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 - – increasing inequality as vast percentage of new income is going to the top 1%, digital marketing is dominated by two big players Facebook and Google, increasing commodity prices, cloud computing is disrupting traditional business models, banking and financial system is disrupted by Bitcoin and other crypto currencies, talent flight as more people leaving formal jobs, geopolitical disruptions,
increasing energy prices, competitive advantages are harder to sustain because of technology dispersion, etc
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 -
Innovation driven organization
– Worker Null is one of the most innovative firm in sector. Manager in Improving Worker Safety in the Era of Machine Learning (B) Harvard Business Review case study can use Clayton Christensen Disruptive Innovation strategies to further increase the scale of innovtions in the organization.
Analytics focus
– Worker Null is putting a lot of focus on utilizing the power of analytics in business decision making. This has put it among the leading players in the industry. The technology infrastructure suggested by Michael W. Toffel, Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla can also help it to harness the power of analytics for – marketing optimization, demand forecasting, customer relationship management, inventory management, information sharing across the value chain etc.
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.
Highly skilled collaborators
– Worker Null has highly efficient outsourcing and offshoring strategy. It has resulted in greater operational flexibility and bringing down the costs in highly price sensitive segment. Secondly the value chain collaborators of the firm in Improving Worker Safety in the Era of Machine Learning (B) HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
Ability to recruit top talent
– Worker Null is one of the leading recruiters in the industry. Managers in the Improving Worker Safety in the Era of Machine Learning (B) are in a position to attract the best talent available. The firm has a robust talent identification program that helps in identifying the brightest.
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.
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.
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.
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.
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.
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.
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 -
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.
Capital Spending Reduction
– Even during the low interest decade, Worker Null has not been able to do capital spending to the tune of the competition. This has resulted into fewer innovations and company facing stiff competition from both existing competitors and new entrants who are disrupting the industry using digital technology.
Need for greater diversity
– Worker Null has taken concrete steps on diversity, equity, and inclusion. But the efforts so far has resulted in limited success. It needs to expand the recruitment and selection process to hire more people from the minorities and underprivileged background.
Slow to harness new channels of communication
– Even though competitors are using new communication channels such as Instagram, Tiktok, and Snap, Worker Null is slow explore the new channels of communication. These new channels of communication mentioned in marketing section of case study Improving Worker Safety in the Era of Machine Learning (B) can help to provide better information regarding products and services. It can also build an online community to further reach out to potential customers.
Low market penetration in new markets
– Outside its home market of Worker Null, firm in the HBR case study Improving Worker Safety in the Era of Machine Learning (B) needs to spend more promotional, marketing, and advertising efforts to penetrate international markets.
Increasing silos among functional specialists
– The organizational structure of Worker Null is dominated by functional specialists. It is not different from other players in the Technology & Operations segment. Worker Null needs to de-silo the office environment to harness the true potential of its workforce. Secondly the de-silo will also help Worker Null to focus more on services rather than just following the product oriented approach.
High dependence on star products
– The top 2 products and services of the firm as mentioned in the Improving Worker Safety in the Era of Machine Learning (B) HBR case study still accounts for major business revenue. This dependence on star products in has resulted into insufficient focus on developing new products, even though Worker Null has relatively successful track record of launching new products.
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.
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 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.
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 -
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.
Reforming the budgeting process
- By establishing new metrics that will be used to evaluate both existing and potential projects Worker Null can not only reduce the costs of the project but also help it in integrating the projects with other processes within the organization.
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.
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.
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.
Buying journey improvements
– Worker Null can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Improving Worker Safety in the Era of Machine Learning (B) suggest that firm can provide automated chats to help consumers solve their own problems, provide online suggestions to get maximum out of the products and services, and help consumers to build a community where they can interact with each other to develop new features and uses.
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.
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.
Learning at scale
– Online learning technologies has now opened space for Worker Null to conduct training and development for its employees across the world. This will result in not only reducing the cost of training but also help employees in different part of the world to integrate with the headquarter work culture, ethos, and standards.
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.
Finding new ways to collaborate
– Covid-19 has not only transformed business models of companies in Technology & Operations industry, but it has also influenced the consumer preferences. Worker Null can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
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.
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.
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 -
Environmental challenges
– Worker Null needs to have a robust strategy against the disruptions arising from climate change and energy requirements. EU has identified it as key priority area and spending 30% of its 880 billion Euros European post Covid-19 recovery funds on green technology. Worker Null can take advantage of this fund but it will also bring new competitors in the Technology & Operations industry.
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.
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.
Stagnating economy with rate increase
– Worker Null can face lack of demand in the market place because of Fed actions to reduce inflation. This can lead to sluggish growth in the economy, lower demands, lower investments, higher borrowing costs, and consolidation in the field.
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.
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 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) .
Consumer confidence and its impact on Worker Null demand
– There is a high probability of declining consumer confidence, given – high inflammation rate, rise of gig economy, lower job stability, increasing cost of living, higher interest rates, and aging demography. All the factors contribute to people saving higher rate of their income, resulting in lower consumer demand in the industry and other sectors.
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.
Shortening product life cycle
– it is one of the major threat that Worker Null is facing in Technology & Operations sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
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.
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.
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.
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.
