ABSTRACT In this study, we examined the effect of digital technologies during COVID-19 pandemic. 104 supply chain managers responded to our survey. Based on their responses we were able to conclude that those firms who invested more in their digital technologies performed better in reducing the severity of COVID-19 pandemic related challenges, felt better prepared in general for the pandemic compared to the competitors, and considers their digital technology investments helped them during COVID-19 on their success which led them to invest further in Technology. We believe our findings will help businesses to consider investing in their digital technologies for better management of uncertainties in their supply chains.

The COVID-19 pandemic has renewed interest in international supply chains. While international supply chains proved to be very robust in the pandemic period of closed borders, restrictions on movement of people and goods, and closures of businesses, the pandemic proved the need for better tools, particularly for policy makers, to ascertain the health and resilience of international supply chains and the impact they have on their respective economies. This report attempts to provide one such tool with the creation of a set of indices to measure the vulnerability of Canadian industries to disruptions in both upstream and downstream international supply chains.

Comparison of cost and return between business as usual case and COVID-19.

The COVID-19 pandemic has renewed interest in international supply chains. While international supply chains proved to be very robust in the pandemic period of closed borders, restrictions on movement of people and goods, and closures of businesses, the pandemic proved the need for better tools, particularly for policy makers, to ascertain the health and resilience of international supply chains and the impact they have on their respective economies. This report attempts to provide one such tool with the creation of a set of indices to measure the vulnerability of Canadian industries to disruptions in both upstream and downstream international supply chains.

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on micro and small enterprises and assess the policy responses in a rapidly changing context, reliable data is imperative, and the need to resort to a dynamic data collection tool at a time when countries in the region are in a state of flux cannot be overstated. The COVID-19 MENA Monitor Survey was led by the Economic Research Forum (ERF) to provide data for researchers and policy makers on the economic and labor market impact of the global COVID-19 pandemic on enterprises.

The ERF COVID-19 MENA Monitor Survey is constructed using a series of short panel phone surveys, that are conducted approximately every two months, and it will cover business closure (temporary/permanent) due to lockdowns, ability to telework/deliver the service, disruptions to supply chains (for inputs and outputs), loss of product markets, increased cost of supplies, worker layoffs, salary adjustments, access to lines of credit and delays in transportation. Understanding the strategies of enterprises (particularly micro and small enterprises) to cope with the crisis is one of the main objectives of this survey. Specific constraints such as weak access to the internet in some areas or laws constraining goods' delivery will be analyzed. Enterprise owners will also be asked about prospects for the future, including ability to stay open, and whether they benefited from any measures to support their businesses.

The ERF COVID-19 MENA Monitor Survey is a wide-ranging, nationally representative panel survey. The baseline wave of this dataset was collected in February 2021 and harmonized by the Economic Research Forum (ERF) and is featured as wave 1 for the enterprise data. In addition, this data includes the panel data that was collected in wave 2, and the panel data that was collected in wave 3. The panel series will be collected approximately each two months.

The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between the Arab countries (Morocco, Tunisia, Egypt and Jordan). All the COVID-19 MENA Monitor surveys incorporate similar survey designs, with data on enterprises within the Arab countries.

Geographic coverage

National

Analysis unit

Enterprises

Universe

The sample universe for the enterprise survey was enterprises that had 6-199 workers pre-COVID-19

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample universe for the firm survey was firms that had 6-199 workers pre-COVID-19. Country-specific sample frames of firms were used. Stratified random samples were used (strata varied by country) to ensure adequate sample size in key strata. A target of 500 firms per country was set. The sampling strategy was incorporated into the weights. Up to three attempts (five in Tunisia) were made to ensure response if a phone number was not picked up/answered, was disconnected or busy, or picked up but could not complete the interview at that time. After the third (or fifth) failed attempt, a firm was treated as a non-response and a random firm from the same stratum was used as an alternate.

Sampling frames The sample frames varied by country as follows: · Egypt: Yellow Pages o Data on broad categories (e.g. gas stations) o Coded into four strata: (1) services, (2) food & accommodation, (3) trade, manufacturing, and agriculture, (4) construction o Restricted to firms with 6-199 workers in February 2020 based on an eligibility question during the phone interview · Jordan: Kinz (a Jordanian corporate data mining website, which had a larger sample of firms than the Yellow Pages in Jordan). o Data on broad categories (e.g. Industry, Marketing) o Coded into five strata: (1) services, (2) food & accommodation, (3) trade and agriculture, (4) construction, (5) industry o Initial frame restricted to firms with 5-250 workers. Further restricted to firms with 6-199 workers in February 2020 based on an eligibility question during the phone interview · Morocco: Yellow Pages (no efficient digital copy available; a physical copy was used) o Data organized geographically, not categorically o Three geographic strata used: (1) Casa-Rabat, (2) North, (3) South o The page ranges for the strata were provided. A random page within a stratum was selected, and then a random firm on that page (without replacement). o The number of firms on the page was recorded and incorporated into the inverse probability weights. o Restricted to firms with 6-199 workers in February 2020 based on an eligibility question during the phone interview · Tunisia: National Institute of Statistics (INS) and Agency for the Promotion of Industry and Innovation (APII) databases o Tunisia did not have a Yellow Pages or similar database, so administrative/statistics data sources had to be used o The sample started with the INS frame with 1,238 firms with 6-200 wage employees § Firms were stratified into: (1) Agriculture (2) Industry (3) Construction (4) Trade (5) Accommodation (6) Service § Firms were also stratified by size in terms of 6-49 versus 50-200 employees § A random stratified sample (order) was selected § Further restricted to firms with 6-199 workers in February 2020 based on an eligibility question during the phone interview § This sample frame was eventually exhausted o After the INS sample was exhausted, the APII sample was used § APII only covered firms with 10+ workers § APII only covered (1) services & transport, and (2) industry o Weights are based on the underlying data on all firms from INS, specifically: Enterprises privées selon l'activité principale et la tranche de salariés (RNE 2019). § We ultimately stratify the Tunisia weights by industry and firms sized: 6-9 employees (since APII only covered 10+), 10-49, and 50-199 in wave one and combine 6-49 and in some cases 6-199 in subsequent waves.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The enterprise questionnaire is carried out to understand the strategies of enterprises -particularly micro and small enterprises- to cope with the crisis as well as related constraints and prospects for the future. It includes questions on business closure (temporary/permanent) due to lockdowns, ability to telework/deliver the service, disruptions to supply chains (for inputs and outputs), loss of product markets, increased cost of supplies, worker layoffs, salary adjustments, access to lines of credit and delays in transportation.

Note: The questionnaire can be seen in the documentation materials tab.

According to Cognitive Market Research, The Global Artificial intelligence AI in Supply Chain and Logistics market size is USD 1.9 million in 2024 and will expand at a compound annual growth rate (CAGR) of 50.50% from 2024 to 2031.

North America Artificial intelligence AI in Supply Chain and Logistics held the major market of around 40% of the global revenue with a market size of USD 0.76 million in 2024 and will grow at a compound annual growth rate (CAGR) of 48.7% from 2024 to 2031.
Europe Artificial intelligence AI in Supply Chain and Logistics accounted for a share of around 30% of the global market size of USD 0.57 million in 2024.
Asia Pacific Artificial intelligence AI in Supply Chain and Logistics held the market of around 23% of the global revenue with a market size of USD 0.44 million in 2024 and will grow at a compound annual growth rate (CAGR) of 52.5% from 2024 to 2031.
South America Artificial intelligence AI in Supply Chain and Logistics market of around 5% of the global revenue with a market size of USD 0.10 million in 2024 and will grow at a compound annual growth rate (CAGR) of 49.9% from 2024 to 2031.
Middle East and Africa Artificial intelligence AI in Supply Chain and Logistics held the major market of around 2% of the global revenue with a market size of USD 0.04 million in 2024 and will grow at a compound annual growth rate (CAGR) of 50.2% from 2024 to 2031.
The sales of software in AI for supply chain and logistics are projected to rise due to increased demand for scalable, customizable solutions offering real-time analytics, predictive insights, and seamless integration capabilities.
The sales of machine learning in AI for supply chain and logistics are poised to surge owing to its ability to optimize operations, forecast demand accurately, and automate decision-making processes, improving efficiency and profitability.

Increasing Availability of Big Data and Analytics Tools to Propel the Market Growth

The increasing availability of big data and analytics tools is poised to propel significant growth in the AI for supply chain and logistics market. As the volume, velocity, and variety of data generated within supply chains continue to expand, businesses are recognizing the value of leveraging advanced analytics and AI-driven insights to optimize their operations. These tools enable companies to extract valuable insights from vast datasets, improving decision-making, forecasting accuracy, and overall supply chain performance. By harnessing the power of big data analytics, organizations can uncover hidden patterns, identify emerging trends, and predict future demand more accurately. Moreover, the integration of AI with analytics tools facilitates the automation of repetitive tasks and the identification of optimization opportunities, leading to enhanced efficiency and cost savings. Thus, the increasing availability and adoption of big data and analytics tools are expected to drive substantial market growth in the AI for supply chain and logistics sector.

Market Restraints of the Artificial intelligence AI in Supply Chain and Logistics

Data Security Concerns to Limit the Sales

Data security concerns pose a significant restraint on the sales of AI for supply chain and logistics solutions. As these systems rely heavily on vast amounts of sensitive data, including customer information, trade secrets, and operational details, the risk of data breaches, cyberattacks, and unauthorized access becomes a prominent issue. Heightened regulatory scrutiny, such as GDPR and CCPA, adds further complexity and compliance challenges to data handling practices within supply chains. Organizations must invest heavily in robust cybersecurity measures, encryption techniques, and access controls to safeguard sensitive data, which can significantly increase implementation costs. Moreover, the reputational damage and financial repercussions resulting from data breaches can deter potential buyers from adopting AI solutions, particularly in industries where data privacy and confidentiality are paramount. Addressing these concerns through stringent security protocols and transparent data governance practices is crucial to fostering trust and driving wider adoption of AI in supply chain management.

Impact of Covid-19 on the Artificial intelligence AI in supply chain and logistics Market

The COVID-19 pandemic has accelerated the adoption of Artificial Intelligence (AI) in ...

The tech industry had a rough start to 2024. Technology companies worldwide saw a significant reduction in their workforce in the first quarter of 2024, with over 57 thousand employees being laid off. By the second quarter, layoffs impacted more than 43 thousand tech employees. In the final quarter of the year around 12 thousand employees were laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of 167.6 thousand employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of 263 thousand laid off employees in the global tech sector by trhe end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.

Abstract

To better understand the impact of the shock induced by the COVID-19 pandemic on micro and small enterprises in Tunisia and assess the policy responses in a rapidly changing context, reliable data is imperative, and the need to resort to a dynamic data collection tool at a time when countries in the region are in a state of flux cannot be overstated. The COVID-19 MENA Monitor Survey was led by the Economic Research Forum (ERF) to provide data for researchers and policy makers on the economic and labor market impact of the global COVID-19 pandemic on enterprises.

The ERF COVID-19 MENA Monitor Survey is constructed using a series of short panel phone surveys, that are conducted approximately every two months, and it will cover business closure (temporary/permanent) due to lockdowns, ability to telework/deliver the service, disruptions to supply chains (for inputs and outputs), loss of product markets, increased cost of supplies, worker layoffs, salary adjustments, access to lines of credit and delays in transportation. Understanding the strategies of enterprises (particularly micro and small enterprises) to cope with the crisis is one of the main objectives of this survey. Specific constraints such as weak access to the internet in some areas or laws constraining goods' delivery will be analyzed. Enterprise owners will also be asked about prospects for the future, including ability to stay open, and whether they benefited from any measures to support their businesses. The ERF COVID-19 MENA Monitor Survey is a wide-ranging, nationally representative panel survey. The wave 2 of this dataset was collected from June to July 2021 and harmonized by the Economic Research Forum (ERF) and is featured as data for enterprise data. The survey is in the process of further expansion to include other waves. The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between other Arab countries (Morocco, Egypt and Jordan). All the COVID-19 MENA Monitor surveys incorporate similar survey designs, with data on enterprises within Arab countries (Egypt, Jordan, Tunisia, and Morocco).

Geographic coverage

National

Analysis unit

Enterprises

Universe

The sample universe for the enterprise survey was enterprises that had 6-199 workers pre-COVID-19

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample universe for the firm survey was firms that had 6-199 workers pre-COVID-19. Stratified random samples were used to ensure adequate sample size in key strata. A target of 500 firms was set as a sample. Up to Five attempts were made to ensure response if a phone number was not picked up/answered, was disconnected or busy, or picked up but could not complete the interview at that time. After the fifth failed attempt, a firm was treated as a non-response and a random firm from the same stratum was used as an alternate.

Use the National Institute of Statistics (INS) and Agency for the Promotion of Industry and Innovation (APII) databases as follow: o Tunisia did not have a Yellow Pages or similar database, so administrative/statistics data sources had to be used o The sample started with the INS frame with 1,238 enterprises with 6-200 wage employees § Enterprises were stratified into: (1) Agriculture (2) Industry (3) Construction (4) Trade (5) Accommodation (6) Service § Enterprises were also stratified by size in terms of 6-49 versus 50-200 employees § A random stratified sample (order) was selected § Further restricted to enterprises with 6-199 workers in February 2020 based on an eligibility question during the phone interview § This sample frame was eventually exhausted o After the INS sample was exhausted, the APII sample was used § APII only covered enterprises with 10+ workers § APII only covered (1) services & transport, and (2) industry o Weights are based on the underlying data on all enterprises from INS, specifically: Entreprises privées selon l'activité principale et la tranche de salariés (RNE 2019). § We ultimately stratify the Tunisia weights by industry and enterprises sized: 6-9 employees (since APII only covered 10+), 10-49, and 50-199

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The enterprise questionnaire is carried out to understand the strategies of enterprises -particularly micro and small enterprises- to cope with the crisis as well as related constraints and prospects for the future. It includes questions on business closure (temporary/permanent) due to lockdowns, ability to telework/deliver the service, disruptions to supply chains (for inputs and outputs), loss of product markets, increased cost of supplies, worker layoffs, salary adjustments, access to lines of credit and delays in transportation.

Note: The questionnaire can be seen in the documentation materials tab.