The manufacturing area is key to sustaining all manufacturing companies. It is necessary to maintain costs at competitive figures, to ensure sustainable growth. Listed below are 11 cost-saving ideas for manufacturing companies.
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Develop and review your strategic plan on a regular basis. Analyze the environment, the market, the competitors, and the available internal resources. Set goals and a roadmap. Review the operation of your company from a global perspective and do not get lost in one department. Visualize the short, medium, and long term. Automate repetitive processes. Introduce the planning of your resources, means of production, and materials. Consider technological evolution and investment, as key elements to achieve manufacturing cost reduction.
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Embrace lean manufacturing initiatives such as Six Sigma, continuous improvement, or Kaizen techniques. Document a Standard Operating Procedure (SOP). It will help employees develop complex work routines. Certify your organization and your products under international standards, such as
Usually, your staff knows more than anyone else where the operational wastes are. They know the opportunities for improvement and they often even know the solutions. It's not necessary to make any large capital investments to tackle improvements.
Involving your staff will improve their well-being and the performance of your business.
Make visible the standards reached by the work teams. Share the goals with regular meetings and internal publications: boards, magazines, mailings, etc.
Your company members need to know where to go and the performance indicators are the trigger. Establish key performance indicators (KPIs) that have a direct impact on your business. It will also drive all the members in the right direction. Combine departmental with interdepartmental indicators. It will favor the interaction of all the members of the organization.
Do things simply and do not get complicated. Get rid of activities that do not add value to your company. Keep only what you need in inventory. Reduce bureaucracy and excessive paperwork. If possible, sell equipment, materials or spare parts that are obsolete or no longer used. They often have a high market value or discard them from your plant. Sometimes, retaining immobilized capital in your company is more expensive than eliminating it. Focus on the core of your business and outsource the rest of the activities. You can outsource activities to some specialized companies. They can carry out:
Check your labor costs on a regular basis. Optimize the use of labor by balancing man-machine activities. Consider versatile multitasking employees. The specialization of those tasks that impact the productivity of the company. Train your human resources in those tasks that impact productivity. They are a good source of leverage. Offer incentives to your employees that introduce new techniques and generate savings.
When material costs are significant, find ways to use fewer materials. Make sure the materials meet the specifications and handle adequate batch sizes. It will reduce the unitary cost. Review your process along the entire chain and look for material waste:
There are many sources of energy in your company, such as electricity, steam, and fuel. Check the facilities and cut down every leakage and source of energy loss. The equipment to check can be boards, transformers, generators, motors, compressors, and pipelines.
Consider the implementation of preventive and predictive maintenance within your organization. Do not wait for machinery failure. Develop a maintenance plan to maintain all your equipment during plant stops. Avoid as much as possible downtime, it will increase your profit loss. Perform a preventive work calendar with your maintenance team:
Consider that all equipment and tools are an investment and not an expense for your company. Replace obsolete equipment and revamp machinery that produces unexpected downtime in your factory. The use of obsolete equipment or in poor condition can create more loss than benefit. Always determine the return on the investment contemplating both, benefits and costs. When acquiring new machinery look for:
Your logistics department is crucial when looking for manufacturing cost-saving ideas. It needs to streamline logistics operations. It guarantees the goods arrive on time and in safe conditions to your clients.
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EVs, power grid management, energy storage requirements, along with growth in renewable energy, will fuel the growth in the battery industry [ 25 ]. For low-cost manufacturing, the abundance of raw materials is a necessary criterion [ 26 ]. At the current usage rate, recent surveys and estimation data indicate that there is enough lithium for the next 365 years [ 27 ]. New processes for lithium extraction from minerals are also being explored [ 28 ]. There is some volatility in spot price of lithium and its commonly used compound, i.e., lithium carbonate. Moreover, lithium is sourced from a few countries and a handful of companies control the major part of the supply [ 29 ]. In March 2016, the peak price was US20,000/ton approximately [ 29 ]. Recent (January 2017) pricing is in the vicinity of ~15,000/ton [ 30 ]. In most cases, the manufacturers alone cannot control the price of the raw materials. Large-scale manufacturers can buy in larger quantities and claim discounted bulk pricing in some cases. However, the manufacturers have more control at every step of in-house manufacturing. It is very important for battery manufacturers to find processing and manufacturing changes that will further reduce the manufacturing cost for lithium and solid-state batteries. The purpose of this paper is to present process variability in battery manufacturing and provide other manufacturing directions that can provide further cost reduction of manufacturing of Li-ion and solid-state batteries.
On the other frontier, the surface transportation sector is also creating a huge market for batteries that actively participate in drive train rather than just supplying starting power to the engine. Energy economics, carbon foot-print reduction and eco-minded consumer behavior are driving the growth of electric vehicle (EV), hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV) markets, and thus, demand for batteries for such applications are also increasing. Further cost reduction of batteries would also bring the price of EV and HEV down, and place them at a more competitive price point with respect to the conventional internal combustion vehicles. Li-ion battery is also used for such applications. In some car batteries, Li account for approximately 5% of materials and less than 10% of the cost [ 22 ]. There is even an example where an EV manufacturer ventured into its own battery manufacturing (Tesla Gigafactory) and has achieved 35% battery cost reduction to lower the cost to below125/KWh [ 23 ]. The same EV company introduced battery storage for PV generated electricity (Tesla Powerwall) [ 24 ].
Energy storage has evolved at a rapid rate in the last couple of decades. For a long time, battery storage was mostly used for starting engines, few emergency backup and portable devices, toys, etc. Ubiquitous zinc-carbon (dry cell) battery and lead-acid battery were the key players for portable applications and the automotive industry, respectively. However, due to the rise of consumer electronics and the convenience of recharging, nickel metal hydride (NiMH) and nickel cadmium (NiCd) batteries also gained mainstream popularity. The arrival of lithium-based batteries changed the scenario by offering higher energy efficiency and density, in addition to its longer shelf life, fast charge and discharge, etc. [ 1 ]. Though it was reported that lithium-ion (Li-ion) batteries have slight memory effect [ 2 ], it is less prominent than NiCd and NiMH batteries. Li-ion batteries have high gravimetric (Wh/kg), high volumetric (Wh/L), high cycle life, and high energy efficiency, etc. [ 3 ]. Li-ion has gravimetric energy density in the range of 110–160 Wh/kg, while such ranges for lead acid, NiMH and NiCd are 30–50, 45–80 and 60–120, respectively in Wh/kg [ 4 ]. Armand et al. [ 5 ] published a detailed comparison of battery chemistries in terms of future applications and their environmental impacts where Li-based batteries performed better than the others did. With ramped up production, Li-battery price is showing a downward trend, and manufacturers, such as Tesla, are utilizing volume manufacturing to push the cost down [ 6 ]. In order to curb dependence on fossil fuel and reduce carbon emissions, the world is leaning more towards renewable energy (wind and solar) for electricity generation, and also towards EVs for surface transportation. Both of these sectors have high degrees of correlation with battery cost and performance. Apart from portable electronics, at this moment, the emerging market for the Li-ion battery can be categorized in two key segments: (a) renewable and grid energy storage, and (b) electric transportation. As sustainable energy sources, there is plenty of renewable energy (sunlight and wind) [ 7 ] for human beings on earth, and in recent years, energy harvesting installations (PV and wind turbine) are growing at an astronomical rate. Due to the intermittent nature of solar and wind energy generation, it is also creating a large demand for energy storage. However, due to the advancements in technology and volume manufacturing, the cost of batteries is following the price reduction trend of photovoltaic (PV) modules [ 8 ]. Cost reduction of battery manufacturing will further reinforce the position of renewable energy as a viable alternative to fossil fuel. Using locally generated direct current (DC) power from PV [ 9 ] and utilizing batteries for local storage can potentially transform the global electrical energy infrastructure [ 10 ]. This scheme also offers better efficiency and resiliency [ 11 ]. As the energy infrastructure is undergoing a gradual transformation, smart grid is also gaining attention. The use of front-of-the-meter (central grid energy storage) and behind-the-meter (energy storage at consumer premises) are increasing and thus, driving up the demand for batteries [ 12 ]. One of the largest batteries in the world, the 400-MW peak hour battery, is based on Li-ion technology [ 13 ]. The PV industry has used practices that were tested, implemented and perfected in semiconductor manufacturing, and thus made significant improvements in reducing the process variability [ 14 ]. This adoption of technology allowed the PV industry to deliver superior performance and to reduce cost. The battery industry can use similar fundamental concepts to transform the battery manufacturing processes. Driven by the continuous increase in energy density and reduction in cost [ 15 ], a recent report predicted 11.6% compound annual growth for Li-ion battery that will reach77.42 billion in 2024 [ 16 ]. Solid-state battery is also garnering attention [ 17 18 ], and it is projected to grow in the near future [ 19 ]. Li-ion with silicon anode [ 20 ], Li-air [ 21 ], etc., are also showing promise for future applications.
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P
) to tolerance (T
) ratio. The ratioP
/T
is defined as [P T = 6 σ precision l i m upper − l i m lower
(1)
precision is square root of sum of repeatability and reproducibility of measurement,lim
upper andlim
lower are upper and lower limits of tolerance, respectively. Semiconductor industry uses a value smaller than 10% forP
/T
[All manufacturing processes have inherent statistical variability. This is also true for the electrochemical and solid-state cells that form a complete battery. For control purposes, the measurement has to be precise enough before any corrections can be made [ 31 ]. In manufacturing, unidentified problems cannot be fixed, and control cannot be meaningful without measurements [ 31 ]. However, the best and most suitable measurement system will be different for each specific process. Therefore, the measurement scheme, data collection, analysis and feedback need to be tailored to each process. A useful measure of this can be expressed in terms of process () to tolerance () ratio. The ratiois defined as [ 32 ]:where σis square root of sum of repeatability and reproducibility of measurement,andare upper and lower limits of tolerance, respectively. Semiconductor industry uses a value smaller than 10% for 32 ]. As the transistor size shrunk, the process control also became more stringent to maintain the final yield [ 31 ]. In spite of having a very small tolerance, the semiconductor industry has achieved high yield for integrated circuits (IC) with ultra-small dimensions. Even, 10 nm transistor technology is now at mass production [ 33 ]. Meticulous attention has been paid to defects of semiconductors and interfaces [ 34 ]. The whole IC industry is a noteworthy example of process control. Their cost reduction is mostly generated by the in-house cost control, i.e., yield improvement, increased process efficiency, increased throughput, etc. Through process improvement, the silicon wafer manufacturers are able to offer superior quality wafers at a lower price. Most importantly, the successful IC manufacturers achieved high yield with tight process control, and are able to ship a larger number of finished products. The manufacturers that used tighter process control and larger Si wafer in their manufacturing process, remained competitive. The reliability of semiconductor products has helped this industry to enjoy stable growth. The reliabilities of transistors and their gates are treated with high importance in semiconductor fabrication [ 35 ]. Now, the reliability of battery and lifetime modeling is also drawing attention [ 36 ]. Apart from all these, researchers in industry or academia may discover that a new electrode, a new electrolyte, particular material substitution, or a new chemical processing step in battery can reduce cost and improve yield. However, this ongoing type of research is beyond the scope of this manuscript. Instead, manufacturing practice and cost reduction will be the key focus, assuming that the desired chemistry, recipe, material, etc. are already selected.
As stated before, the PV industry has made immense improvements in processing by adapting many well-known practices from the semiconductor industry [ 37 ]. The battery industry can also adapt some proven practices and processes from PV and semiconductor industries.
In this article, we will refer to an individual electrochemical or solid-state cell as “cell”. When several such cells are combined together, it will be referred as “battery pack”, which may include the battery management system (BMS) with sensors, controllers, processing unit, etc. On many instances of real life application, cells may be arranged according to the need to achieve a desired voltage level and capacity.
Due to tolerances during the manufacturing process, the final product may exhibit variability. This is applicable for battery manufacturing as well. These variability issues present a big challenge for batteries since the manufacturing process consists of the assembly of sub-components (e.g., electrolyte, electrode, separator, etc.) which may come from different locations and vendors [ 38 ]. However, cell-to-cell variability had been analyzed from the viewpoint of chemical and physical phenomena, and the system-level design requires analytical models that account for the variability in the manufacturing process [ 38 ]. The electrochemical models, such as pseudo 2-dimentional (P2D), single particle (SP), porous electrode polynomial (PP), etc. are powerful tools and their contribution should not be underestimated. Detailed comparisons of these models can be found in review articles, such as [ 39 ]. It is possible to use an electrochemical model to accurately estimate the effects of process variation on a cell. However, the system-level design and variation of electric systems might be handled effectively by analytical electrical models [ 38 ]. Stroe et al. [ 40 ] also used an electrical model to characterize battery pack performance. When multiple electrochemical cells are present, it is important to understand the role of variability on the behavior of the final battery pack. Zhang et al. [ 41 ] demonstrated one such example by experiment, modeling and parameter extraction for a multi-cell battery.
Shin et al. [ 38 ] demonstrated a combined cell-to-cell variation model by: (i) identifying the target lithium battery structure, (ii) modeling capacity and internal resistance, and (iii) model parameter analysis. By random variable assignment (based on mean and variance), 10,000 random cells were generated, arranged in arrays and simulated in MatLab Simulink [ 38 ].
Figure 1 [38 ] shows a statistical distribution of cell performance for Li-ion cells in terms of capacity and resistance. The desired statistical distribution of the cell capacity in Figure 1 should have (a) a mean centered at higher value, i.e., the mean should move right, and (b) have a smaller (narrower) variation. Similarly, the statistical distribution of resistance should have (a) smaller mean value, i.e., move towards left, and (b) have a smaller (narrower) variation. As shown in Figure 2 38 ], it was demonstrated that if an array consists of cells with less variance, it could perform better than the one that has more variance among its constituent cells. Therefore, with narrower statistical distribution, it is possible to extract more power from the array of cells.We may consider the single electrochemical cell as the building block, and can connect such cells to form a battery pack of desired voltage (e.g., 12 V). Using smaller battery packs in series and parallel combination, higher voltage (e.g., 48 V) and higher capacity (e.g., 100 KWh) can be achieved. Therefore, the variation of output from one cell to another is unwanted as its adverse effect will ripple to the battery pack and eventually, to the whole (large) battery installation. Therefore, variation reduction should start from the smallest constituents -the cells, and then, applied on subsequent steps or blocks gradually. Variation reduction among cells can contribute significantly in boosting the overall performance of a large energy storage installation. While investigating the thermal imbalance between cells, Christen et al. [ 42 ] mentioned that variation in resistance can significantly reduce the lifetime of a battery.
Any kind of high variation in electrochemical cell performance may require discarding many of the manufactured cells. Moreover, due to the presence of different types of materials, e.g., anode, cathode, solvents, and other chemicals, the statistical distribution can become wider, and eventually produce inferior end results. Santhanagopalan and White [ 43 ] have shown that the variation in cathode thickness and cathode particle size can cause measurable differences in their Nyquist responses.
The importance of process control and variation can be illustrated through a very relevant example from the PV manufacturing process. A PV module is the series and parallel arrangement of solar cells. Due to the series and parallel types of connection, the worst performing solar cell dominates the overall performance of the whole PV module. The statistical distribution and non-uniformity among solar cells causes lower efficiency in energy conversion in a PV module when compared to the efficiency of the best performing solar cell. The improvement in individual solar cell efficiency does not translate directly into improved PV module efficiency. Cell-to-cell performance variation can reduce the achievable power output, because the worst performing cell will pull down the overall performance of the module [ 14 ]. The percentage change in module efficiency from single cell efficiency can be considered as an indicator of how closely the process could be controlled and what are the degrees of variation among cells. A smaller change in a silicon solar cell to module efficiency is a clear indicator that these cells can be manufactured with the least amount of variation. Due to process uniformity, the efficiency of the silicon PV module is only slightly lower than the efficiency of the individual cell, and it is best among commercial solar panels [ 44 ]. On the other hand, for many R&D cells, high variability and inability to control the process are key hurdles for entering mass production [ 45 ]. This is a stellar example of what can be achieved through process control.
Li-ion batteries employ BMSs to ensure safe and reliable operation of the battery pack. The BMS needs to monitor many parameters (e.g., total and individual voltage and current, temperature, impedance, etc.). It also performs critical tasks of estimation of battery states which includes state of charge (SOC), state of health (SOH), state of function (SOF), etc. Moreover, depending on the specific application, BMS may perform on-board diagnosis, safety control and alarm, charge control, equalization, thermal management, networking, information storage, etc. SOC estimation can be done through several methods, such as open circuit voltage, ampere-hour integral, battery model-based estimation, neural network model, Fuzzy logic, Kalman filter, sliding mode observer, etc. Each of these has its own advantages and drawbacks, and they are significantly different in their computational loads and error margins [ 46 ]. Lu et al. [ 46 ] also presented a table comparing different BMS chips available on the market. Stuart and Zhu [ 47 ] demonstrated BMS for large Li-ion cells used in EVs. If more uniformity is present among cells and they perform in a harmonious way, a less computationally powerful (and cheaper) BMS may be used. Equalization can be done at battery (with several cells) level, or at an individual cell level. The non-uniformity in remaining capacity and the need for equalization can be caused by, among other reasons, non-uniformity of self-discharge or non-uniformity in Coulombic efficiency [ 46 ]. Uniform electrical performances may lead to a greater application of passive BMS, which is cheaper than active BMS.
In essence, for achieving higher performance and lower manufacturing cost of batteries, the electrochemical cells need to have smaller process variation, i.e., uniform physical and chemical properties.
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