Supply Chain Planning & Analytics CHAPTER 5

Supply Chain Planning & Analytics CHAPTER 5. Padilla, Levi May MMGT 633 - Operations Management.

What is supply Chain?. According to Ganehsan & Harrison “ A supply chain is a network of facilities and distribution options that perform the function of procurement of material transformation of these materials into intermediate and finished products, and the distribution of these finished products to customers.”.

What Is Supply Chain Analytics ?. Refers to the processes organizations use to gain insight and extract value from the large amounts of data associated with the procurement, processing and distribution of goods..

Importance of supply chain planning & analytics. By analyzing customer data, supply chain analytics can help a business better predict future demand . It helps an organization decide what products can be minimized when they become less profitable or understand what customer needs will be after the initial order..

How do organizations evolve?. Stage 1 summarize and report data to decision makers to inform them about what happened in the past. Stage 2 develop the capability to analyse real-time data to understand what is currently happening. Stage 3 predict what will happen in the future. (in advanced stage uses prescriptive analytical models to determine what should happen)..

Problems may arise ( RESULTS IN POOR SUPPLY CHAIN PLANNING & EXECUTIONS):.

Three major activities:. Demand planning Sales and operation planning Supply planning.

Demand Planning is concerned with developing a point of view on what future demand will be over some forthcoming period of time..

Forecasting methods. Forecasting methods Informal Formal Qualitative Life cycle analysis Surveys Delphi Method Historical Analogy Expert Opinion Consumer Panels Test Marketing Quantitative Time Series Analysis Causal Simple regression Exponential smoothing Moving average Box-Jenkins Econo-metric Multiple regression.

Factors Influencing the Choice of Forecasting Method First, l ong-range forecast/Short-range forecast Second , if the data are available, one quantitative forecasting methods can be used. Otherwise, non-quantitative techniques are required Third, the greater the limitation on time or money available for forecasting, the more likely it is that an unsophisticated method will have to used. Fourth, with the advent of computers, the cost of statistical forecast based on historical data and the time required to make such forecast have ne reduced significantly..

Time Series Analysis Is simply a set of values of some variable measured either at regular points in time or over sequential intervals of time. Components of a Time Series T for Trend S for Seasonal Variation C for Cyclical Variation R for Random Variation.

Trend. The trend shows the general tendency of the data to increase or decrease during a long period of time. A trend is a smooth, general, long-term, average tendency. It is not always necessary that the increase or decrease is in the same direction throughout the given period of time ..

Seasonal Variation. Seasonality in a time series can be identified by regularly spaced peaks and troughs which have a consistent direction and approximately the same magnitude every year, relative to the trend. The following diagram depicts a strongly seasonal series. There is an obvious large seasonal increase in December retail sales in New South Wales due to Christmas shopping. In this example, the magnitude of the seasonal component increases over time, as does the trend..

Cyclical Variations. The variations in a time series which operate themselves over a span of more than one year are the cyclic variations. This oscillatory movement has a period of oscillation of more than a year. One complete period is a cycle. This cyclic movement is sometimes called the ‘Business Cycle’..

Models of Time series analysis. Moving Averages In this method, the moving average is calculated by doing the sum and average of the values mentioned in a time series over periods that are specified on a repetitive basis..

Exponential Smoothing This method determines forecasts by using weighted averages that are based on past observation. More importance is given to recent data or values in a particular series..

Thank you!. To be continued….