Shippers and carriers need accurate dimensional data to run their operations. This information is the source for receiving, slotting, inventory management, picking, packing, cost calculations, invoices, for EVERYTHING.
Without dimensional data operations & systems cannot work. With inaccurate dimensional data operations & systems becomes inefficient leading to a 30% and up increase in operational costs.
This is why we’ve named inaccurate dimensions “dirty dimensional data”, as it brings hurt to multiple supply chain processes.
Let’s look at the impact of not having accurate dimensional data to rely on in different operations.
Overview of processes affected by dirty dimensional data:
- Storage
- Inventory accuracy
- Picking
- Packing
- Truck load building
- Cost calculations
- Sales order creation
- WMS set-up
- Vertical lift utilization
In storage
The impact of dirty dimensional data begins at the inbound stage, where dimensional data is inaccurately collected.
As inventory arrives, warehouse workers rely on this data to assign storage spaces within the warehouse. However, when the provided dimensions are incorrect, it triggers a chain reaction that resonates throughout the storage process.
Workers assign spaces based on wrong information -> Space is occupied by items that do not fit to the assigned dimensions.
- If a box or a pallet is too big for an assigned slot, workers need to relocate it which leads to time loss.
- If a box or a pallet is too small for an assigned slot, either it is left there which means wasted space as it would fit in a smaller slot, or again workers need to relocate it.
In inventory accuracy
Warehouses maintain detailed information about their products:
- SKUs - dimensions and weight
- Master cartons (also known as outer cartons) - dimensions, weight, and SKU quantity inside
- Pallets - dimensions, weight, SKU and master carton quantity on the pallets, and number of layers per pallet
The availability of this kind of multi-level information contributes to inventory accuracy. Understanding the dimensions, weights, and quantities allows accurate counting of stock levels.
Without this data companies face estimation of inventory levels.
Picking a master carton while knowing how many SKUs have left a warehouse becomes tricky without dimensional data because figuring out how many SKUs are inside becomes a guessing game.
In the absence of dimensional data, companies resort to more stock-counting efforts.
In picking
Pickers navigate the warehouse to retrieve items from shelves & racks and slots, and these routes can be complex, with various parameters to consider.
Often there are constraints on max. volumes and max. weight in one pick tour. This can only be considered when warehouses have the data on their inventory.
Not knowing the (right) dimensions of items means wrong-sized boxes and pallets are chosen for pick tours.
The sequence in which items are picked is also important. Warehouses need to know the constraints of maximum volume or weight in a single-pick tour.
Without knowledge of the biggest or heaviest items to pack for a pick tour leads bad packing – in boxes & on pallets.
In packing
Wrong dimensions of SKUs and master cartons leads to wrong selection of shipping boxes & pallets in packing.
What does this mean? Items not fitting in the selected box or on a pallet.
- If a box is too small, rehandling is required which leads to slower order fulfillment.
- If a box is too big, and a packer chooses not to repack it, this leads to shipping air and wasting carton.
In truck load building
Guessing or using the wrong dimensions of boxes and pallets to create load plans for orders can lead to ordering the wrong number of trucks - too many or too few - which leads to higher than necessary transport costs.
- Ordering too many trucks means:
- Higher transportation cost
- Lower fill rates for each truck
- Higher CO2 emissions
- Ordering not enough trucks means:
- Not being able to ship all your goods
- Longer delivery times = unhappy customers
In shipping cost calculations
In shipping cost calculations, the importance of accurate dimensional data cannot be overstated.
When actual dimensions do not match recorded dimensions, this means wrong invoices and shipping cost calculations for customers & carriers.
If you’re a carrier, this means:
- You’re losing money since you cannot correctly bill your customers
- More disputes with shippers and bad carrier-shipper relationship
If you’re a shipper, this means:
- Higher transportation costs
- More disputes with carriers and bad shipper-carrier relationship
In sales order creations
The process of sales order creation, particularly during website checkout, includes the calculation of shipment costs.
Companies have to pass on shipment costs to their customers or, at the very least, provide a reliable shipping cost indication. This objective relies on the availability of accurate dimensional data.
Since successful sales order creation requires capabilities in packing (for cartonization) and for truck load building, incorrect shipment cost calculations impact customer satisfaction.
In WMS set-ups
Dimensional master data is a prerequisite for configuring the WMS to efficiently manage inventory and warehouse spaces.
The absence of precise master data means the system lacks the foundational information required for optimal functioning.
This means lost financial resources due to wait time in the WMS implementation phase and time loss.
In vertical lift utilization
No dimensional master data = No knowledge of what size the shelves should be, to store the SKUs.
Dimensional data of SKUs & master cartons is a prerequisite if companies choose to implement vertical lift systems such as Kardex.
Bottom Line
This lack of dimensional data can lead to a 30% increase in operational costs. If a company faces issues in more than one of the mentioned areas, this cost increase could be even higher.
Wrong measurements or lack thereof cause disruptions & inefficiencies in the supply chain, it's time to focus on getting the measurements right. This isn't just about fixing past mistakes; it's about making supply chain work more efficient.
What’s the quality of your master data?