As we head into the new year, you have already read many articles predicting supply chain trends for 2023. I am not a big fan of predicting trends when it comes to supply chains. My primary reasoning is that these trends are not a surprise for seasoned supply chain leaders and executives. These leaders and executives experience the challenges associated with these trends on a day-to-day basis in their roles. This article, therefore, aims to look beyond the trends to understand how we can use this year to build foundation in an area that can help build supply chains that can address challenges associated with these trends: Effective supply chain data management. If you analyze the trends carefully, you will find that the trends identified in most of the articles will not change significantly in short to mid-term. The result of this is that you could recycle some trends from last year this year, since they will hold true in 2023, and then use some of them in 2024 as well. If you think this is inaccurate, here are a few trends for 2023 for the supply chain that you can bookmark and re-visit in 2024 and maybe in 2025 as well, and they will still hold true in some form:

• International trade (Includes the outsourced manufacturing to China aspect) and Geopolitical challenges (current example is the Ukraine war)
• Macroeconomic trends (Inflation/Recession)
• ESG and Sustainability in supply chains
• Evolving supply chain complexity (due to aspects like digitalization, the proliferation of SKUs, channels, etc.)
• Evolving customer demand

The first exercise that you need to do is to read a few articles that touch upon 2023 supply chain trends, and you will find that you can easily assign most of them to the five buckets above. The second exercise is to bookmark these five trends and then re-visit them in December 2023 or January 2024. You will find a bulk of them are still relevant and the majority of hands-on supply chain managers and leaders are already aware of these trends. We also need to address the question "How do we build supply chains that can run robustly while facing the challenges associated with these trends?” This is where supply chain data management can play an important role. Technology is one of the three components needed to build supply chains (people and processes being the other two). But remember that there is no magic bullet. There is no one technology or even a portfolio of technologies that will act in a magical way to manage these trends better. You have integrate technology with strategically designed processes and aligned skillset. We can throw terms like IoT, Al, ML, control towers, advanced analytics, and whatnot. But the fact is, there is no scenario where you can invest in these technologies in 2023 and expect your supply chain to fully address these trends in late 2023 or early 2024. That is just not feasible and realistic. Scaling the value from technology investments takes time, no matter what was promised to you when you bought the solution. While you evaluate your specific tools and technologies requirements, you can use this year to focus on building foundations in the areas of data and analytics. These are the areas that can help you build some solid foundational capabilities. Then you can use this foundation to gradually build a supply chain that can handle most of the curveballs thrown at it. At the core of it all, is data, and if you are not there already in terms of capabilities, make 2023 the year of building foundational supply chain data capabilities. But how can investing in data (and analytics) capabilities help you address these trends? While not comprehensive, illustration 1 highlights how at the core of the capability to address the challenges created by these trends lies the data (and the subsequent analytics) capabilities that organizations have developed or need to develop. In this article, we will discuss, at a high level, how you can use 2023 to build foundation for your supply chain data management capabilities.

Illustration 1: Addressing challenges associated with trends with effective data and analytics.

Building a supply chain data management foundation

The key area that you need to focus on is building a data foundation. Consider the example of supply chain visibility. As you can see in illustration 1, many of the trends above or the challenges arising due to many of the trends, can be addressed through optimal supply chain visibility. However, when we think about supply chain visibility, we immediately start thinking about visibility platforms or supply chain control towers, or some other fancy tools and applications. but at the core of it, all is data. Any application you decide to leverage whatever fancy name it has is essentially helping you consolidate your supply chain data in one single view, hopefully in real-time. And the fact is just like every other application, even these tools would primarily rely on the data fed into these tools. While supply chain visibility platforms that are available today are very useful in the sense that they provide you visibility beyond your network, your immediate focus should be to build immaculate visibility internally. And you cannot do that unless you have the required data infrastructure and capabilities. When we discuss modernizing data, we immediately start throwing keywords or buzzwords like data governance and data strategy. We start exploring how we manage the data that we have and throw in more keywords like harmonization etc. And you will find these keywords in this article as well. However, the first thing you need to do and that should be your focus in 2023 if you have not already done so, is to evaluate your entire data infrastructure. Based on that evaluation, you will identify the gaps and then formulate a future state data strategy. To illustrate the steps you can follow to start working on building a robust data foundation in 2023, we will use few examples: Do a visibility gap analysis. Let us start with an example of challenges associated with international footprint of  supply chains. An effective way to manage the challenge associated with this trend is to have visibility with minimum latency. As mentioned before, visibility eventually pertains to data availability, with desired information latency level. Your visibility gap analysis should eventually highlight the gaps in that data availability and data latency issues. This is the most comprehensive step of this entire process and the success of subsequent steps depend on how robustly this analysis was executed. While it has been captured in a single simplified sentence here, you will need to leverage an elaborate method for this gap analysis. The output of this gap analysis is eventually the work that needs to be done in areas like data quality management, data integration and data harmonization. An example of this, highlighted in our recently published inventory management and optimization report, can be inventory accuracy data. Your inventory visibility is not complete unless all the data that is being captured through technologies like barcodes or labeling or RFID, is accurate and aligned. The majority of organizations have invested in infrastructure that allows them to capture their inventory data across all their distribution and logistics network. But they grapple with accuracy and consistency challenges. And this means that though they may have captured the data, that data is not as useful as it can be. And the fact is no matter how much you invest in a fancy control tower solution, unless you fix these underlying data issues, it will always be garbage-in -garbage-out. Understand the drivers. Once you know the gap areas the next step is to understand how to resolve those gap areas. And we will continue with the inventory data example that we brought up before. If you know that there are inconsistencies there are data integrity issues in your inventory data, you then need to formulate a strategy to address them. Whether it is the sub-optimal assignment of SKU codes, bad description of SKUs, or erroneous packaging data, all that needs to be documented comprehensively. Formulate strategy based on the remedy: You need to identify the root cause behind the drivers, and then integrate the remedies for those issues in your data strategy and data governance architecture. Note that no organization develops a data strategy or data governance process or even a data architecture exclusively for their supply chain. What this means is that these remedies and the associated data strategy and data governance elements, need to be integrated into an organization's overall data strategy and data governance process. Evaluate the impact on other strategies: Once you understand what needs to be done, start working in that direction. This is where you bring in additional elements of your strategy, like cloud strategy. As an example, as you start developing the remedies to your data architecture gaps, you will figure out that there may be a scalability challenge due to your on-premise infrastructure and that is why you may need the cloud infrastructure. Hence, your cloud strategy comes into play here.