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3 Insights on Using Data and Analytics for Better Warehousing

Back then decision-making is more about guesswork and hopefully everything would turn out fine later.

But to stay competitive and get ahead in this modern society, various companies and industries now are becoming more data-driven. This includes the warehousing, logistics and inventory management industries. Although right now companies in these industries are already taking advantage of data in better operations, there might be still a few opportunities untapped and insights still left unexplored.

Using descriptive, predictive and prescriptive analytics

Dynamic industries such as warehousing already have huge data resources (and they get bigger every day). That’s because every activity is actually an opportunity to collect data and gather insights from. These activities include:

  • Unloading and receiving goods from suppliers, importers and manufacturers
  • Transferring goods onto pallets for short-term or long-term storage
  • Storing goods in prescribed conditions (racks, cold areas, freezers)
  • Picking the goods from warehouse shelves and preparing them for transport (usually in response to customer orders)
  • Loading the customer orders onto vehicles (for later transport to the customer’s site)

Each activity presents an opportunity for improvement. And notice that these activities require movement and the use of forklifts. These activities also require several trips from point A to point B.

This presents a huge opportunity for achieving higher speed and better operational efficiency (thereby improving cost effectiveness). What if we could reduce the number of trips and the distance being covered by the forklifts whenever we’re receiving and unloading goods from the suppliers? What if we could minimise the downtimes through better fleet rotation and maintenance scheduling among our forklifts?

These are only possible if we start using data more wisely. For example, we can look at the usage data of forklifts and rotate them more effectively. If forklift A is logging in twice as many hours as point B, it’s time to switch or reduce the workload of forklift A. This way, the parts and engine of forklift A won’t be worn out too fast. In other words, more effective fleet rotation results to better equipment utilisation.

Aside from optimal fleet rotation, accurate and timely usage data can also be used for optimal maintenance scheduling. If forklift has logged a certain number of hours or covered a certain distance range, a user-definable maintenance alert could kick in. Instead of waiting for forklift A to break down, a timely maintenance could be done on the material handling equipment. Or, instead of waiting for 6 months or 1 year before performing any maintenance on forklift A, we can be more proactive and practical by basing the maintenance schedule on the forklift’s hours, rather than an arbitrary 6 months or 1 year.

This is the power of descriptive, predictive and prescriptive analytics. With the use of data, we can make more proactive and practical decisions. As a result, we can improve our operational efficiencies and better match supply and demand. With the forklifts in top condition, there will be zero or minimal downtimes in our warehousing operations.

This goes way beyond looking at historical data and preparing management reports. This is more about being more responsive and proactive in better warehousing and inventory management. Aside from describing the current operations, the data we have can also be used for predictions and prescriptions (resulting to productive actions).

Well, this is what already happens in the operations of Fortune 500 corporations. They track almost everything and create extensive documentations for monitoring and improving current operations. But this might not be the case in relatively smaller warehouse operations. Good news is they can still benefit for using data and analytics for better warehousing.

Smaller warehouse operations are a bit different

Small and medium-sized businesses often have different requirements and priorities compared to multinational conglomerates. Aside from the scale and capacity, SMEs also have more apparent financial limitations.

For instance, creating a data warehouse and other IT infrastructure is a tremendous investment for SMEs. Also, hiring or training personnel who can manage these systems requires huge investments and time, knowledge and financial resources. There’s also the case of uncertainty and magnification of effects. Many SMEs live day by day and any huge fluctuation in their expenses and orders could mean survival for them.

On the other hand, Fortune 500 corporations all have the financial resources to invest in huge data infrastructure and other systems to improve productivity. It’s just a drop in the bucket in their financial statements. Moreover, huge corporations can better weather the fluctuations.

Perhaps it’s one reason why SMEs don’t invest much on technologies that can lead to operational improvements. Perhaps it’s better to purchase new forklifts instead of investing on new technologies on top of the core operations.

But still, SMEs involved in warehousing can still benefit from proper use of data and analytics. Here are some ways SMEs can accomplish things using available data:

  • Placing the fast-moving goods near entry or exit for faster unloading, loading and receiving of goods (given that we’ve identified beforehand those items)
  • Designating low-speed and high-speed areas in the warehouse (e.g. low-speed for areas where accidents commonly occur)
  • Analysing usage data for optimal maintenance scheduling and fleet rotation (which also applies to large-scale warehousing)

Using data for SMEs might be more about looking at historical data than reacting on real-time. But these businesses can still move further by having alert systems for accidents and impacts. If a forklift collided with another forklift (or crashed into a wall or shelf), the manager or engineer in charge should be able to respond promptly (e.g. give immediate first aid to driver, evacuate the area in case there’s a chemical spill).

Aside from the alert system, a data recording system should also be in place for later analysis of the events. If there’s an accessible record of the accidents (e.g. information about time and driver), we can then analyse them and formulate to prevent them in the future. Even in warehouses with just 4 or 5 forklifts this is still useful. Take note that warehouse operations are dynamic and accidents may occur each week (especially if you have a very busy warehouse or there’s a surge of orders and requests).

What about big data & artificial intelligence?

Artificial intelligence is set to transform not just warehousing, but every industry that’s active right now. One of the most promising (and perhaps most profitable) application of AI in accurate forecasting of demand.

With the availability of sufficient data, AI systems can search for and exploit patterns in the different facets of warehousing and inventory management. Using past data, AI systems can come up with optimal ways on when to order the goods (including how many) and where to place them. This requires analysing the product interactions, relationships among various data points and other things that might affect supply and demand. With a more accurate forecast (plus its certainty), businesses can better exploit opportunities by promptly meeting the demand.

Again, this level of technology may require huge investments in computer and data infrastructure (plus sensors for multi-parameter monitoring of almost everything). In addition, hiring or training the talent required for this level is too costly especially for SMEs.

Good news is even without the use of AI, SMEs can still take advantage of the data available to them. For instance, the use of EquipManager® allows managers to have timely and accurate usage data about their fleet. Analysis of that data can result to optimal maintenance scheduling, fleet rotation and equipment utilisation.

Aside from better operational performance, having an effective fleet management system such as EquipManager® reduces costs associated with product and equipment damages. More importantly, a more effective fleet management can result to a safer workplace and zero or fewer injuries in the warehouse.

It’s just a start. Data can be further exploited to spot opportunities for better operational performance. But it’s always recommended to pay attention to the low-hanging fruits first before looking at very costly AI technologies. As you grow your operations and stabilise your business, you can always make bigger technological investments later on.

Perhaps right now you have everything you require to achieve a 10% higher productivity in your warehouse. With the insights you’ll acquire from analysing data, you can readily reduce product damages and minimise downtimes and worker injuries. Indeed, better warehousing is attainable with the right tools and mindset.

You can begin with mapping the entire process from receiving the goods until dispatching them. In every step lies an operational improvement. It’s especially the case in forklift operations wherein they do most of the movements.

Moving goods from point A to point B could be a lot safer and more efficient with a fleet management system in place. Here at ShockWatch, we provide such system for better warehouse management. Contact us today if you want more information.