Introduction
Australian industry is developing rapidly and manufacturing is one of the oldest industries employing the largest number of people. Manufacturers play a key role in driving the Australian economy, so modernizing Australian manufacturers is a priority not just for the government but for businesses to remain competitive in the global marketplace. Efficiency is the key in the production process and manufacturers need to be able to produce according to a "plan", which is carefully formulated and takes into account all the ups and downs of market demand as well as the factors related to the supply chain.
Manufacturers should be able to predict the demand in the market to generate enough inventory of items, not more than enough to increase the cost of stocking and possibly expirations, and not less than enough stock to empty and leave the customer with no choice but to look for alternatives in the market. The word "forecast" may sound simple, but in reality, experienced production planners know that finding the magic number is not easy. Despite the advancements in technology, results from the surveys reveal that this process is people-oriented, meaning that the production planner runs the analysis based on his or her own experience.
The challenge is that making mistakes leads to expensive production costs, which manufacturers should try to avoid. In this short article, I will outline the main challenges of demand forecasting, and then describe the various requirements forecasting methods that might be used. I won't go into the details of each method (maybe in another article I'll cover these methods in more detail). It is expected that it summarizes the top challenges for Australian manufacturers to do demand forecasting and highlight the need for the use of modern new technologies like AI and machine learning to achieve it.
Challenges in Demand Forecasting
When it comes to demand forecasting for manufacturers in Australia, there are some challenges that they are facing if they don’t get the numbers right. Here is the summary of the top ones on the list.
1. Forecasting demand too low
Planning to meet needs that are less than what you need will leave you scrambling to catch up. When something unexpected happens and you don't have enough materials and labour, you end up with delayed shipments because you have to scramble to find workers and what you need to make the product. Most companies deliberately avoid forecasting demand too low because they can't find additional materials and workers fast enough without incurring huge costs.
2. Forecasting demand too high
Planning to meet more demand than is realized leads to overstaffing and inventory. This will result in overtime costs and potential inventory costs. It is also expensive to anticipate excessive demand because overtime costs can mount rapidly and morale drops when employees have to do nothing. Low morale can lead to attrition, which puts your company back in a chaotic mode of finding employees as the costly cycle of relying on inaccurate forecasts continues. You also run the risk of overproduction, which will tie up your funds while finishing the finished product.
3. Supply chain dependencies causing last-minute changes
Your companies on either side of the supply chain can affect your ability to keep promises. Any shortfall in your demand projections will amplify this. Suppose your supplier ships you late and you predict demand for 100 units this week. You only have enough material on hand to produce 80 pieces, so 20 pieces are on hold until they arrive. Now you can ship it late, or you can ship it as soon as the order comes in, on time. In this case, if you get an order and want to ship it on time, once the goods arrive, you have to find extra workers or have your existing staff work around the clock.
4. Forecasting demand too early
Predicting demand too early means you will almost certainly miss your target. The further out you plan, the less accurate your predictions will be. For some businesses, this is less of a problem because their demand is predictable. But for most businesses, the goal is to achieve real-time response to requirements. Getting as close to this point as possible by forecasting demand is the next best thing
5. Relying too rigidly on forecast demand
Having a plan is great, but sticking to it so rigorously that you can't respond to the reality around you is a surefire way to get your business into trouble. No matter how you forecast demand, it will have a degree of inaccuracy. It is common to predict demand and claim 95% accuracy, meaning that there is an acceptable range of variation from the predicted demand level of 5%. This can translate into significant costs associated with overstaffing, or not being prepared even in a best-case forecast scenario. More likely, it's not even 95% accurate. Relying too much on forecast demand can lead to companies not paying attention to what is happening around them and making changes to respond to the situation.
These challenges significantly impact the efficiency of the process as well as the financial impact of the business. As I said earlier, many production planners are still doing some basic analysis or just guessing demand based on their experience. But in the next section, I'll introduce some of the approaches that might be used here.
Method of Demand Forecasting
No one can predict the future with absolute certainty. However, there are several requirements forecasting techniques that can help you make an educated guess. Using predictive models will help you make better business decisions. There are many different ways to create forecasts. Here are five of the top demand forecasting methods.
1. Trend projection
Trend projection uses your past sales data to project your future sales. It is the most straightforward demand forecasting method. It’s important to adjust future projections to account for historical anomalies. For example, perhaps you had a sudden spike in demand last year. However, it happened after your product was featured on a popular television show, so it is unlikely to repeat. Or your eCommerce site got hacked, causing your sales to plunge. Be sure to note unusual factors in your historical data when you use the trend projection method.
2. Market research
Market research demand forecasting is based on data from customer surveys. It requires time and effort to send out surveys and tabulate data, but it’s worth it. This method can provide valuable insights you can’t get from internal sales data. You can do this research on an ongoing basis or during an intensive research period. Market research can give you a better picture of your typical customer. Your surveys can collect demographic data that will help you target future marketing efforts. Market research is particularly helpful for young companies that are just getting to know their customers.
3. Salesforce composite
The salesforce composite demand forecasting method puts your sales team in the driver’s seat. It uses feedback from the sales group to forecast customer demand. Your salespeople have the closest contact with your customers. They hear feedback and take requests. As a result, they are a great source of data on customer desires, product trends, and what your competitors are doing. This method gathers the sales division with your managers and executives. The group meets to develop the forecast as a team.
4. Delphi method
The Delphi method, or Delphi technique, leverages expert opinions on your market forecast. This method requires engaging outside experts and a skilled facilitator. You start by sending a questionnaire to a group of demand forecasting experts. You create a summary of the responses from the first round and share it with your panel. This process is repeated through successive rounds. The answers from each round, shared anonymously, influence the next set of responses. The Delphi method is complete when the group comes to a consensus. This demand forecasting method allows you to draw on the knowledge of people with different areas of expertise. The fact that the responses are anonymized allows each person to provide frank answers. Because there is no in-person discussion, you can include experts from anywhere in the world on your panel. The process is designed to allow the group to build on each other’s knowledge and opinions. The result is an informed consensus.
5. Econometric
The econometric method requires some number crunching. This technique combines sales data with information on outside forces that affect demand. Then you create a mathematical formula to predict future customer demand. The econometric demand forecasting method accounts for relationships between economic factors. For example, an increase in personal debt levels might coincide with an increased demand for home repair services.
Conclusion
This article gives ideas about why we need demand forecasting, challenges of demand forecasting, and methods of demand forecasting. Demand forecasting helps drive informed business decisions. It doesn't matter whether you choose simple or complex methods. Demand planning based on sales data, market research, and economic factors will help your business stay strong. The accuracy of the forecast also affects overruns and underruns in production, further reducing time and resource waste.
Sharon Li, Business Analyst
Sharon is an IT business analyst at High Tech Masterminds. She also works on ICT and technical support for bridging the gap between IT and business using data analytics to assess processes, determine requirements and deliver data-driven recommendations.
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References:
https://resource.optimalnetworks.com/blog/2018/07/10/implementation-vs-adoption-technology https://www.apty.io/blog/erp-adoption https://www.acumatica.com/blog/what-is-the-erp-implementation-process/ https://theoreminc.net/5-customer-engagement-challenges-how-to-fix-them/ https://www.apty.io/blog/erp-adoption
Top challenges for Australian manufacturers for demand forecasting