Statistical forecasting

Three recycling news items are very briefly reported upon a Canadian-developed pyrolysis technology that converts plastics scrap into alpha-olefins, a scrap-plastics-to-monomers system under construction in Scotland, and statistical forecasts on chemical recycling in Germany for 1996. 

The demand planning module is used for short-term and midterm sales planning. It covers basic statistical forecasting methods, but is also capable of taking additional aspects into account. For example, these may be promotions in shortterm sales planning or the consideration of product lifecycles in midterm sales planning. 

Country Studies. Often managers need an in-depth, yet broad and up-to-date understanding of a country s strategic market potential and situation before the first field trip or investment proposal. There are over 190 country studies available. Each study consists of analysis, statistics, forecasts, and information of relevance to managers. The studies are continually updated to insure that the reports have the most relevant information available. In addition to raw information, the reports provide relevant analyses which put a more general perspective on a country (seen in the context of relative performance vis-a-vis benchmarks). 

The book presents a well-defined procedure for adding or subtracting independent variables to the model variable and covers how to apply statistical forecasting methods to the serially correlated data characteristically found in clinical and pharmaceutical settings. The standalone chapters allow you to pick and choose which chapter to read first and hone in on the information that fits your immediate needs. Each example is presented in computer software format. The author uses MiniTab in the book but supplies instructions that are easily adapted for SAS and SPSSX, making the book applicable to individual situations. 

Recency. Fildes and Goodwin also found that overoptimism tends to lead to erroneous positive adjustments, while negative adjustments are based on more realistic expectations. Finally, they found a bias toward recency—that is, emphasizing the most recent history while treating the more distant past as bunk. This focus on recency tended to undermine the process of statistical forecasting. 

It is also good practice to compare the statistical forecast to a naive forecast. (The naive forecast is a simple technique where the forecast equals the volume of goods sold in the prior forecasting period.) Naive forecasts, in some situations, can be surprisingly difficult to beat, yet it is very important that the organizations ensure that software and a statistical modeler improve on the naive model. The focus needs to be on continuous improvement. If the software, modeler is not able to do this, it makes sense to implement better software, improve the skills of the modeler, or just use the naive model as a baseline forecast. 

In this section, it will be performed a literature review for each one of the four categories of the Demand management - Statistical Forecast, Sales and Operations Planning (S OP), Collaborative Plaiming and Forecasting Replenishment (CPFR) and Vendor managed Inventory (VMI). This review allowed identify the DDSC characteristics for each category which was used to develop the five level maturity model. 

The last case is when a product has low demand variability, and in this case, a data driven statistical forecast should be applied, as it will allow capture the benefits of a push system. The approach described above brings light to help define when a company should be demand driven or forecast driven. Based on Croxton et al. (2002), it is proposed to expand the matrix to also include the tools and approaches that can be used in each one of the three situations, as detailed and illustrated in Fig. 4.4. 

For data driven forecast, it is suggested to apply statistical forecast models, which will generate good forecast accuracy results, and will also automate the forecasting calculation, saving demand planners time to devote to more complicated and/or variable SKUs. 

Quantitative skills because forecasting often involves the use of statistical forecasting methods and algorithms. 

Regarding the steps required to perform a statistical forecast, Makridakis (1998) proposes five steps to forecast when quantitative data is available as detailed in Fig. 4.8. 

Generate information for Sales and Marketing people to use in developing the new forecast Sales analysis data, statistical forecast reports, etc. 

Step 2 Unconstraint Statistical Forecast The second step is to generate the unconstraint forecast, and consists of two elements ... 

Run statistical forecast models to predict future volumes, open by business unit, geographic regions, product family, SKU ... 

No statistical forecast methods or only very basic models (e.g., moving average) are used to plan business volume. 

No forecast tool is in place to automate statistical forecast process. 

Statistical forecast methods (e.g.. Exponential Smoothing, Box-Jenkins, Holt and Holt-Winters) are used to plan business volume for short term period (1 week to 4 months). Combined forecast methods are also used to improve forecast accuracy. 

Performance of planners and forecast are tied to compensation and rewards. Senior management understands, support and value demand planning function. Forecast tools are in place for both short and long term forecast, and are used to automate statistical forecast process, increasing planners capability to simulate different models. 

Both statistical forecast tool and demand visibility in the supply chain are in place to generate forecast and define replenishment needs. 

Same as in level 4, but in addition more than 80% of the company sales volume is sold using a pull system and only 20% remains using statistical forecast (mainly low variability SKUs). 

When used, statistical forecast shows high accuracy performance (greater than 90% on a SKU, week and plant level). 

There is a formal monthly Sales Operations Planning (S OP) process that covers (1) Data gathering, (2) Unconstraint statistical forecast, (3) Demand Plaiming, (4) S OP analysis, (5) Pre-S OP meeting, (6) Executive S OP meeting. 

For Demand Management, Statistical Forecast should be the basis due to the industry still applies the make to stock approach to optimize its asset base and reduce fixed cost. 

For demand management, it is suggested to focus in Statistical Forecast and Vendor Managed Inventory as described below ... 

For Statistical Forecast, it is important to define a process to formally analyze and cluster the SKUs sold in different customers and channels based on sales volume and demand variability, in order to apply an approach that combines statistical forecast for SKUs with low variability and actual POS demand information for SKUs with high variability. It is also suggested to implement a root cause analysis to map and understand the reasons of low forecast accuracy by SKU, and then, implement an effective action plan to fix the problems. 

Brown, R. G. 1959. Statistical Forecasting for Inventory Control. New York McGraw Hill. Chase, C. 2009. Demand—Driven Forecasting A Structured Approach to Forecasting. Hoboken, NJ John Wiley Sons. 

E. Lorenz Empirical orthogonal functions and statistical weather prediction. Tech. Rep. 1, Statistical Forecasting Project, Department of Meteorology, Massachusetts Institute of Technology, Cambridge, MA, 49 pages (1956)... 

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