Vendor: ALOM
Customer: ALOM
Customer’s Primary Industry: Supply chain management
Timeframe: 18-20 months
Timeframe: January 2021 – December 2021
Business Objective: Lineage Logistics leveraged ndustrial’s nsight Coincident Peak solution to save over $2.5 million in energy savings in 2021. Additionally, the savings removed 2MW of energy off the utility grid during peak energy demand periods. To make sure the amount of electricity fed into the electricity grid is equal to the amount of electricity consumer, ndustrial specifically developed the nSight CP solution to forecast electric grid peaks, allowing factories to avoid expensive power consumption periods and to help manage the electric grid. Lineage Logistics and other facilities have increasing pressure to maintain quality production standards while simultaneously reducing their energy costs, while operating at the lowest possible cost-per-unit of production. Additionally, reducing or adjusting energy use at the right time requires a comprehensive understanding of all facilities’ operational and energy patterns. ndustrial was one of the first in the industry to develop this software-only approach to help factories reduce their energy demand charges through a proprietary machine learning algorithm. Lineage notified ndustrial when their monthly peak hour was going to occur, allowing them to reduce power consumption during the predicted time window. This process enabled Lineage to proactively adjust their production schedules, cooling set points, lighting levels or any other energy consuming function during the window. At the end of each month, ndustrial matched the savings prediction against the results of their load control to calculate savings and produce a monthly report.
ROI: As a result of the implementation, Lineage has saved $2.5 million in energy spend in 2021 and removed 2MW of energy off the utility grid during peak energy demand periods.
Vendor: Fine Tune Expense Management
Customer: Fortune 1000 global manufacturer of technically advanced specialty materials and complex components 
Customer’s Primary Industry: Business consulting and services
Timeframe: April 2020 – Present
Business Objective: Fine Tune and its client of 4.5 years—a $3 billion global manufacturer of technically advanced specialty materials and complex components—underwent an intensive optimization project that saved the client hundreds of thousands of dollars in uniform rental category spend. Utilizing its proprietary Expense Management Optimization and Auditing Technology (eMOAT), Fine Tune worked with the client’s sourcing leaders to review spend and inventory summaries across 16 sites. Doing so uncovered and implemented substantial savings, including facility service item inventories and service frequencies, uniform quantities, direct sale/restroom product buying strategy and loss and ruin charges.
ROI: As a result of Fine Tune’s deep dive into spend and utilization, the client company spent nearly $100,000 less in 2021 on uniform and non-garment rental than in 2020, and more than $205,000 savings is expected over the client’s remaining uniform rental program term. Additionally, the project resulted in sustainability improvements—an important benefit of Fine Tune’s optimization projects—with reductions in wasted space within facilities where unused product “sat,” extra trips by the uniform supplier for unnecessary item servicing, uniform wash cycles and new uniforms injected into the program. Finally, the client’s Commodity Manager reported an added benefit of being able to “focus on more strategic efforts across the business” as Fine Tune executed and managed the project, and in everyday operations moving forward.
Vendor: inVia Robotics
Customer: Leading entertainment merchandise 3PL
Customer’s Primary Industry: Fashion and beauty, entertainment 
Timeframe: April 2021 – Present
Business Objective: This 3PL was using completely manual processes, and the warehouse team was picking at an average rate of 30 UPH. It chose inVia to implement a comprehensive warehouse execution system (WES) that could create greater efficiencies in its Carlsbad, Calif., distribution center. This project entailed using artificial intelligence (AI) to better organize storage and movement of inventory, providing tools that would help employees be more productive and using robots to automate picking and replenishment tasks to save on labor costs. Using inVia’s robotics-as-a-service (RaaS) model, this 3PL was able to adopt all of these optimizations at its own pace without disrupting any of the existing operations. It started by implementing the inVia Logic WES and the inVia PickMate productivity tool. First inVia Logic identified the ideal location for inventory to store in reserve and in forward pick locations. Using inVia’s system to do this was particularly important because all of the orders are e-commerce and depend on random access to all SKUs. inVia Logic also functions as the labor management system. It collects and uses data on each worker to assign fulfillment tasks, tracking elements such as pick rates, travel time and time to productivity. Furthermore, it dynamically reassigns labor throughout the day according to progress against SLAs. Additionally, employees started using inVia PickMate on their existing handheld devices to receive intuitive, step-by-step instructions to complete fulfillment tasks. This helped boost both efficiency and accuracy rates, leaving no steps in the process open to guessing. After using the software alone for 3 months and seeing doubled pick rates, the 3PL added inVia PickerWall using a fleet of 42 inVia Picker autonomous mobile robots (AMRs). This workflow leverages the strengths of robots on one side of a daily put/pick wall and the strengths of people on the other side.
ROI: This 3PL was able to increase productivity rates 1,000% higher than what they were achieving before implementing the inVia Robotics automation system. Before, it was averaging 30 UPH per picker, and now, averages 334 UPH per picker. This increased efficiency and reduced labor needs by 60%, which was the difference for it be able to stay in business given the tight labor market. Using the machine precision of robots to pick and putaway items also increased accuracy rates to 99.9%.
Vendor: ndustrial
Customer: Lineage Logistics
Customer’s Primary Industry: Energy software and technology 
Timeframe: January 2021 – December 2021
Business Objective: Lineage Logistics leveraged ndustrial’s nsight Coincident Peak solution to save over $2.5 million in energy savings in 2021. Additionally, the savings removed 2MW of energy off the utility grid during peak energy demand periods. To make sure the amount of electricity fed into the electricity grid is equal to the amount of electricity consumer, ndustrial specifically developed the nSight CP solution to forecast electric grid peaks, allowing factories to avoid expensive power consumption periods and to help manage the electric grid. Lineage Logistics and other facilities have increasing pressure to maintain quality production standards while simultaneously reducing their energy costs, while operating at the lowest possible cost-per-unit of production. Additionally, reducing or adjusting energy use at the right time requires a comprehensive understanding of all facilities’ operational and energy patterns. ndustrial was one of the first in the industry to develop this software-only approach to help factories reduce their energy demand charges through a proprietary machine learning algorithm. Lineage notified ndustrial when their monthly peak hour was going to occur, allowing them to reduce power consumption during the predicted time window. This process enabled Lineage to proactively adjust their production schedules, cooling set points, lighting levels or any other energy consuming function during the window. At the end of each month, ndustrial matched the savings prediction against the results of their load control to calculate savings and produce a monthly report.
ROI: As a result of the implementation, Lineage has saved $2.5 million in energy spend in 2021 and removed 2MW of energy off the utility grid during peak energy demand periods.
Vendor: Verusen
Customer: Fortune 500 global packaging company
Customer’s Primary Industry: Packaging 
Timeframe: 2021
Business Objective: The Fortune 500 global packaging company was looking to offset inflation by reducing its inventories. It had just completed an acquisition and had a massive amount of relevant data living across many systems, but this data was highly disjointed, incorrect and disparate, providing an inaccurate view of inventories. This resulted in ongoing overstocking, a lack of visibility into where items are located, wasted spending and lost revenue. Using Verusen’s AI platform, the packaging company could harmonize its data, creating transparency and integrity across all catalogs of data. This affected over 55 facilities and over 400,000 individual SKUs, saving the customer over $6 million in MRO space in less than 24 months. The customer was also able to identify over $21 million of verified cost reduction opportunities and over $200 million value of on-hand inventory related to MRO materials. In addition, by implementing Verusen’s AI platform, the team reduced risk across the supply chain significantly. For example, one of the plants experienced two critical asset failures causing production to cease. The reliability manager could not find any available replacement parts to perform the maintenance needed to get the asset back up and running. The storeroom team first searched the plant inventory manually, with no result. Next, multiple colleagues initiated material searches within SAP, with no matching result. Finally, they contacted their manufacturer supplier and were told it would be a 4-week lead time to get the part, which would have cost over a $1 million loss in downtime. By using Verusen’s AI Global Material Search capability, the company was able to locate the critical material in real-time at four sister plants, all within proximity. The material was dispositioned for overnight delivery, and the maintenance was performed, enabling the asset to be back up and running in less than 3 days with minimized downtime.
