IT in Manufacturing: Industry 4.0

Information Technology (IT) and Operational Technology have grown up side by side. Modern manufacturing equipment has been on a collision course with standard IT for decades as more computerization is added to the machine tools used in manufacturing. With the emergence of convergence between the technologies, the manufacturing sector is beginning to become more reliant on the same skills that have traditionally been used in IT. Along with the skill set of hardware technological support and programming support, IT leaders going forward will need to understand the operational mindset of the managers they interact with.

Metrics

Industry 4.0, an initiative that began in Germany in 2011, sometimes called Manufacturing 4.0, represents the convergence of activities. Manufacturing activities have always been metric centric. How many widgets can be made by a piece of equipment in a given time with what rate for rejected pieces is used to calculate the effective throughput of a given device. This calculation is added to the BOM (Bill of Material) and employed in planning calculations within MRP.

In the past, this information was manually determined and entered into the BOM. The Internet of Things (IOT) has created the means to provide this information electronically, allowing for better measurements and quicker reactions to variations than ever before.

Quality metrics based on the throughput and yield are also impacted by the ability to communicate this data in real-time. Sensors being built into systems that perform the SPC (Statistical Process Control) activity provide up to the minute data for analysis.

Production Line

Reporting

Still, this is only the beginning of the ways in which the data can be used. Data from these two areas can be used to create analytical studies for finance departments to better understand the depreciation and efficient use of capital investments. Engineers can design better more efficient processes and sales, forecasting and customer service departments can get more insightful information to provide customers better delivery dates, and inventory level information.

Operational leaders who are looking into or actively implementing robotic manufacturing depend heavily on interconnected systems with automated reporting to reduce cost and improve throughput in the manufacturing environment. Smart factories that practice Lean Manufacturing take advantage of the analytical reporting generated by the interconnected operations technology to shift labor and operational staff to areas to maximize their production staff and increase capacity.

Security

The adoption of IOT has resulted in more wired and wireless factory shopfloor connected devices, remote access, programming, and set-up operations. Manufacturing machines with embedded operating systems, usually have a “lite” version of the operating system with a limited capacity to configure and execute sophisticated commands. This lower technological threshold has resulted in security breaches which, if part of a fully connected network, lead major systems to be compromised. While it is IT Security’s responsibility to address these vulnerabilities, IT must also ensure that manufacturing can still continue to run on a 24X7 basis. This applies in particular as robotic devices replace manually administered equipment.

Production Support

As the manufacturing moves into the digital world, IT will increasingly be called upon to support production equipment at the same level that it supports end users. The data from this equipment will make its way to senior managers who make decisions on customer pricing, continuing existing relationships with suppliers and customers, the fate of manufacturing facilities and product lines. Our service delivery for software and infrastructure support as well as user education and assistance will need to encompass all levels within the organization from shop floor and assembly line staff to the C-suite.

How Predictive Analytics Can Help Businesses Secure Funding and Boost Sales

There are several key factors that contribute to business model success. When businesses are looking for investor financing or when professionals seek to salvage a failing business, they look to see if these factors are in place and working well together.

Once business owners understand what these factors are, they can strengthen any weak areas they find in their business.

Zbig Skiba discussed successful business modeling and the book, Business Model Generation, in a LinkedIn article.

The 9 key elements of a good business model the book outlined were:

  • Knowledge of your customer segment and, “distinct target market;” people with similar desires and needs and the ability to pay for your product or service.
  • Your unique value proposition. What does your single product or service or bundle of products and services offer your customer segment?
  • Having efficient channels. Channels are the way you communicate with your customers. There are sales channels, delivery channels, follow-up channels etc.
  • Customer relationships. What is your experience and interaction with your clients? Are they satisfied with your business?
  • Revenue Streams, money coming in.
  • Key Activities, things you must do to conduct business every day. These might include manufacturing and design, etc.
  • Key Resources, your staff, financial resources, physical and intellectual property etc.
  • Key Partnerships, your vendor relationships and partnerships that make your business better, more affordable and more efficient; and
  • Your Cost Structure, the money you have to spend to run your business, including payroll, loan payments, raw materials, delivery costs, licensing fees and rent etc.

Many business owners and managers have yet to learn that comprehensive data analysis or predictive analytics can help strengthen weak areas in their business models.

How?

  1. Predictive analytics organizes big data. This helps businesses clearly see what their target market needs, wants and can afford to pay for.
  2. Once business owners and managers understand their target market at this level, their value proposition becomes clearer.
  3. Businesses can then start using communication and delivery channels that are convenient to their best prospects and therefore the most effective.
  4. Customers feel heard when businesses meet their needs and provide the products and services they desire. Satisfied customers turn into long-term clients and brand advocates.

Predictive analytics can also boost sales by helping businesses:

  • Identify their best revenue streams
  • Understand what activities they should continue and which ones to stop
  • Know which employees are right for each job
  • Know which vendors to partner with to satisfy their target market better
  • Budget more efficiently and stop wasting money on expenses they don’t need