1. There are many ways to forecast business trends and all of them fall under two extensive categories like qualitative & quantitative. Let’s discover more about these models:
2. The Qualitative Approach - This model is useful with short-term assumptions where there is lesser scope for forecasting. The figures in this method can be expert-driven depending on the view of the market experts or the trend of the market. If you want to strategize a successful plan for the short-term, then the qualitative model is what you should seek. The model does have its limitation in regards to the measurable data, but then again, it is an assumption on the 1st hand.
3. Delphi model where the field experts are asked for their general opinions which are later on compiled into the forecast.
4. Conducting market research where the opinions of a large number of people are taken on a specific service or product to see how many people use it and what their feedback is.
5. The Quantitative Approach - The quantitative model rules out any human assumption and depends on data generated by computers. This is because the qualitative model is infused with fickle and hasty assumptions that are too risky to be counted in. With the quantitative method, variables like real estate, GDP, and other long term measurements are gauged
6. The time series method comprises of different ways that are used to predict the events ahead. This model uses fine calculations and details, like providing more reliable data. Through tracking the past figures, the time series approach gives a more trustworthy view of the average future prediction.
7. An econometric model is a mathematical approach for indicating the figures. This model analyzes the internal consistency of data sets over time and how the information strengthens as time graduates. The model is many times used as custom indicators used in the academic fields for evaluating its economic policies.
8. The indicator model analyzes the relationship between unemployment rates, GDP, and other economic aspects. Close monitoring determines the lagging indicators in any strategy that will make the economy face a dump.
LIMITATIONS OF BUSINESS FORECASTING
1. Live data generated will always become old at some point. Future does not guarantee in a foolproof way about the validation of the generated data.
2. Unexpected events or any other externalities can cause a sudden meltdown of the generated data and set assumptions ruling out every alteration.
3. Forecasts have their own impact, which can be accurate or inaccurate. The business actions are based on factors that cannot be included as variables. This concept has a knot and in the undetermined scenario, the hierarchy becomes too much dependent on the figures.
ERP PLANNING METHODS ARE OFTEN SIMPLISTIC RULES OF THUMB
1. ERP systems will always offer min, max, safety stock, reorder point, reorder quantity, and forecasts to drive replenishment decisions.
2. But what about the underlying methods used to calculate these important drivers? In nearly every case, the methods provided are nothing more than rule-of-thumb approaches that don’t account for demand or supplier variability.
3. Some do offer “service level targeting” but mistakenly rely on the assumption of a Normal distribution (“bell-shaped curve”) which means the required safety stocks and reorder points recommended by the system to achieve the service level target are going to be flat out wrong if your data doesn’t fit the ideal theoretical model, which is often gravely unrealistic. Such over-simplified calculations tend to do more harm than good.
4. Most often, if you were to implement a new ERP solution, your old data would be stranded. So, any native ERP functionality for forecasting, setting stocking policy such as Min/Max, etc., cannot be used, and you will be forced to revert back to cumbersome and error-prone spreadsheets for at least two years (one year to implement at earliest and another year to collect at least 12 months of history).
EVALUATING ERP FORECASTING FUNCTIONALITY
1. Consider what is meant by “demand management”, “demand planning”, and “forecasting”. These terms imply certain standard functionality for collaboration, statistical analysis, and reporting to support a professional demand planning process.
2. However, in most ERP systems, “demand management” consists of executing MRP and reconciling demand and supply for the purpose of placing orders, i.e., “order management.”
3. It has very little to do with demand planning which is discrete process focused on developing the best possible predictions of future demand by combining statistical analysis with business knowledge of events, promotions, and sales force intelligence.
4. Most ERP systems offer little statistical capability and, when offered, the user is left with a choice of a few statistical methods that they either have to apply manually from a drop-down list or program themselves.
5. It’s baked into the order management process enabling the user to possibly how the forecast might impact inventory. However, there isn’t any ability to manage the forecast, improve the quality of the forecast, apply and track management overrides, collaborate, measure forecast accuracy, and track “forecast value add.”
MODERN ERP SOLUTION WITH REAL-TIME DATA AND BUSINESS INTELLIGENCE
1. Modern ERP solutions give businesses greater control and insight over the data being generated each and every day. From the front office to the warehouse, you can use ERP to extract and analyze data in a number of ways and for a number of reasons. ERP delivers several forecasting capabilities that support a clear plan for a successful future:
2. Demand forecasting: Determine which time of year is busier than others or which products are trending. ERP enables you to prepare demand forecasts by customers, products, geography, as well as seasons. Knowing what’s coming up positions your managers for making strategic, profitable decisions with procurement, scheduling and labor management.
3. Financial forecasting: Keep your finger on the pulse of income and spending. Monitor expense reports and budgets in real-time, predict capital requirements or product demands, and watch for changing costs which can impact profit margins. With real-time data, managers can watch operational or customer-specific spending in real-time and react faster to deviations.
4. Supply forecasting: Planning procurement, production and distribution activities are essential for meeting customer demands. Strengthen relationships with suppliers and get a tighter grip on inventory within ERP. Understanding supplier lead times, and preparing for back-up, could mean the difference between delivering on time and production shut-downs and backorders.
5. Job Scheduling: Although not traditionally seen as an ERP forecast, job scheduling relies on analysis of previous job data and a reaction to this analysis. You should know how long each operation will take based on engineering standards or past history. Without ERP forecasting you cannot simply expect a production job to be at a work center at 10:14 PM and arrange for components to be on hand at exactly that moment.
6. External Forecast Integration : Not only do you have our own ERP data to analyze but you can add all kinds of external data looking for correlations and a basis to possibly improve our forecasts. Governments all have a variety of indices they publish. The Dow Jones has a long history and many forecasts of its future value. Scientists predict El Niño weather and global warming. These and many other data sets can be integrated with your ERP forecasting.
7. Capturing and analyzing data isn’t easy when using disparate business systems. ERP offers built-in business intelligence (BI) features and reporting capabilities. Role-tailored dashboards, charts and other visualizations, as well as detailed reporting templates, streamline data capture and analysis. Dashboards can be configured to meet the needs of each manager.
8. For example, sales leaders can monitor the entire sales cycle from lead to post-sale follow-up. They can extract key metrics and identify potential trends in product or customer behavior. Then, your leaders can use this insight to develop stronger marketing campaigns, make prudent procurement decisions and schedule production operations to meet these trending opportunities.
FACTORS TO A SUCCESSFUL INTELLIGENT ERP FORECAST
1. Explore data nuances with smart insights - With intelligent forecasting features available in modern ERP, you can use natural language and visual explanations that can simplify the most complicated set of data & nurture clarity. Machine learning and demand analytics allow financial planners to manage and compare the limitless amount of data to minimize data redundancy and promote efficiency.
2. Create an insight-driven culture - Manufacturing forecasting software systems give businesses quick access to departmental data with ‘search to insight’ features and conversational AI. It lets you generate visualizations in real-time to understand underlying business trends for the future.
3. Cross-validate forecast result - Intelligent forecasting can considerably increase forecast accuracy. Users can cross-validate forecast results and access large-scale data sets and manage them in a library of state-of-the-art models. You can use hindsight to previous actual values on the basis of algorithms.
4. Predict the future with accuracy – foster growth - When data is stored on a single platform and is organized properly, forecasting becomes hassle-free. Intelligent ERP helps the organization choose a definite course of action with the purpose of fostering growth and success.
5. Less errors and expedited operations with statistical methods - SAP Business One uses statistical methods to evaluate large chunks of business data and historical events. Embedded with Big Data technology, SAP Business One Hana empowers you to visualize the information in insightful charts for easy viewing and manage the data in real-time.
Source:
http://www.exactlly.com/blog/index.php/business-forecasting-with-erp/#:~:text=Financial%20Forecasts%3A%20ERP%20predicts%20the,long%2Dterm%20projects%20are%20considered.
https://www.optiproerp.com/blog/forecasting-made-easy-for-manufacturers-with-intelligent-erp-software/
https://www.realsteelsoftware.com/blog/how-are-erp-solutions-helpful-in-forecasting/
https://smartcorp.com/blog/erpforecasting/
https://www.erpfocus.com/five-ways-erp-forecasting-can-improve-business-efficiency-1804.html
