3 Manufacturing Analytics Use Cases to Simplify End-to-End Production

September 9, 2021 | 6 minute read
Roxanne Bradley
Product Marketing Manager
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Manufacturing is by its very nature cross-functional.

Industrial scale production touches countless processes up and down the supply chain. It is reliant upon the sourcing and procurement of raw materials, goods, labor, and services. It mandates an accurate accounting of inventory amounts and their placement within the warehouse. Input costs, planned obsolescence, and throughput performance influence the financial bottom line. And of course, production output has a material impact on an organization’s ability to fulfil customer orders by agreed-upon delivery dates. 

The chain of events that produce the successful delivery of goods to customers is complex, often spanning supply chains across different time zones and industries. With so many moving parts, the ability to maximize profit necessitates operational efficiency.

Analytics has become a vital tool within the Manufacturer’s arsenal, providing alignment across materials, labor, and machinery throughout every step of the process. Using analytics, users can combine information from disparate data sets to obtain a bird’s eye view of end-to-end production operations. Analytics solutions allow manufacturing professionals to understand the impact of the smallest actions on the company’s overall manufacturing plan.

Deck

Below are several examples of ways in which analytics ensure cross-functional alignment, enabling manufacturing professionals to take strategic action towards production goals.

Use Case 1: Work Order Prioritization

Within the world of manufacturing, process optimization is key.

When reviewing the vast amount of customer orders placed, it is often difficult for manufacturing teams to determine which projects and production runs to prioritize. As a production supervisor, how do I know which work orders to start first? How should I prioritize builds? Moreover, do I have access to the specific inventory needed for each order?

Analytics can help me answer these questions. Via analytics, manufacturing leaders can work across departments and data silos to bring together all the data needed to make informed decisions. Analytics allows companies to compare manufacturing data (work orders) against sales orders, and inventory on-hand. The ability to see sales order data, or even “need by” dates allows production superintendents to develop a larger conceptual view of the ways in which various production runs fit into the overall manufacturing strategy. With analytics, manufacturing professionals can enhance and refine these strategies to include critical cross-functional information, such as labor availability and procurement details.

For example, let’s assume I’m in charge of building out my organization’s manufacturing plan. Using analytics, I can better understand plan effectiveness by viewing the exact materials required for each production run alongside the quantity of goods and parts required week by week, and inventory on-hand.

In this scenario, I’ve detailed all materials required for week 12 of the production process. I need to understand which items have a shortage, as this will impact my work orders. Specifically, analytics allows me to bring together data from many different sources to view inventory exceptions related to my planned build.

With access to end-to-end production details across departments, I can determine whether I have enough materials to partially fill the customer orders for week 12. Adding inventory data to my analysis allows me to see whether the warehouse has the additional component parts that I need on-hand, and whether these materials are already assigned to another project. This additional layer of visibility ensures that our plant doesn’t cannibalize inventory designated for our own capital projects.

 

If I need to shift my production schedule based on the availability of specific materials, I can bring procurement data into my investigation. Analytics affords users the ability to align many data types to answer the following questions: should I shift the manufacturing of these items to next week, or to next month? Can I even fill the customer order, or is there a serious purchasing error here?

With analytics, I can see which items are on PO, and what their expected delivery dates are. This allows me to reconfigure the dates of my production run, and to alert my customers to any potential delays in advance.

Alternatively, if I’m working on producing a high profitability product, or if I’m in the midst of producing items for a critical partner, I can use analytics to flag the importance of these production runs over others. An analytics solution allows me to identify which teams to work with to prioritize these work orders. All of the components required for critical production will be flagged as well, so that others within the company know not to use them for alternate projects.

Inventory Analysis

 

Use Case 2: Optimizing Routine Maintenance Schedules

“Unscheduled downtime due to equipment failure, operator error, or nuisance trips is the nemesis of all manufacturers.” -- International Society of Automation

To avoid encountering this “nemesis,” many manufacturers employ routine maintenance strategies. In a 2020 study conducted by Plant Engineering, 22% of global respondents indicated that they allocated more than 15% of their plant’s annual operating budget to maintenance. Facilities surveyed specified that they spent an average of 20 hours per week on scheduled maintenance. Shockingly, however, 47% of respondents admitted their reliance on in-house created spreadsheets and printed reports to determine maintenance schedules.

Piecing together disjointed spreadsheets to schedule routine maintenance introduces a higher risk of errors. Analytics solutions provide users comprehensive visibility into production runs for all machines and work centers across the organization. Access to updated information rather than stale data ensures greater accuracy and lessens idle time.

From a labor perspective, maintenance professionals can be particularly costly. Expert knowledge of certain machinery can mandate the use of specialists, whose knowledge and experience are in high demand.

However, on any given work day, “...only 24.5% of the average maintenance worker’s time is spent performing productive tasks (Plant Services).” The following is commonplace: a maintenance crew is scheduled to travel to the field to perform work, yet upon their arrival onsite, they discover that production is running the equipment in question due to looming deadlines.

Idle maintenance time is expensive, yet performing maintenance when the equipment is needed for production results in lost revenue.

Analytics can be leveraged to more clearly visualize production schedules according to machines in use. Scheduling maintenance when a piece of equipment is not in use enables cost savings in the form of reduced downtime, and reduced opportunity costs of labor, parts, and capital. Enhanced visibility into work center and machinery schedules allows for more focused maintenance efforts that are rooted in cost avoidance.  

Use Case 3: Inventory Reconciliation

Analytics can help simplify the complexities inherent in global production. Gaining a detailed understanding of inventory on-hand across multiple entities or locations is incredibly valuable when it comes to reducing excess spend, or gaining operational efficiencies. Let’s take the example of joint ventures, or integrating a newly acquired company into existing operations.

In this example, I’m in charge of 3 production platforms in the eastern Mediterranean that are run under different joint ventures within the oil and gas industry. For accounting purposes, they must be kept as separate inventory organizations. Yet, the ventures serve similar purposes, and use synergistic parts.

The inventory for all three production platforms is sourced from a common warehouse. For example, I could have an item needed for Platform A sitting on the shelves within the Platform B inventory area in the same warehouse. But, without analytics, I wouldn’t have this insight.

I can leverage analytics to understand the inventory quantity on-hand among all the different organizations. When afforded this cross-organizational visibility, I can easily locate inventory and perform an inter-organizational transfer, or even ship out an order on another organization’s behalf. Knowledge of inventory across all warehouses allows my team to make quick tactical decisions, thereby improving efficiency. Merging inventory data from multiple organizations eliminates the added cost of ordering new parts and delaying production until they arrive.

Accelerate Productivity with Oracle Analytics for Manufacturing

For professionals within the manufacturing industry, advanced analytics platforms —such as Oracle Analytics— are a key tool with which to solve cross-functional problems. By blending data from many internal and external sources, analytics helps manufacturing organizations streamline operations and cut costs. Gaining efficiencies within the manufacturing space can be realized in the form of increased throughput, minimal downtime, and ultimately, improved margins.

Analytics are critical to the success of resource management and operational planning across manufacturing processes -- Oracle’s analytics offerings can provide the clarity needed to increase productivity at every stage of production.

Learn more about Oracle Analytics. Follow us on Twitter @OracleAnalytics and connect with us on LinkedIn.

Roxanne Bradley

Product Marketing Manager


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