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Improving Worker Safety in the Era of Machine Learning (B) SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis

Case Study SWOT Analysis Solution

Case Study Description of Improving Worker Safety in the Era of Machine Learning (B)


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Authors :: Michael W. Toffel, Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla

Topics :: Technology & Operations

Tags :: Forecasting, Health, Personnel policies, Policy, Regulation, Research & development, Supply chain, SWOT Analysis, SWOT Matrix, TOWS, Weighted SWOT Analysis

Swot Analysis of "Improving Worker Safety in the Era of Machine Learning (B)" written by Michael W. Toffel, Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Worker Null facing as an external strategic factors. Some of the topics covered in Improving Worker Safety in the Era of Machine Learning (B) case study are - Strategic Management Strategies, Forecasting, Health, Personnel policies, Policy, Regulation, Research & development, Supply chain and Technology & Operations.


Some of the macro environment factors that can be used to understand the Improving Worker Safety in the Era of Machine Learning (B) casestudy better are - – wage bills are increasing, digital marketing is dominated by two big players Facebook and Google, central banks are concerned over increasing inflation, geopolitical disruptions, cloud computing is disrupting traditional business models, banking and financial system is disrupted by Bitcoin and other crypto currencies, there is increasing trade war between United States & China, talent flight as more people leaving formal jobs, increasing transportation and logistics costs, etc



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Introduction to SWOT Analysis of Improving Worker Safety in the Era of Machine Learning (B)


SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Improving Worker Safety in the Era of Machine Learning (B) case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Worker Null, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Worker Null operates in.

According to Harvard Business Review, 75% of the managers use SWOT analysis for various purposes such as – evaluating current scenario, strategic planning, new venture feasibility, personal growth goals, new market entry, Go To market strategies, portfolio management and strategic trade-off assessment, organizational restructuring, etc.




SWOT Objectives / Importance of SWOT Analysis and SWOT Matrix


SWOT analysis of Improving Worker Safety in the Era of Machine Learning (B) can be done for the following purposes –
1. Strategic planning using facts provided in Improving Worker Safety in the Era of Machine Learning (B) case study
2. Improving business portfolio management of Worker Null
3. Assessing feasibility of the new initiative in Technology & Operations field.
4. Making a Technology & Operations topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Worker Null




Strengths Improving Worker Safety in the Era of Machine Learning (B) | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis

The strengths of Worker Null in Improving Worker Safety in the Era of Machine Learning (B) Harvard Business Review case study are -

Operational resilience

– The operational resilience strategy in the Improving Worker Safety in the Era of Machine Learning (B) Harvard Business Review case study comprises – understanding the underlying the factors in the industry, building diversified operations across different geographies so that disruption in one part of the world doesn’t impact the overall performance of the firm, and integrating the various business operations and processes through its digital transformation drive.

Training and development

– Worker Null has one of the best training and development program in the industry. The effectiveness of the training programs can be measured in Improving Worker Safety in the Era of Machine Learning (B) Harvard Business Review case study by analyzing – employees retention, in-house promotion, loyalty, new venture initiation, lack of conflict, and high level of both employees and customer engagement.

Learning organization

- Worker Null is a learning organization. It has inculcated three key characters of learning organization in its processes and operations – exploration, creativity, and expansiveness. The work place at Worker Null is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Improving Worker Safety in the Era of Machine Learning (B) Harvard Business Review case study emphasize – knowledge, initiative, and innovation.

Strong track record of project management

– Worker Null is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.

Digital Transformation in Technology & Operations segment

- digital transformation varies from industry to industry. For Worker Null digital transformation journey comprises differing goals based on market maturity, customer technology acceptance, and organizational culture. Worker Null has successfully integrated the four key components of digital transformation – digital integration in processes, digital integration in marketing and customer relationship management, digital integration into the value chain, and using technology to explore new products and market opportunities.

Successful track record of launching new products

– Worker Null has launched numerous new products in last few years, keeping in mind evolving customer preferences and competitive pressures. Worker Null has effective processes in place that helps in exploring new product needs, doing quick pilot testing, and then launching the products quickly using its extensive distribution network.

High brand equity

– Worker Null has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Worker Null to keep acquiring new customers and building profitable relationship with both the new and loyal customers.

Sustainable margins compare to other players in Technology & Operations industry

– Improving Worker Safety in the Era of Machine Learning (B) firm has clearly differentiated products in the market place. This has enabled Worker Null to fetch slight price premium compare to the competitors in the Technology & Operations industry. The sustainable margins have also helped Worker Null to invest into research and development (R&D) and innovation.

Effective Research and Development (R&D)

– Worker Null has innovation driven culture where significant part of the revenues are spent on the research and development activities. This has resulted in, as mentioned in case study Improving Worker Safety in the Era of Machine Learning (B) - staying ahead in the industry in terms of – new product launches, superior customer experience, highly competitive pricing strategies, and great returns to the shareholders.

Ability to lead change in Technology & Operations field

– Worker Null is one of the leading players in its industry. Over the years it has not only transformed the business landscape in its segment but also across the whole industry. The ability to lead change has enabled Worker Null in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.

Superior customer experience

– The customer experience strategy of Worker Null in the segment is based on four key concepts – personalization, simplification of complex needs, prompt response, and continuous engagement.

Low bargaining power of suppliers

– Suppliers of Worker Null in the sector have low bargaining power. Improving Worker Safety in the Era of Machine Learning (B) has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Worker Null to manage not only supply disruptions but also source products at highly competitive prices.






Weaknesses Improving Worker Safety in the Era of Machine Learning (B) | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis

The weaknesses of Improving Worker Safety in the Era of Machine Learning (B) are -

Employees’ incomplete understanding of strategy

– From the instances in the HBR case study Improving Worker Safety in the Era of Machine Learning (B), it seems that the employees of Worker Null don’t have comprehensive understanding of the firm’s strategy. This is reflected in number of promotional campaigns over the last few years that had mixed messaging and competing priorities. Some of the strategic activities and services promoted in the promotional campaigns were not consistent with the organization’s strategy.

Products dominated business model

– Even though Worker Null has some of the most successful products in the industry, this business model has made each new product launch extremely critical for continuous financial growth of the organization. firm in the HBR case study - Improving Worker Safety in the Era of Machine Learning (B) should strive to include more intangible value offerings along with its core products and services.

High operating costs

– Compare to the competitors, firm in the HBR case study Improving Worker Safety in the Era of Machine Learning (B) has high operating costs in the. This can be harder to sustain given the new emerging competition from nimble players who are using technology to attract Worker Null 's lucrative customers.

Aligning sales with marketing

– It come across in the case study Improving Worker Safety in the Era of Machine Learning (B) that the firm needs to have more collaboration between its sales team and marketing team. Sales professionals in the industry have deep experience in developing customer relationships. Marketing department in the case Improving Worker Safety in the Era of Machine Learning (B) can leverage the sales team experience to cultivate customer relationships as Worker Null is planning to shift buying processes online.

Compensation and incentives

– The revenue per employee as mentioned in the HBR case study Improving Worker Safety in the Era of Machine Learning (B), is just above the industry average. Worker Null needs to redesign the compensation structure and incentives to increase the revenue per employees. Some of the steps that it can take are – hiring more specialists on project basis, etc.

High cash cycle compare to competitors

Worker Null has a high cash cycle compare to other players in the industry. It needs to shorten the cash cycle by 12% to be more competitive in the marketplace, reduce inventory costs, and be more profitable.

Lack of clear differentiation of Worker Null products

– To increase the profitability and margins on the products, Worker Null needs to provide more differentiated products than what it is currently offering in the marketplace.

Slow decision making process

– As mentioned earlier in the report, Worker Null has a very deliberative decision making approach. This approach has resulted in prudent decisions, but it has also resulted in missing opportunities in the industry over the last five years. Worker Null even though has strong showing on digital transformation primary two stages, it has struggled to capitalize the power of digital transformation in marketing efforts and new venture efforts.

No frontier risks strategy

– After analyzing the HBR case study Improving Worker Safety in the Era of Machine Learning (B), it seems that company is thinking about the frontier risks that can impact Technology & Operations strategy. But it has very little resources allocation to manage the risks emerging from events such as natural disasters, climate change, melting of permafrost, tacking the rise of artificial intelligence, opportunities and threats emerging from commercialization of space etc.

Ability to respond to the competition

– As the decision making is very deliberative, highlighted in the case study Improving Worker Safety in the Era of Machine Learning (B), in the dynamic environment Worker Null has struggled to respond to the nimble upstart competition. Worker Null has reasonably good record with similar level competitors but it has struggled with new entrants taking away niches of its business.

Slow to strategic competitive environment developments

– As Improving Worker Safety in the Era of Machine Learning (B) HBR case study mentions - Worker Null takes time to assess the upcoming competitions. This has led to missing out on atleast 2-3 big opportunities in the industry in last five years.




Opportunities Improving Worker Safety in the Era of Machine Learning (B) | External Strategic Factors
What are Opportunities in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis


The opportunities highlighted in the Harvard Business Review case study Improving Worker Safety in the Era of Machine Learning (B) are -

Developing new processes and practices

– Worker Null can develop new processes and procedures in Technology & Operations industry using technology such as automation using artificial intelligence, real time transportation and products tracking, 3D modeling for concept development and new products pilot testing etc.

Reconfiguring business model

– The expansion of digital payment system, the bringing down of international transactions costs using Bitcoin and other blockchain based currencies, etc can help Worker Null to reconfigure its entire business model. For example it can used blockchain based technologies to reduce piracy of its products in the big markets such as China. Secondly it can use the popularity of e-commerce in various developing markets to build a Direct to Customer business model rather than the current Channel Heavy distribution network.

Redefining models of collaboration and team work

– As explained in the weaknesses section, Worker Null is facing challenges because of the dominance of functional experts in the organization. Improving Worker Safety in the Era of Machine Learning (B) case study suggests that firm can utilize new technology to build more coordinated teams and streamline operations and communications using tools such as CAD, Zoom, etc.

Leveraging digital technologies

– Worker Null can leverage digital technologies such as artificial intelligence and machine learning to automate the production process, customer analytics to get better insights into consumer behavior, realtime digital dashboards to get better sales tracking, logistics and transportation, product tracking, etc.

Remote work and new talent hiring opportunities

– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Worker Null to expand its talent hiring zone. According to McKinsey Global Institute, 20% of the high end workforce in fields such as finance, information technology, can continously work from remote local post Covid-19. This presents a really great opportunity for Worker Null to hire the very best people irrespective of their geographical location.

Loyalty marketing

– Worker Null has focused on building a highly responsive customer relationship management platform. This platform is built on in-house data and driven by analytics and artificial intelligence. The customer analytics can help the organization to fine tune its loyalty marketing efforts, increase the wallet share of the organization, reduce wastage on mainstream advertising spending, build better pricing strategies using personalization, etc.

Creating value in data economy

– The success of analytics program of Worker Null has opened avenues for new revenue streams for the organization in the industry. This can help Worker Null to build a more holistic ecosystem as suggested in the Improving Worker Safety in the Era of Machine Learning (B) case study. Worker Null can build new products and services such as - data insight services, data privacy related products, data based consulting services, etc.

Changes in consumer behavior post Covid-19

– Consumer behavior has changed in the Technology & Operations industry because of Covid-19 restrictions. Some of this behavior will stay once things get back to normal. Worker Null can take advantage of these changes in consumer behavior to build a far more efficient business model. For example consumer regular ordering of products can reduce both last mile delivery costs and market penetration costs. Worker Null can further use this consumer data to build better customer loyalty, provide better products and service collection, and improve the value proposition in inflationary times.

Better consumer reach

– The expansion of the 5G network will help Worker Null to increase its market reach. Worker Null will be able to reach out to new customers. Secondly 5G will also provide technology framework to build new tools and products that can help more immersive consumer experience and faster consumer journey.

Low interest rates

– Even though inflation is raising its head in most developed economies, Worker Null can still utilize the low interest rates to borrow money for capital investment. Secondly it can also use the increase of government spending in infrastructure projects to get new business.

Building a culture of innovation

– managers at Worker Null can make experimentation a productive activity and build a culture of innovation using approaches such as – mining transaction data, A/B testing of websites and selling platforms, engaging potential customers over various needs, and building on small ideas in the Technology & Operations segment.

Increase in government spending

– As the United States and other governments are increasing social spending and infrastructure spending to build economies post Covid-19, Worker Null can use these opportunities to build new business models that can help the communities that Worker Null operates in. Secondly it can use opportunities from government spending in Technology & Operations sector.

Using analytics as competitive advantage

– Worker Null has spent a significant amount of money and effort to integrate analytics and machine learning into its operations in the sector. This continuous investment in analytics has enabled, as illustrated in the Harvard case study Improving Worker Safety in the Era of Machine Learning (B) - to build a competitive advantage using analytics. The analytics driven competitive advantage can help Worker Null to build faster Go To Market strategies, better consumer insights, developing relevant product features, and building a highly efficient supply chain.




Threats Improving Worker Safety in the Era of Machine Learning (B) External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis


The threats mentioned in the HBR case study Improving Worker Safety in the Era of Machine Learning (B) are -

Regulatory challenges

– Worker Null needs to prepare for regulatory challenges as consumer protection groups and other pressure groups are vigorously advocating for more regulations on big business - to reduce inequality, to create a level playing field, to product data privacy and consumer privacy, to reduce the influence of big money on democratic institutions, etc. This can lead to significant changes in the Technology & Operations industry regulations.

Trade war between China and United States

– The trade war between two of the biggest economies can hugely impact the opportunities for Worker Null in the Technology & Operations industry. The Technology & Operations industry is already at various protected from local competition in China, with the rise of trade war the protection levels may go up. This presents a clear threat of current business model in Chinese market.

Increasing international competition and downward pressure on margins

– Apart from technology driven competitive advantage dilution, Worker Null can face downward pressure on margins from increasing competition from international players. The international players have stable revenue in their home market and can use those resources to penetrate prominent markets illustrated in HBR case study Improving Worker Safety in the Era of Machine Learning (B) .

New competition

– After the dotcom bust of 2001, financial crisis of 2008-09, the business formation in US economy had declined. But in 2020 alone, there are more than 1.5 million new business applications in United States. This can lead to greater competition for Worker Null in the Technology & Operations sector and impact the bottomline of the organization.

Increasing wage structure of Worker Null

– Post Covid-19 there is a sharp increase in the wages especially in the jobs that require interaction with people. The increasing wages can put downward pressure on the margins of Worker Null.

Technology disruption because of hacks, piracy etc

– The colonial pipeline illustrated, how vulnerable modern organization are to international hackers, miscreants, and disruptors. The cyber security interruption, data leaks, etc can seriously jeopardize the future growth of the organization.

Instability in the European markets

– European Union markets are facing three big challenges post Covid – expanded balance sheets, Brexit related business disruption, and aggressive Russia looking to distract the existing security mechanism. Worker Null will face different problems in different parts of Europe. For example it will face inflationary pressures in UK, France, and Germany, balance sheet expansion and demand challenges in Southern European countries, and geopolitical instability in the Eastern Europe.

High level of anxiety and lack of motivation

– the Great Resignation in United States is the sign of broader dissatisfaction among the workforce in United States. Worker Null needs to understand the core reasons impacting the Technology & Operations industry. This will help it in building a better workplace.

Easy access to finance

– Easy access to finance in Technology & Operations field will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Worker Null can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.

Technology acceleration in Forth Industrial Revolution

– Worker Null has witnessed rapid integration of technology during Covid-19 in the Technology & Operations industry. As one of the leading players in the industry, Worker Null needs to keep up with the evolution of technology in the Technology & Operations sector. According to Mckinsey study top managers believe that the adoption of technology in operations, communications is 20-25 times faster than what they planned in the beginning of 2019.

Aging population

– As the populations of most advanced economies are aging, it will lead to high social security costs, higher savings among population, and lower demand for goods and services in the economy. The household savings in US, France, UK, Germany, and Japan are growing faster than predicted because of uncertainty caused by pandemic.

Backlash against dominant players

– US Congress and other legislative arms of the government are getting tough on big business especially technology companies. The digital arm of Worker Null business can come under increasing regulations regarding data privacy, data security, etc.

Learning curve for new practices

– As the technology based on artificial intelligence and machine learning platform is getting complex, as highlighted in case study Improving Worker Safety in the Era of Machine Learning (B), Worker Null may face longer learning curve for training and development of existing employees. This can open space for more nimble competitors in the field of Technology & Operations .




Weighted SWOT Analysis of Improving Worker Safety in the Era of Machine Learning (B) Template, Example


Not all factors mentioned under the Strengths, Weakness, Opportunities, and Threats quadrants in the SWOT Analysis are equal. Managers in the HBR case study Improving Worker Safety in the Era of Machine Learning (B) needs to zero down on the relative importance of each factor mentioned in the Strengths, Weakness, Opportunities, and Threats quadrants. We can provide the relative importance to each factor by assigning relative weights. Weighted SWOT analysis process is a three stage process –

First stage for doing weighted SWOT analysis of the case study Improving Worker Safety in the Era of Machine Learning (B) is to rank the strengths and weaknesses of the organization. This will help you to assess the most important strengths and weaknesses of the firm and which one of the strengths and weaknesses mentioned in the initial lists are marginal and can be left out.

Second stage for conducting weighted SWOT analysis of the Harvard case study Improving Worker Safety in the Era of Machine Learning (B) is to give probabilities to the external strategic factors thus better understanding the opportunities and threats arising out of macro environment changes and developments.

Third stage of constructing weighted SWOT analysis of Improving Worker Safety in the Era of Machine Learning (B) is to provide strategic recommendations includes – joining likelihood of external strategic factors such as opportunities and threats to the internal strategic factors – strengths and weaknesses. You should start with external factors as they will provide the direction of the overall industry. Secondly by joining probabilities with internal strategic factors can help the company not only strategic fit but also the most probably strategic trade-off that Worker Null needs to make to build a sustainable competitive advantage.



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