Data-Driven Supply Chain Success: The Secret to Your Logistics Projects

In today’s rapidly evolving business world an efficient and reliable supply chain is more important than ever. Companies that can successfully manage supply logistics have a significant competitive edge. But how do they get that edge?

The answer often lies in effective data utilization. Data has become the lifeblood of modern supply chain management, and harnessing its power in projects to improve these processes can set you up for extraordinary results.

A high-quality data set allows you to make confident decisions and adapt more easily to scope changes and post-analysis revisions. It also facilitates clearer presentation of project results/findings, which is helpful for internal stakeholders and assures your credibility in their eyes.

Before we delve further into the role of data, let’s determine what we mean by a supply chain project.

What is a Supply Chain Project?

Unlike routine business supply operations, a supply chain project involves a specific, time-bound effort to create a new product or service, implement a major change within the supply chain, or address a unique business need. Examples include:

Launching a new product line
Opening a new manufacturing or distribution facility
Implementing a new supply chain management technology
Entering a new market or location
Responding to a supply chain disruption

Clearly, no company will embark on such a project without studying the options and making a plan, and this is where it’s important to gather all the right information.

The Importance of Data in Supply Chain Management

Attempting supply chain management without sufficient and well-prepared data poses significant risks because there can be many components and processes involved, all of which require a certain degree of visibility and accuracy. Failure to carefully consider these aspects can lead to excess costs, stockouts, delivery delays and potential disruptions.

With thorough data analysis, companies gain visibility into the full spectrum of supply chain activities – from procurement and production to distribution and delivery. This comprehensive view allows for well-informed, strategic decisions within the supply chain instead of relying on intuition, ensuring that managers utilize valid data points to drive positive outcomes. This point carries over into demand planning, where analysis of historical sales stats, market trends, and seasonal fluctuations is crucial for forecasting.

Data-driven risk management helps identify potential curve-balls like natural disasters, supplier disruptions or geopolitical instability, and proactively develop mitigation strategies to minimize supply chain disruptions. Furthermore, granular details of each stage reveal areas with inefficiencies or bottlenecks – highlighting the root causes of delays, for example – and this allows companies to optimize supply chain processes.

The end result of optimal supply chain management is that companies can achieve substantial cost reductions through strategies such as perfecting shipping routes, negotiating better transportation rates and identifying cost-effective suppliers. Moreover, supply chain solutions have a key role in building customer loyalty and driving positive brand reputation.

It contributes to the ability to assess customer preferences, buying patterns and delivery expectations, which leads to personalized service and consistent delivery of products on time.

All of this significantly impacts the bottom line, highlighting the merits of investing in robust data collection and analysis systems to maximize supply chain performance.

What Makes a Great Data Set?

All conversations about data analytics typically stress the importance of quantity, quality and relevance with respect to each data set. Let’s see how these factors come into play in a supply chain context.

1. Quantity of data available – Do you have enough data to properly reflect the current state of business? For example, if working on an analysis for an apparel company, you need to make sure you have at least a year’s worth of data to properly capture seasonal effects.

2. Quality of data – Your data needs to be error-free to ensure that the analysis is accurate and relevant. Metrics such as currencies or weight must be converted so that all entries use the same units of measurement. And if working with shipping data that requires address fields or suite numbers, are there any blank entries? These must be eliminated.

3. Level of Detail – Making sure all the required data components are available is critical to getting accurate results, and careful study project requirements will avoid situations such as “apples to oranges” comparisons. Fields where data is only partially present can significantly limit the types of analysis that can be done.

For example, if you are issuing an RFP to new carriers for your LTL shipments, and providing them with a historical shipment file, you need to include dimensions, pallet level information, and proper freight class. Without these elements you won’t be able to accurately compare carrier responses and could potentially miss out on competitive pricing offers.

Attention to each of these factors will save time in the long-run, preventing the need for repeat analyses.

Harnessing the Power of Data: Practical Examples

Here are some examples of how the latest data analysis techniques are transforming supply chain management:

Predictive Analytics: Analyzing historical data and real-time trends can help companies predict future customer demand, potential disruptions, or the need for capacity adjustments. This allows organizations to meet demand proactively and manage challenges.
Internet of Things (IoT): IoT devices connected throughout the supply chain can collect vast amounts of data on parameters such as shipment location, temperature, humidity, and vibrations in real time. This provides unprecedented visibility and security across the entire supply chain, often preventing problems before they occur.
Transportation Management Systems (TMS): TMS software leverages real-time stats on route optimization, carrier rates and vehicle capacity. This enables efficient transportation planning, leading to lower costs and improved delivery times.
Big Data and Data Management: Managing vast troves of supply chain entries requires specialized platforms and tools. Big data analytics can reveal patterns and correlations that are difficult to discern with traditional or manual analysis. This unlocks insights that help companies make more strategic decisions and boost their supply chain performance.

Tips for Leveraging Data to Maximum Effect

Here are some Pro Tips for applying data analysis in your supply chain operations.

Tip #1: Share your data. When soliciting RFQ bids from supply chain providers, for example, if the provider can see all the details of your business requirements, then they will have confidence in giving competitive rates. They won’t feel like they have to “hedge their bets” and add margin to cover hidden costs or to allow for issues that might arise.

Tip # 2: Always get more data than you think you need. One analysis or project often leads to another, which may need to incorporate additional parameters. Gathering all available data at the start of the project will save a lot of time later on when revisions need to be made, or if the scope of the analysis needs to be changed.

Tip # 3: Always summarize the data you plan to work with and allow stakeholders to review it. They can validate it before you get too deep into the project. This is where extra pairs of eyes can check whether data is missing, or if there’s an element of the business that is changing and might impact the analysis.

Getting Started with Data-Driven Supply Chain Management

Embracing a data-centric approach to supply chain management can be transformative. So how does a company incorporate this culture?

As a first step, companies need to invest in tools and technologies to collect information from various sources in the supply chain, such as sales figures, inventory levels and supplier information. This is then channeled to a centralized repository for better organization within the company’s IT systems, and integrated with its other business management apps.

Once collected, the raw data needs to be cleaned and formatted to ensure that it is compatible with the company’s analytics tools, so that reliable and high-quality insights can be derived. User-friendly visualization tools and dashboards can provide a clear understanding of supply chain performance, allowing companies to look for patterns, trends, and areas for improvement.

The next step is to define relevant Key Performance Indicators (KPIs), like on-time delivery percentage, inventory turnover rate and freight costs per shipment. Regular monitoring of the KPIs helps identify areas of success, as well as potential challenges. Finally, companies must not lose sight of maintaining a culture of continuous improvement centered around data. This means always encouraging teams across the organization to seek insights and use them to solve problems, streamline processes, and drive decision-making through appropriate use of supply chain solutions.

Data is Your Compass in the Supply Chain Landscape

Achieving supply chain success is a complex endeavor, often fraught with challenges such as disruptions, fluctuating demand, and unforeseen obstacles. However, these complexities can be effectively managed with the right tools and strategies. Data plays a crucial role, providing valuable insights and guiding decision-making for greater efficiency, cost reduction, and increased resilience. By harnessing the power of data and implementing proven strategies, businesses can turn potential setbacks into opportunities, paving the way for comprehensive supply chain optimization at every step of your journey.

At Armstrong, we understand the transformative power of data. We’re more than just a supply chain solutions provider – we’re your partner in harnessing the full potential of your data. Our expertise and customized solutions can help you unlock insights, streamline processes, and achieve supply chain excellence.

Ready to embark on your data-driven supply chain journey? Contact Armstrong today. Let’s turn your data into actionable strategies and drive your business forward.