Using Analytics to Measure S&OP Effectiveness

Having a strong Sales & Operations Planning (S&OP) strategy ensures that your forecasts, raw materials availability and production capacity all match up and that the factory floor can meet its defined deadlines. But simply putting an S&OP strategy in place isn’t enough. You need to know that you are implementing your sales and operations plans effectively.  Key to that is understanding what you should measure to assess your S&OP effectiveness, where to set your Key Performance Indicators (KPIs) and the most optimal methods you should use to analyze your data to improve performance.


A Few Important KPIs for Tracking S&OP Performance

As a long-time provider of business intelligence applications for manufacturing and distribution businesses, Silvon has played a key role in helping our clients define and implement performance metrics that align their S&OP initiatives with their corporate goals. Below are a handful of important KPIs that should be tracked to ensure that your S&OP goals stay on track.

On-Time Delivery (OTD)

On-Time Delivery (OTD) is a primary metric used to determine the overall efficiency of the supply chain. Its usefulness in S&OP is in helping maintain Service Level Agreements (SLAs) related to delivery windows.

As a ratio of units delivered on time to total deliveries made over the same time period, most companies express OTD using a range of dates such as X days before and Y days after the due date. Delivery metrics can vary in terminologies, measurement methods, and variables from one company to the next.  As a result, companies need to define and agree on the standards and definition of “on time” in the contracts, agreements, terms and conditions they share with their customers and partners (e.g., logistics firms).

Following are just a few examples of how measuring on-time delivery can be difficult:

  • Volume of ordered goods. If you deliver 90 of 100 ordered items and the customer acknowledge the deficit, will this count as an OTD delivery? If you ship 80 products on time, and the customer agrees to receive the remaining goods later, will this be a 100% or 80% on-time delivery?
  • Delivery date. If a customer agrees to receive the order as early as three days before the agreed-upon delivery date, but is also willing to accept the items three days after the promised date, will you consider that six-day window an on-time delivery?
  • Quantity of goods in the order. If customers purchase five different items in the same order, should you transport all five goods on schedule to obtain a 100% OTD delivery?

Again, you need to be specific in how you define what OTD is.

Production Plan Adherence (PPA)

Measuring how well production meets its planned output goals of the sales and operations plan is key to maintaining tight delivery windows. The Production Plan Adherence (or PPA) metric is calculated as (planned production – actual production)/planned production. This KPI is useful in determining if machinery is performing up to standards, or if a facility or production line is falling behind another in terms of output so stakeholders can work with the production team to find and eliminate the cause.If the PPA metric is too high or too low, it has an effect on the company both in terms of investment and performance. Ideally, you should aim to either keep this variance low or close to zero.

You can also use PPA to create a baseline by which you can measure future line output and ensure that a facility can sustain the expectations required to meet growing customer demands.

Forecast Accuracy

Forecast Accuracy is a very good gauge of overall planning success. The first thing you need to do is understand your specific situation and how heavily to weigh this metric. For example, if your company is a producer of fresh foods, forecast accuracy is critical to avoiding waste and the costs associated with it. On the other hand, if your business deals in the manufacture of products with indefinite shelf lives, then other aspects of planning have higher importance.

  • Forecast accuracy should be measured at both the SKU and product family levels.
  • If your products have long lead times, lead time lag should be taken into account when assessing forecast accuracy.
  • Be sure to determine whether your planning and forecasting system’s statistical forecast or the override forecast submitted by planners is more accurate.
  • Assess whether forecasts are consistently too high or too low, indicating a bias in the statistical forecast or in forecast adjustment.
  • Also be sure to perform root cause analysis on items with high forecast errors to learn what’s causing them.
 Capacity Utilization

The Capacity Utilization measure is vital for measuring your company’s efficiency in terms of resource usage and in planning for the future. In essence, this metric is a percentage of total production capacity, providing an indication relative to how close to full capacity your plant is operating at.

Using Capacity Utilization as a metric in your manufacturing analytics can help you pinpoint areas where your production line is being wasteful, inefficient, or under-utilized. To track your capacity utilization rate, you need to capture and measure data related to your resource capacity, such as data from purchase reports, inventory movements, cycle times, and output capacity. You should also measure actual output-related data such as gross production numbers, resource usage, and total materials input against output.

Order Cycle Time

Order cycle time (OCT) is the total time from when an order is placed to when it is received by the customer.  The real importance of this KPI metric is that it provides an accurate depiction of your production, inventory, supply chain, and shipping and can give you a clear indication of the health of your end-to-end supply chain. Order cycle time is a valuable measurement of S&OP effectiveness because it directly impacts customer satisfaction.

Many factors that can affect order cycle time, including the number of line items ordered, the complexity of the assembly process, and the lead time for each component. Order cycle times can also be affected by seasonal fluctuations and changes in customer demand.


Alert-Based Analysis

With today’s sophisticated analytics and reporting tools, you also need to have the ability to receive early warnings that certain KPI’s like those outlined above are projected to exceed tolerance levels so you can take corrective action before they become a real issue.

For example, while a supply order may arrive only one day late (which may be within tolerance from a supplier management perspective), the consequence could be that a major new customer order will be delivered later, or even worse, result in a lost sale the negatively impacts gross margin.  Such an occurrence should cause an alert to be sent to appropriate managers so they can take immediate action.


Managing by Exception

Closely related to alerting is the ability to drill down to the exceptions that cause a particular KPI to exceed its tolerance levels so you can better understand the root cause of the issue. Today’s advanced business intelligence and reporting solutions are key to providing this capability versus the traditional approach of performing ad hoc analysis and data extracts with spreadsheets.


Using analytics to measure S&OP effectivness is key to making the most of your S&OP plan and gaining the highest ROI on your efforts. We suggest you start with the metrics and approaches outlined in this article and progress to building your company’s own unique set of KPIs so your supply chain keeps moving effectively and your products keep being delivered precisely when your customers expect them.


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