Sustainability Management imperatives are taking more prominence due to environmental concerns and will continue to be in focus in the coming days with increased awareness on the subject from governments, business & society at large.

Businesses have a significant role to play & sustainable practices are considered a corporate responsibility with leading companies taking sincere initiatives to measure and minimize environmentally unfriendly operations and to address various stakeholders’ (internal & external) priorities:

• Brand reputation – As companies introduce sustainable methods in their manufacturing systems, their reputation among investors, stakeholders and consumers improves
• Operational efficiency – Reduced usage of energy and other resources leads to reduction in costs which eventually leads to improved operational efficiency
• Societal impact – Creating a strong image with sustainable methods sends out a strong message to the society and this in return creates a positive impact on consumers’ minds
• Transparency from Business Partners – Companies are expecting from their partners Sustainable business engagement & moving away from ties with partners at risk
• Shareholders – Beginning to use ESG scores as one of the criteria to make investment decisions
• Regulatory Requirements: These are becoming more stringent
• Customer perspectives – Becoming more conscious about sustainable Products & Practices & do not like Greenwashing

The manufacturing industry is setting ambitious and sustainable targets for improving the planet with meaningful environmental changes. Intricately connected with this are corresponding opportunities for technology companies. According to recent surveys, the sustainability market size for green technologies is expected to grow significantly with a Compound Annual Growth Rate (CAGR) of >25%, with market size expanding to almost $45b+ by 2028. The management of greenhouse gas emissions, energy consumption, waste management, green product development, and water conservation—is seen no longer as a cost but as a critical value differentiator.

Challenges faced by manufacturing companies:

Operational Inefficiencies

• Manual Method of collecting sustainability data & transformations leading into errors
• Auditability/ Traceability issues due to manual methods of operation
• Deficiencies in ability to do basic analysis on the data

Newer Asks:

• Suppliers following the organization’s environmental standards – Whether the suppliers are complying with the organization’s green standards with the components that they are providing
• Lifecycle assessment of products – Assessing the sustainability footprint of the product (e.g., from cradle to grave)
• Climate Risk Analysis (e.g., while setting up new units – Challenges of setting up new factories as per climate standards and how these setups are going to affect the environment)
• Evolving regulatory frameworks & higher reporting frequencies

These challenges need to be intrinsically addressed by a digital solution to improve efficiencies, enable auditability, reduce non-compliances & make the enterprise future-ready.

Conclusion

When it comes to sustainability in manufacturing companies, a remarkable change is afoot, resulting in more significant thinking—especially on the factory floor and value chain partners. Manufacturers prepared to adopt the change will find opportunities for innovation—with sustainability targets inspiring green design, manufacturing, sourcing and novel technology applications. The time has come for data platform solutions for ESG and other newer asks that are emerging.

Author:

Sandip Mitra,
Business Consulting Group, 91¶¶Ňő

The post Sustainability Imperatives in Manufacturing Companies appeared first on 91¶¶Ňő.

AI-ML concepts can be applied to various components of the supply chain – demand forecast, logistics &transportation, inventory management, production planning, and procurement.

In this article, we will discuss one of the areas of the supply chain, which is an elephant in the room. ĚýHowever, it can be easily addressed by Machine Learning. It is an essential component of the supply chain; however, many industries are still struggling with it or using rudimentary methods. If performed accurately, many issues which planners face in the later stages can be tackled effectively. This value is used for production planning, inventory management, procurement, and eventually logistics and transportation. It is Demand Forecasting.

Demand Forecasting Ěý

Demand forecasting isĚýthe process of using predictive analysis of historical data to estimate and predict customers’ future demand for a product or service. Statistical methods of performing forecasting are Simple Moving Average, Holt’s Winter Method, Croston Method, and Syntetos-Boylan Approximation (SBA). Croston and SBA are majorly used for intermittent forecasting.
The Machine Learning approach to forecasting involves –

• Time Series Analysis
• Regression Modelling
• Deep Learning Modelling

In this blog, we will focus on Machine Learning Approach for Demand Forecasting. What all data pre-processing steps can be employed? Which models can be built under each approach, their benefits and limitations? How to perform multivariate forecasting under these approaches? What KPI metrices can be used? And how can the model check and autocorrect its output?

Data Pre-processing Ěý

It is an essential approach in any ML project. However, it gets tricky when we are dealing with Time Series Data. We need to pre-process the data ensuring that trend and seasonality are not getting impacted. Following pre-processing can be performed –

• Anomaly Detection & Correction – It isĚýthe identification of rare events, items, or observations that are suspicious because they differ significantly from standard behaviors or patterns. Anomaly in Time-Series data can be tricky as trend and seasonality must be considered while finding anomalies.
• Breakpoint Analysis – Breakpoint analysis isĚýa way of looking at demand data to determine when there are shifts or breaks in demand levels. This can eliminate previous historical data which are no more significant.
• Seasonality and Trend Calculation – Seasonality and trend can be calculated using seasonal_decompose. This gives an idea about the overall trend and any seasonality.

Time Series Analysis Ěý

ARIMA is a way to perform Time Series Analysis. There are few variants of ARIMA –

• ARIMAX – Standard ARIMA with an additional feature to train using exogenous factors.
• SARIMA – Seasonal ARIMA
• SARIMAX – Seasonal ARIMA with exogenous factors
• Auto ARIMA – Automatically tries to find best fitted ARIMA considering seasonality and exogenous factors

Pros Ěý

• Accuracy is very high
• Easy to implement
• Can generate a model with few data points. In a case, a minimum of 12 data points were used to generate this model

Cons Ěý

• In a few cases, it fails to identify trends or seasonality
• In some cases, it identifies the incorrect trends and extrapolates the data exponentially

Regression Modelling ĚýĚý

Regression models like Random Forest & XGBoost can also be used to forecast demand for the future. In our case, XGBoost has outperformed Random Forest. 24 data points are used as input and the 25th data points as prediction; the entire data set must be broken down into sets of 24+1 values. A minimum number of inputs in the training set should be 24 (if it is monthly data) to identify seasonality.

Pros Ěý

• High accuracy
• It can automatically identify seasonality and trends

Cons

• Needs more data points at least 30 (in this case) to perform this regression.

Deep Learning Modelling Ěý

ANN and LSTM models can be used for forecasting. LSTM gives better results as compared to ANN. In LSTM, the training set should have 24 inputs to predict next month’s output.

Pros ĚýĚý

• Its accuracy is better than XGBoost Regression

Cons Ěý

• It needs several data points

Causal Factor Integration Ěý

We have added causal factors which have a strong correlation with demand data. These causal factors are – Holiday, Discounts Data, Promotions, and Price Change Points.

Auto-Correction Algorithm Ěý

Once the final forecast is generated, the algorithm performs a sanity check of the output. It calculates trends and standard deviation of the output to check if the algorithm is fit properly. If there is any issue in the output, the model calculates the forecast using Statistical Method – Holt’s Winter Algorithm.

Conclusion:ĚýWe have used MAPE (Mean Absolute Percentage Error) as our KPI. We are adopting these principles can achieve accuracy up to 85% and for products, it is above 90%.

Author:

Shrestha Priya
Lead Consultant

The post Machine Learning Models for Demand Forecasting appeared first on 91¶¶Ňő.

Most of the manufacturing companies are driving rapid digital transformation. This has given a very positive direction to manufacturers. In this scenario, Manufacturing Execution System (MES) has become an integrated part by helping companies transform digitally. Manufacturing Execution System serves as a single platform for the entire factory. MES comes with different modules like Production, Quality, Maintenance, etc. The advantage of having different modules is that it gives unique value to the customer, further helping manufacturers to achieve the goal of digital transformation.

This module allows Users to optimize production quality through strategic planning and monitoring and Maintenance Personnel to maintain critical assets based on real-time data. By keeping it modular, deployment of MES can be customized as per requirement and executed easily in phases. Further, it lets the company test the waters with low investment, thereby realizing ROI within months from the deployment of the first phase of the project, while the other modules are still under development. Our experience shows that the customers are more focused towards deploying the Production module. While this might work for few industries, having a healthy equipment is equally important to drive the efficiency in any manufacturing set up. Over 75% of the industries are taking more than 20 months to breakeven (BEP) and one of the reasons for the delay is low prioritization of maintenance module.

We are seeing a shift in the approach of deployment post-Covid pandemic. Now industries are inclined to give equal importance towards maintenance related activities. We are seeing many industries are specifically asking for Total Productive Maintenance (TPM). Basically, this shift is because of the long pause of all the equipment’s due to pandemic. After the relaxation of the lockdown, most of the companies are facing challenges in using the assets efficiently. Hence Maintenance in MES has become a driving force for many industries to be flexible and at the same time effective and efficient.

Once the production started post lockdown, many challenges were faced by companies- few of them are equipment health status, estimating the effort in identifying the problem and fixing it. Usually, most of the effort invested by the maintenance team member was on what the problem is and what tool to be used to fix it. Few companies also have an issue on the prioritization of the problem. This challenge is leading to the realization of maintenance module within MES. Value of Maintenance Module is taking heights due to many reasons, listed below are few:

• Clear visibility on the downtime with reason code: Line operators will have a provision to register the downtime with small description along with specific reason code. This helps maintenance personnel understand and get prepared to solve the problem before coming to line. This has helped industries to minimize the response time leading to improved productivity. Maintenance personnel will also be able to put details of the defect steps taken which will help in keeping the record of health of the equipment.
• Real-time visibility of the downtime: Andon System will help the maintenance team to easily identify the station which is under breakdown. Further, the supervisor will also know in real time the time spent to make the station up and running.
• Analysis on the equipment downtime to show trend: MES can give out the data for each equipment, data like Mean Time to Repair (MTTR), Mean Time Between Failure (MTBF), Asset utilization for each equipment. This data will help to analyze the severity and will also lead to proactive approach in solving the repeated issues.
• Predictive Maintenance: Moving towards the predictive maintenance has become very important. MES provides predictive maintenance capability, when configured it can monitor machines and trigger alerts so that work is only done when necessary.
• Autonomous Maintenance: A very critical step towards achieving Total Productive maintenance (TPM). It’s an activity which operator does before starting the production in that station/equipment. Operator will go through each checkpoint of the checklist to check if values are within limits. This ensures the equipment availability thereby helping to reduce the downtime within the shifts. MES provides this functionality in more simplified way, which can be traced in real time.

Today’s fast-paced manufacturing environment demands complete, accurate and accessible data from the shop floor at the equipment level. Maintenance module within MES system will provide effective monitoring and synchronization throughout the factory to achieve high productivity and ensure responsiveness to market demand. To know more on the product please fill free to connect with us, we can guide you towards your digital transformation.

Author:

Vishal S. Arya
Principal Consultant
BCG, 91¶¶Ňő

Nishant Jain
Associate Consultant

The post Leveraging MES Maintenance Module for higher productivity appeared first on 91¶¶Ňő.

Imagine you are buying a few bags of potato chips to snack on. One bag has fewer chips than expected; the chips do not match the weight mentioned on the package. You will be disappointed. You will either complain to the manufacturer or simply stop trusting the brand. Worse, you could take to social media to express your anger, affecting the broader reputation of the brand. If there are a few extra chips in the bag, you will not mind it. But those extra chips can quickly add up to massive losses for the manufacturer. Either way, not controlling the grammage of products leads to unwelcome outcomes. This is not a new problem for the F&B industry. But new solutions are now available to contain and practically eliminate-the problem.

Variances in packaged food products is expected. The manufacturer of a 78 g bag of potato chips can perhaps tolerate a variance of +/- 2 grams. But by identifying the actual variance and tracking trends, upstream and downstream systems can be improved, costs can be lowered, compliance norms can be met, waste reduced and customers kept happy by offering a more consistent product. However, usually individual checkweigher or multi head weighers and baggers are used at the packaging stage to accept/reject products for the market—when it can be too late. To overcome this, the science of Extra Grammage (EGA) Optimization needs to be mastered.

The weight of packaged food products is a tricky affair. Something as simple as extra moisture in the chips or extra oil can increase the weight. The thickness of input materials (for example, potato slices, masala or salt deposition etc. ) or even the temperature of the cooking oil or frying time can cause higher or lower weights, or the vibratory nature of the manufacturing equipment can lead to variances.ĚýĚýĚý

Often the selling price of a product cannot be changed. In such cases the manufacturer must resort to controlling the weight.

EGA optimization is a latest Industry 4.0 solution integrates all weigher “Machine Data” & “Process Parameters” and where continuously monitors the trend of Underweight & Overweight Rejections and Extra Give Away of Materials.Ěý Then Auto-Insights are generated basis factual data at frequent intervals, minute level & KPIs are measured EGA%, rejections% etc. and any deviations beyond acceptable limits are alerted to respective operators to fix the production parameters causing the variance immediately, minimizing the volume of rejected products at the end of the line. The solution works for both linear and vertical manufacturing and packaging processes.Ěý

Vertical manufacturing and packaging processes need to be dealt with a little differently from linear processes (example: potato chips versus cookies). In a vertical process, the product drop happens to multiple buckets during production. The weight may vary because of product breakage or due to residual recipe material, accumulated in the assembly line, sticking to the product.

In current practice, many CPG companies have not integrated their Machine data. Right set of Analytics not used to create meaningful alerts for operators and to improve output quality. Our implementation of this solution in a large food production plant has shown significant results, that even a 1% impact is saving thousands of dollars. But this is what production plant managers will like to hear the most: ROI in our implementation was less than six months. This is a high-impact, quick-return intervention that every manufacturer must consider.

Author:

Siddaraju G,
Senior Principal Consultant,
Business Consulting Group,
91¶¶Ňő

The post A “must-have” packing weight variation control technique appeared first on 91¶¶Ňő.

Vision-based computing or computer vision, aka machine vision, has grown tremendously in the last few years. The key reasons for the growth lie in the availability of affordable hardware, the growth in data sets and maturity in the science of Artificial Intelligence (AI) and Machine Learning (ML). With the COVID-19 pandemic accelerating digital adoption, vision-based analytics is bound to find wide-spread applications in several industries including manufacturing. The extraordinary drive for work place safety, the need for automation to improve productivity, and access to better quality will be the key drivers for the growth in vision-based computing. One indicator of its bullish future is reflected in a that found AI in computer vision market size would reach $144.46B by 2028 from $7.04B in 2020 (a CAGR of 45.64% from 2021 to 2028). Manufacturers who do not study use cases for vision-based computing in their organizations could lose out on the promise of generating business value and creating competitive differentiation.

An area where vision-based technology can be applied with great success is quality inspection. Take the case of manufacturing cookies in the food and beverage industry. Operators are deployed on production lines to check the quality of the output. They must examine each cookie for conformance to color, texture, shape, size, nut coverage and several other factors and benchmarks. These are important in an industry where consumers assess products visually. Human bias – even human fatigue or distraction — can affect the inspection process. Options such as interval sampling of products in a lab can improve the process but they too leave the gates open to flawed products reaching the market.

The solution is in using AI and ML-based algorithms to examine each cookie. Cameras scan the live production line before packaging, accurately picking out cookies that do not meet the pass range for quality. Even when samples are examined in an off-line mode in a lab, vision-based computing removes subjective biases.

Once a cookie on a production line is identified for non-conformance, a simple drop mechanism removes the cookie from reaching the packaging process.

Vision-based computing has several applications in factory environments. Most manufacturing plants already have several CCTV cameras used to monitor activity, human safety and business security. The number of cameras on a production floor can number anywhere between 30 and 100 – a number impossible for humans to continuously scan for aberrations. This is why these cameras are usually used after the fact, to investigate incidents.

However, the camera feeds can be analyzed in real time based on EHS guidelines and even used for automated risk audits. This means the investment already made in video monitoring can go the extra mile by identifying hazards in different work areas such as slippery floors, accumulation of materials, obstacles in pathways, missing or inadvertent obstructions around firefighting equipment, violation of safety norms by employees (such as not wearing hard hats or fall arrestors), unauthorized entry of personnel in restricted areas, and the entry and exit of vehicles in plants, warehouses, collection points and so on.

Not only can vision-based computing prevent issues or generate real-time alerts, but the data can also improve processes. For example, supervisors can immediately know how many vehicles are parked in a certain area, what type of vehicles are parked and for how long, the time taken to offload material from any vehicle—each data point allowing the supervisor to take quick and accurate decisions.

Vision-based computing is here to stay. And the race to adopt it has just begun.

Author:

Siddaraju G,
Senior Principal Consultant,
Business Consulting Group,
91¶¶Ňő

The post Enhancing product quality and plant safety with vision-based computing appeared first on 91¶¶Ňő.

It is never easy to stay on top of the factors that determine product development. These factors range from gathering information on what customers used in the past, their levels of satisfaction with existing products, how they can be persuaded to change their minds with a change in packaging or display, the impact of word-of-mouth on decision-making, competitive action, regulatory requirements, the intellectual property landscape, patents, available technology and ingredients, recipe suppliers…the list is long and daunting. This is where digital platforms come into play, supporting the effort to capture knowledge and let it flow into new product development.

There is an additional level of complexity involved. CPG organizations need to address a variety of time frames to get new products before consumers. Reactive changes to existing products may be required in the short-term. Some changes, such as shifting to sustainable ingredients, may be required in the mid-term. And launching a fundamentally new product, replacing existing products, may be required in the long-term. Also, long term sustainability and growth of the business would require the CPG organization to move quickly, focusing on product IP creation and raising barriers to entry. The question hounding every CEO pursuing new product development is, “Am I executing innovation and NPD process using the correct systems and methodologies, so that I can have the right products available just when required, to be used as the winning ammunition on the battlefield of the market?”Ěý

To answer that question with confidence, the CEO needs to have a digital platform that takes products from concept to launch. 91¶¶Ňő’s PLM based Digital Platform fills the gap. The platform is enabled with stage-gate driven accelerators and templates. It integrates business functions in the organization such as engineering, testing, packaging, graphics, commercial, marketing and legal, and enables them to collaboratively contribute to the innovation process at various stages. The platform, through its interactive dashboard, provides visibility into portfolio, programs and projects, captures who is working on what activity of which project, and the stage at which each project is. What the digital platform delivers is a system of urgency, flagging delays, identifying bottlenecks, sensing what is happening in the market and bringing it to the notice of the organization, and enforcing collaboration.

The need for such a platform has been felt for long. But the urgency has grown in recent years as end-customers are going digital. They now have the tools to look for customized, personalized and improved products. In response, CPG organizations need to work faster, conceptualize products overnight, come up with prototypes, test them and get them into the market before competition does. The only way to do this effectively is with a PLM based digital platform, such as the one provided by 91¶¶Ňő, specifically designed to manage new product development and innovation.

Author:

Debjit Banerjee
VP, Business Consulting,
91¶¶Ňő

The post Faster, higher, stronger: How digital platforms can win the race for new products innovation appeared first on 91¶¶Ňő.

Business Intelligence makes data driven decision-making possible at large scale. Using self-service analytics capability, the business leaders can tweak different parameters and simulate different scenarios before decision making. With this, they are better equipped to draw useful insights and make better informed decisions with higher probability of success.

Importance of Business Intelligence in manufacturing

Availability of technologies to source data at low cost from equipment and IT applications makes manufacturing, one of the most suited industry for applying BI. Data is already being measured and recorded in real-time using IoT devices, PLC, SCADA, MES, ERP, CRM and other platforms. Most of the times, this data collected is used only for general operations purpose. Getting insights from data is an area most organizations have not really looked at. Processing data efficiently can unlock a lot of value and can lead to great tangible monetary benefits. For example, in FMCG, processing quality data using advanced analytics and re-aligning process in real-time can lead to big savings by avoiding potential rejections and rework.

Business Intelligence tools convert text and numbers into images which are more readable and comprehendible.

Here are few applications of BI tools in manufacturing –

Inventory Management – Without complete visibility of products on hand, it may be challenging to minimize inventory costs without compromising service levels. BI dashboards take various data points like change in supply and demand, product obsolescence and other factors into consideration and help the manufacturers optimize safety stock, assess reordering point, control inventory costs, avoid out-of-stocks and improve the service levels.

Supply Chain Management – BI tools can help the supply chain managers get more insight into routes, carriers, wait times, freight delivery status and payments. It helps in finding more opportunities and do better negotiations. Tracking KPIs like OTIF helps identify delivery issues in real-time and address them.

When combined with Advanced Analytics, BI can solve even more complex problems like the following –

1. ĚýEnergy Analytics – Energy cost is a major chunk of conversion cost in any manufacturing unit. There is a need to optimize performance of utilities like boiler, chiller and other energy intensive units. In absence of insights, the plant operators are more focused on meeting production targets than on optimization. 91¶¶Ňő has developed an advanced analytics-based solution on PTC Thingworx to address this challenge. The solution predicts optimum efficiency and prescribes operating parameters to achieve that. It leads to a 5-20% savings in energy costs.
2. Product Quality Analytics – 91¶¶Ňő helped a global FMCG giant to collect data from existing PLC & SCADA systems, apply artificial intelligence, make data models and show meaningful KPIs in real-time on responsive dashboards using which the operators could control the quality of products being manufactured. This has led to quality yield improvements of 1-2%.

Recommendations –
Now before you decide on your business intelligence tool, here are some points to keep in mind –

• Involve both Business management and IT team in developing BI tools
• Decide right KPIs from start
• Build database, don’t use data directly from different sources because different sources may have different benchmarks
• Select BI tools that allow customized report delivery and scheduling
• Develop BI tools which can work on different devices including smartphones to make it more useful
• Don’t delay the development thinking you don’t have enough data. Most companies already have enough data to start with
• In conclusion, BI tools are a must for manufacturing organizations today. It makes the whole process faster, optimized and transparent. The investments made are lesser compared to the long-term gains.

Author:

Nishant Jain
Associate Consultant, Business Consulting Group
91¶¶Ňő

Reference:

1. Energy Advisor from 91¶¶Ňő. Leveraging IOT and Data Analytics for Efficient and Greener Manufacturing

The post Let your operations take off using the wings of Business Intelligence appeared first on 91¶¶Ňő.

One thing that leading manufacturing companies did during their low demand days is that they invested in preparing for the future. During the economic recession of 2008-2009, forward looking manufacturing companies invested substantially in training and development of their employee’s skills. A major portion of the workforce time was invested in driving continuous improvement projects, conducting trials, process innovations, etc., that were typically difficult to manage during business as usual scenario. Clearly, these companies benefited from their investments and were able to scale up quickly compared to competitors. The importance and role of operational excellence tools like Lean Manufacturing, Six Sigma, TPM and TQM in enhancing the maturity of the manufacturing sector can neither be questioned nor overlooked. Over the past few years, adoption of Information Technology in the manufacturing sector has increased significantly and has driven the continuous improvement journey.

Again, forward-looking companies would like to use the current scenario to accelerate their Digital Manufacturing journey—but the business situation doesn’t support it. While a few companies have been able to manage and continue the digital transformation journey, most manufacturers have put their plans on hold on account of financial constraints. As markets are slowly opening across the globe, normalcy will take time to return Most CXOs believe and understand the importance of investment in IT at this point of time but uncertainties in the economic scenario, cash flow challenges and short term returns in IT investments make them inclined to favor a delay in their Digital Manufacturing plans.

91¶¶Ňő understands the importance of technology in a manufacturing setup and has accordingly modified solutions and offerings to address some of the challenges of a CXO. In this blog, we talk about our 4D-MES framework that can be leveraged by companies to manage their ongoing Manufacturing Execution System rollouts or start the MES journey that has been put on hold. The 4D MES Framework allows manufacturers to extract maximum value at optimal cost from their MES landscape. The framework optimizes cost in the current program and generates additional value from the existing solution without new capex or major investments. The dimensions of the 4D framework are as follows:

• Domain Leadership – By pre-building industry templates leveraging our access to manufacturing plants of our parent company ITC Limited and utilizing the knowledge of domain experts, we reduce MES implementation lead time by almost 30% and help realize return on investment in a much shorter duration in addition to implementing industry best practices.
• Depth of Technology Expertise – Our Technology Assessment Framework is used for improving MES components utilization and to extract maximum value from the existing MES landscape. Automation toolkit (DevOps & Test Automation) helps automate the end-to-end MES lifecycle resulting in development and deployment effort optimization and business disruption reduction.
• Delivery Efficiency – The current team structure is designed and equipped to work remotely at 100 percent efficiency, helping us design, develop, test and deploy solutions even in a scenario where mobility is severely restricted. The team structure leverages the concept of shared resources to improve resource utilization and optimize overall development cost.
• Disruptive Business Model – The proprietary assessment framework helps quantify benefits from an MES implementation and enables us to work on commercials linked to an outcome-based model. Subscription-based pricing linked to the number of users and factories, variable bandwidth-based support engagements and commercials linked to outcomes help manufacturers reduce the overall initial investment and risks associated with a milestone-based payment model.

Recent surveys and articles by leading consulting companies highlight that technology will play a vital role for manufacturing and supply chains in the post COVID-19 era. CXOs believe in its importance and commit to its adoption but, with few exceptions, also agree that their digital journey is not well funded. The implication is clear: organizations should leverage the innovative and compelling business models and differentiated solutions being offered by technology companies to accelerate their digital manufacturing journey and prepare for the post COVID-19 era.

The post Manufacturing Execution System- During COVID-19 and Beyond appeared first on 91¶¶Ňő.

Today’s technology can change that. Using Robotic Process Automation (RPA), Cognitive Intelligence, Machine Learning and Natural Language Processing (NLP), etc., it can turn what used to be a frustrating experience into a stress-free – even delightful – interaction. The real reason why these wonderful experiences are difficult to come by is because IT systems designers are out of touch with end users. They don’t know how to go about using technology to redesign processes and meet user expectations.

Over the last decade, there has been dramatic change in technology. Today, organizations can use chatbots over mobiles, instant messengers and websites to deliver multi-lingual voice and text support. Next Gen technology can accurately handle customer queries and arrive at precise decisions using massive knowledge banks, Artificial Intelligence (AI), Big Data Analytics and even the Internet of Things (IoT) and Blockchain! In fact, now is the time for organizations to place the power of self-healing and self-service in the hands of users. Using these technologies and methodologies, it is possible for users to reduce resolution and response wait times to minutes instead of days, sending the time when “raising a ticket” was the norm into history.

While re-casting processes and replacing technologies, it helps to bear in mind the three different categories of end users we have today:

The Digital Native:

A person who has grown up in the era of ubiquitous technology, popularly known as the millennial and the centennial generation, for whom the technical world poses no challenge. These are the people who want the best-in-class service.

The Digital Immigrant:

This is a person born before the digital era, popularly known as the Xennial generation, which has had an analogue upbringing but has learnt the digital ropes. They are making the effort to feel at home with technology but they want simple-to-use technology.

The Digital Handicap:

This is the “old school” and digital has not been their ally…yet. It requires patience and perseverance to make sure this user can work his/her way through technical problems. For these users, customer service needs to be 100% on point since they stand to lose the most if their issues do not get addressed properly.

The type of service used depends heavily on which of the above three categories the user falls in. This brings us to the key question: If users have evolved over the last decade, why are we still offering the same services to everybody, as though there is no distinction in the way these users approach their digital world?

The answer lies in the mind-set of service providers. It is the service provider that needs to refresh the technology that forms the backbone of these services and which intuitively adjusts to end user needs.

What are your thoughts on this? Why have IT teams been slow to change? Why have they been reluctant to keep up with the shifts in consumer behaviour as well as expectations? Stay tuned to this blog to know more!

Author:

Sujoy Chatterjee is the Vice President @ ITC INFOTECH. In his current role he is responsible to incubate new technology, alliances and build solutions in the IT Infrastructure space. He drives End User Computing services to global customers.

With his 25+ years of IT Industry experience, he helps organizations to re-look at the way the end user services are delivered and to transform these services to a user experience led delivery.

The post The Changing Dispositions of End Users appeared first on 91¶¶Ňő.