Supply Chain Planning

This Supply Chain Planning (SCP) market research delivers current market analysis plus a five year market and technology forecast. The study is available in multiple editions including worldwide, all regions, and most major countries.  This supply chain planning research covers digitally transforming the end-to-end supply chain, demand planning, inventory optimization, network design, agility, resilience, strategic analysis, market size, industry trends, historical analysis.

The Supply Chain Planning Market Continues to Grow by Double Digits 

According to ARC Advisory Group research, the supply chain planning software market grew at a double-digit rate over the last several years, even during the pandemic. It is unusual for a mature, multibillion-dollar market to grow so fast. Clearly, the pandemic, although now post-pandemic, continues to be a driver of growth for this multibillion-dollar market. The pandemic highlighted the need for companies to be agile and resilient. Agility and resilience continue to be key goals among C-level executives. Executives largely do not believe that we will have smooth and predictable supply chains that were prevalent several years ago. Supply chain planning is one key technology for achieving these goals.

Supply Chain Planning Strategic Issues

In addition to providing a five-year market forecast, the Supply Chain Planning market research provides detailed quantitative current market data and addresses key strategic issues.

Beware the Digital Twin Hype

A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or process. The term “virtual representation” in SCP means a model. We should not be speaking of a digital twin, singular, but digital twins, plural. Different types of models are good for solving different kinds of problems. The demand model is different from the supply model. In terms of supply modeling, many companies will find that they need one model for supply chain design, another for tactical planning, and another for operational planning. Models are getting broader and deeper, and this is to be applauded. Where a supply network model used to consist of a model of multiple factories and warehouses, today we are seeing models that include a much broader set of constraints: purchase agreements, store replenishment, transportation, ESG, and others as well. These broader models are better models.

Do Not Customize the Software

Over the years, ARC has come across many companies that purchased supply chain software and then customized it so that it would better fit their processes and culture. The implementations take longer when customization is involved. Furthermore, as the software was customized, customers were reluctant to upgrade the software because of the large costs associated with upgrading software that has been customized. This reluctance to upgrade customized software can continue for several years and the buyers forego product enhancements. After some number of years, while falling further and further behind the current version of the software, the companies realize that they have legacy software. If they want to use a modern solution, the costs will be so high that they may as well go through a new vendor selection process and a brand-new implementation.

There is a marked trend toward purchasing cloud-based solutions. This is not just because cloud solutions are sold in a Software-as-a-Service (SaaS) model that lowers upfront costs but also because public cloud solutions typically do not allow for customization and thus protect the upgrade path. In short, avoiding customization improves the total cost of ownership based on faster implementations, easier upgrades, and improved maintenance. However, there are still some companies that can plausibly argue that off-the-shelf software does not suit their needs. In supply chain planning, that often means they believe that the addition of a particular algorithm, or algorithms, will improve the optimization to such an extent that the extra costs do not matter. A few providers of SCP have public cloud solutions that allow custom algorithms to be put in a library attached to the platform; this is done in such a way that the upgrade path is still protected. That is the best of both worlds.

Public cloud solutions also allow for best practice benchmarking. For example, a demand/inventory optimization solution running in a public cloud allows the supplier to access the data, anonymize it, and then generate benchmarks that allow companies to see how they are performing on measures of forecast accuracy and inventory success measures relative to their peers.

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Embrace Demand Sensing and Other Data Sources

Traditional demand forecasting is based on the presumption that history repeats itself. Even in the best of times, this method has limitations. During the pandemic, it completely failed. For companies trying to predict demand in March of 2020 as the world was descending into lockdown and everything was being turned upside down, what happened in March of 2019 had little to no relevance. The pandemic created seismic changes in customer behavior. Companies that made use of planning systems that combined demand sensing – the use of multiple, real-time signals (like sales in a particular store or shipments from a retailer’s warehouses to their stores) – and machine learning, produced more accurate forecasts in almost all instances. In times of dramatic swings in demand, demand sensing forecasts are even better–they adjust to the swings in demand much more quickly.