Bottom Line: Analytics expertise is a crucial driver enabling smart factories’ growth, with ten key metrics essential for keeping the next generation of smart manufacturing centers on track.
43% of manufacturers had smart factory projects underway in 2017, growing to 68% this year, according to the new Smart factories @ scale Capgemini survey. Capgemini found that discrete manufacturing leads all other segments in its planned transition to smart factories by 2024. Manufacturers are also planning to launch 40% more smart factories in the next five years, increasing their annual investments by 1.7 times compared to the previous three years.
Only 14% of smart factories say they are succeeding, however. That’s a sure sign every smart manufacturing pilot underway needs greater accuracy, clarity, and precision when it comes to measuring each pilots’ progress. The bottom line is the future of smart manufacturing is being built on a foundation of analytics today, and ten key metrics emerge as essential for their success.
IMPROVING SMART MANUFACTURING OUTCOMES STARTS BY CHANGING BEHAVIORS
Flat-screen monitors mounted above production floors reflect current production rates, load rates by machine and percent machinery utilization, yield rates, and in some plants, percent plant utilization. In the best-run manufacturing plants, every member of the production team knows how their activity, behaviors, and decisions contribute to the overall teams’ goals getting accomplished. The best-run manufacturing plants use analytics and the metrics that comprise them to achieve the following:
- Creates and reinforces ownership of outcomes. Walking plant floors where manufacturers have accomplished this creates an energy level not seen in other plants. When production teams own the results and have control over their work, analytics and metrics provide the feedback that keeps them motivated to do their best.
- Analytics and metrics can change everyone’s behavior in a plant quickly, so only choose those that balance collaboration, efficiency, and quality. Of the smart manufacturing pilots that I’ve been a part of, the most successful ones redesign end-to-end process workflows to break down silos and increase collaboration. The greater the knowledge sharing, the greater the potential product quality will improve.
- Metrics need to measure end-to-end production process improvement from a product lifecycle approach first with a strong focus on manufacturing process improvements. Smart factories are being designed to support lifecycle-based production methods that provide a more accurate cost of production and a more realistic view of how flexible products are to customization or not. Order Cycle Time, Gross Contribution Margin Analysis by Product and Segment, and Fill Rate Effectiveness as a Percent of All Orders are examples of metrics that provide end-to-end manufacturing process visibility.
- Avoid vanity metrics that celebrate the success of a siloed department and replace them with measures of collaboration and team accomplishment. It’s easy to spot vanity metrics because they always end up with the department creating them shown in the far right uppermost corner of a quadrant. Vanity metrics are a waste of time and need to be replaced with accurate measures of team performance. Vanity metrics that are irrelevant to total team performance include number of functional specifications written, order acceptance rate, total order activity within the department, and activity indices of in-department activity only.
DECIDING WHICH METRICS MATTER THE MOST IN SMART FACTORIES
The urgent need to know how the many decisions on the shop floor impact financial results are forcing manufacturers to replace legacy systems with integrated platforms. Of the dozen smart factories I’ve visited, all share the common attribute of being able to aggregate and provide insights in real-time of how each phase of the manufacturing process reduces costs or improves customer experiences. Based on discussions with a dozen manufacturers over the last year, the following are the most valuable types of metrics for managing manufacturing operations today:
- Financial metrics that are based on real-time data monitoring from the shop floor
- Customer responsiveness and satisfaction metrics
- Supplier and product quality metrics
- Efficiency-based metrics
- New Product Development & Introduction (NPDI) time-to-market performance
Operations teams are also designing their smart factory IT and Operations Technology (OT) systems to capture real-time data when possible on the following types of metrics:
- Asset and maintenance metrics including preventative metrics
- Inventory management, turns, and velocity
- Compliance metrics
Smart factories’ analytics’ strategies are designed to optimize the trade-offs of cost, production flexibility (as measured by scaling for higher and lower production volumes), product & service quality, and time. Making trade-offs across these four constraints is driving faster use of flow metrics over traditional schedule-based measures of performance. They’re also making it easier for production teams to change their behavioral-based approaches to getting work done by focusing on the optimal sequences of jobs and teamwork over choosing the most straightforward jobs to do first.
The following are the 10 most valuable metrics to manufacturers as they plan, pilot, and launch smart factories:
1. Carrying Cost Of Inventory: Combines the most challenging costs to capture for managing inventory, including put-away labor and storage costs, costs of obsolescence, and how effective warehouse management is at reducing logistics and fulfillment costs. Carrying the cost of inventory is a must-have because it’s invaluable in tracking how much working capital is being allocated to inventory as well.
2. Customer Satisfaction Levels: Measured through periodic customer satisfaction audits, customer satisfaction scores are a metric that needs to be designed to measure end-to-end manufacturing process performance. Manufacturers building smart factories rely on creating their customer satisfaction metrics, factoring in order delivery times.
3. Demand Forecast Accuracy: A must-have metric to determine if the supply chain planning, procurement, production scheduling, and fulfillment systems are synchronized with each other. Demand Forecast Accuracy also indicates the variation in real or actual demand and what is forecasted at the factory level.
4. Fill Rate Effectiveness As a Percent Of All Orders: Another excellent metric for measuring the level of collaboration between supply chain operations, planning, and production, Fill Rate Effectiveness as a Percent of All orders directly reflects how well supply chains are providing smart factories with the materials they need to fulfill orders.
5. Gross Contribution Margins by Product, Production Facility, and Business Unit: An essential metric for measuring the financial outcomes of manufacturing decisions. Every smart factory pilot I’ve been involved with tracks Gross Contribution Margin (GCM) performance levels by product, region, and production center or factory.
6. Inventory Turnover: Defines how many times a given plant’s inventory is consumed to build salable products and replenished in a specific period. Inventory Turns are most often calculated using the Sales by Average Inventory factoring for specific accounting periods. The second approach is to divide Cost of Good Sold (COGS) by the average inventory level for a specific accounting period.
7. Order Cycle Time: Defined as the total elapsed time it takes from when a customer places an order to when they receive it. Order Cycle Time is an excellent metric for determining how collaboratively the entire production team is working. Smart factory pilots using this metric are attempting to quantify the contribution of inventory management, supply chain, manufacturing, and fulfillment performance levels.
8. Order Pick, Pack, and Ship Accuracy: An essential metric for measuring how effectively the main functions of an inventory management system are performing and how well integrated they are to fulfillment systems. By definition, pick, pack & ship is the logistics process of locating inventory and packing items for shipment to customers.
9. Perfect Order Performance: Measures how effective a production facility is in delivering accurate, damage-free orders to customers on or before their delivery due date. It’s often defined as the (Percent of orders delivered on time) * (Percent of orders complete) * (Percent of orders damage free) * (Percent of orders with accurate documentation) * 100.
10. Supplier Quality Index: A useful metric for determining how integrated inventory management, quality, and compliance systems are and how effectively they can isolate supplier quality problems before they impact production. In regulated industries, it’s required to track supplier quality and compliance, often to the lot and vendor level. Medical products manufacturers need to provide this level of visibility to comply with the U.S. Food and Drug Administration mandate, 21 CFR Part 11.
The true power of metrics in smart factories is providing everyone with visibility into how their contributions to planning, producing, selling, and servicing products matter. Analytics is the cornerstone that keeps smart factories focused on customers and their changing needs. Their greatest potential, however, is in providing production teams with a sense of purpose and meaning to continually keep striving to improve manufacturing process performance, product quality, and customer satisfaction.
- Liu, Y., Wang, L., Wang, X. V., Xu, X., & Zhang, L. (2019). Scheduling in cloud manufacturing: state-of-the-art and research challenges. International Journal of Production Research, 57(15-16), 4854-4879.
- Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2019). Smart manufacturing: characteristics, technologies, and enabling factors. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233(5), 1342-1361.
- Qu, Y. J., Ming, X. G., Liu, Z. W., Zhang, X. Y., & Hou, Z. T. (2019). Smart manufacturing systems: state of the art and future trends. The International Journal of Advanced Manufacturing Technology, 1-18.
- Salehi, M. (2019). Bayesian-based predictive analytics for manufacturing performance metrics in the era of industry 4.0. KIT Scientific Publishing.
- Zhu, K., Joshi, S., Wang, Q. G., & Hsi, J. F. Y. (2019). Guest Editorial Special Section on Big Data Analytics in Intelligent Manufacturing. IEEE Transactions on Industrial Informatics, 15(4), 2382-2385.
originally posted on forbes.com by Louis Columbus