It Takes Two: Capacity Planning and Supply Chain Network Modeling Work Best Together

One of our clients, a major consumer products manufacturer, successfully applies analytics to long range manufacturing capacity planning. Along the way, the manufacturer discovered that capacity planning and supply chain network modeling are two of a kind. They go together like surf and turf.

There’s an intersection between supply chain network modeling and long-range manufacturing capacity planning. When taking advantage of this pairing, the client’s capacity planning process produces results with profound depth and range. It’s far better than ordinary spreadsheet-based analytics. It substantially sped up the time to build out its supply chain network models.

A quick recap of our partnership

Years ago, the client first engaged NTT DATA to help with capacity planning and supply chain network design. In the past, the client’s supply chain design needs were primarily concerned with distribution components. However, when the manufacturer reached out to NTT DATA, it had developed a great interest in fully developing its manufacturing components.

Manufacturing interest was the catalyst that led to a profound assessment of the union between capacity planning and supply chain network design. Specifically, they have many things in common—data requirements, a focus on the manufacturing network and the need to answer similar questions surrounding the network. We had a critical question: How do we effectively serve both client needs? In answering that not-so-simple question, the coming together of these things began to take shape.

Jumpstarting Supply Chain Network Design with Capacity Planning

It wasn’t always this way, but capacity planning models are now the foundation for supply chain design models. Essential manufacturing components, line-SKU capabilities, line-SKU planning rates, plant calendars, and bill of materials (BOM) are developed and vetted as part of a bi-annual capacity planning process. The elevated level of detail delivered by capacity planning makes conversations with the client’s manufacturing and planning subject matter experts(SMEs) possible. When vetting the results of a model, the line usage discussions are the precise areas where the client’s manufacturing and planning SMEs are particularly well-versed.

As the capacity planning cycle winds down, network model refreshes are getting started. The great thing is that the manufacturing parts of the model are already in place at the level of detail required, only needing some aggregation. Yes, the entire distribution end of the network model still needs to be built, but there’s a lot less complexity in that part of the network. Suppose you started building the entire network model — manufacturing plus distribution — without input from capacity planning. In that case, we figured it’d take two-to-three times longer to get it done.

Secure success by aligning people and processes

The successful application of software to the capacity planning process is only part of the bigger picture. NTT DATA has worked with this client to combine technology with the right teams, data and methods that create results that matter.

Thoroughly engaging executive-level supply chain leadership in the capacity planning process is a critical driver for success. SMEs provide data and business context to the modeling teams and play an essential role in validating results. NTT DATA’s day-to-day role entails coordinating with the client’s teams to develop accurate supply chain network models and deliver reporting capabilities that help team members quickly understand the 100+ combinations of platforms and scenarios.

Realizing benefits on multiple levels

Beyond its primary benefits, the data delivered by capacity planning enhances supply chain network modeling decision support. There are some specific benefits gained from the capacity planning process including:

• Actionable, credible platform-level usage results
• Cross-functional collaboration that develops a single Capacity Planning storyline for each business unit(BU) key segments
• A repeatable, trusted capacity planning process with agreement from mid-level management through executive leadership
• Robust data quality validation, such as demand forecasts and planning run rates per line-SKU
• Data governance prioritization and a firm foundation for supply chain network models

Besides the vital benefits of capacity planning, there are additional advantages to applying supply chain network modeling technology within the capacity planning process:

• Capacity planning results that are far superior, in range and depth, compared to spreadsheet-based analytics
• Capability to produce multi-scenario capacity panning results with ease
• A two-to-three times faster transition to supply chain network models that can address critical strategic questions beyond capacity planning’s scope
• Views of peak seasonal utilizations
• Usage views into finished goods and primary work in progress(WIP) products
• Insights on prioritizing plants and lines, that is, which plants or lines to fill first during periods of lower demand
• Insights on optimal line-level SKU mix

External hybrid modeling teams can perform supply chain network modeling. With much larger client organizations, internal teams with modeling competencies can do the job. Based on our experience, we strongly recommend that clients don’t assign network modeling responsibilities to business planners who lack the requisite network modeling skills and experience.

Capacity planning answers the critical “when” and “what platform(s)” questions in the network. The answers to those questions influence capital investment decisions that can be in the multi-millions of dollars and with implementation timelines that can span several years. Network design models answer the manufacturing network’s pivotal “where” and “how” questions. There’s a natural intersection across many of these essential manufacturing questions that endows multi-level benefits to companies applying network design modeling within their capacity planning processes.

— By Dan Sobbott and Aravind Krishna