What is Reverse Supply Chain & Why is it Important?

Posted on September 8, 2022March 9, 2023 by manasa

It’s appealing to commit all your business efforts to the forward supply chain, but the “reverse” practice is also crucial for many businesses. From coordinating defective goods returns to picking up empty packaging to collecting old household appliances and other used products, these operations necessitate a streamlined and effective reverse supply chain.

Why would a business need to implement and then reverse a supply chain? Isn’t it perplexing? Let’s dive right into the details.

What is a reverse supply chain?

It’s the series of activities to retrieve a used product/part from a customer, either dispose of it or reuse it. And for a growing number of manufacturers in industries ranging from handbags to supercomputers, the reverse supply chain has become an essential part of their business.

In some regions of the world, companies are forced to set up reverse supply chains because of ESG or consumer pressures.

Let’s consider Kodak; it remanufactures its single-use cameras after the film has been developed. Over the past 10 years, the company has recycled millions of cameras in more than 20+ countries. Few companies are using reverse supply chains as an integral part of new and existing businesses, such as Bosch.

When a company establishes a reverse supply chain by choice or necessity, it faces many challenges. It should educate its customers and establish new points of contact with them, decide what activities to outsource and what to do, and figure out how to keep costs to a minimum while discovering innovative ways to recover value.

Key Components of the Chain

To understand the structure of a reverse supply chain, let’s divide the chain into its five key components:

Acquisition:

The task of retrieving used products is key to creating a profitable chain. Product returns’ quality, quantity, and timing need to be carefully understood and managed. Furthermore, companies may be flooded with returned products of such variable quality that they make remanufacturing impossible. Companies often work closely with retailers and distributors to coordinate collections.

Reverse Logistics:

Once collected, the products need to be transported to facilities for various processes such as inspection, sorting, and disposition. There is no one “best” design for a reverse logistics network; each must be tailored to the products involved and the economics of their reuse. Bulky products like refrigerators, for instance, will require very different handling than small but fragile products like cameras, lenses, etc. Companies consider not only the costs for shipping and storage but also how quickly the value of the returned products will decline and the need for control over the products. In many cases, companies outsource the logistics to a specialist, which makes better sense in some scenarios.

Sort, Sweep & Segregate(3S):

The check, sort, sweep, segregate, and grading of returned products are labor-intensive and time-consuming tasks. But the process can be streamlined if a company subjects the returns to quality standards and uses technologies to automate (such as sensors, bar codes, etc.) to track and test them. A business should learn to make 3S decisions based on quality, product spec, or other variables at the earliest possible stage during the return process. This helps eliminate many logistics costs and gets remanufactured products to market faster.

Refurbishment – Renovation:

Companies may capture value from returned products by extracting and reconditioning components for reuse or remanufacturing the products for resale. Reconditioning and reproduction processes are much less predictable than traditional manufacturing because there can be a large degree of uncertainty in the timing and quality of returned products. Making smart decisions while accepting and sorting returns will help largely reduce manufacturing variability and costs.

Market Re-entry:

If any company plans to sell a recycled product, it first determines whether there is a demand for it or if a new market must be created. The company needs to invest heavily in consumer education and other marketing efforts if it’s the latter. Potential customers for remanufactured products or components include not just the original purchasers but also new customers in different markets. The company will sometimes target customers who cannot afford the new products but would jump at the chance to buy used versions at lower prices.

Artificial Intelligence for Reverse Supply Chain

Using AI for reverse supply chain management enables businesses to address waste management and environmental sustainability issues.

The reverse supply chain is a part of a more significant concept known as the circular economy, which seeks to tackle problems such as waste reduction, pollution, biodiversity loss, and climate change. Considering sustainability, governing bodies around the world create product take-back laws, which hold manufacturers financially accountable for factors related to waste recycling, handling of perishable goods, and to produce circular products. Managing these activities effectively offers businesses a more significant challenge in reverse logistics. Using new approaches and tools like AI for reverse supply chains can help overcome the challenges associated with reverse logistics.

To optimize the performance of the reverse supply chain, there is a need to establish an effective and efficient infrastructure via optimal network design. Generally, reverse supply chain network design is concerned with establishing an infrastructure to manage the reverse channel, which often consists of final users, collectors, and remanufacturers.

Using Artificial Intelligence (AI) for reverse logistics offers a great range of solutions to get the world closer to the ideal circular economy. Artificial Intelligence driven data analytics, image processing, and other techniques can simplify reverse logistics.

Classifying Waste Materials

Artificial Intelligence based waste recognition systems feature image classification and processing to remove inconsistencies from the process of material sorting and composition analysis. Thus, waste materials’ identification and categorization are optimized to maximize recycled goods’ quality. Artificial Intelligence-based machine vision recycling systems enables waste managers and recycling department to receive valuable insights to increase the recycling rates and product value. Machine vision-aided image processing holds the potential to add new dimensions to the waste management and product recycling processes. Using 2D and 3D imaging, Artificial Intelligence allows the businesses to optimize waste material segregation.

Repurposing Returned Goods

Automated analytics and AI are potent tools for repurposing goods that your customers have returned. So, the business can choose whether it must liquidate, re-shelve, recycle, or scrap a returned product after an Artificial Intelligence-based tool assesses and predicts the usability and condition of such goods. Additionally, Artificial Intelligence also predicts and provides recommendations about how businesses can dispose of returned products at the point of return. So, selecting the best option for processing a returned item can be automated, thereby increasing its speed and effectiveness in decision-making and reducing the expenses incurred for each returned item.

Artificial Intelligence can also be deployed to reroute products in reverse logistics that their buyers have returned. Using its decision-making tools and inventory management systems, assess a returned product’s condition and various other factors, such as demand for the same product in other regions, before recommending to the sales team to market the product in such areas.

Designing Circular Products

Artificial Intelligence helps speed up circular products’ overall design and development processes to make them arrive in commercial markets sooner. Machine learning-based systems produce components, materials, and products that fit the ideal circular economy. Artificial Intelligence enables designers to select product designs faster as the technology analyzes large amounts of data and quickly suggests optimal designs and their adjustments. Once the base design with various tweaks and changes is suggested, the product designer can start creating the product that will be the most sustainable and recyclable. The insights generated by Artificial Intelligence are responsible for manufacturers creating circular products quickly in the most cost-effective way.

Reverse Supply Chain Use Cases

The best example of a business leveraging Artificial Intelligence to resell its returned goods is the return management strategy by IKEA. The renowned conglomerate deployed an Artificial Intelligence-based solution to ensure that the large and expensive merchandise returned is not wasted. It has been found that one in every ten products comes back from being sold by the Swedish company globally. To reduce the losses, IKEA chose Oporto, a specialized reverse logistics-based software company, to design a predictive Artificial Intelligence tool that provides suggestions backed by data for the best possible destination for returned merchandise. The Artificial Intelligence tool allows IKEA’s product managers to know whether a returned product should be sold to a third-party retailer, donated to charity, or brought back to the floor.
H&M is a clothing brand that has used the concept of “Reverse Logistics” innovatively. H&M accepts old clothes of any brand. They take the used clothes to create their all-recycled clothing line. The idea behind this is to connect people with the brand by selling to them and making them involved with the brand by giving away their old clothes.
Apple manufactures various products, such as the iPhone, MacBook, etc., and sells them in its stores worldwide. Apple lets its customers return their old Apple phones when they want to upgrade to newer ones. They take their customers’ old phones and provide a discount on the new ones. Apple takes the old phones back to their factories and uses the parts of the old phones to manufacture new phones. This not only makes them more profit but also helps manufacture products in an environmentally friendly way.
Amazon is one of the largest e-commerce websites and a pioneer in selling online. Initially, it was difficult for e-commerce websites to gain customers’ trust. Hence, Amazon started a free-of-cost product return and replacement policy on certain products only under specific conditions & also when the customers have a genuine reason to return products. Amazon handles its reverse logistics process through various third-party vendors and organic resources such as Genco, FedEx, and many other small vendors. This way, they gain their customers’ trust and ensure that their sellers provide good quality products to the customers so that fewer returns occur.

Few KPI’s considered

Disposition Cycle Time: Cycle time is the total time it takes to move a unit from the beginning to the end of a physical process.
Handling Cost: Handling costs are the cost of holding products in the inventory.
‍Cost of Repair or Refurbishment: Understanding the cost of repair or refurbishment is a common KPI that is measured.
Rate of Recovery: How much effort did you spend repairing the item? How long did it take for the item to return to stock? How much did it sell for on an online eCommerce channel? The recovery rate will show you how much value you recovered after selling the item.

Final Words

Companies that have had the most success with their reverse supply chains are those that precisely synchronize them with their forward supply chains, resulting in what is known as a closed-loop system. The approach significantly reduces inspection and disposal expenses, allowing the business to profit from remanufactured tools. Forward-thinking yields significant returns, even in reverse supply chains.

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5 Key Factors in Industry 4.0: The Game Changer for Manufacturers in 2022

Posted on May 2, 2022March 9, 2023 by manasa

The 4th industrial revolution ushers every Industry into immense transformations, with enormous advantages and implementation challenges. The goal of Industry 4.0 is to integrate physical and digital technologies into a cyber-physical system (CPS) that reflects the digital world in the physical world and vice versa; in addition to that, it also enhances the customer experience. For organizations, implementing Industry 4.0 is a daunting challenge. The process starts with understanding the existing workflow of the business, identifying the bottlenecks, and selecting the right technologies to overcome the workflow and business problems. In order to make this process simpler, our research team and industry experts suggested 5-key factors that help organizations implement Industry 4.0 with fewer complications and faster success.

5 Key Factors- Effective ways to implement Industry 4.0 in your organization

There are numerous factors that organizations ought to consider in order to embrace Industry 4.0 successfully. However, here are the five most essential factors that help shape Industry 4.0 at its core and make the implementation process hassle-free. When integrated, each of these pieces create a capability that can help in bringing transformation.

1. Identifying and Implementing Right Technologies

Customer-centric technologies have enabled OT IT Convergence, highly recommended to manufacturers, mandating them to integrate their IT (Information Technology) and OT (Operational Technology) and transform real-time data into actionable intelligence. Enterprise information technology must securely collect data from OT equipment before processing it and sharing the necessary insights with OT professionals and other internal and external stakeholders to maximize IIoT project return on investment (ROI).

By Integrating OT Plant Data with IT Systems such as ERP, WMS, and other third-party IT suites, whether on-premises or in the cloud, there would be clear shop floor data visibility by integrating Computerized Maintenance Management Systems (CMMS) with a SCADA system, Cloud/Edge-based Remote Monitoring Solutions with these operations.

The next point to mention is that AI (Artificial Intelligence) brings flexibility to adapt various technologies that enable software and machines to perceive, comprehend, act, and learn independently or to augment human operations.

Industrial production can be more convenient than manual processes by incorporating artificial intelligence. These technologies offer vast potential for manufacturing companies to work even faster and more flexibly, which helps achieve the best possible quality while cutting down on the resources used and driving greater production efficiencies.

2. Selecting The Right Use Case for Your Need

When a new technology is introduced, the scope and significance of its advantages are usually unclear until after it is put to use. Early IIoT adopters and digital transformation pioneers served as role models for other sectors who were always interested in exploring the new avenues offered by emerging technologies. It takes a particular vision to apply use cases to a different business, but it eliminates the risk of buying on trust.

Use cases are just the beginning, and the idea is to learn about what others have done or are doing and seek techniques to make the outcomes they’re getting relevant to your business. The first approach is to understand why the company is still attempting to figure out its IIoT strategy and needs to build a list of problem statements planning to resolve. The next step is to look to peer companies, research analysts, and solution providers’ case studies to understand digital transformation use cases, entry points, techniques, and rewards. Start with a PoC having low resources, low effort, and high impact; when the results are visible, start implementing use case simultaneously following the same strategy.

Few use cases offered by Affine for different functions:

3. Identifying the skill gap, filling it with right skillsets

Organizations are recognizing IoT in the paradigm of a highly autonomous production line to envision all of the skills that are required to implement Industry 4.0. It could comprise additive manufacturing techniques, CNC lathes, and newer machines capable of executing highly variable, multi-step processes with the help of robotic vision, artificial intelligence, and cobots that work alongside humans. We now have a technology landscape that requires multiple skill sets and the blending of those skills to cut across silos and unique skills to create entirely new categories of technology professionals—those who understand the convergence of operational and information technologies.

Now is the time to think cross-discipline or multi-discipline. People claim the Internet of Things is about digitizing things when they talk about them. It’s all about digitizing business processes. As a result, engineers, network specialists, application developers, prominent data architects, UI [user interface] designers, and businesspeople must communicate and comprehend each other. Industry 4.0 will necessitate the organization of multidisciplinary teams to solve complex challenges. Of course, specialists will be required, but they will also need to broaden their knowledge to cover other IT technologies like cloud, AI, analytics, and operational technologies such as robotics and process automation that keep factories and assembly lines operating. To do so, train the people, develop continuous learning programs as a regular practice, hire people with good knowledge who are needed to bridge the gap, and have new people on board to help cross-learn and motivate the team. This is to unlock their potential to create a sustainable workplace.

4. Change Your Vision and Transform Your Company Culture

The force behind the “Digital Transformation” has become an extensive expression used across several industries and contexts to describe the process through which a company adopts and implements digital solutions that benefit its activities. It is crucial to notice the narrative around digital transformation as it enables a cultural shift in the company. Remarkably, how often do we discuss culture as a consequence of an event rather than as the driving force behind it? This approach may still be missing in some essential aspects.

The advantage of a digital tool, no matter how good it is or what benefits it offers, will be lost if the company is not prepared to handle it. The project’s true potential is hidden, resources are wasted, and it is on the brink of failure. Aligning business with right operational practices denote the company culture, key factors that facilitate digital transformation approach to drive cultural change, and shop floor employees’ engagement with the vision and road map makes it simple. This transparent process guides CDOs and digital leaders. As organizations prepare for and impose digital transformations, it is crucial to promote a culture where everybody is tech-savvy, and security is everyone’s consideration.

5. Finding the Right Partners/Vendors

Plan the scope of your business and align your goals with the company’s general strategy by selecting that the right vendor/partner for implementing the IR 4.0 applications plays a pivotal role. Start with pilot projects, validate results, and systematize the learning mechanisms initially to understand the scope aligned with your requirements. To achieve this, model projects and “best practices” should be promoted and invest in a digital learning technique.

Whether they recognize it or not, most production managers today are already in a race. It’s a culture to adapt and implement new manufacturing systems and technologies; as we all know, integration and collaboration are at the core of Industry 4.0. Create a strategy before embarking on a long journey without a map, and you should do the same with Industry 4.0 adoption. This is a crucial step in the procedure. Once you’ve determined your desired maturity level, you’ll need to create a thorough implementation strategy to assist you in achieving your objectives.

A potential and effective partner will monitor your current functioning, detect traps, comprehend obstacles, and provide a healthy way to proceed or a new course of action. Ultimately, this will solve your existing challenges and assist you in generating new value from them. In the usual approach, they should give you a road plan for pushing your business to the next level. And they should let you comprehend it in a logical fashion without a slew of technical jargon.

Conclusion

Manufacturers and the manufacturing industry as a whole are seeking direction as we approach 2022, not least due to the ongoing global COVID-19 pandemic. However, the last few years have given us much learning that signifies resilience, innovation, and the sector’s ability to persevere in the face of adversity.

There are numerous opportunities for all industrial sectors, ranging from acquiring fresh talent to exploiting data more effectively to contribute to a more sustainable world. Smart Manufacturing and Industry 4.0 solutions and efforts will always be vital in manufacturing and many other sectors.

Are you looking to know how your industry will change in the era of Industry 4.0 and want to be a winner in this new world? Listen to our industry leaders and experts and ask your questions at our virtual event on “Demystifying Industry 4.0”. Our speakers are sharing real-life use cases and insights into opportunities that are driving their growth and success with Industry 4.0. Go ahead, Register Now – this is going to be a great event!

Stay tuned for more information!

Assessing Top 5 Challenges of Implementing Industry 4.0!

Posted on April 20, 2022December 28, 2022 by Website Manager

Today, the entire world is grappling with the COVID-19 pandemic, which has intensified supply chain concerns and prompted many businesses to rethink their sourcing strategies. Several businesses are focusing on localization for two reasons: one, to be closer to the source, and the other, to minimize the risk of disruption. In the case of manufacturing, the movement of Industry 4.0 is caused by volatile market demands for better and quicker production techniques, shrinking margins, and intense contention among enterprises that are impossible without potential technologies like AI, Data Analytics, and Cloud. However, SMEs and MSMEs are still struggling with several challenges in adopting Industry 4.0 initiatives. These obstacles may dissuade some manufacturing companies from adopting Industry 4.0, causing them to fall behind their peers.

The Top Five Challenges!

SMEs and MSMEs still experience difficulties achieving Industry 4.0 goals, although smart manufacturing is often associated with Industry 4.0 and digital transformation. Here are the five challenges:

1. Organization Culture:

This is one of the immense challenges for any organization to evolve from ad-hoc decisions to data-based decision-making. Part of this is driven by the data availability and the CXO’s awareness and willingness to adopt new Digital technologies. Navigating the balance between culture and technology together is one of the toughest challenges of digital transformation.

2. Data Readiness/Digitization:

Any Digital Revolution succeeds on the availability of data. Unfortunately, this is one of the most significant opportunities for SMEs. Most of the manufacturing plants in SMEs lack basic data capture and storage infrastructure.

Most places have different PLC protocols (e.g., Siemens, Rockwell, Hitachi, Mitsubishi, etc.), and the entire data is encrypted and locked. This either requires unlocking encryption by the control systems providers or calls for separate sensor or gateway installations. Well, this is a huge added cost, and SMEs have not seen any benefits so far, as they have been running their businesses frugally.

3. Data Standardization and Normalization:

This is a crucial step in the Digital Transformation journey, enabling the data to be used for real-time visibility, benchmarking, and machine learning.

Most SMEs grow in an organic way, and there’s an intent to grow most profitably. Typically, IT and OT technology investments are kept to a bare minimum. As a result, most SMEs are missing SCADA/MES’S systems that integrate the data in a meaningful way and help store it centrally. As a result of missing this middleware, most of the data needs to be sourced from different sensors directly or PLCs and sent via gateways.

All this data cannot be directly consumed for visualization and needs an expensive middleware solution (viz., LIMS (Abbott, ThermoFischer), and LEDs- GE Proficy); this is again an added cost.

Additionally, the operational data is not all stored in a centralized database. Instead, it is available in real-time from Programmable Logic Controllers (PLCs), machine controllers, Supervisory Control and Data Acquisition (SCADA) systems, and time-series databases throughout the factory. This increases the complexity of data acquisition and storage.

4. Lack of Talent for Digital:

Believe it or not, we have been reeling under a massive talent crunch for digital technologies. As of 2022, a huge talent war is attracting digital talent across all services, consulting, and product-based companies.

As a result, we don’t have enough people who have seen the actual physical shop floor, understand day-to-day challenges, and have enough digital and technical skills to enable digital transformation. A systematic approach is needed to help up-skill existing resources and develop new digital talent across all levels.

5. CXO Sponsorship:

This is a key foundation for any digital transformation and Industry4.0 initiative. Unless there’s CXO buy-in and sponsorship, any digital transformation initiative is bound to fail. For CXO’s to start believing in the cause, they need to be onboarded, starting with just awareness of what’s possible, emphasizing benefits and ROI as reasons to believe.

Once there’s a top-down willingness and drive, things will become much easier regarding funding, hiring of technical talent or consulting companies, and execution.

Final Takeaway

It should go without saying that the above stats do not include all the challenges manufacturers encounter when they embark on the Industry 4.0 journey. Additionally, more industries and professionals should actively engage in skill improvement initiatives for immediate implementation and prepare employees for the future. Industry 4.0 is more than a vision of the future of manufacturing; it’s a blend of potential technologies, processes, and business models that will create new ways to make anything at a previously impossible scale.

What impact would that have on Manufacturers? Who stands to benefit from Industry 4.0? How is it going to be the case? How will it begin in earnest?

Compelling insights will be unveiled at the Demystifying Industry 4.0 event on 13th May 2022.

Click Here to Register Now!

Stay tuned for more information!

Statistical Model Lifecycle Management

Posted on March 15, 2022March 17, 2023 by affine

Organizations have realized quantum jumps in business outcomes through the institutionalization of data-driven decision making. Predictive Analytics, powered by the robustness of statistical techniques, is one of the key tools leveraged by data scientists to gain insight into probabilistic future trends. Various mathematical models form the DNA of Predictive Analytics.

A typical model development process includes identifying factors/drivers, data hunting, cleaning and transformation, development, validation – business & statistical and finally productionisation. In the production phase, as actual data is included in the model environment, true accuracy of the model is measured. Quite often there are gaps (error) between predicted and actual numbers. Business teams have their own heuristic definitions and benchmark for this gap and any deviation leads to forage for additional features/variables, data sources and finally resulting in rebuilding the model.

Needless to say, this leads to delays in the business decision and have several cost implications.

Can this gap (error) be better defined, tracked and analyzed before declaring model failure? How can stakeholders assess the Lifecycle of any model with minimal analytics expertise?

At Affine, we have developed a robust and scalable framework which can address above questions. In the next section, we will highlight the analytical approach and present a business case where this was implemented in practice.

Approach

The solution was developed based on the concepts of Statistical Quality Control esp. Western Electric rules. These are decision rules for detecting “out-of-control” or non-random conditions using the principle of process control charts. Distributions of the observations relative to the control chart indicate whether the process in question should be investigated for anomalies.

X is the Mean error of the analytical model based on historical (model training) data. Outlier analysis needs to be performed to remove any exceptional behavior.
Zone A = Between Mean ± (2 x Std. Deviation) & Mean ± (3 x Std. Deviation)
Zone B = Between Mean ± Std. Deviation & Mean ± (2 x Std. Deviation)
Zone C = Between Mean & Mean ± Std. Deviation.
Alternatively, Zone A, B, and C can be customized based on the tolerance of Std. Deviation criterion and business needs.

Rule	Details
1	Any single data point falls outside the 3σ limit from the centerline (i.e., any point that falls outside Zone A, beyond either the upper or lower control limit)
2	Two out of three consecutive points fall beyond the 2σ limit (in zone A or beyond), on the same side of the centerline
3	Four out of five consecutive points fall beyond the 1σ limit (in zone B or beyond), on the same side of the centerline
4	Eight consecutive points fall on the same side of the centerline (in zone C or beyond)

If any of the rules are satisfied, it indicates that the existing model needs to be re-calibrated.

Business Case

A large beverage company wanted to forecast industry level demand for a specific product segment in multiple sales geographies. Affine evaluated multiple analytical techniques and identified a champion model based on accuracy, robustness, and scalability. Since the final model was supposed to be owned by client internal teams, Affine enabled assessing lifecycle stage of a model through an automated process. A visualization tool was developed which included an alert system to help user proactively identify for any red flags. A detailed escalation mechanism was outlined to address any queries or red flags related to model performance or accuracies.

Fig1: The most recent data available is till Jun-16. An amber alert indicates that an anomaly is identified but this is most likely an exception case.

Following are possible scenarios based on actual data for Jul-16.

Case 1

Process in control and no change to model required.

Case 2:

A red alert is generated which indicates model is not able to capture some macro-level shift in the industry behavior.

Any single data point falls outside the 3σ limit from the centerline (i.e., any point that falls outside Zone A, beyond either the upper or lower control limit)

Two out of three consecutive points fall beyond the 2σ limit (in zone A or beyond), on the same side of the centerline
Four out of five consecutive points fall beyond the 1σ limit (in zone B or beyond), on the same side of the centerline
Eight consecutive points fall on the same side of the centerline (in zone C or beyond)

If any of the rules are satisfied, it indicates that the existing model needs to be re-calibrated.

Key Impact and Takeaways

Quantify and develop benchmarks for error limits.
A continuous monitoring system to check if predictive model accuracies are within the desired limit.
Prevent undesirable escalations thus rationalizing operational costs.
Enabled through a visualization platform. Hence does not require strong analytical
expertise.

Product Life Cycle Management in Apparel Industry

Posted on March 15, 2022March 17, 2023 by affine

Product Life Cycle Estimation

“Watch the product life cycle; but more important, watch the market life cycle”
~Philip Kotler

Abstract

The product life cycle describes the period over which an item is developed, brought to market and eventually removed from the market.

This paper describes a simple method to estimate Life Cycle stages – Growth, Maturity, and Decline (as seen in the traditional definitions) of products that have historical data of at least one complete life cycle.

Here, two different calculations have been done which helps the business to identify the number of weeks after which a product moves to a different stage and apply the PLC for improving demand forecasting.

A log-growth model is fit using Cumulative Sell through and Product Age which helps to identify the various stages of the product. A Log-Linear model is fit to determine the rate of change of product sales due to a shift in its stage, cet. par.

The life span of a product and how fast it goes through the entire cycle depends on market demand and how marketing instruments are used and vary for different products. Products of fashion, by definition, have a shorter life cycle, and they thus have a much shorter time in which to reap their reward.

An Introduction to Product Life Cycle (PLC)

Historically, PLC is a concept that has been researched as early as 1957 (refer Jones 1957, p.40). The traditional definitions mainly described 4 stages – Introduction, Growth, Maturity, and Decline. This was used mainly from a marketing perspective – hence referred to as Marketing-PLC.

With the development of new types of products and additional research in the field, Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) were added to the traditional definition to give the Engineering PLC (or E-PLC). This definition considers the cost of using the product during its lifetime, services necessary for maintenance and decommissioning of the product.

According to Philip Kotler, ‘The product life cycle is an attempt to recognize distinct stages in sales history of the product’. In general, PLC has 4 stages – Introduction, Growth, Maturity, and Decline. But for some industries which consist of fast moving products, for example, apparel PLC can be defined in 3 stages. PLC helps to study the degree of product acceptance by the market over time which includes major rise or fall in sales.

PLC also varies based on product type that can be broadly divided into:

Seasonal – Products that are seasonal (for e.g. mufflers, that are on shelves mostly in winter) have a steeper incline/decline due to the short growth and decline periods.
Non-Seasonal – Products that are non-seasonal (for e.g. jeans, that are promoted in all seasons) have longer maturity and decline periods as sales tend to continue as long as stocks last.

Definition of Various Stages of PLC

Market Development & Introduction

This is when a new product is first brought to market before there is a proved demand for it. In order to create demand, investments are made with respect to consumer awareness and promotion of the new product in order to get sales going. Sales and Profits are low and there are only a few competitors in this stage.

Growth

In this stage, demand begins to accelerate and the size of the total market expands rapidly. The production costs and high profits are generated.

Maturity

The sales growth reaches a point above which it will not grow. The number of competitors increases and so market share decreases. The sales will be maintained for some period with a good profit.

Decline

Here, the market becomes saturated and the product is no longer sold and becomes unpopular. This stage can occur as a natural result but can also be due to introduction of new and innovative products and better product features from the competitors.

This paper deals with the traditional definition of PLC and the application in Fashion products.

Why do Businesses Need PLC and How Does it Help Them?

Businesses have always invested significant amounts of resources to estimate PLC and demand. Estimating the life cycle of a new product accurately helps business take several key decisions, such as:

Provide promotions and markdowns at the right time.
Plan inventory levels better by incorporating PLC in demand prediction.
Plan product launch dates/season.
Determine the optimal discount percentages based on a product’s PLC stage (as discussed later in this paper).

Businesses primarily rely on the business sense and experience of their executives to estimate a product’s life cycle. Any data driven method to easily estimate PLC can help reduce costs and improve decision making.

How Does the Solution in this Paper Help?

The solution detailed in this paper can help businesses use data of previously launched products to predict the life cycles of similar new products. The age at which products transition from one life cycle phase to another as well as the life cycle curves of products can be obtained through this process. This also helps to identify the current stage of the products and the rate of sales growth during stage transition.

Below is an overview of the steps followed to achieve these benefits:

To identify products similar to a newly released product, we clustered products based on the significant factors affecting sales. This gives us a chance to obtain a data based PLC trend.
Next, sales is used to plot the Cumulative Sell Through Rate vs Product Age (in weeks).
A log-growth model fit across this plot will provide the Life Cycle trend of that product or cluster of products.
The second differential of this curve can be analyzed to identify shifts in PLC phases, to estimate the durations of each of the PLC phases.

Detailed Approach to Estimate PLC

The process followed to determine the different PLC stages is a generic one that can be incorporated into any model. However, in this paper, we have described how it was employed to help determine the effect of different PLC stages on sales for the apparel industry.

The procedure followed has been described in detail in the steps below:

i. Product Segmentation

The first step in estimating PLC is to segment products based on the features that primarily influence sales.

To predict the life cycle factor in demand prediction of a new product, we need to find similar products among those launched previously. The life cycle of the new product can be assumed to be similar to these.

ii. Identification of PLC Stages

To identify various stages, factors like Cumulative Sell through rate and Age of product were considered. The number of weeks in each stage was calculated at category level which consists of a group of products.

Cumulative sell through is defined as the cumulative Sales over the period divided by the total inventory at the start of the period. Sales of products were aggregated at category level by using the sum of sales at similar product age. For example, Sales of all products when the age was 1 week being aggregated, to get the sales of that category on week 1.

After exploring multiple methods to determine the different stages, we have finally used a log-growth model to fit a curve between age and cumulative sell through. Its equation is given below for reference:

Note: Φ1, Φ2 & Φ3 are parameters that control the asymptote and growth of the curve.

Using inflexion points of the fitted curve cut-off for different phases of product life cycle were obtained.

The fitted curve had 2 inflexion points that made it easy to differentiate the PLC stages.

The plot above shows the variation of Cumulative sell through rate (y-axis) vs Age (x-axis). The data points are colored based on the PLC life stage identified: Green for “Growth Stage”, Blue for “Maturity Stage” and Red for “Decline Stage”.

Other Methods Explored

Several other methods were explored before determining the approach discussed in the previous section. The decision was based on the advantages and drawbacks of each of the methods given below:

Method 1:

Identification of PLC stages by analyzing the variation in Sell through and Cumulative Sell through.

Steps followed:

Calculated (Daily Sales / Total Inventory) across Cumulative Sell through rate at a category level
A curve between Cumulative Sell through rate (x-axis) and (Daily Sales / Total Inventory) in the y-axis was fitted using non-linear least square regression
Using inflexion points of the fitted curve cut-off for different phases of product life cycle is obtained

Advantages: The fitted curve followed a ‘bell-curve’ shape in many cases that made it easier to identify PLC stages visually.

Drawbacks: There weren’t enough data points in several categories to fit a ‘bell-shaped’ curve, leading to issues in the identification of PLC stages.

The plot above shows the variation of Total Sales (y-axis) vs Age (x-axis). The data points are colored based on the PLC life stage identified: Green for “Growth Stage”, Blue for “Maturity Stage” and Red for “Decline Stage”.

Method 2:

Identification of PLC stages by analyzing the variation in cumulative sell through rates with age of a product (Logarithmic model).

Steps followed:

Calculated cumulative sell through rate across age at a category level.
A curve between age and cumulative sell through rate was fitted using a log linear model.
Using inflexion points of the fitted curve cut-off for different phases of product life cycle is obtained.

Drawbacks:

Visual inspection of the fitted curve does not reveal any PLC stages.
This method could not capture the trend as accurately as the log-growth models.

Application of PLC stages in Demand Prediction

After identifying the different PLC phases for each category, this information can be used directly to determine when promotions need to be provided to sustain product sales. It can also be incorporated into a model as an independent categorical variable to understand the impact of the different PLC phases on predicting demand.

In the context of this paper, we used the PLC phases identified as a categorical variable in the price elasticity model to understand the effect of each phase separately. The process was as follows:

The final sales prediction model had data aggregated at a cluster and sales week level. PLC phase information was added to the sales forecasting model by classifying each week in the cluster-week data into “Growth”, “Maturity” or “Decline”, based on the average age of the products in that cluster and week.

This PLC classification variable was treated as a factor variable so that we can obtain coefficients for each PLC stage.

The modeling equation obtained was:

In the above equation, “PLC_Phase” represents the PLC classification variable. The output of the regression exercise gave beta coefficients for the PLC stages “Growth” and “Maturity” with respect to “Decline”.

The “Growth” and “Maturity” coefficients were then treated such that they were always positive. This was because “Growth” and “Maturity” coefficients were obtained w.r.t. “Decline” and since “Decline” had a factor of 1, the other 2 had to be greater than 1.

The treated coefficients obtained for each cluster were used in the simulation tool in the following manner (more details given in tool documentation):

If there is a transition from “Growth” to “Maturity” stages in a product’s life cycle – then the PLC factor multiplied to sales is (“Maturity” coefficient / “Growth” coefficient).

If there is a transition from “Maturity” to “Decline” stages in a product’s life cycle – then the PLC factor multiplied to sales is (“Decline” coefficient / “Maturity” coefficient).

If there is no transition of stages in a product’s life cycle, then PLC factor is 1.

Conclusion

The method described in this paper enables identification of PLC stages for the apparel industry and demand prediction for old and new products. This is a generalized method and can be used for different industries as well, where a product may exhibit 4 or 5 stages of life cycle.

One of the drawbacks of product life cycle is that it is not always a reliable indicator of the true lifespan of a product and adhering to the concept may lead to mistakes. For example, a dip in sales during the growth stage can be temporary instead of a sign the product is reaching maturity. If the dip causes a company to reduce its promotional efforts too quickly, it may limit the product’s growth and prevent it from becoming a true success.

Also, if there are a lot of promotional activities or discount are applied, then it’s difficult to identify the true-life cycle.

References

Below are links to certain websites referred to:

Top 5 Challenges- Implementing Industry 4.0!

Posted on March 14, 2022March 1, 2023 by affine

Well, necessity is the mother of invention, and this is undeniably true for technological innovations, precisely Industry 4.0 solutions. Evolution is the result of real hardship. In the case of manufacturing, the movement of Industry 4.0 is caused by volatile market demands for better and quicker production techniques, shrinking margins, and intense contention among enterprises that is impossible without smart technology.

Smart Manufacturing will be based on digitization and Industry 4.0 and large enterprises are inclining towards digital innovation. However, SME’s and MSMEs are still struggling with several challenges to adopting the Digital Transformation and Industry 4.0 initiatives. These obstacles may dissuade some manufacturing companies from adopting these technologies, causing them to fall behind their peers.

The Top Five Challenges!

SMEs and MSMEs still experience difficulties achieving Industry 4.0 goals, although smart manufacturing is often associated with Industry 4.0 and digital transformation. Here are the five challenges: