How digital twin applications can help manage and optimize inventory

Digital twins—essentially virtual representations or simulations of physical operations—allow manufacturers to see how a process or operation performs in real time and predict how it may perform in the future. This type of simulation will increasingly become table stakes in the smart factories of the future.

One RSM client, a midsize industrial food production company, used digital twins to optimize its bulk inventory management capabilities and ultimately determine which investments in personnel, storage tanks and other supporting storage capacity were necessary. Through simulation, the business was able to refocus investments on areas of the business that would yield more value. We explore the factors at play in putting this transformative technology into action.

Determining future capacity

At the outset of the project, the industrial food production company had numerous priorities: making operations as efficient as possible, improving business processes and reducing operating expenses while also prioritizing capital investment. To achieve those objectives, the company had purchased production line equipment that would enable it to produce a greater volume of product, but it needed to understand the upstream implications before increasing capacity.

That’s where a simulation analysis proved helpful. The company fed operational data into a digital twin model and ran various simulations to determine the appropriate size of production equipment that would be needed to meet the production line demands. Solving that question had many variables, including product mixes, batch sizes, upstream processes’ capacity, new and existing equipment, and plans for future growth.

The main challenge was to determine whether the future-state production system could meet the company’s sales goal of producing an additional 9.5 million pounds of product per year, and how to make sure the organization understood the broader change and impact that growth would have on operations.

RSM’s engagement with the company involved a three-phase approach using digital twins to address that challenge:

1. Discovery phase

• Document simulation questions, assumptions and model scope
• Collect existing process performance data and plan operations studies
• Document, review and approve on-paper model of in-scope processes and future-state scenarios

2. Analysis phase

• Conduct process studies and finalize model inputs
• Build simulation model of existing system
• Validate simulation model against current performance
• Finalize future-state scenarios

3. Scenarios and impact phase

• Modify model to reflect future-state scenarios and answer simulation questions
• Summarize scenario findings into a final deliverable concluding with a readout for executives and other key stakeholders

Key benefits

Understanding specific inventory and staff levels is just one aspect of the use case for digital twin simulations. This technology also helps organizations understand the design and layout needs of their warehouse and production facilities and improve stakeholder understanding of impending changes by using 3D representations to explain the change and supporting rationale.

The simulation’s flexible model and dynamic scenario levers enabled the company to experiment with real-world variability that showed the range of expected performance across multiple simulation runs. Models are an enduring asset, and easily modified to evaluate new operating scenarios in the future.

Other key benefits included:

• Apples-to-apples comparisons of key performance measures across alternative scenarios versus current state
• Simulations that are easily modified for future projects
• Mitigation of uncertainty and risk of initial plan, allowing the client to pivot before missing customer commitments
• Identification of additional process improvement opportunities with upside of an additional 1 million pounds in annual production

From insights to decisions

The foundational importance of digital twin simulations lies in the fact that they can help businesses transform data-driven insights into real-world decisions. For many manufacturers, this capability will be paramount as economic and margin pressures require many companies to shift from a “grow-at-all-costs” strategy to one that focuses on profitability.

Businesses should investigate how digital twins might help them optimize operations through the evaluation of multiple potential scenarios, including:

• Diversifying and optimizing product offerings with a focus on high-margin products
• Focusing on production efficiencies across the entire value chain, with continued investment in automation and productivity-boosting technologies
• Renegotiating contracts with suppliers and working with customers to negotiate cost reimbursements
• Streamlining processes and upskilling employees to improve efficiencies

All of this can ultimately help organizations better understand where to focus their investments and adapt operations as needed.

^{This article originally appeared in the Manufacturing Leadership Journal, a publication of the Manufacturing Leadership Council.}