Best Practices for Integrating S&OP

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Closeup image of businessman drawing 3d graphicsMost manufacturing outfits in the modern world already have some kind of S&OP—but sometimes it can feel like even your most integral processes aren’t evolving quickly enough to keep up with the times. What started as an annual meeting of stakeholders in sales and production planning might now be under pressure to incorporate more digital data and technology. Or, you might be looking to add or upgrade your sales planning software, which might mean reimagining your S&OP process from the ground up.

If you’re in one of the positions sketched out above—or a similar position regarding your sales planning workflows—there’s no time like the present to get a handle on the most important best practices for S&OP integration. Even as digitization works to decrease the complexity inherent to quarterly or yearly sales planning processes, there’s still a lot of nuance to get a handle on, and a slapdash integration can result in supply-demand mismatches down the road—resulting in expensive production shortages or overages.

 

Map Out Product Structures and Network Models

To begin with, it’s important to remember that S&OP is a process that’s aided—but not necessarily defined by—software. In other words, it’s a process that benefits from smart, digital forecasting, complex network modeling, and even AI integration; but, at the same time, it requires interpersonal collaboration and structure to get off the ground.

To this end, the first thing you’ll need to collaborate on is defining your network structure, such that it can be modeled in digital space by your planning solution. This involves reaching out to product managers to get hard and fast information about the production constraints and considerations for each of the products in your portfolio, e.g. what the bill of materials is for each, what customization options exist, etc. You’ll need to work with production managers to model your production processes, logistics managers for specific elements of your transport network, and so on until your end-to-end sales and production processes have been mapped out.

If you’re utilizing a digital S&OP solution of some kind, it will no doubt prompt you for this information—but it’s important to approach this process with input from a variety of stakeholders in order to ensure that your model is as accurate as possible.

 

Optimize Your Forecasts

Ultimately, the goal of any S&OP process is to ensure that your production capacity matches up with real demand as closely as possible. Old-school, pen-and-ink planning flows can help you get a rough sense of how to adjust capacity going forward, but true optimization requires you to forecast demand accurately and effectively. This requires not just digitization, but advanced analytics integration as well. By either starting out with S&OP software that has AI, machine learning, and other analytics capabilities built in or adding on a separate analytics module, you can turn data streams from up and down the supply chain into more accurate forecasts than a human planner could hope to achieve. Because forecasting is step one when it comes to S&OP, your ability to optimize forecasts in this way will set the tone for the success or failure of the workflow as a whole. Unfortunately, an inaccurate forecast—one that’s rooted in past demand but fails to account for changing market conditions and other signifiers—can doom an otherwise perfectly thought-out sales plan from the get-go.   

 

Automate

Ideally, once your system had created a demand forecast that reflected the likely order realities for the coming quarter or year, that same system would be able to automatically generate efficient production plans that account for all of the constraints and parameters that you’ve already mapped out in your network model. More than that, you’d want your solution to automatically adjust in real-time as conditions changed, accounting for different logistics scenarios and product structures. Here, your analytics integration will be just as crucial as it was above.

This shouldn’t be a black box, i.e. a system that takes in data and spits out an answer with no explanation of how that answer was reached. At the end of the day, your sales planning tools and processes are there to empower you, which means that something like explainable AI (AI that walks the user through the logical steps it took to reach a conclusion) can be extremely valuable—especially if you’re changing your assumptions as you go. This can form the basis of a system whereby planners utilize digital simulations to test potential planning scenarios and use these as the basis of collaborative decision-making flows. Here, increased digitization is used to power increased visibility, which in turn helps you leverage all of your resources more effectively.

 

Utilize S&OE

S&OP processes operate on different timetables: one might cover a six-month long planning horizon, while another might seek to plan the next 18 months in advance. Whatever length of time you’re covering, however, it’s unlikely that your S&OP process is built around tiny, day-to-day or week-to-week variations in supply chain activity. On the one hand, it’s useful not to lose the forest for the trees by getting too bogged down in the minutiae. At the same time, it is important to monitor things like inventory levels and transport routing plans against your expectations as the planning horizon unfolds.

Often, when reality deviates from forecasted outcomes, planners will overreact—for instance, they might put in a huge and expensive restock order for a certain part due to a demand spike that was destined to normalize the following week. To stave off these situations (or the reverse, where no one takes any action to keep the plans on track), it’s helpful to supplement your S&OP process with an S&OE (sales and operations execution) workflow. What does this entail? Basically, S&OE planners monitor live supply chain data and check it against expectations, making minute daily and weekly adjustments to inventory levels and transport routes as needed. As they take in data and make adjustments, they can also provide other functions with a snapshot of supply chain functioning as a way of offering real-time planning and production feedback. The result is that your longer term plans are better matched to reality over time. This might sound like a tall order—but once you have the infrastructure to take in up-to-the-minute data from various touchpoints on the supply chain, it’s just a matter of establishing S&OE best practices and spinning up a new function accordingly.

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