In high-performing modern supply chains, effective logistics planning begins with a rigorous, data-driven understanding of how an operation actually runs. Three foundational data points form the baseline of the automation design process: Sales order history, inventory profiles, and 3-year, 5-year, and 10-year growth projections. TGW Logistics’ dedicated data consultants translate these inputs, along with SKU velocity, order diversity, and peak demand, into actionable system designs and logistics services that reflect operational reality. The best results are rooted in deep-dive data analysis, translating operational outputs into precise, scalable solutions.
The thoughtful, precise use of raw data enables smarter logistics planning that aligns with both current performance needs and long-term growth, equipping industrial operations to evolve over time without disruption. To be future-proof, automation plans must be rooted in how a facility actually functions, so the final design can take shape before any technical equipment is selected. Working with an experienced, forward-looking, and data-driven systems integrator yields an automation solution that fits the facility, supports efficient logistics services, and scales seamlessly as your business expands and demand increases.
Why Raw Data Is the Starting Point for Any Automation Design
Averages alone don't tell the entire story. Without understanding the variance and distribution behind them, they can conceal emergent problems that are difficult to address later. The best and most informed logistics planning starts with highly specific data to avoid these harmful generalities. Your facility’s SKU velocity, order diversity, peak throughput patterns, and volume shifts across seasons and channels are all operational outputs that should be considered, as pointedly as possible, in your design process. While calculating averages and “most-of-the-times” may sound like a clever shortcut, we’ve seen systems built for average performance fail to keep pace during multi-week peak windows.
TGW Logistics’ data consultants know their way around heaps of raw data. These professionals collaborate directly with our customers’ operations managers and data teams to generate the numbers needed, rather than relying on tidy, filtered summaries that may be used for other purposes in other parts of the organization. Part of this process includes assessing data quality, identifying gaps, inconsistencies, or structural issues that could skew analysis if left unaddressed. While summaries serve their purpose, it's granular raw data that uncover non-obvious issues and illustrate the facility's full operational reality. For example, aggregate throughput numbers may hide picking inefficiencies, or an average SKU velocity may not convey that a small percentage of SKUs are driving a disproportionate share of picks and are causing congestion in specific zones. Averaged data underestimates variability, resulting in planned facilities that perform well on paper but not in practice. Raw data establishes the operational truth across the board: quirks, bottlenecks, obsolete stock, and all.
Translating Data into a Logistics Planning Framework
Once the data has been collected, the solution design team’s focus shifts to turning analysis into strategy through collaborative scenario planning. Rather than planning around a single, idealized forecast, our professionals build multiple models based on conservative, expected, and aggressive growth assumptions to represent a range of possible operational futures. This all-inclusive approach helps to resolve the inherent tension between relying on historical performance data and anticipating future demand. It also opens the door to incorporating a company’s ambitions and aspirations into the data. What could your operation look like with more sales, regions, or SKUs? Modeling allows you and our solutions design team to visualize the possibilities.
Scenario planning across a range of operational circumstances ultimately provides stakeholders with invaluable tools for making informed decisions on the flexibility that should be built into the automation design, as well as the appropriate financial investment. Each modeled outcome is tied to actual customer data and forms the foundation of a strong business case for automation investment. They quantify what’s possible and show how different automation choices impact throughput, storage capacity, and labor needs over time.
What the Data Actually Reveals in Industrial Logistics Operations
As we’ve said, detailed data analysis is an indispensable step in logistics planning because it provides a full picture of how the facility operates and where opportunities for improvement lie. Digging into the raw numbers almost always surfaces insights that high-level reviews created for executives and other teams consistently miss.
Here are some examples of the pain points the data analysis process might expose:
- SKU analysis: May identify the small subset of items driving a disproportionate share of picks, so you can design around the reality and not the average.
- Order diversity: Accounts for both combined single-line ecommerce and multi-line wholesale orders, which may significantly change throughput characteristics.
- Peak season analysis: Reveals the shape of a volume surge (whether that be a climb, hold, or drop) rather than just sizing for the highest possible volume.
These data points are also useful in determining how much physical throughput capacity should be built into the automation design and how much flexibility to allow for tweaking processes and labor. This supports the creation of a system that’s sized to reliably handle expected volumes and adapt to peak demand periods without initial overbuilding. Again, we want to know how the operation truly behaves rather than relying on averages and assumptions.
Another upside to studying operational data granularly is that it may reveal bottlenecks and pain points that can be solved with simple operational tweaks rather than added automation equipment. For example, data might reveal that a single high-velocity SKU is creating downstream bottlenecks. In one case, implementing a pre-bagging process upstream for that SKU improved overall throughput.
From Analysis to Solution: How TGW Logistics Builds the Case Internally and for You
At TGW Logistics, a resilient, effective final solution design requires alignment across our design, simulation, and realization teams to confirm that what’s been envisioned on paper will work on the warehouse floor. The simulation team validates the proposed design using a detailed simulation model and real customer data, testing it against peak demand, evaluating throughput limits, and locating potential bottlenecks. Simulations test critical variables like peak throughput ceilings, equipment utilization rates, and buffer capacity under surge conditions. This step routinely surfaces design adjustments that are far less costly to make in the model than after installation. And all of this happens before installation even begins.
Cross-functional collaboration fully stress-tests the design in a range of scenarios, leading to fewer unpleasant surprises when the system goes live. Additionally, a business case illustrated with solid data, scenario modeling, and simulation output is much easier for senior stakeholders to understand and approve.
The Right Automation Solution Starts with the Right Data
Operations managers know that industrial logistics operations are far too variable, complex, and specific to be well-served by standard automation decisions. The most successful logistics planning and warehouse automation design processes start where the organization is now, by thoroughly analyzing existing raw data. This step guarantees that you’re designing for a real operation rather than an imagined, idealized version.
TGW Logistics achieves successful logistics planning outcomes by offering our customers the depth of data analysis and industrial logistics experience required to build a solid foundation for personalized automation design. Collaboration across our teams is vital, but collaboration with the customer and your stakeholders is the most important puzzle piece of all. Partnering on this process from the earliest stages keeps priorities aligned and controls quality from beginning to end. Additionally, we’ve found that working closely with our customers throughout the automation design journey builds trust among us and in the solution we’ll design together.
Ready to learn how TGW Logistics data consultants can convert your raw data into actionable insights to create a design plan for a facility that thrives in your current operational reality and will grow with you to achieve future goals? Get in touch with us today to find out what’s possible.
TGW Logistics is a foundation-owned enterprise headquartered in Austria and a global leader in warehouse automation and warehouse logistics. As a trusted systems integrator with more than 50 years of experience, we provide end-to-end services: designing, implementing, and maintaining fulfillment centers powered by mechatronics, robotics, and advanced software solutions.
With over 4,600 employees across Europe, Asia, and North America, we combine expertise, innovation, and a customer-centric dedication to help keep your business growing. With TGW Logistics, it's possible to transform your warehouse logistics into a competitive advantage.
