One of the biggest questions in establishing a new business forecasting process is the level of detail of the forecast from a product dimension as well as a geography/channel dimension. The third key dimension is time. The process stakeholders in operations usually want to drive all of the forecast dimensions down to the finest level of time detail, which is typically weekly, while finance is focused on the quarterly reporting time period and the monthly forecasts which build up into that time period.
The process owner can’t make everyone happy, or can they?
What are the factors which should drive that decision? The first issue is always whether there is the organizational bandwidth to go to weekly forecasting. If true weekly forecasts are desired then the organization needs to have the people and processes in place to manage and be accountable for the weekly numbers. A simpler way to put it is never plant a lawn bigger than the one you want to mow.
A second factor is whether or not the new business process system eliminates at least half of the Excel spreadsheets that have the word ‘Forecast” in their title. If it doesn’t, then the new system is probably forecasting at a level which is going to be too high for the needs of your organization. That’s a sure sign that there is a need for weekly forecasting as well as other drill downs on product and geographic/channel attributes.
Do weekly and monthly numbers ever have to true up? If they do, you probably need to support both weekly and monthly in a formal system and have business rules to convert the forecasts back and forth between weekly and monthly.
Regardless of your decision, software like RoadMap GPS has the flexibility to operate in a weekly or monthly mode, as well as the ability to switch back and forth between modes at the touch of a button.
No industry in the world is as exciting as, or more difficult to forecast than consumer electronics, where waves of better, faster and cheaper products flood the marketplace every six months. RoadMap’s technology planning software was developed in concert with industry leaders such as Dell Computers, General Electric and Samsung.
As with the Life Sciences industry, product life cycle management is critical to an accurate forecast. RoadMap’s ability to phase-in and phase-out forecasts for product lines, as well as the ability to forecast price compression curves are essential to producing accurate forecasts.
In many consumer electronics industries, such as printers and copiers, forecasting sales of spare parts and consumables is as important as forecasting original equipment. Higher margins on sales of these service items allow the manufacturers to lower introductory prices of new models of equipment. However, without accurate forecasts of consumables, it is impossible to forecast margins and overall profits.
RoadMap’s ability to forecast across multiple hierarchies allows high technology companies to forecast their sales by brand, by end-user segment (Home vs. Small Business) as well as by classes of product (e.g. equipment vs. consumables).
How to Quench Your Thirst for Knowledge from Big Data
Daily Point of Sale Data from 3,000 stores for Ten of Thousands of SKUs
Daily RX Data for Hundreds of Thousands of Individual Patients
Real Time Demand for Electricity At Each Point Along a 250,000 Square Mile Electric Grid
We all thirst for information and learning from these huge data sets. But, in the time it take to read this post, terabyte upon terabyte of transactions data has been piling up in corporate databases around the world. How can we harness the power of Big Data, while not drowning in it? Identify the Strategic Drivers of Your Business By identifying what is really important to know in your business and focusing on analyzing just those issues, you can cut down the real time and daily data requirements for your business. Key Performance Indicators or KPIs have been around for a generation. It’s very important to have a subclass of these indicators which are real time KPIs. This enables you to whittle down you Big Data set into hopefully a manageable one. These KPIs can be as unique as the industries that they are in.
Mobile Phones: Pre-orders for Soon to be Launched Phones
Fashion Apparel: Color Mix Across all Styles and Lines
Pharmaceuticals: List of Patients More than 7 Days Overdue for a Refill
Utilities: Hourly Sunshine and Temperature Forecasts by City
On-line Retailer: Recommendation Engine
Using Automated Tools to Analyze the Data As Yogi Berra once said, “you can’t think and hit at the same time” – meaning that a ball player does not have a lot of time to think about whether or to swing or not and what type of swing to take, it must be a trained instinct. In a similar manner, predictive analytics for Big Data must be analyzed automatically. In other words, if it takes you a day to some up with a forecast of tomorrow’s sales, you are never going to be able to come up with a forecast in time to make a decision based on the forecast. RoadMap’s forecasting algorithms make extensive use of re-entrant coding as well as in-memory data caching which allows for fast and efficient automated processing. Look for a Total Solution, Not an ERP Suite or a Single Package Depending on the complexity of the predictive model, your existing suite of software may just not have enough horsepower. That’s why open source projects such as the R Project for Statistical Computing can be such as valuable part of the total solution. R, an Open Source project which is supported by numerous corporations, universities and hospitals has developed over 5000 statistical models called packages. So, if your Big Data analysis engine needs to be turbocharged, it’s a good bet that there will be some predictive model in R which will help fill the bill. Your analytic needs may also exceed your IT infrastructure. That’s why cloud based solutions work so well with Big Data. IT departments usually work off a 18 month long backlog of applications requests. Strategic projects can’t wait 18 months; they have to be done immediately, with whatever resources are available. In summary, corporations that can cut their predictive analytics down to size by focusing on strategic imperatives, develop automated systems, and use multi-vendor approaches, which may even include open source or could solutions, stand the best chance of gaining business insight.