How To Avoid The S&OP Blind Spots

S&OP implementations are complex, which is one reason at least three critical areas are too often overlooked, and a fourth is worth highlighting in order to gain a competitive advantage.

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At its heart, sales and operations planning (S&OP) is a collaborative process, connecting business strategy to the supply chain, bringing together players from across the organization—sales, finance, production, distribution. The S&OP process aims to close the gaps between these parties in a transparent and iterative way.

The process itself can be complex—getting buy-in from the stakeholders is the first hurdle, followed by the challenge of staying on track despite the distractions key players will face from shorter term considerations of the business. In light of this complexity and competing interests, it’s easy to understand why many organizations overlook critical S&OP components and find it difficult to keep up with evolving S&OP technology that could provide a competitive advantage.

In my experience of S&OP implementations across the globe, I’ve observed three areas that are often overlooked, and a fourth that’s also worth highlighting.

1. The optimal plan
Just as S&OP is iterative and collaborative, so must be the planning and optimization process that underpins it. A typical S&OP system often functions as a black box, with the system delivering a “perfect” plan, which is discovered to have no application in the complicated and unpredictable reality of the business world. That’s because the knowledge on which it was built is inaccurate, over-simplified or out of date.

An effective plan incorporates all relevant data, including key performance indicators, network configuration, costings and more.

  1. Where possible, there should be automated feedback loops creating a self-learning mechanism. This keeps the input accurate and up to date.

  2. Planners and decisions makers must interact with the plan, combining the technology’s speed and intelligence with the user’s expertise and insight.

  3. Real-time collaboration across the organization gives users the opportunity to automate some of the more common processes. It also provides visibility of all business processes and access to information affecting performance. Company-wide transparency is fundamental to smarter and faster decision-making. When key users are notified of changes occurring at any stage of a process, they can selectively re-optimize the plan.

2. Maintaining data quality
A robust plan is built on the quality of the data and the insights derived from them. If a system can capture and process data from various sources in your business in real time, the data can be continuously updated and analyzed for patterns and insights. Using the insights derived from this process of machine learning, it’s possible to generate increasingly accurate predictions, enabling users to make ever-better decisions based on actuals instead of hunches. This is what Quintiq calls the Self-Learning Supply Chain.

When working with what-if scenarios—the starting point for any S&OP plan—the importance of the quality of the data cannot be understated. But, data quality starts deteriorating the instant the data is generated. Disparate spreadsheets to record demand; integration of systems; and merging of sources; system lags, all of these factors degrade the quality and reliability of the data. In any complex planning process, the volume of data is likely to defeat its owners very quickly, within just a few months, well short of the typical 18-month time horizon of the S&OP process.

Therefore, it is important for companies to assess not only the quality of their data, but the performance of their systems that underpin their S&OP process, particularly in light of the rapidly evolving technology in this area. One of the most reliable indicators of data quality is the measurement of adherence to plan – the gap between what you planned and what actually happened. When the data going into the planning process is inaccurate, adherence drops.

3. The product design process
The product design process is often ignored during the S&OP process. This is a missed opportunity. In fact, I’d call it a crucial oversight. In many cases, this work is done by an entirely different, isolated team that doesn’t interact with those involved in the S&OP process. When the product reaches the S&OP stage, it’s passed the point where it can have any influence on the product’s characteristics. To ignore the specifications of the products coming online in the next year or two is to ignore a significant opportunity for optimization.

For example, with the input of the shape, size, weight and packaging of the product, production and distribution can be optimized at a much earlier stage. Integrating product design insights into the S&OP process brings a deeper understanding of the consequences of design decisions. A new generation of S&OP solutions are able to better incorporate the design process by better predicting the consequences of design decisions and integrating them into S&OP plans. And, S&OP teams will be able to give feedback to product development teams on future products, particularly in the areas of cost of production, storage and distribution.

4. Automatic hierarchy construction—finding the forecasting sweet spot
There are two questions at the opening of every S&OP process:

  • At what level should the forecasts be made?

  • How should products and services be grouped?

Typically, a combination of approaches and technologies is required to answer these two questions with any degree of certainty. Groupings are often done manually and mirror the structure of the business. A new technique to automate and optimize this process promises a faster and more effective alternative. The technology analyzes data patterns to determine the right groupings of products and services. Grouping optimally based on data similarities drives up forecast accuracy.

It relies on machine-learning principles to sort through the possible combinations of clusters and forecasting levels to arrive at the optimums. The increased accuracy means a sizable reduction in safety stock levels and waste.

Always pushing at the boundaries of technology, Quintiq has been testing automated hierarchy construction, which has already delivered promising results.

In my experience, by expecting more from your optimization, data quality and the incorporation of product design you have the potential to bring significant improvements to your S&OP process and its outcomes. Assessing your current processes and the systems you have in place and keeping an eye out for new capabilities could prove to be the difference between a successful S&OP cycle and a failed one.

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