Small and midsize (SMB) manufacturers are critical drivers of innovation and productivity, and agility often gives them a competitive advantage over larger organizations. But they have unique challenges compared to larger manufacturers who have the resources and capital to achieve greater economies of scale. To take advantage of their agility, SMBs can adopt a data-centric and analytics-first strategy by deploying analytics and artificial intelligence (AI) in their day-to-day processes faster than larger manufacturers -- if approached in the right manner.
A common challenge for SMB manufacturers is determining where to start with analytics, since a clear path to value may not be obvious. Over two-thirds of SMB manufacturers make decisions based on “gut feel,” and given the demands on the business, the C-suite often chooses to allocate resources elsewhere. Their teams are not trained in artificial intelligence and data science, yet senior management understands that data science is a key to being competitive in 2021 and beyond.
The current economic environment, which has resulted in a shortage of qualified workers and raw materials, means that many manufacturers have even fewer staff able to devote time to developing data science talent. According to Forbes, 55%of small business owners in the manufacturing industry reported significant or moderate lost sales opportunities due to staffing shortages. In some cases, the lack of skilled production line workers has resulted in design engineers, demand planners and other similar job functions working the manufacturing line. This presents an even bigger challenge in striving to embrace AI, as fewer people can execute on analytic initiatives.
The SMB Group surveyed 761 SMBs in North America and found that more than 60% of all SMBs, regardless of industry, find it important or extremely important that any new technology solutions have the following embedded capabilities:
- Pre-built connectors to integrate other solutions.
- Reporting and analytics.
- Real-time collaboration streams.
- Artificial intelligence/machine learning.
Few vendors offer all these capabilities within a single platform. SAS provides this breadth of capabilities in a cohesive ecosystem of analytics, decisioning and data.
The SMB organizations that succeed in their first foray into analytics take a pragmatic approach, focusing on top impact areas, but starting small enough to drive rapid return on investment. Three key accelerators that allow SMB manufacturers to adopt and benefit from analytics include:
First and foremost, companies should focus on addressing business challenges that offer the highest return on an analytic investment in the shortest amount of time. While this may seem straightforward , there are many factors to consider when reconciling “highest return” with “shortest amount of time” in order to identify a “right-sized” analytic initiative.
Quality and supply chain are key starting points for analytic innovation with the highest financial impact, fastest time-to-value and highest executive level visibility. These areas have shown a compelling ROI for companies with limited resources and minimal data- science expertise. We will explore these areas in subsequent blogs to show how SAS has helped achieve 10% yield improvement and 5% reduction in inventory with analytics.
2. Available data
Excel often serves as a backbone for SMB planning and reporting, more so than for large organizations, who are typically further along in their systems modernization and thus data maturity. SMB manufacturers face unique data challenges and often lag behind other industries in data maturity, so it’s important to choose your first effort based on those areas of your business that have the most mature data. Vendors that facilitate ingesting and rationalizing widely disparate data enable manufacturers to proceed in a purposeful manner focused on the business issue and rapid ROI rather than spending most of their time breaking down barriers of siloed data.
This shift is a cultural change since manufacturing subject matter experts, such as demand planners, operators and engineers, have experiential knowledge that has served their company well for decades. The ability to operationalize internal processes and systems to drive analytic insights is crucial, since nothing can be measured if it is not adopted. SAS calls this “decisioning”. That is, take the results of analytics and embed them into mission-critical processes through integrated systems. SAS can embed analytics into real-time streaming and sensor data as needed.
Understandably, some SMB manufacturers are hesitant since changing mission-critical processes, such as control settings of equipment, can be nerve-racking. For example, if the new control settings do not produce the desired results in terms of product quality and yield, then the initiative can have a negative impact on profitability, and possibly an impact on machine performance. Most customers start small, implementing those sorts of changes by testing on one line for one SKU, seeing improvement, extrapolating out the ROI and expanding the reach of the analytic recommendations.
How SAS can help
A study published by Deloitte and The Manufacturing Institute cites as many as 2.1 million manufacturing jobs will be unfilled through 2030 and 77% of manufacturing executives surveyed said they expect to have trouble attracting and retaining workers this year and beyond. Given that SMB manufacturers can be hesitant to act until they develop internal analytic talent, and the current shortage of qualified workers in the US, vendors that can help with all aspects of implementing analytics for manufacturers play a vital role.
SAS has 40+ years of leading analytic initiatives for manufacturers. SAS helps you align your aspirations with your ability to execute by:
- Determining right-sized areas of focus.
- Defining and measuring ROI.
- Ensuring that results are actionable and resultant decisions made.
- Providing the technologies that allow you to integrate analytic results into your processes.
- Gathering data from disparate systems such as LIMS, ERP, Historian, financial, and other operational data sources.
- Ensuring our AI and data science is leading edge and relevant to manufacturer’s business problems.
SAS’ cloud offerings and partnership with Microsoft help SMB manufacturers rapidly embrace analytics by providing an analytic platform that can easily ingest on-premises and cloud data. Regardless of whether the data is stored in an on-premises historian, streaming sensor data generated by robotics equipment, legacy ERP, cloud-resident data lake or other data repository, SAS can bring it together in the SAS Azure cloud. Once the data is integrated in the SAS Azure analytic environment, our subject matter experts, working in conjunction with your manufacturing, engineering, design and supply chain teams, create purpose-built AI and machine learning models in the cloud for deployment.
Customers also have the option of choosing Amazon Web Services and Google Cloud Platform as their environment or can leverage our AI and machine learning capabilities on-premises.
SAS has 40+ years of manufacturing and CPG domain and analytic expertise. This allows you to accelerate any analytic effort, whether it’s supply chain planning, quality or yield improvement or predicting warranty issues before they occur. These industry experts can jump start your first foray into analytics, allowing you to train staff and embrace analytics at a pace that accounts for your current operational challenges.
We bring together our subject matter experts, and those of our partners, to provide measurable impact in an agile manner. We can also provide staff augmentation to ensure adoption of analytics. We continually hire industry experts, engineers and members of IT that have implemented operational systems, statisticians, demand planners and experts that have worked the shop floor -- and all are members of our manufacturing practice.
Industry 4.0 and IoT are changing the nature of manufacturing, but that does not mean that SMB manufacturers have adopted, or will fully embrace in the short term, an initiative to deploy those concepts across their manufacturing ecosystem.
Transforming and refactoring the manufacturing floor and all associated systems takes significant time and investment. As Industry 4.0 efforts progress, a parallel path of embracing AI as described here allows manufacturers to realize some of the value of Industry 4.0 with a significantly smaller up-front investment and faster returns. The days where only budget, resource and data rich large manufacturers can benefit from AI and data science are gone, and as a result, SMB manufacturers are seeing accelerated adoption of AI and machine learning.