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Demand-Supply Analysis Explained

Demand-Supply Analysis Explained

In a market economy, the level of demand and supply of all goods and services jointly determines the price level and quantity of that good (or service) in the economy.

A Little more on What is Supply Demand Analysis

The law of demand states that (with a few exceptions) as price rises, the quantity demanded of any good or service would be lower. The law of supply implies that higher the price received by a supplier, the quantity supplied will rise. Thus, demand is often a downward sloping curve in the price-quantity plane, while supply is an upward sloping curve.

The intersection of the supply and demand curve denotes the market equilibrium, which in turn determines the equilibrium levels of price and quantity of the particular good (or service) in the economy.

If the present demand for a good (or service) in the economy is higher than the equilibrium quantity, the situation is described as that of an “excess demand”. Excess supply is also defined in a similar fashion.

Changes in Supply and demand (and thus the equilibrium price and quantity) of any good or service could be governed by a lot of factors, such as:

• changes in policies,
• unpredictable shocks to the economy,
business cycle fluctuations like a recession or a boom, or
• even simply over time (long run versus short run).
• It also depends on the nature of the market (whether the market is perfectly competitive or monopolistic etc.).

The analysis of all the above could be termed as the study of the supply and demand, or simply, ‘Demand Supply Analysis’.

References for Supply and Demand Analysis

References for Demand-Supply Analysis

  • · Entry and empirical demand and supply analysis for competitive industries, Veloce, W., & Zellner, A. (1985). Journal of Econometrics, 30(1-2), 459-471. This research aims to provide an improved look at price, output, and yield across competitive industries. Using data taken from the Canadian househould furniture industries, this study attempts to improve on earlier theoretical and empirical models that don’t accurately account for entry cost and demand.
  • · The market for public accounting services: Demand, supply and regulation, Benston, G. J. (1985). Journal of Accounting and Public Policy, 4(1), 33-79. This research demonstrates that in the field of public accounting, owners and their employees benefit from monitoring. Through the research the demand for public accounting is derived, and comparative advantage and regulation is analyzed. The paper concludes with a real-world case study by applying their analysis to the 1972 U.S. House and Senate committee staff reports.
  • · Supply, demand, and technology in a multiproduct industry: Texas field crops, Shumway, C. R. (1983). American Journal of Agricultural Economics, 65(4), 748-760. This research examines the levels to which different Texas field crops produce different yields. Variables in this study include technology, supply, and demand for the production of the crops individually and in mixed ratios. Relationships between outputs, fertilizer, and labor are also examined.
  • · Analysis and design of focused demand chains, Childerhouse, P., Aitken, J., & Towill, D. R. (2002). Journal of Operations Management, 20(6), 675-689. This research uses the long-term case study of a UK lighting manufacturer to examine facility layout and supply chain organization. The case study follow’s the manufacturer over time to analyze changes in engineering, supply chain management, and consumer preference, all in relation to varying levels of business performance. Results determine that careful analysis in product selection can have substantial savings in manufacturing costs and lead times.
  • · Creating satisfaction in the demandsupply chain: the buyers’ perspective, Cambra-Fierro, J. J., & Polo-Redondo, Y. (2008). Supply Chain Management: An International Journal, 13(3), 211-224. This paper analyzes the concept of satisfaction in relationships between firms and suppliers. Set within the context of Spanish industry, it finds that suppliers are able to pinpoint the elements that affect satisfaction, and if they wish to cultivate long-term relationships with companies, they need to be able to identify these values. This research uses both quantitative approaches and model-based analysis in reaching these conclusions.
  • · Information sharing in a supply chain: A note on its value when demand is nonstationary, Raghunathan, S. (2001). Management science, 47(4), 605-610. This paper examines how a manufacturer can effectively use a retailer’s point-of-sale (POS) data. By using analysis and simulation, the researchers determine that if a manufacturer has a large enough body of historical POS data, intelligent use of this data can help them more accurately predict future orders. These findings can also help prevent uneccesary expenses in improving manufacturer information systems.
  • · Planning nervousness in a demand supply network: an empirical study, Kaipia, R., Korhonen, H., & Hartiala, H. (2006). The International Journal of Logistics Management, 17(1), 95-113. This research examines the planning processes in a supply network that experiences rapid market fluctuations and product changes. Interviews were conducted with decision-makers at various points in the system, and a data analysis was created to analyze the quality of the plans. The results demonstrate the differences in the effectiveness of planning, and the relationship between planning and the bullwhip effect.
  • · Demand, Supply, and Price Relationships for the Beef Sector, Post-World War II Period, Langemeier, L., & Thompson, R. G. (1967). Journal of Farm Economics, 49(1_Part_I), 169-183. This research examines the supply and demand relationship at work in the beef industry. The research builds on and refutes earlier studies to show that different types of beef have different demand levels, and that these demand levels can be inter-related with each other as well as being effected by the changes in levels of consumer income.
  • · A decision support framework for global supply chain modelling: an assessment of the impact of demand, supply and lead-time uncertainties on performance, Acar, Y., Kadipasaoglu, S., & Schipperijn, P. (2010). International Journal of Production Research, 48(11), 3245-3268. This research employs a simulation model to demonstrate new ways to optimize a supply chain network. Different variables can be introduced to create various outcomes. Those results can be studied to provide more efficient delivery of goods and greater levels of customer service.
  • · Demand-supply mismatches and stock market reaction: Evidence from excess inventory announcements, Hendricks, K. B., & Singhal, V. R. (2009). Manufacturing & Service Operations Management, 11(3), 509-524. This paper shows that over a 12-year period there is a provable relationship between announcements of excess inventory and negative stock market movement. By using actual announcements and analysis of historical data, differences can be seen in announcements made by firms of varying size, debt-level, as well as differences made in certain types of inventory announcements.
  • · A structured analysis of material requirements planning systems under combined demand and supply uncertainty, Brennan, L., & Gupta, S. M. (1993). The International Journal Of Production Research, 31(7), 1689-1707. This research examines the effect that changes in demand and upredictable lead times can have on systems that form long-term plans. Using simulation models and statistical analysis, various outcomes are explored. The effects of multiple variables such as product mix, cost rations, and lot-sizes are also examined. The effects of inter-relationships of these variables are also explored.
  • · The dynamic effects of aggregate demand, supply and oil price shocks—a comparative study, Bjørnland, H. C. (2000). The Manchester School, 68(5), 578-607. This paper examines the effect that changes in demand, production, and price of oil effects the GDP and unemployment levels in major US and European countries. Various price shocks over time are examined, and statistical models help identify and explain them, including those that are related to the recessions of the 1970s and 1980s. The results show that in most cases price shocks negatively affect output.

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