Article
– should you outsource it or keep it in-house?
However, there is a downside to outsourcing forecasting. It can be argued that the quality of the forecasting process as a whole may be affected, as the decision-making that takes place in-house will be based on a forecasting result that has been created outside, thus lacking the understanding of how the forecast result has been obtained. In other words, outsourcing forecasting can be seen as a “black box” in which you input historical sales data, and you get an output which represents your future sales, but what happens in-between is completely unknown. Needless to say, “black box” solutions may not always please everyone.
Another consequence of outsourcing forecasting with a “black box” setup is the difficulty of enriching the outsourced forecast baseline. That is, if you are a company that has outsourced the generation of the forecast, you cannot know how much information, such as promotions, capacity constraints, sales strategies etc., has already been incorporated in the calculation of the resulting baseline. Therefore, enriching forecasts with market intelligence can become a big challenge for demand planners and could even worsen the quality of the final forecast.
We all agree that the quality of the forecast is the key driver when considering different forecasting strategies. Generally, companies providing forecasting services use very advanced and quite complex forecasting algorithms. Even though methods like machine learning or advanced statistical models may sound interesting, scientific literature shows that using complex algorithms does not create better results than simple techniques. Advanced forecasting algorithms have shown to be good at predicting the past, but what about the future? Are these methods able to capture the impact in the forecast of future events such as capacity issues, increased sales, promotions or changes in market conditions? The answer is, possibly, no. These types of events are extremely difficult to predict simply by using advanced history-based statistical models. Spyros Makridakis, one of the world’s leading experts on forecasting, argues that complex statistical models fit past data well but do not necessarily predict the future accurately, whereas simple models do not necessarily fit past data well but predict the future better. In short, no advanced statistical models will be accurate because the future, in fact, cannot be written.
READ MORE about simple forecasting in Spyros Makridakis’ book: Forecasting: Methods and Applications.
Keeping supply chain parts and partners working together is already a big challenge in modern organisations, and outsourcing forecasting may not influence in the desired direction. In particular, outsourcing may lead to a loss of control in the forecasting process, which can result in a loss of control in the overall planning processes as well as organisational fragmentation. For example, Marketing, Sales and Finance will no longer talk to Operations in the same way as if a collaborative demand forecasting process would take place in-house. We have seen that when forecasting is outsourced, each organisational area does its own prediction for future sales, that is, Finance will do a value forecast, and Operations will do a volume forecast.
When outsourcing forecasting, there is not only a risk of losing control of business processes, but there is also a risk of losing people with specific knowledge and expertise in demand planning. Outsourcing will naturally lead to a reduction of the number of demand planners needed, which poses a risk of losing valuable knowledge which is not easy to reacquire.
Finally, the last point that needs to be considered here is the willingness to transfer critical information outside the organisation. Security concerns may arise when considering outsourcing forecasting, as some of the inputs required for generating a demand forecast consist of sensitive information such as product launches, future trends or expansion strategies.
Keep it simple – keep it in-house
At this point you may be wondering – why not keep forecasting activities in-house?
We see many arguments to decide to keep forecasting capabilities within organisations.
First, by maintaining the planning process in-house, not only do we ensure a higher degree of control of operations and processes, but also a higher degree of flexibility and reactivity. Imagine, for example, that your company is facing an unforeseen event such as a supply shortage that will have an important impact on the forecast in the future months. In an outsourcing scenario, you will need to wait until the next forecasting cycle to incorporate this market knowledge in the forecast calculation. However, in the case where forecasting is done in-house, you could exceptionally recalculate your forecast to see the impact on your baseline right away and adjust your supply chain operations to mitigate it.
Second, forecasting is also about incorporating market knowledge to your demand prediction, and who, if not your internal demand planning team, can do this task better? Over time, demand planners have acquired relevant company- specific knowledge that should not be ignored. Having the knowledge of why the forecast values look like they do enables fully informed decision-making, increasing the degree of visibility and transparency across the supply chain network.
Third, in-house forecasting means being in control of the company’s intellectual property, as sensitive data is not shared with external providers. In addition, the process becomes less complex because no data loads are required, which we have observed in some companies to be quite tedious to manage.
And last but not least, forecasting can be kept in-house and done in a simple way! There is no need for fancy algorithms to predict your future demand. Simple statistical forecasting methods are, at least for repetitive decisions, typically superior to expert judgements. They are also easy to understand and are therefore easier to add input to. Thus, proactive and robust sales forecasts can be created by combining simple and easy-to-understand statistical forecasting methods with the market knowledge of the people in the organisation. With simple statistical methods, higher stability can be achieved, enabling better decision-making. We have seen that with outsourcing, forecast results vary significantly from one release to the next, making it difficult to make consistent decisions over time. However, when forecasting is done in-house using simple methods and applying simple criteria and rules, we can achieve a more stable forecast signal, thus allowing a superior decision process.
