Benefits of Market‐Driven Demand Forecasting Approach Improving service while reducing costs With customer expectations rising companies are pushed to focus more on customer service. They should realise that this service comes at a certain cost, like extra inventory or extra capacity. As margins are shrinking increased costs are often not an option and improving operational performance is the best alternative. However, for most companies this is a hard nut to crack in a globalised economy with long supplier lead times and high market volatility. As we will illustrate using a recent case of Corus Tubes, a steel tubes manufacturer, we believe a good demand planning process is one of the keys to improve customer service while reducing operational costs. Corus Tubes is one of Europe’s leading manufacturers of welded steel tubes, in a range of diameters, for a variety of wall thicknesses and different grades of steel. The main application areas are automotive, construction projects, greenhouses, pipelines and household appliances. A best practice demand planning framework Best practice demand planning processes typically consist of two steps. In a first step, statistical techniques are used to generate a "base" forecast. In a second step, sales, marketing and product development add information to that base forecast. Skipping the first step can result in a waste of valuable time. To create the base forecast sales people will on an individual basis try to mimic their sales budgets in the forecast. Budgets are often a higher level of aggregation in the product hierarchy. This makes the translation into operational forecasts on SKU (Stock Keeping Unit) level a cumbersome task. Statistical methods are far more efficient in executing this task. They know how to incorporate the last period’s sales, how to detect and extrapolate seasonality, how to incorporate leading indicators... Providing a good statistical base forecast will increase the commitment of sales and marketing as it makes their role in the process more focused and more efficient. Some companies try to skip the second step. Supply chain or production departments may distrust sales and marketing and try to predict the market themselves. This is plain self‐deception. Supply chain or production can never be the owner of the forecast. Sales needs to understand that its responsibility does not stop by selling product or services but it has a role in controlling costs and service in the supply chain. It plays that role as owner of the forecast information. A forecast will always be wrong. But as we will illustrate with the case of Corus Tubes, improving the forecast accuracy is beneficial to both costs and service. The customer service level will increase because of the reduced risk for stock‐outs, whereas cost can be reduced by further inventory optimisation based on the forecast. A business case for improved demand planning • Step 1: Simple statistics Using simple statistical methods to generate a demand forecast based on historical data is an important first step in reducing the demand uncertainty. Simulations at Corus Tubes have shown this can lead to a reduction of uncertainty. Before the demand planning approach there was a budget‐driven process based on historical figures, with an equivalent accuracy on SKU‐level of 25%. As a result of the transition towards a demand planning approach based on simple statistics the forecast accuracy on SKU‐level could be improved to 35%. This reduction in uncertainty allows for a service level improvement of 4% resulting in less lost sales and an inventory reduction of 5% based on the right inventory levels for the right product. • Step 2: Advanced statistics In a volatile market, more advanced statistical methods will yield better results as they take into account trends and seasonal effects. Applying these advanced statistics may be more difficult to understand and often requires an expert in house if you want to avoid a ‘black‐box’ feeling. Typically it requires the purchase of a software package. Nevertheless, detailed case calculations have indicated that this could increase the accuracy of your forecast drastically, allowing inventory level reductions to a more optimal levels, both on finished goods and for raw materials. Service will be further improved as stock‐outs are limited, resulting in less lost sales or the possibility of allocating inventories to A‐customers. Forecast simulations for the case at Corus Tubes resulted in an average forecast accuracy of 55%, equivalent for an improvement of the service level of 9% and a reduction of the inventory costs of 10%. • Step 3: Introduce Market Intelligence However generating a statistical forecast can only be just a start. The real benefits are in collaboration with the sales team to come to efficient demand shaping. Their market knowledge and information from key customers can create a tight feedback loop from the actual market activity to the demand assumptions and plans. This demand‐driven process will result in a single demand forecast, adjusted and validated by the company sales team. This validation process can take place on SKU level or on more aggregated levels, like product families of geographical markets. Though often a manual forecast adjustment process, the benefits from the improved forecast accuracy on service and inventory are convincing. Input from Corus Tubes sales people showed that this market‐driven process results in forecast accuracies of 70% and higher. For this steel manufacturer implementing a market‐driven demand planning process means an improvement of the customer service level with 20% and a potential reduction of inventory costs of 20% compared with the current situation. This demonstrates the negative impact on customer service and inventory levels of each functional area having its own version of demand forecast truth. • Step 4: Continuous Improvement Finally continuous improvement and monitoring of the forecast accuracy will allow even more forecast performance improvements. Therefore it is important to create an awareness of the impact of poor forecast accuracy and to measure the forecast performance. In Summary The majority of the companies have some form of demand forecasting system in place, however, they need to focus on becoming demand‐driven in their approach. This means that the company needs to focus on capturing the real customer demand, an approach where the engagement of sales in the process must be ensured at all times. The demand planning process should align the goals of the sales department with the demand planning department with respect to forecasting metrics. With an improved market‐driven demand planning process companies are now able to further optimise inventory levels and furthermore improve service. The case of Corus Tubes, a steel manufacturer, shows convincing benefits with a 24% increase of the company service levels while reducing inventory costs by 22%. What MÖBIUS can do for you MÖBIUS can assist you in building your business case for demand planning improvements. Based on your company specific sales history, we can assess the improvement potential in both inventory and service for introducing simple and more advanced statistical techniques, involving sales and marketing in the demand planning process. Interested? Contact the authors or visit our website on www.mobius.eu. Bram Desmet ‐ Bram.Desmet@mobius.eu
Peter Sterckx ‐ Peter.Sterckx@mobius.eu