How to improve your S&OP thanks to forecast KPI
The S&OP process requires the correct use of demand forecasting methods, as described in previous articles. However, it is essential to efficiently monitor forecasts accuracy in order to improve your supply chain performance. If the forecast accuracy level is low, the production planning process will be impacted causing excessive inventories or product shortages and availability, with consequent delays in deliveries to customers, or in worst cases loss of sales and business. Within this context, selecting indicators to adequately evaluate and monitor the accuracy of a demand forecasting process must be implemented as a key practice. However, this task may be complex, and SKU Science can help you achieve this goal. Some important concepts need to be considered before defining how KPI will be measured, in order to get the most value from their results. Here we present some approaches and methods, with their main characteristics, advantages, and shortcomings. Try our fast & simple demand forecasting solution Sign up for free to SKU Science today!Pre-loaded sample date – No credit card required SIGN UP FREE Key Reliability Parameters The first concept to be aware of is the difference between the precision of a forecast and its bias.First of all, the error is defined as the forecast minus the demand. Our First Forecast KPI: The BIAS The BIAS will represent the historical average error, indicating the overall error direction. The precision will measure how much spread you will have between the forecast and the actual value, indicating the magnitude of errors, which can be visualized in figure 1. Figure 1: Precision and Bias. Hence, the performance of your forecast accuracy indicator can be evaluated for its effectiveness through a quantitative criterion. This will enable you to make better decisions on adjustments, or will direct you toward the use of another metric. MAPE: The Mean Absolute Percentage Error This KPI is measured by the sum of the individual absolute errors divided by the demand (each period separately). This model divides each error individually by the demand; so the main disadvantage of this is that high error levels during low demand periods will have a major impact on its result, compromising the effectiveness of your forecast. For this reason, we recommend your avoiding this particular KPI. MAE: The Mean Absolute ErrorMAD: The Mean Absolute Deviation MAE and MAD are similar. These forecast KPIs are defined by means of absolute error. However, since they are not scaled to the average demand, the result is an absolute number which cannot help directly determine if your demand forecast is good or bad—unless you are fully familiar with numbers from your business. To resolve this issue, it is common practice to divide the MAE by the average demand in order to get a percentage indicator, which then provides a very good KPI to measure your forecast accuracy. RMSE: The Root Mean Square Error RMSE is defined as the square root of the average squared error. Just as for MAE, it is not scaled to the demand, and therefore should be similarly adjusted by the average demand to produce a percentage indicator. Compared to MAE, RMSE does not treat each error in the same way and gives more importance to the biggest errors, so one big error will be sufficient to get a very bad RMSE result. Try our fast & simple demand forecasting solution Sign up for free to SKU Science today!Pre-loaded sample date – No credit card required SIGN UP FREE Important Factors to Consider When Choosing Forecast KPI Measurement When comparing all forecast KPI alternatives, you may have noted that MAPE should be avoided as it allocates a high weight to forecast errors when demand is low. This situation is not observed on the other two, as MAE targets the median demand, while the RMSE aims at the average demand. But what then would be the best alternative? For many products, you will observe that the median is not the same as the average demand, particularly with seasonal markets. This means that a median based forecast like the MAE will generate a bias, resulting in a high offset compared with the actual demand.RMSE, on the other hand, is not free either of a few shortcomings. As seen earlier, RMSE gives greater importance to the highest errors. This comes at a cost: sensitivity to outliers. For example, one exceptional month on a certain product demand will, due to external factors, strongly impact the average demand, and impact in turn, the reliability of the RMSE measurement. Additionally, intermittent demand, which is common to many supply chains, should be taken into consideration. If your demand doesn’t follow any common pattern, the median will not be a reliable way to analyze its evolution, and therefore the MAE should not be used in these cases. Considerations for Starting Your First Forecast Accuracy KPI In conclusion, MAE provides good protection against outliers, whereas RMSE will procure an unbiased forecast. Additionally, if your business deals with low-demand items on a weekly basis, you should consider aggregating the demand to a higher time horizon, using monthly or even quarterly periods in order to get better forecast results. As you may have already noticed, choosing which forecast KPI to use is not that easy and there isn’t a definitive answer. You should experiment and adjust your parameters based on your learning curve, checking both the BIAS and the precision of the chosen error model. Knowing some characteristics of your demand will start you off in the right direction: Does your business have a constant or seasonal demand? How susceptible is your market to outliers? Do you face any intermittent demand? Based on management experience and the overall knowledge of your business, answering these questions will not be difficult. You are now ready to try various options. Most importantly, keep in mind the importance of a proper forecast accuracy indicator as a key tool to manage your demand forecasting process. SKU Science can definitely help you select the appropriate KPI to optimize your S&OP. Additional
How to get your S&OP Process right
Within the flow of a company’s supply chain planning process, the breakdown of functional silos and the integration of the various business areas involved, have long been endorsed. However, even with the adoption of integrated management systems, achieving tangible and quantifiable results remains a challenge for most companies. Within this scenario, companies are increasingly implementing the Sales and Operations Planning (S&OP) process, using straightforward practices that aim to simultaneously achieve improvements in terms of cost (inventory levels and cost of production) and service (product availability). These overall results are achieved by improving the sales and production planning process, based on a balance not only between demand and product availability (encompassing production and supplies) , but also between volume and product mix. Critical Success Factors S&OP is basically a fairly simple process, that encompasses two activities – demand forecasting methods and inventory planning – already discussed at meetings and carried out by a company. However, to be successful, the S&OP process requires a combination of factors to be implemented, as follows: Commitment of the company The first aspect to be considered concerns the degree of effective involvement of the company’s management body in the S&OP process. How often are managers present at meetings and what is their level of interaction when it comes to decision-makings?The second aspect concerns the involvement of all areas directly or indirectly affected by the material inventory planning process. As an example of an indirectly related area, we can cite maintenance or engineering. In some cases, changes from scheduled downtime to maintenance may be necessary to free up production capacity in order to meet the demand planning. Based on this example, it is possible to identify the importance of not only the presence of all areas with some relation to material requirements planning, but also that managers in these areas have the authority to make decisions. Meeting Planning Due to the many unfolding events that can impact sales and production planning, S&OP meetings are very often subject to lack of focus and to discussions about specific and less relevant points. Thus, meetings should be planned in such a way as to follow a set agenda for the main points to be discussed in order to prevent minor issues gaining undue importance. Try our fast & simple demand planning solution Sign up for free to SKU Science today!Pre-loaded sample date – No credit card required SIGN UP FREE Definition of responsibilities In addition to clarifying the responsibilities of the managers of each area within the process, it is advisable to designate a sponsor and a person responsible for the S&OP process as a whole. It is the sponsor’s responsibility to keep the entire company’s attention on the process; to remove possible impediments to incorporate necessary additional resources (participation of new people, or acquisition of supporting tools), and finally, to provide general support to the process. With respect to the person in charge of the process, their main responsibility is to manage the execution of each stages of the process, including the fulfillment of the defined deadlines, and to conduct planning meetings. Planning horizon This relates not only to the total planning period, but also to the point at which planning becomes more detailed (for example, planning for five months, but detailing the production mix for the next month only), and the presence or absence of a frozen planning period. The importance of these definitions is based on the specific need for planning by each area involved. For example, the supply area may require demand planning for three months due to purchase constraints, while production may be indifferent to the long-term, but requires a plan for the next month due to its production scheduling. Supporting tools In any planning process that requires some demand forecasting techniques, the use of digital resources and data models is a strong indication that the decisions made adequately take into consideration all trade-offs involved in the problem. For example, there is room to optimize some forecast models by applying several demand planning tools, and using software that enables more accuracy, transparency and agility during the process.It is worth noting that the use of support tools for each of the planning activities does not depend on the existence of an S&OP process, which may occur before or after the implementation of the S&OP process. The use of these tools, together with the S&OP process, has the effect of amplifying their potentialities; migrating from obtaining local gains to an integrated gain in planning efficiency. Financial follow-up S&OP meetings can be used to monitor and review the company’s overall budget, not only from a volume standpoint but also from revenue and profitability. This influence on the financial aspect occurs through possible changes in the planning of the sales mix. In this way, it is advisable to evaluate the impact of all planning alternatives discussed throughout the meetings. This evaluation can be carried out before the actual meeting, or even during the meeting, which gives greater flexibility and dynamics to the process. However, it does require a higher level of efficiency from decision support tools. Documentation of the process The degree of formalization of an S&OP process usually has a strong relationship with the documentation level of the process. Thus successful processes are expected to have a documented detailed planning policy that includes the participants, the responsibilities, the deadlines, and the objectives for each step of the process. Performance Monitoring In line with the financial monitoring aspect, monitoring the S&OP process should address both the performance as a whole, as well as the activities of each area involved in the process that may have an impact on its final performance.From the point of view of the final outcome of the process, it is important to monitor the availability of products for sales and inventory levels, in addition, of course, to the financial results of the company as a whole discussed above. In this respect, it is advisable to use a proper supply chain dashboard. With regard to
