Perspectives on Various Issues of Interest

Capacity Planning Using Emerging Technologies

Capacity planning involves ensuring that a business has the resources it needs to meet current and future demand. Emerging technologies are transforming capacity planning by providing more accurate forecasts, optimizing resource allocation, and enhancing operational efficiency.

Artificial Intelligence (AI) and Machine Learning (ML) algorithms help with capacity planning by aiding more accurate demand forecasting. Organizations can analyze historical data, market trends, and external factors to predict future demand accurately. This allows companies to plan capacity based on realistic projections. Using optimization algorithms, machine Learning models optimize resource allocation, production schedules, and staffing levels by identifying patterns and predicting bottlenecks. Amazon uses AI-driven demand forecasting to optimize inventory levels and warehouse capacity, ensuring efficient order fulfillment.

Internet of Things (IoT) provide insights into current capacity and performance by enabling real-time monitoring whereby real-time data from equipment and production lines are collected. Siemens utilizes IoT sensors and predictive analytics for proactive maintenance of industrial equipment, minimizing downtime and optimizing production capacity.

Blockchain enables transparency and traceability by providing a secure, transparent ledger for tracking the movement of goods and resources across the supply chain, improving capacity planning accuracy. Smart contracts automate and enforce capacity-related agreements between suppliers and manufacturers, ensuring timely delivery and resource availability. Walmart implements blockchain for tracking produce from farm to shelf, enhancing transparency and efficiency in capacity planning.

Cloud Computing platforms offer scalable infrastructure that can be adjusted based on demand, providing flexible capacity planning. Cloud-based tools facilitate collaboration across different departments and geographies, enhancing coordination in capacity planning. Netflix uses cloud computing to dynamically scale its IT infrastructure based on user demand, ensuring seamless streaming experiences.

Advanced Analytics and Big Data tools process large datasets to uncover trends and insights that inform capacity planning decisions. Big data enables companies to simulate various scenarios and their impact on capacity, helping in strategic decision-making. Procter & Gamble (P&G) leverages advanced analytics to optimize production schedules and inventory management, ensuring efficient use of capacity.

Digital Twins create virtual replicas of physical assets and processes, allowing companies to simulate different scenarios and optimize capacity planning. This enables real-time monitoring where digital twins continuously updates the digital model with real-time data, providing a dynamic view of capacity and performance. General Electric (GE) uses digital twins to optimize the performance and capacity of their wind turbines, enhancing energy production efficiency.

Robotic Process Automation (RPA) automates repetitive tasks in capacity planning, such as data entry and report generation, freeing up human resources for more strategic activities. This helps with reducing the risk of human error in capacity planning processes, improving accuracy and efficiency. DHL employs RPA to automate warehouse operations and logistics planning, optimizing capacity and improving service delivery.

Augmented Reality (AR) and Virtual Reality (VR) provide immersive training experiences for employees, improving skills and preparedness for managing capacity-related challenges. These tools enhance the visualization of capacity data and planning scenarios, making it easier for decision-makers to understand and act on insights. Boeing uses AR for assembly line workers to improve accuracy and efficiency, optimizing production capacity.

Long Short-Term Memory (LSTM) Networks for Business Intelligence

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) designed to effectively learn and remember long-term dependencies in sequential data. LSTMs address the limitations of traditional RNNs, which struggle with long-term dependencies due to issues like vanishing and exploding gradients. 

Architecture of LSTM Networks

LSTMs have a more complex architecture than standard RNNs. The key component of an LSTM is its memory cell, which maintains information over long periods. Each LSTM cell contains several gates that regulate the flow of information:

1. Forget Gate (𝑓_𝑡): Decides what information to discard from the cell state.
2. Input Gate (𝑖_𝑡): Determines which new information to add to the cell state.
3. Cell State (𝐶_𝑡): Represents the memory of the network.
4. Output Gate (𝑜_𝑡): Decides what information from the cell state to output.

These gates are controlled by sigmoid and tanh activation functions, which output values between 0 and 1 to modulate the information flow.

LSTM Equations:

LSTM Cell Structure

Here's a diagram representing the structure of an LSTM cell:

 

1. Forget Gate: This gate uses the sigmoid function to decide which parts of the cell state 𝐶_𝑡−1 to forget.
2. Input Gate: It controls which values to update with new information. The sigmoid layer decides which values to update, and the tanh layer creates new candidate values ​ to be added to the cell state.
3. Cell State Update: The cell state 𝐶_𝑡 is updated by combining the old cell state (scaled by the forget gate) and the new candidate values (scaled by the input gate).
4. Output Gate: This gate determines the output of the LSTM cell. It first uses a sigmoid function to decide which parts of the cell state to output, then applies the tanh function to scale the cell state before multiplying by the output gate's result.

Applications of LSTMs

LSTMs are widely used in various fields due to their ability to capture long-term dependencies:

• Natural Language Processing (NLP): For tasks like machine translation, text generation, and sentiment analysis.
• Time Series Prediction: Used for forecasting stock prices, weather prediction, and anomaly detection.
• Speech Recognition: LSTMs help in converting speech to text by maintaining the context over long audio sequences.

In the context of operations and supply chain management, LSTM networks are particularly well-suited for time series forecasting due to their ability to learn and retain dependencies over long sequences. Several companies across various industries have successfully implemented LSTM networks for forecasting purposes. Here are some real-world examples:

1. Amazon

Use Case: Demand Forecasting and Inventory Management

Amazon employs LSTM models to predict product demand based on historical sales data, seasonal trends, and promotional activities. These predictions help optimize inventory levels across its vast network of warehouses, reducing stockouts and overstock situations. The accuracy of LSTM in capturing temporal patterns improves Amazon's ability to meet customer demand efficiently.

2. Google

Use Case: Cloud Usage Forecasting

Google Cloud uses LSTM networks to forecast resource usage and manage cloud infrastructure more efficiently. By predicting future demand for computing resources, Google can optimize server allocation and reduce operational costs. The LSTM models consider past usage patterns, workload seasonality, and other relevant factors.

3. Uber

Use Case: Ride Demand Forecasting

Uber utilizes LSTM models to forecast ride demand in different geographical areas and at various times of the day. These predictions help in dynamically adjusting pricing (surge pricing) and ensuring an adequate supply of drivers. By accurately forecasting demand, Uber improves rider experience and driver satisfaction.

4. Netflix

Use Case: Content Demand Forecasting

Netflix uses LSTM networks to predict the demand for different types of content. By analyzing viewing patterns, user preferences, and seasonal trends, Netflix can make informed decisions about content acquisition and production. This helps optimize their content library and personalize recommendations for users.

5. Siemens

Use Case: Predictive Maintenance

Siemens employs LSTM networks for predictive maintenance of industrial equipment. The models analyze sensor data from machinery to predict potential failures and maintenance needs. By forecasting equipment health and performance, Siemens can schedule maintenance proactively, reducing downtime and extending the lifespan of equipment.

6. JD.com

Use Case: Sales Forecasting

JD.com, a major Chinese e-commerce company, uses LSTM models to forecast sales for various product categories. These forecasts inform inventory management and procurement strategies, ensuring that popular items are adequately stocked while minimizing excess inventory. The models consider factors like historical sales data, promotional events, and market trends.

7. Facebook

Use Case: Infrastructure Capacity Planning

Facebook uses LSTM networks to predict server loads and optimize the allocation of computational resources across its data centers. By forecasting demand for different services, Facebook can efficiently manage its infrastructure, ensuring high availability and performance while controlling costs.

8. Walmart

Use Case: Supply Chain Optimization

Walmart employs LSTM models for various aspects of supply chain optimization, including demand forecasting, inventory replenishment, and logistics planning. The models analyze vast amounts of data, including sales history, weather patterns, and economic indicators, to improve the accuracy of forecasts and enhance supply chain efficiency.

Key Benefits of Using LSTM for Forecasting

• Capturing Long-Term Dependencies: LSTM networks excel at understanding long-term dependencies in time series data, making them ideal for forecasting tasks that require consideration of historical patterns.
• Handling Non-Linear Relationships: LSTMs can model complex, non-linear relationships in data, providing more accurate predictions compared to traditional statistical methods.
• Adaptability: LSTM models can be trained and fine-tuned to adapt to various forecasting needs across different industries and use cases.

These examples illustrate the versatility and effectiveness of LSTM networks in forecasting applications across diverse industries, enabling companies to make data-driven decisions and optimize their operations.

Handling computational complexity, data requirements, and interpretability 

To handle computational complexity, it would be necessary to optimize the model architecture. Depending on the context, simple versions of LSTMs (e.g., Vanilla LSTMs, Gated Recurrent Units) might suffice and they are less computationally intensive. One could start with few layers and then add complexity as needed. Dropout regularization can help in preventing overfitting as well as computational load during training. The models used for LSTM can be compressed using techniques such as quantization (i.e., reducing the model’s precision from floating points to fixed point representation) and pruning (i.e., removing connections in the network that are not significant).

While reduction in complexity by optimizing model architecture and model compression will be helpful, it is still going to be important to deploy efficient hardware and software (e.g., high performance computing that utilize Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) and facilitate parallel processing. Firms can also take advantage of distributed computing frameworks (e.g., Apache Spark, TensorFlow’s distributed computing) that allows for models to be trained across multiple nodes.

For managing data requirements, steps such as normalization of the data, using synthetic data to augment training set, integrating data from external sources that might be relevant for forecasting, using batch processing and mini-batch gradient descent (specifically, to effectively deal with large datasets), and dimensionality reduction (e.g., using Principal Component Analysis to reduce the number of features) can go a long way.

To improve interpretability, one could implement attention mechanisms within the LSTM architecture to highlight which parts of the input sequence the model is focusing on during prediction or could use saliency maps to visualize the importance of different input features and time steps in the prediction process. There are also some methods such as help in understanding of each feature to LSTMs prediction (e.g., SHAP – Shapley Additive Explanation) and to explain individual predictions by approximating the LSTM model with a simpler interpretable model locally (e.g., LIME - Local Interpretable Model-agnostic Explanations). Sometimes it is also helpful to integrate multiple models, e.g., using LSTM + Decision Trees or LSTM + Linear Models so as to have clarity on the predictions.

As is always the case, proper documentation and reporting protocols can ensure that the knowledge gained by implementing these tools are preserved and enhanced as a company continues the journey of using these tools.

Artificial Intelligence driven Supply Chain Planning Algorithms

AI-driven supply chain planning leverages complex algorithms to enhance efficiency, accuracy, and responsiveness across various supply chain functions. Organizations need to not only follow systematic steps to achieve performance outcomes, but also be cognizant of factors that present challenges in reaping associated benefits. 

The foundation of AI-driven supply chain planning lies in Data Collection and Integration, i.e., gathering vast amounts of data from diverse sources. Internal data sources include sales history, inventory levels, production rates, order fulfillment data etc. On the other hand, external data sources include market trends, supplier performance, economic indicators, weather conditions, social media sentiments etc. Data is integrated into a centralized system, often facilitated by cloud computing, to create a comprehensive dataset for analysis.

The next step involves Data Preprocessing to ensure quality and consistency before feeding the data into AI algorithms. In this stage the data is cleaned by removing duplicates, correcting errors, and handling missing values. Normalizing helps in scaling the data to a consistent range to improve algorithm performance. Categorical data is often transformed into numerical values using techniques such as one-hot encoding.

Feature engineering involves selecting and transforming relevant data features to enhance the predictive power of the model. Key variables that have significant impact on supply chain performance (e.g., lead times, demand patterns) are identified and new features are derived from existing data, such as calculating average lead times or seasonal demand variations.

After these initial steps are undertaken, Model Selection ensues depending on the specific supply chain task. For instance, demand forecasting calls for time series models (e.g., ARIMA, Prophet), regression models (e.g., linear regression, gradient boosting), and deep learning models (e.g., LSTM networks). Reinforcement learning models are used for inventory optimization where these models learn optimal stocking policies through simulations. Optimization algorithms (such as genetic algorithms, ant colony optimization) and neural networks are used for route optimization so that efficient delivery routes can be identified.

Once the models are selected, using historical data the Training phase enables the algorithm to learn patterns and relationships within the data using historical data, adjusting its parameters to minimize prediction errors and the Validation phase helps in evaluating the model's performance on a separate validation dataset to ensure it generalizes well to new data. 

Once trained, the AI models help with Prediction and Optimization of supply chain operations. These could include such areas a demand forecasting (predicting future sales based on historical data and external factors), inventory management (recommending optimal inventory levels to balance holding costs and service levels), production scheduling (allocating resources and scheduling production runs to meet predicted demand efficiently), logistics and distribution (optimizing delivery routes, shipment schedules, and transportation modes to minimize costs and delivery times) and others.

AI algorithms then enable Real-Time Decision-Making by continuously analyzing incoming data and updating predictions and recommendations. With adaptive learning, the models adapt to new data, improving their accuracy over time. Automated adjustments help the system to automatically adjust supply chain parameters (e.g., reorder points, production schedules) based on real-time insights.

The final step in AI-driven supply chain planning involves a feedback loop for Continuous Improvement. Through performance monitoring KPIs (e.g., order fulfillment rates, inventory turnover) are tracked to measure the effectiveness of AI-driven decisions. Periodically model retraining with new data helps in maintaining and enhancing performance. Running "what-if" scenario analysis prepares a firm for potential disruptions and optimizes contingency plans.

Notwithstanding these step-by-step approach for using AI algorithms for supply chain planning and their associated advantages, many firms fail to reap those benefits. These challenges often stem from several key factors. Many companies struggle with the quality and integration of data from disparate sources within their supply chain. Inconsistent or incomplete data can lead to inaccurate predictions and suboptimal decision-making. For instance, if historical data is incomplete or inconsistent, AI algorithms may produce unreliable forecasts, impacting inventory management and demand planning. As an example, a multinational consumer electronics company implemented AI-driven demand forecasting software to optimize inventory management. The software struggled with integrating data from various retail partners, resulting in inconsistent demand forecasts. Due to this shortcoming the company experienced issues with inventory levels, leading to excess stock in some regions and stockouts in others, impacting profitability and customer satisfaction.

Companies face difficulties in implementing and fine-tuning complex AI models. These algorithms require specialized expertise in machine learning and data science to develop and maintain. Without sufficient internal expertise or resources, companies may struggle to effectively deploy AI-driven solutions and optimize them for their specific supply chain needs. Resistance to change within organizations can hinder the successful adoption of AI-driven supply chain planning. Employees may be reluctant to trust AI recommendations over traditional methods or may lack the necessary training to interpret and act upon AI-generated insights. Without buy-in from key stakeholders and adequate change management strategies, companies may fail to leverage AI's full potential. For example, a global pharmaceutical company adopted AI-powered supply chain optimization tools to streamline logistics and reduce costs; however, middle management and operational teams were resistant to AI recommendations, preferring traditional planning methods. Despite the potential for cost savings and efficiency improvements, the company failed to fully realize the benefits of AI, resulting in missed opportunities for operational optimization.

The upfront costs associated with implementing AI-driven supply chain solutions can be substantial. Companies may struggle to justify these investments without clear, measurable returns on investment (ROI). Moreover, accurately estimating the ROI of AI initiatives can be challenging due to factors such as implementation costs, ongoing maintenance, and the complexity of measuring intangible benefits like improved decision-making.

Integrating AI-driven algorithms with existing legacy systems and processes can be complex and time-consuming. Legacy systems may lack the flexibility or compatibility required to seamlessly integrate with AI solutions, leading to operational disruptions and delays in realizing benefits. For example, a leading automotive manufacturer invested in AI algorithms to optimize production scheduling and supplier management. Integration issues arose when attempting to link AI systems with existing ERP platforms and legacy systems. Delays in data synchronization and decision-making processes affected production timelines and supply chain visibility, impacting overall operational efficiency.

In industries with stringent regulatory requirements or ethical considerations, implementing AI-driven supply chain algorithms may raise concerns about data privacy, algorithm transparency, and compliance with regulations. Companies must navigate these challenges to ensure that AI solutions comply with legal and ethical standards while delivering operational benefits. For example, a global food and beverage conglomerate implemented AI algorithms to optimize its supply chain, including procurement, production planning, and distribution. The AI algorithms used by the company to optimize procurement decisions inadvertently favored suppliers with lower costs, sometimes sourced from regions with questionable labor practices or environmental standards. This raised ethical concerns within the company and among stakeholders, including customers and advocacy groups. Additionally, there were regulatory concerns related to compliance with labor laws, environmental regulations, and fair trade practices in different regions. The ethical and regulatory concerns posed reputation risk since negative publicity and stakeholder backlash affected the company's brand reputation and consumer trust. The company faced compliance issues since regulatory authorities scrutinized the AI algorithms and supply chain practices, leading to potential fines or legal repercussions for non-compliance. The company had to reassess its AI algorithms and supply chain policies to ensure alignment with ethical standards and regulatory requirements. This involved revising procurement criteria, enhancing supplier vetting processes, and implementing transparency measures in supply chain operations.

Overall, addressing these challenges requires a holistic approach that includes improving data quality, investing in AI expertise, fostering a culture of innovation and adaptability, managing costs effectively, ensuring regulatory compliance, and aligning AI initiatives with strategic business objectives. Companies that successfully navigate these challenges can harness the full potential of AI-driven supply chain planning to achieve competitive advantages in today's rapidly evolving market landscape.

Supply Chain Planning with Emerging Technologies

Supply chain planning has become increasingly sophisticated with the advent of new technologies. There are several technologies that are being used to improve planning. 

Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of modern supply chain planning. These technologies enable with demand forecasting by predicting future demand with high accuracy by analyzing historical data, market trends, and external factors. Inventory optimization is facilitated where these technologies help with balancing inventory levels to meet demand without overstocking or stockouts. Predictive maintenance is aided where these technologies monitoring equipment health and predicting failures before they occur to minimize downtime.

Big Data and Analytics are helping in supply chain planning by aiding with data integration to aggregate data from multiple sources (sales, market trends, supplier information) and thereby gain comprehensive insights. Real-Time Analysis enables making data-driven decisions on the fly to quickly respond to market changes. Performance Monitoring aids in tracking key performance indicators (KPIs) and identifying areas for improvement.

Internet of Things (IoT) devices collect real-time data from various points in the supply chain, enabling enhanced visibility for real-time tracking of goods and assets throughout the supply chain, condition monitoring of goods in transit (e.g., temperature, humidity) to ensure quality, and asset management for tracking the location and status of equipment and inventory.

Blockchain technology that offers transparency by creating an immutable ledger of all transactions, ensuring transparency and traceability. The technolgoy also enhances security by preventing tampering and fraud, and creation of smart contracts that automates contract execution when certain conditions are met, improving efficiency.

Robotics and Automation are used for warehouse automation where robots are used for sorting, picking, and packing goods, which increases efficiency and reduces labor costs. Automated Guided Vehicles (AGVs) helps in transporting goods within warehouses and production facilities without human intervention.

Cloud-based solutions that provide scalability that enables easily scaling up or down based on demand. It facilitates collaboration enabling seamless collaboration among supply chain partners through shared platforms and enhances cost efficiency reducing the need for on-premises infrastructure and maintenance.

Advanced Planning Systems (APS) that are designed to optimize production schedules to ensure that manufacturing processes align with demand forecasts and for efficient resource allocation to maximize productivity and minimize costs. APS also helps with scenario Analysis running "what-if" scenarios to plan for various contingencies.

3D printing is revolutionizing supply chains by helping with on-demand production, producing parts and products as needed, reducing the need for large inventories; customization, allowing for the customization of products to meet specific customer requirements; and reducing lead times, speeding up the production process by creating parts directly from digital designs.

Digital twin is a virtual replica of a physical supply chain that allows for simulation and optimization, testing different scenarios and optimizing processes before implementing changes in the real world. It also helps with predictive insights where data from digital twins is used to predict outcomes and improve decision-making and with proactive maintenance by simulating equipment wear and tear to plan maintenance activities proactively.

Finally, Augmented Reality (AR) and Virtual Reality (VR) technologies are used for training, providing immersive training experiences for supply chain workers.It helps with maintenance, assisting technicians with virtual overlays of instructions and equipment details. Digital twins helps with visualization of complex supply chain networks and operations.

Some real-world examples of AI-driven supply chain planning:

1. Amazon

Amazon leverages AI to optimize its vast and complex supply chain. The company uses machine learning algorithms to forecast demand, manage inventory, and streamline logistics. AI predicts consumer demand based on historical data, seasonal trends, and market conditions, ensuring that the right products are available in the right quantities. Additionally, Amazon's AI-driven robotics in fulfillment centers automate sorting, packing, and transportation of goods, significantly speeding up order processing and reducing operational costs.

2. Walmart

Walmart employs AI to improve its supply chain efficiency and customer satisfaction. AI-powered analytics help Walmart predict demand, manage inventory levels, and optimize routes for its delivery fleet. For instance, the company uses AI to anticipate stock shortages and automatically reorder products, minimizing out-of-stock situations. Moreover, Walmart's AI-driven supply chain visibility tools track shipments in real time, enabling proactive management of delays and disruptions.

3. Zara

Fashion retailer Zara uses AI to stay ahead in the fast-paced fashion industry. Zara's AI systems analyze real-time sales data and social media trends to predict fashion trends and consumer preferences. This information helps Zara rapidly adjust its inventory and production schedules, ensuring that popular items are available in stores quickly. The company's AI-driven logistics also optimize delivery routes and warehouse operations, reducing lead times and operational costs.

4. Procter & Gamble (P&G)

P&G utilizes AI to enhance its supply chain planning and efficiency. The company's AI-driven supply chain management platform, known as the Control Tower, provides real-time visibility into its global supply chain. AI algorithms analyze data from various sources to predict demand fluctuations, optimize production schedules, and manage inventory levels. This proactive approach helps P&G reduce waste, lower costs, and ensure product availability.

5. Coca-Cola

Coca-Cola employs AI to optimize its supply chain and improve sustainability. The company uses AI to analyze data from sales, weather patterns, and market trends to forecast demand more accurately. This helps Coca-Cola optimize production schedules and reduce excess inventory. Additionally, AI-driven route optimization ensures efficient distribution, minimizing fuel consumption and carbon emissions.

6. Unilever

Unilever's AI initiatives focus on enhancing supply chain resilience and efficiency. The company's AI-driven demand forecasting tools analyze historical sales data, market trends, and external factors to predict consumer demand accurately. Unilever also uses AI to optimize its manufacturing processes and supply chain logistics, ensuring timely delivery of products while reducing operational costs.

7. Siemens

Siemens leverages AI to improve its supply chain and manufacturing operations. AI-driven predictive maintenance tools monitor equipment health and predict potential failures, reducing downtime and maintenance costs. Additionally, Siemens uses AI to optimize inventory management and production schedules, ensuring efficient use of resources and timely delivery of products.

8. DHL

DHL, a global logistics company, uses AI to enhance its supply chain operations. AI-driven analytics help DHL forecast demand, optimize warehouse operations, and improve delivery routes. The company also employs AI-powered robots in its warehouses to automate sorting and packing, increasing efficiency and reducing operational costs.

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These examples highlight how AI is revolutionizing supply chain planning across various industries. By leveraging AI technologies, companies can enhance demand forecasting, optimize inventory management, streamline logistics, and improve overall efficiency. As AI continues to evolve, its impact on supply chain planning will only grow, enabling businesses to achieve greater levels of agility, sustainability, and customer satisfaction.

The integration of these technologies into supply chain planning is transforming how businesses operate, making supply chains more efficient, responsive, and resilient. As these technologies continue to evolve, they will further enhance the capabilities of supply chain management, driving innovation and competitive advantage.

Taming the Counterfeiting Epidemic

In a recently published article, we present the findings of our research that suggests that successfully combating counterfeits requires the attention of a broad collection of organizational functions. It works best with a multilayered strategy encompassing diverse methods and engaging the entire organization and its partners. We recommend that organizations tackle counterfeiting with a cohesive plan for identifying, containing, and preventing it. This comprehensive process means routinely keeping tabs on contract manufacturers and charting how products move through the supply chain. It includes scoping out what is for sale in consumer markets, deploying covert markings, reviewing warranty claims, educating customers, and partnering with key agencies and competitors.

While companies may never completely eliminate bad actors from copying products or diverting goods from authorized channels, they can significantly reduce counterfeiters’ market penetration and restore lost revenue. Successful approaches may involve cutting off counterfeiters’ access to markets, eliminating their supply of unauthorized goods, or making a product more difficult to copy. Building on insights from our case studies and discussions with practitioners, we provide details on the three activities that together help in developing a robust action plan comprises: identification, containment, and prevention. Identification helps in gaining Insight Into the extent and nature of the counterfeiting problem. Containment helps in limiting the problem’s spread. And prevention helps in stopping future counterfeit operations before they start

Source: Taming the Counterfeiting Epidemic - A multilayered approach is essential to effectively combat counterfeiting and unauthorized sales. MIT Sloan Management Review, Forthcoming (Fall 2023).

 

The mutual causality between supply chain disruption and innovation in low-income markets

In a recently published article, my co-author and I argue that low-income markets have unique constraints that trigger the co-evolution of innovation and disruption in such markets. When disruptions occur in global supply chains, and in particular, in low-income markets, they spur innovations that may be necessary to address both existing and potential future disruptions. However, such innovations in turn create disruptions to existing supply chains, and they may also create new supply chains. We highlight the unique opportunities and constraints that exist in the low-income market contexts that result in interesting and unique dynamics between innovation and disruption. Further, we identify the mechanisms through which innovation-disruption mutual causality occurs—as well as the boundary conditions for these mechanisms.

Using several case examples including Chotukool, Lagos, Nigeria Apapa port, MPesa, Momo, E-Choupal, Zipline drone technology, Dabbawallah, Danone South Africa, Kobo 360, Nestlé’s miniaturized “pure water” sachets, Barefoot College, Diagnostics for all, and Haul247 we discuss how disruption and innovation reinforce each other. Our study presents an integrated framework that considers the underlying characteristics of supply chain disruption and innovation. Supply chain disruption is characterized by impact (which can be low or high), probability (which is high), and level of control (which is low). We characterize innovation by considering whether it is affordable, available, accessible, has high level of awareness/relevance, and has low resource requirement. The mutual causality between supply chain disruption and innovation is strengthened by human and social capital that organizations in low-income markets are able to develop and creative resourcing that needs to move from autonomous to directed over time. Our study also emphasizes that weak institutions can paradoxically strengthen the force with which disruption and innovation reinforce each other. In addition to these moderating factors that strengthen the relationship between disruption and innovation, we also discuss the motivations, cognitive and social mechanisms that link supply chain disruptions to innovation, as well as information availability, structural changes and social equity that link innovation to supply chain disruption. The integrated framework presented in the paper is as follows:

This study offers directions for policy makers and managers regarding the macro-level and micro-level factors that influence innovation and disruption, as well as how to navigate the interplay between disruption and innovation in LIMs. It is noteworthy that this interplay can act as both a deterrent and an opportunity. An understanding of how LIM micro-level and macro-level factors influence disruption and innovation should help in decision-making and policy formulation. In addition, this study provides guidance to policy makers to work closely with organizations, as new innovations within these companies help to manage disruptions emanating from LIM constraints, thereby requiring innovative thinking on the policy front as well. Public-private partnerships can ensure that resource-constrained innovations at the organizational level are optimized and strengthened through policy innovations at the macro level.

Source: Goke, A., Nair, A. 2023. From Chaos to Creation: The mutual causality between supply chain disruption and innovation in low-income markets. Journal of Supply Chain Management, Vol. 59, Iss. 3, 20-41.

 

Strategic supply chain technology

According to the 2022 Gartner Supply Chain Technology User Wants and Needs Survey, supply chain organizations are prioritizing (i) increasing productivity and efficiency, (ii) improving resiliency, continuity, risk and security, and (iii) fostering innovation around new operating models and processes. The report presents the following framework for broader strategic supply chain technology themes for 2023.

 

 

Technologies focused on employee engagement, mobile asset optimization, and industry cloud platforms, help in optimizing operational performance. Cyber resilient supply chains, supply chain integration services, and compassable application architecture help in scaling by securely building the connectivity, resiliency, agility and flexibility for growth and maturity. Smart operations and actionable AI are pioneering technologies that help in enhancing decision making by leveraging asset and human connections.

 

These technologies are expanding and building on technologies focused on ecosystem enablement, sustainability, and digital supply chain twin that have already evolved. These technologies are also forming the building block for new technologies such as multi agent orchestration platforms, logistics platform business models, last-mile delivery, and industrial metaverse that are entering into the landscape.

Business Deals Across Different Industry Sectors

Business deals can mainly take eight distinct forms – Venture Financing, Acquisition, Equity Offering, Debt Offering, Asset Transaction, Private Equity, Partnership, and Merger. Between 2019 and 2022, in terms of volume, venture financing is the dominant form of business deal followed by acquisition, equity offering, debt offering, asset transaction and private equity.

Source: MarketLine

In terms of volume, most deals were in the telecom and IT industry. This was followed by financial services, pharmaceuticals and healthcare, construction and real estate, business and consumer services and energy and utilities.

In terms of value, the sequence in descending order is debt financing, acquisition, private equity, equity offering, asset transaction and venture financing (with the exception of 2021 when venture financing was higher in value than asset transactions).

Source: MarketLine

In terms of value, financial services and telecom and IT industry represent the dominant industry sectors for business deals followed by energy and utilities, construction and real estate, pharmaceuticals and healthcare and business and consumer services. The number of deals in 2022 was lower than previous three years in these industry sectors, except energy and utilities.

Source: MarketLine

In terms of volume, the telecom and IT industry saw most of business deals in the form of venture financing, acquisition, and private equity.

(Venture Financing)

Source: MarketLine

(Acquisition)

Source: MarketLine

(Private Equity)

Source: MarketLine

Construction industry saw the most number of business deals in the form of equity offering.

Source: MarketLine

Financial services saw the most number of business deals in the form of debt offering.

Source: MarketLine

Asset transaction is the dominant mode in energy and utilities sector.

Source: MarketLine

Business deals in pharmaceutical and healthcare sector is mostly done in the form of partnerships.

Source: MarketLine

Merger is the common mode for business deal in business and consumer services, financial services and telecoms & IT.

The Impacts of digital technologies on coping with the COVID-19 pandemic in the manufacturing industry

The COVID-19 pandemic’s impacts have been devastating for the global economy, and particularly for manufacturing companies. Many firms were unprepared for a crisis of this magnitude and adapted their operations in an ad-hoc manner. Some of these adaptations were facilitated by digital technologies (DTs). In a recently accepted article, my co-authors and I examine extant literature using a systematic review methodology in order to extend our understanding of what DTs have been used in manufacturing companies during the pandemic, how DTs have helped manufacturing companies cope with the pandemic, and how these innovations can be used in the post-pandemic world. The major outcome of our study is a novel classification of DT applications in the pandemic setting in the manufacturing industry. In addition, we propose an agenda for future research.

Source: Ardolino, M., Bacchetti, A., Dolgui, A., Franchini, G., Ivanov, D., & Nair, A. (2022). The Impacts of digital technologies on coping with the COVID-19 pandemic in the manufacturing industry: a systematic literature review. International Journal of Production Research, 1-24.

Adapting supply chain operations in anticipation of and during the COVID-19 pandemic

In a recently accepted article, my co-authors and I investigate the impacts of the COVID-19 pandemic and their proactive mediation by adaptive operational decisions in different network design structures in anticipation of and during the pandemic. In generalized terms, we contribute to the understanding of the effect of preparedness and recovery decisions in a pandemic setting on supply chain operations and performance. In particular, we examine the impact of inventory pre-positioning in anticipation of a pandemic and the adaptation of production-ordering policy during the pandemic. Our model combines three levels, which is not often seen jointly in operations management literature, i.e., pandemic dynamics, supply chain design, and operational production-inventory control policies.

The analysis is performed for both two- and three-stage supply chains and different scenarios for pandemic dynamics (i.e., uncontrolled propagation or controlled dispersal with lockdowns). Our findings suggest that two-stage supply chains exhibit a higher vulnerability in disruption cases. However, they are exposed to a lower system inertia and show positive effects at the recovery stage. Supply chain adaptation ahead of a pandemic is more advantageous than during the pandemic when specific operational recovery policies are deployed. We show that it is instructive to avoid simultaneous changes in structural network design and operational policies since that can destabilize the production-inventory system and result in higher product shortages. 

Source: Rozhkov, M., Ivanov, D., Blackhurst, J., & Nair, A. (2022). Adapting supply chain operations in anticipation of and during the COVID-19 pandemic. Omega, 110, 102635.