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To invest in industry 4.0, you need to avoid these pitfalls

from January 22 to 25, Davos world economic forum was held in Switzerland. This Davos again focused on industry 4.0. The theme of the forum is globalization 4.0: building the global structure in the era of the fourth industrial revolution, which shows the heat of the topic. Boston Consulting Company found that there are still many misunderstandings and traps for enterprises to participate in industry 4.0

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the prospect of industry 4.0 is promising

manufacturers use new digital technologies to improve flexibility, productivity, quality, speed and safety, thereby gaining great value. These technologies can help enterprises achieve the highest level of operational excellence, so it is crucial for enterprises

although the solutions of industry 4.0 are becoming more and more powerful, and the cost is also declining, many manufacturers still invest money, time and resources in insignificant projects, without touching the most fundamental pain in their own operations

enterprises need to be particularly vigilant against four hidden dangers when adopting new technologies: pursuing small profits but ignoring the potential goal containing huge profits, digitizing the process without solving the fundamental low efficiency problem, ignoring the behavioral root causes of process problems, blindly pursuing high-tech solutions without evaluating the implementation cost, and regardless of whether they can achieve the same effect with simpler methods and lower costs

in order to avoid these hidden dangers, enterprises must have a detailed and in-depth understanding of their own operation problems before investing in industry 4.0. Successful enterprises will use these insights to develop a comprehensive plan to achieve operational excellence and the best application of digital technology

forget the 80/20 law

many enterprises investing in industry 4.0 have forgotten such a general law that 80% of the value is obtained by solving 20% of the problems. 80/20 law is based on the research of economist Vilfredo Pareto, which has important reference value for effectively overcoming operational problems. In short, manufacturers should focus on solving a few problems that can bring most profit opportunities

unfortunately, enterprises often don't know what problems to focus on, because they don't quantitatively evaluate the impact of operation improvement on finance. In addition, production experts may be too eager to adopt advanced analysis and cutting-edge technology, so that they forget to measure the relative importance of the problems to be solved

for example, a pharmaceutical enterprise found that the market demand for a major drug was unexpectedly high, but the efficiency of the drug production line was low, resulting in the inability of the enterprise to expand production, leaving the entire distribution network in a state of shortage. Enterprises cannot increase production shifts because there are not enough technical staff. The high-tech operation experts of the enterprise black belt did not choose to directly increase the production capacity of the production line, but used advanced analysis method to simulate and reduce the material waste (i.e. scrap rate) in the production process. When the scrap rate is 1.5%, it is indeed a costly problem. However, the goal of experts is to reduce the scrap rate to less than 1%. If it can be achieved, the production capacity can be increased by 0.5 percentage points, which is the most urgent problem faced by the production line

however, the value of reducing the scrap rate is not worth mentioning compared with improving the overall efficiency (OEE) of production line equipment. OEE refers to the ratio of actual output to the fastest output in theory when the production line is operating without interruption. At present, the OEE of the enterprise is less than 40%. The two main reasons for the low OEE are the planned downtime (such as line change time) and the failure rate, accounting for 30 percentage points each. Significantly reducing the occurrence of such events is more effective than reducing the scrap rate. Halving line changes and other planned downtime will greatly increase production capacity, which is at least 30 times higher than the result of the scrap rate scheme. Recognizing this, the pharmaceutical company repositioned and shifted its focus to reducing planned downtime, resulting in a huge capacity increase

in order to reduce planned downtime, experts and production line leaders must understand and assist in changing the behavior of production line operators, rather than prematurely adopting complex algorithms or advanced technologies. This requires close cooperation with operators to analyze the relevant steps of planned downtime in detail, summarize all inputs, and find the right way to externalize, parallel, shorten or remove the steps in the line change process. Once the newly designed process is in place, operators and production line leaders need training and mobilization to actively follow the new changes. Experts should instill the necessary behavior changes as assistants and coaches

in order to solve the most important problems first, leading enterprises will make a formal and rigorous annual summary of current performance and determine positive expansion plans to bridge the gap between current and future performance. In order to ensure the right direction, they will quantify and rank all promotion schemes based on their possible financial impact. Although continuous improvement from top to bottom is not a good idea, it can help enterprises locate the most influential areas. In contrast, continuous improvement from bottom to top has a significant effect on encouraging operators and instilling correct corporate culture, but it is rarely quantified in economic benefits

digital waste

it is common for enterprises to indiscriminately adopt high-tech automation or digitalization of behaviors or processes that have no added value (also known as waste in lean terms). Looking back at such behavior, we blindly improve the existing working methods, but do not solve the fundamental problem of low efficiency. When applying technology and promoting action plans, enterprises should aim to minimize or eliminate the root causes of waste in the future

by understanding the root causes of seven types of lean waste, enterprises can find appropriate ways to improve the efficiency of the process before introducing the digital scheme proposed by industry 4.0 (see Figure 1)

the following cases fully illustrate the opportunities contained therein

1, overproduction and inventory

manufacturers usually invest in automated and complex IT systems to manage huge inventory warehouses. But a better way is to first understand the reasons for the high inventory. Many enterprises overproduce in order to prevent emergencies or urgent needs and maintain a high inventory level. Sufficient reserves leave sufficient buffer room for enterprises to prevent inaccurate supply or demand forecasts, mismatches with the pace of the supply chain, or major production fluctuations and sudden changes in suppliers

instead of using automation to reduce the operating costs of warehouses, enterprises should find forward-looking solutions to reduce or eliminate the requirements for warehouses. Such schemes may include the following measures:

strictly abide by the unified sales and operation planning process

the pull system is adopted, and the inventory is replenished only when it is lower than the preset level

cooperate with customers to share real-time inventory information of dirty distributors or points of sale

the difference between the temperature of the air flow near the box wall and the center temperature of the flow field is usually 2 ~ 3 ℃ and the role and coordination of the organization, so as to eliminate the behavior that may lead to more or less than expected

use artificial intelligence to improve the accuracy of expectations

reduce the proliferation of inventory units

speed up the line change and improve the flexibility of production through the analysis of these data

optimize factory configuration and distribution footprint

2, the length of equipment suspension and idle time is the main reason for low productivity, which is usually caused by human factors. Manufacturers can solve this problem by strengthening front-line management

3, transportation and mobility

many enterprises invest a lot in conveyor systems or automated guided vehicles in order to transport goods faster and more efficiently in factories and warehouses. However, before making such investments, they should consider whether they can reduce the need for internal transportation, such as changing the layout or introducing a pull system to reduce inventory points

4, over processing

if an enterprise adds unnecessary processing steps and the customer is unwilling to pay, it belongs to over processing. For example, if the process parameters exceed the normal range, the enterprise may arrange the induction and control system and add additional processing links in order to return the parameters to the normal level. In addition, they may use cisterns or buffer stocks to temporarily store defective products, and then return to the site for rework or reprocessing. However, enterprises usually do not find out the reasons for the excessive parameters when adopting these methods

in order to gain insight and make correct corrections, enterprises need to understand the basic principles of each link. By using big data modeling and simulation processing (ideally in the design stage), enterprises can find the right way to improve the stability of processing, thereby reducing the need for induction and control systems, as well as reservoirs and buffer inventory

5, defective products

many enterprises use advanced intelligent technologies, such as visual systems, to help find and classify defective products or batches. Generally speaking, these technologies are necessary short-term solutions, but enterprises should also use technology to identify the root causes of defective products and reduce or eliminate them

big data and analysis allow enterprises to draw data from new sources (such as customers) to better understand the location, causes and root causes of defective products. Because many defective products have behavioral origins, enterprises can reduce the defect rate by improving the enthusiasm or ability of operators. Low cost or low technology solutions, such as error proofing equipment with simple mechanical pause or visual signal function, can also be very effective

ignore the root causes of behavior

according to our work experience in the industry, consumer goods and pharmaceutical fields, enterprises often ignore the root causes of problems. For example, we found that many equipment failures and malfunctions are caused by improper human operation, which has nothing to do with technology

the filling and packaging production line of a food and beverage company often breaks down. Irregular production suspension will not only disrupt the plan, increase pressure on the schedule, but also waste materials. Enterprise leaders appreciate the innovative concept of industry 4.0 to avoid failures through early maintenance. They know that advanced sensors can detect the vibration, durability, noise level and other fault warning parameters of the equipment, and artificial intelligence algorithms can predict the faults that will occur several hours or even days in advance. The factory can arrange relatively cheap technical maintenance according to the forecast to avoid sudden failures

but pre maintenance is still maintenance. It does not touch the behavioral root cause of the failure. For example, every few hours, the workshop operators of food and beverage enterprises must clean, inspect and lubricate the production line and set parameters correctly (this link is called central line control). As these tasks require high discipline and accuracy, employees must have sufficient enthusiasm and motivation to successfully complete the task. If employees do not follow the prescribed steps for maintenance, the equipment needs more intervention than normal, and eventually fails

when we analyze the causes of failures in food and beverage enterprises, we find that the root cause is that enterprises sometimes lack sufficient operating standards, and when there are sufficient standards, employees do not abide by them. These situations reflect the behavior problems of the leaders: the person in charge of production did not train, motivate and supervise the operators to implement the necessary standards (see Figure 2)

at this time, the machine will automatically print out the sample number, the values of f1p1, f2p2, f3p3 and the