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Profitable use of Forecast Services

Profitable use of Forecast Services

Matching supply and demand ensures sustainable corporate success

Outsourcing non-core business processes is a proven way to reduce costs while simultaneously enhancing performance. Initially enterprises focused on functions such as finance and HR but have now turned to outsourcing supply chain processes such as demand forecasting, enabling them to focus on their existing business processes while gaining access to leading-edge expertise and capabilities. The main reason, effort should be put into demand planning is that with an accurate forecast you ensure that you manufacture the right products in the right amount at the right time. This means that no products must be sold with a smaller margin, or sales lost as a result of stockouts. Both decrease the profitability of a business.

Essentially, four key figures can be optimised with better forecasting:

Demand planning as a major challenge

Shorter product cycles, pricing wars with competitors, and high volatility in demand make the world of demand planning difficult to succeed in. Effective demand planning requires a great deal of work and, to move forward, companies must invest in highly trained employees, expensive software systems, and continuous improvement programs. Savings can be made in all these costs if a company opts to outsource demand planning and tasks experts with doing the work. Additionally, analytics in general are becoming increasingly important for most enterprises: industry and forecasting experts help provide analytics expertise and capabilities.


Performance improvement through Forecast as a Service (FaaS)

Small and medium-sized enterprises are increasingly turning to forecasting services to help grow and scale their operations.


Advantages in cloud-based forecast services in comparison to traditional on-site ERP solutions:

 

Manufacturing businesses, in particular, aim to gain a competitive advantage and boost their operational performance. Optimised demand forecasting can simultaneously keep working capital low and minimise lost sales. With TenglerGluttig’s forecast engine, enterprises of all sizes can increase their planning accuracy and therefore move closer to operational excellence.

How to effectively use Forecast as a Service?

The FaaS process begins with providing all relevant sales history data on Stock Keeping Unit (SKU) level and adjusting for non-repetitive influencing events and missing values. After cleaning the data, a forecast engine based on various algorithms generates accurate demand forecasts. A mix of sales history and current market intelligence is needed to ensure an accurate forecast. Trends and seasonality of sales history based on SKU level must be considered for long-term forecasts, while current market conditions are used to change and adapt forecasts in a shorter period. Since the forecast engine is based not only on statistics but also on relevant market knowledge, expert insights are essential in addition to actuality.


The performance of this statistical forecast will be measured with detailed forecast metrics and benchmarked against the company’s previous forecast accuracy. TenglerGluttig experts will meet regularly with key stakeholders and establish forecast accuracy reports to observe the impact of the collaboration process.

Which key performance indicators can be optimised with FaaS?

Forecast accuracy is directly influenced by the forecast error. One way to measure forecast accuracy is the root mean squared error (RMSE). A statistical forecast engine can effectively decrease RMSE through algorithms, boosting a company’s forecast accuracy.

Forecast value add can be described as the added value in each forecasting process step. If in one step the forecast is less accurate than in the previous step, this particular step has added no value. Manual overrides must add value. If not, they have to be eliminated from the forecasting process. A statistical forecast can serve as a baseline.

Forecast bias is the tendency to over- or underestimate demand. The root of a biased forecast lies in internal processes. Incentives for planners should be based on overall company success – rather than on the objectives of different departments – to reduce bias in a supply chain. Again, a statistical forecast engine works with a cleaned sales history to generate objective forecasts.

How does forecast accuracy influence operational performance?

With forecast accuracy being a main lever against uncertainty, it helps a business to exploit a variety of benefits. An increased forecast accuracy reduces safety stock levels and thus inventory costs which are typically correlated with the forecast error.

                      

Manufacturing systems and logistical processes can be better managed when forecast accuracy is higher, a company knows what to produce, and it is more likely that products will be available when the customer attempts to order them. In summary, forecast accuracy helps to decrease costs and working capital while simultaneously improving customer service. A small improvement in forecast accuracy leads to significant benefits across the entire value chain.

Conclusion

Largely due to a lack of internal resources, companies may rely on spreadsheets, outdated ERP software or rules of thumb. These planning methods inevitably result in poor customer service and excess inventories.

With TenglerGluttig’s forecast engine businesses can gain access to specialised Supply Chain Management expertise as well as the latest trends and technologies in demand planning and therefore sustainably improve your performance.



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