Manufacturing is one sector that is clearly benefitting from big data analytics. Using data gathered from the production line as well as suppliers and distributors, big data manufacturing can substantially streamline operations, improve quality, and reduce waste. Returns on big data manufacturing can be substantial, since big data analytics can reveal flaws in the production line, underperforming suppliers, and business performance factors that can affect production and profits.
To demonstrate the potential big data has for manufacturing, McKinsey & Company points to the real-time statistics available from the production floor, including monitoring production equipment. In the case of pharmaceuticals, for example, manufacturers can monitor more than 200 variables to ensure production flow and the purity of the end product. Two batches of the same drug can vary in yield between 50 and 100 percent. By segmenting the manufacturing process and using statistical analyses of production data, one drug company was able to identify interdependencies. As a result, nine parameters were isolated that had the biggest impact on production, which resulted in a vaccine yield increase of 50 percent and annual savings of $10 million.
Demonstrate the Impact of Big Data Manufacturing
The best way to sell big data manufacturing is to demonstrate how big data insights can bring value. Talk to the manufacturer about their specific business challenges and how big data can help. Here are a few common use cases related to manufacturing:
Logistics and supply chain – A tremendous amount of data is generated from procurement, shipping, distribution, and warehousing. Using this data, manufacturers can identify bottlenecks in the supply chain. For example, RFID data can track the exact location of any product to show where to optimize shipping and storage.
Customer support – Most manufacturers maintain customer call centers to handle warranty issues and complaints. Big data analytics can highlight areas of customer discontent using speech and text recognition to process unstructured data. Correlating this information with other data can identify product flaws, design issues, or customer service challenges.
Customer sentiment – In addition to direct customer response, big data also can provide customer sentiment analysis, drawing from social media and other data sources. Coca-Cola, for example, has more than 63 million fans on Facebook and uses social media to track customer loyalty and sentiment
Preventive maintenance – The proliferation of RFID technology and the Internet of Things lets manufacturers gather ongoing performance data about machines, tracking variables such as temperature, humidity, speed, oil levels, etc. Machine logs can be included as part of big data analytics to identify maintenance problems before machines fail and disrupt production.
Selling more profitable products – For complex manufacturers, big data can track the cost of build-to-order goods to assess profits against production costs. Big data also can manage production schedules, staffing, and operations.
Improving Six Sigma performance – Integrating big data manufacturing into the Six Sigma DMAIC framework (define, measure, analyze, improve, control) reveals how each phase of DMAIC cam affect and improve manufacturing.
These are only a few ways that manufacturers benefit from big data manufacturing. Once you show them the value, you have to build consensus.
Build Consensus Around Big Data Value
The biggest challenge in selling big data to manufacturers is getting everyone to agree to work together for big data success. In manufacturing particularly there are siloes of expertise, and the experts often don’t want to listen to new ideas.
A TCS study shows that the biggest impediment to big data manufacturing success is getting the data scientists and the functional managers to trust each other. Other factors include getting managers to make decisions based on the data and not intuition, getting business units to share data, and determining what to do with the insights uncovered by big data. All these issues can be overcome if you engage in the right way.
Remember that big data is a value sale, not a technology sale. You are selling insight, not infrastructure, so by demonstrating the potential impact of big data on operations, you are selling its value to managers who care about operating efficiency. Start the sales process at the top. Talk to the executive decision-makers and get them to embrace the value of big data manufacturing first. Then you will have the right high-level support when you approach other stakeholders.
Be sure to get buy-in from all the stakeholders, including line managers and IT. Illustrate the potential returns each can gain from big data manufacturing. Use the support of senior management to overcome opposition but try to build cooperation and faith in the outcome. Start with a use case you can use as a proof of concept that will prove the value of big data manufacturing. Once you demonstrate the potential ROI from big data manufacturing they will be anxious to take on bigger projects.