innius: a machine user’s business case

Introduction

Building a solid business case for investing in innius, a Smart Industry and IIoT solution, is something you have to do deliberately. Several renowned writers from companies like McKinsey & Company, Cap Gemini and LNS Research have recently published research findings indicating that companies struggle with the financial quantification of business case to invest in IIoT technology. Reasons for this struggle include:

• Lack of digital transformation skills. The complexity of an IIoT platform, often including state-of-the-art technologies like cloud computing and advanced data analytics, requires significant effort to understand. Applying such IIoT solution to the own business situation is even more complex and the impact of change can hardly be understood by an average business person.
• Lack of knowledge: one does not know what one does not know. It is unclear what can be done with IIoT, where it can be applied and what gains could be achieved. Therefore it is hardly possible to develop the investment proposal.
• Lack of trusted advisors: one does not know what questions to ask to an advisor, and conversely the advisor himself does not understand the opportunities that are offered by IIoT.
• Lack of reference stories. There is not yet an extensive set of customer reference stories in the IIoT domain, due to the lack of finished IIoT projects and the confidential nature of many projects.
• Conservative nature of many manufacturing companies, who want to see proof of IIoT successes at others and who want to understand the technology in depth before engaging in an IIoT project.

The business case that is developed in the remainder of the blog post is based on the excellent research paper of Prof. Dr. Michael J. Capone when he worked for Capgemini Consulting Services.

Our machine customer: Acme Chips Ltd., a potato chips production company

Acme Chips is a Dutch food production company, with a turnover of € 54.8M. Acme employs 250 people and uses 80 machines to produce 20 million cartons with bags of chips per year. The average production cost per carton is € 0.50 and the average material cost per carton is € 0.75. Eric is the plant manager at Acme Chips. His primary concern is the uninterrupted production of potato chips to guarantee the output of 20 million units. Eric uses the OEE performance indicator to get insight of the availability, performance and quality of the plant. Currently the OEE of Acme Chips is 64%, which is far below the target of 80% that was agreed upon in Acme Chips management team. Eric has learned about the possibilities of real-time production monitoring using Industrial Internet of Things technologies, and considers investing in the innius IIoT solution. In order to convince the management team of this investment Eric must develop a comprehensive production optimization proposal including a solid financial business case over 36 months.

Eric has already outlined the key requirements for this improvement project:

1. The production of Acme Chips must not be disturbed by this project.
2. The implementation of the project must be incremental.
3. The project must align with the continuous improvement using Lean 6σ, which had been initiated some time ago in the Acme organization.
4. The project must be relevant for all people in the organization as Eric believes that the technical solution must support the employees in their daily tasks.
5. The solution must be standard software, integrated with other software solutions in the organization.

Elements of the business case

The business case addresses a number of improvements in production, machinery, maintenance and organization. The improvements are discussed in the research papers mentioned above and are applied to the Acme Chips scenario. The improvements are all based on the insights that once real-time data from sensors is captured, one develops knowledge about the actual situation, the bottlenecks and the possible optimizations. The following improvements are included:

1. Availability. The availability of the Acme Chips production line to actually produce output is impeded by a variety of reasons. People may not be available, change-over may be too often, materials may not be available, or maintenance may have been delayed. Appropriately placed sensors convey information about the actual stock levels in storage bins, or can indicate that a certain sequence of production orders is not efficient with regards to change-over and cleaning.
2. Downtime. Machine downtime is one of the biggest factors that influence production volume. There are several reasons for downtime, including failure of parts, failures in raw materials or finished products, refill of materials, adjustments to machine programs or recipes, machine maintenance or machine change-over. Sensors that monitor machine state, machine usage, product conditions and material levels generate real-time data which is analyzed and extrapolated to generate recommendations for better usage or planned maintenance.
3. Waste. Production facilities are often stuck with significant waste in its operations. Next to physical waste, like spilled products or materials, waste exists in inefficient operations like excessive cleaning regimes or superfluous movements. Even inefficient administrative tasks for operators dealing with production orders is a source of waste. Sensor data can be used to warn about storage overflows, time spent in various tasks or wrong tuning of machinery. Note that #2 Downtime is often conceived as a waste as well.
4. Safety. Safety of personnel is a core HR responsibility. Incidents in the production line occur often, causing personnel unavailability and production disruption. Sensors in sensitive areas can warn operators about unsafe actions, and trends in machine usage indicate needs for better schooling of personnel.
5. Maintenance. It is clear that condition-based or smart maintenance, which is performing maintenance at the right time, is offering optimization of maintenance planning in sync with production planning, lower costs for maintenance personnel and improved maintenance management. Machine and part sensors that count usage but also measure specific conditions offer insight in wear and tear of the machinery.
6. Spare part stock. If real-time monitoring can indicate when certain parts are likely to break, and consequently when they need to be replaced, then an optimization in spare part inventory is easily achieved. Note that this may either be within the company, or even outsourced to the machine manufacturer.
7. Spare part pricing and transport. If one knows the requirements for spare parts, one can negotiate better prices than having to acquire a part in an emergency situation. Next to that it is significantly cheaper to get spare parts in house by normal delivery than by express delivery.
8. Service contract. The negotiations with the service provider, be it the machine manufacturer or a third-party service company, can be held more effectively and with better outcomes when one is able to articulate the service needs based on actual data as has been accumulated through the use of sensors.
9. Energy. Energy consumption is a high attention area in today’s environment-conscious production. Savings can be achieved by analyzing in detail when the machines are using energy, whether is is optimally applied and if the machine is optimally configured. A combination of sensors on the energy inputs, sensors on the machine state and sensors on the actual production quantities provide insight of the energy costs per machine status and per product quantity.

Innius gathers real-time data from sensors, analyzes the data to gain more insight, calculates performance indicators (KPI’s) that are used for monitoring and benchmarking  and supports threshold evaluation, user alerting and task assignments. The improvements from the business case all utilize the real-time sensor and KPI information that innius delivers. With these capabilities the Acme Chips organization, including Eric the production manager and his team of supervisors and operators, is able to continuously define, measure, analyze,  improve and control the production in the Acme Chips plant.

Financial model

The financial model is based on the improvements listed above, a conservative estimate of the gains that can be realized and a significant projected investment in organizational change, external consultancy and technology.

Improvements

Investment

The estimations for the various costs in the project are listed below:

Net result

The net result of the project over 36 months is estimated to be € 2,414,841, which is 4.4% of annual revenue. The return on investment for this project is 129.8%

Conclusions

Eric has written the process improvement proposal and includes the financial business case for Acme Chips Ltd. He requests a project budget of about € 2.25 M over 3 years. The management team of Acme decides unanimously to accept the proposal.