Tulip Interfaces > Case Studies > Frozen Food Supplier Enhances Operational Efficiency with Tulip

Frozen Food Supplier Enhances Operational Efficiency with Tulip

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 Frozen Food Supplier Enhances Operational Efficiency with Tulip - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Real Time Analytics
  • Cybersecurity & Privacy - Identity & Authentication Management
Applicable Industries
  • Equipment & Machinery
  • Food & Beverage
Applicable Functions
  • Facility Management
  • Maintenance
Use Cases
  • Real-Time Location System (RTLS)
  • Supply Chain Visibility
The Challenge
The frozen food supplier, a leading provider of bakery solutions, was grappling with two major challenges. Firstly, the company was heavily reliant on paper-based processes to record machine downtime and the reasons for it. This method was often inaccurate and difficult to aggregate, leading to a lack of visibility into the plant's metrics. Consequently, line supervisors and plant management lacked the critical information needed to prioritize and address issues impacting throughput. Secondly, the company had to perform cumbersome and time-consuming audits to comply with the stringent requirements of the food and beverage industry. These audits ranged from PPE audits, pre-production checks on equipment, and periodic checks on the metal detector. The combination of these problems resulted in a lack of visibility into the plant's performance and the issues that needed to be mitigated.
The Customer

This company is a leading supplier of frozen bakery solutions

About The Customer
The customer in this case study is a leading supplier of frozen bakery solutions to the food service and in-store bakery business segments. The company combines advanced bakery technologies with a collaborative business approach to create and manufacture on-trend bakery concepts for leading global, national, and regional customers. Operating in the food and beverage industry, the company has to comply with stringent HSE regulations. The high-throughput nature of the plant makes it critical to keep machines running efficiently. The company was facing challenges with paper-based processes and a lack of visibility into plant metrics.
The Solution
To address these challenges, the company implemented Tulip, a digital machine monitoring solution. Tulip starts recording information on machine errors or malfunctions that cause downtime either when machines output a fault code via the OPC UA server or when an operator manually stops a machine. This triggers a downtime state within Tulip, alerting operators and allowing them to swiftly address and fix the problem. The solution also enables team members to report scrap whenever material fails to meet quality standards. All this information is aggregated into visual and real-time dashboards that line supervisors and plant managers can use for data-driven decision-making. For audits, the team built digital audits that can be performed from tablets. These apps are composed of intuitive and user-friendly checklists that can be performed on predefined schedules. The data is synced in real-time, alerts are sent to teams when areas have been released, and all past audits are securely stored for future reference.
Operational Impact
  • The implementation of Tulip has provided the frozen food supplier with unprecedented visibility into the plant’s operational state. The team now has a real-time, detailed view of the entire plant and its operational state. Historical data is aggregated to provide the team with trends on the performance of equipment across the plant. The team can view which issues cause the most time-consuming faults so that they can prioritize their improvement efforts. This insight is enabled by Tulip’s analytics engine, which aggregates and displays visual charts and information recorded in Tables. The team can configure a suite of analyses to display metrics from the plant overview and drill down into data on specific pieces of equipment. The company is also in the process of integrating Tulip with Netsuite to pull data from the work orders that are scheduled to further augment the app suite. This will enable the team to better schedule and plan production runs.

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