production periods (Jacobs, 2013). There is a need for Coca-Cola as a company to find out the events that affect the company production wise. The occurrences include expectations of sales, and whether they will cause an increase or a decrease in the share price of the company. Nonetheless, forecasting provides a necessary benchmark for Coca-Cola, which has a long-term perspective of operations.
Forecasting mode has become a necessity for the organization to lean on. Borousan claimed that implementation of an accurate forecasting process helps companies is trying to obtain financing from investors. This method is a prediction based on previous sales performance and analyse of expected market conditions. There is important thing for
Based on the case, there were two fundamental changes to standardize and improve the accuracy of forecasts. The first area was to "switch the focus of the focus of the forecasting process from sell-in to sell-through". This meant tracking closely what was sold in one region and shipped from another made forecasting market demand a more accurate exercise. The second area centered on ignoring capacity constraints to estimate demand. In the past, "forecasting was affected by perceptions of present and future supply chain capacity".
Forecasting demand is the art and science of predicting future demand. There are several different techniques that can be employed alone or in combination with each other, depending upon the firm’s particular situation and the point in the product’s life cycle, and they are further classified as to the time horizon they represent. Forecasts are generally quantitative (relying on historical data) or qualitative (such as variable personal experiences).