Decreasing the Pressures of Inventory Dynamics 

 A Mobile Inventory Solution Provides Energy Company with Quick 61% ROI

A North American energy company was losing money and identified that their inventory process could benefit from a mobile system. The manual processes they used to manage inventory transactions with contributed to inaccurate data and delays in entering inventory balances into their system. Here’s how they saved $15,000 within two weeks of implementing DataSplice.

Oil and Gas Workers Scanning Inventory items into Maximo with iPad

The company’s manual processes, which included cycle counting and material issues, were identified as inefficient and ineffective. Here’s what they were doing: The company’s wall-to-wall inventory counting within the warehouses took two to four weeks. It required that the warehouse be “frozen,” whereby no receipts were issued or accepted within the system, in an attempt to ensure that correct balances would be obtained at the end of the cycle. Operations, however, were not frozen and continued to issue and receive items. Upon the re-opening of the warehouse, they entered the backlog of manually-recorded transactions into the system. Also, the company hired third-party contractors to perform the inventory counts, creating additional cost. Finally, the manual process itself included recording information on paper, which was then physically entered into IBM Maximo®, sometimes incorrectly.  The result of this cumbersome inventory process was a count variance as high as 14%.

Similar costly inefficiencies arose with issuing materials. This was also a manual process which created problems. For one, the transcription of the handwritten information into Maximo contributed to information errors. Two, the manual process of recording information and then separately entering it into the system at a later date created delays. Up-to-date system inventories were unavailable because the entry of these issues could take several days. These manual processes contributed to the high variance rates experienced with the physical counting of items in the warehouse.

To address these issues, they deployed a mobile solution to accurately account for transactions in a timely and effective manner.

REQUIREMENTS

The energy company needed a solution to optimize their inventory count and sign-out processes. Expectations included:

  • Must be a handheld device.
  • Must be easy to learn and use.
  • Must include barcode scanning capability.
  • Must have the ability to sync data with Maximo.

SOLUTION

The DataSplice solution runs on handheld devices that can be utilized in a connected or disconnected mode. Physical counts are entered into a handheld device by scanning item barcodes, then the data syncs directly to Maximo when a connection is present. If the warehouse technician works in an area that has no connectivity, or if the systems are unavailable, the job of counting items can continue as planned with the data stored on the device. Operators are now able to issue materials directly to work orders by scanning the item with a handheld device, recording this information as it occurs and ensuring up-to-date information.

BENEFIT HIGHLIGHTS

  • Decreased the number of days the warehouses were frozen for inventory counting.
  • Increased the accuracy of data entered into Maximo.
  • Decreased the cost of material issues.
  • Ended redundant inventory steps.
  • Correctly recorded inventory.
  • Decreased count variances by half.
  • Increased confidence in inventory reports.

Through the implementation of DataSplice, the energy company realized a savings of over $100,000 in less than one year, with a 61% ROI within seven months. Rather than freezing a single warehouse for up to four weeks, physical counts now take just over one week for two warehouses. The count variances are improving, decreasing to 7% within the first year. This solution has created confidence in their inventory balances, saved time and saved money.

 PROJECT SPECIFICATIONS

  • Deployment Type: On-premise
  • Integrated Systems: Maximo
  • Departments: Storerooms
  • Implementation Team: DataSplice
  • Use Case Type: Inventory