AIOU Course Code 8418-1 Solved Assignment Autumn 2021
ALLAMA IQBAL OPEN UNIVERSITY ISLAMABAD
(Department of Business Administration)
Course: Production & Operations Management (8418) Semester: Autumn, 2021
Total Marks: 100 Pass Marks: 50
ASSIGNMENT No. 1
Note: Attempt all the questions.
- 1 What is Production and Operation Management? How could you analyze the problems of production management? Discuss with examples. (20)
The very essence of any business is to cater needs of customer by providing services and goods, and in process create value for customers and solve their problems. Production and operations management talks about applying business organization and management concepts in creation of goods and services.
Production is a scientific process which involves transformation of raw material (input) into desired product or service (output) by adding economic value. Production can broadly categorize into following based on technique:
Production through separation: It involves desired output is achieved through separation or extraction from raw materials. A classic example of separation or extraction is Oil into various fuel products.
Production by modification or improvement: It involves change in chemical and mechanical parameters of the raw material without altering physical attributes of the raw material. Annealing process (heating at high temperatures and then cooling), is example of production by modification or improvement.
Production by assembly: Car production and computer are example of production by assembly.
Production and Operations Management
Importance of Production Function and Production Management
Successful organizations have well defined and efficient line function and support function. Production comes under the category of line function which directly affects customer experience and there by future of organization itself.
Aim of production function is to add value to product or service which will create a strong and long lasting customer relationship or association. And this can be achieved by healthy and productive association between Marketing and Production people. Marketing function people are frontline representative of the company and provide insights to real product needs of customers.
An effective planning and control on production parameters to achieve or create value for customers is called production management.
As to deliver value for customers in products and services, it is essential for the company to do the following:
Identify the customer needs and convert that into a specific product or service (numbers of products required for specific period of time)
Based on product requirement do back-ward working to identify raw material requirements
Engage internal and external vendors to create supply chain for raw material and finished goods between vendor → production facility → customers.
Operations management captures above identified 3 points.
Production Management v/s Operations Management
A high level comparison which distinct production and operations management can be done on following characteristics:
Output: Production management deals with manufacturing of products like (computer, car, etc) while operations management cover both products and services.
Usage of Output: Products like computer/car are utilized over a period of time whereas services need to be consumed immediately
Classification of work: To produce products like computer/car more of capital equipment and less labour are required while services require more labour and lesser capital equipment.
Customer Contact: There is no participation of customer during production whereas for services a constant contact with customer is required.
Production management and operations management both are very essential in meeting objective of an organization.
- 2 What is product design? Discuss and analyze the evaluation of product design with examples. (20)
The definition of product design describes the process of imagining, creating, and iterating products that solve users’ problems or address specific needs in a given market.
The key to successful product design is understanding the end-user customer, the person for whom the product is being created. Product designers attempt to solve real problems for real people by using empathy and knowledge of their prospective customers’ habits, behaviors, frustrations, needs, and wants.
Ideally, product design’s execution is so flawless that no one notices; users can intuitively use the product as needed because product design understood their needs and anticipated their usage.
Good product design practices thread themselves throughout the entire product lifecycle. Product design is essential in creating the initial user experience and product offering, from pre-ideation user research to concept development to prototyping and usability testing.
But it doesn’t end there, as product design plays an ongoing role in refining the customer experience and ensuring supplemental functionality and capabilities get added in a seamless, discoverable, and non-disruptive manner. Brand consistency and evolution remain an essential product design responsibility until the end of a product’s lifespan.
And it’s much more than just what users see on their screens. System design and process design are critical behind-the-scenes components that eventually drive users to see and interact with the interface design.
Product design is an outgrowth of a very similar discipline called industrial design.
According to the Industrial Designers Society of America:
“Industrial design is the professional practice of designing products used by millions of people worldwide every day. Industrial designers not only focus on the appearance of a product but also on how it functions, is manufactured and ultimately the value and experience it provides for users.”
Before the mass-production era of manufacturing, craftspeople built products primarily by hand. This meant there were fewer products available for sale and that they cost more. Then, the industrialization of manufacturing allowed businesses to mass-produce products inexpensively.
To help sell their products to the millions of people who could now afford them, manufacturers enlisted the help of industrial designers to create products that were not only functional but also aesthetically pleasing.
Over time, a subset of industrial design has evolved into its own category: product design. This is because industrial design today connotes physical products such as furniture and household appliances. In contrast, product design can refer to any product—even digital, virtual products such as software apps.
Type of product s
What different companies think of today as product design jobs might include several roles under different names. For example:
User-experience and interaction designers focus on refining a product based on how their research into user behavior suggests people will get the most satisfaction from using the product. UX designers aim to increase users’ happiness.
The most artistic job within product design is creating the graphics, icons, logos, and other visual elements of the product experience. Their purview is as broad as selecting a color scheme to as narrow as tweaking individual pixels.
If the product experience involves elements “moving”—be it slick transitions or a user-controlled avatar—these specialists work on this extremely complicated part of the design. They don’t create the art, but they bring it to life.
In a large enough product design organization, they are solely focused on understanding customers. Interviewing, running usability studies, presenting prototypes and mockups for feedback, and building out demographics and personas that fall under their purview.
These designers focus on user research and other data to identify ways to improve a product’s layout, feature set, and visual aesthetic. In other words, their primary role is a scientific one, but they are also designers.
Prototypers are the product team members who bring the team’s ideas to a tangible state to help the company quickly validate with users the product’s features and other characteristics. In a company that makes physical products, prototypers will hand-craft mockups. For digital companies, the prototyping team will develop wireframes or other virtual mockups.
Of course, in many cases, a company will hire a person to handle several of the roles above and others under a product designer job. Other companies will handle some of the bigger picture, strategic elements of developing new product ideas. There, other professionals in the organization take responsibility for things like—user research, UX design, information architecture, etc.
The details of the product design process will vary from company to company, but these professionals tend to follow a similar philosophy or framework when it comes to design thinking. As Cam Sackett explains, the design-thinking process involves several steps:
Empathize with people
Define the problem
Ideate a solution
Build a prototype
Test the solution
Sackett also points out that the design process doesn’t necessarily move in a linear path, although arranged linearly. Sometimes the results learned in a given step lead the team back to repeat or refine an earlier step.
The product design process never truly stops, even once a product reaches maturity. That’s because technology and how users interact with it keep evolving.
Take the ever-increasing importance of mobile devices. For years, mobile apps and phone-friendly websites had limited capabilities, while the bulk of the user experience required a full computer.
But product designers have had to keep pace with shifting usage patterns, bringing more and more functionality to smaller screens to meet the real-world usage preferences of customers. And with each new technological innovation, product design must determine its potential impact on the user experience and adjust accordingly.
Lean Product Design
Bringing innovative products to market as quickly as possible is the bedrock of Lean, Agile, and other popular approaches to software development. But Lean Product Design goes one step further, introducing rapid iterations to the pre-coding product development phase.
The process identifies the product’s key value proposition and differentiators, speedily introducing a working-yet-limited product to spark the feedback loop immediately and begin generating sales or queue up interesting prospects to establish and quantify product-market fit.
Facilitating this expeditious journey to product introduction requires lots of cross-functional interactions and collaboration. It’s not uncommon for product designers to partner with product managers or business-side experts for the initial concept development before joining forces with a lone developer or small team to generate working prototypes and early versions of the product.
These Lean teams succeed because they share a common goal and both welcome and incorporate user feedback swiftly. The focus on reducing waste—a holdover from Lean’s manufacturing origins—applies in these cases to maximizing resource utilization and not sweating the small stuff until the major components are proven to resonate with users, solve their problems, and create value and satisfaction.
Lean Product Design only works in organizations that embrace continuous learning and accept that they already know everything. Moving forward with unknowns and unanswered questions doesn’t always sit well in larger enterprise settings. In these settings, they build, measure, and learn in the face of well-plotted master plans. The plans stretch out years into the future.
Chunking out larger solutions into smaller, discrete products or features may calm some of that trepidation. It gives stakeholders a chance to see progress and watch how the continuous feedback loop and rapid iterations result in solutions that truly meet the market needs in short order. It may feel risky at first, but Lean Product Design is actually a far safer bet than building a huge product over months or years with zero external input until it ships or moves into a beta program.
Because it covers a broad range of disciplines, the role requires several different types of tools. Among these are:
Journey mapping apps
Graphic design apps
Research and data analytics tools (e.g., spreadsheets, sophisticated A/B testing apps)
CAD (computer-aided design) software
Project management apps (e.g., Trello)
Product roadmap apps (e.g., ProductPlan)
Product design is a far broader, far more strategically central role than most people realize. It is not simply the process of making a product look better. As Eric Eriksson writes, “product design is the whole process.” You evaluate problem validation, as well as crafting, designing, testing, and shipping the solution.
See also: Product Designer, Product Architecture, Design Ops, Design Thinking.
- 3 How a manager involves employees in TQM process to control the quality of product? Discuss with examples. (20)
Total employee involvement is an organization methodology and set of management principles that encourages individual contributors, team members, and employees to participate much more in the problem solving, decision making and planning processes that affect their organization.
Within total employee involvement, team members are encouraged to learn more about their organization, contribute ideas, feel more engaged, and look for new opportunities that will help the organization be more competitive and effective. Traditionally this type of work and responsibility has resided with only managers and leaders in the organization.
This philosophy engages your workforce to consistently contribute directly to improving their organization. It may contribute to greater overall retention rates, in addition to increased employee motivation to give ideas and help innovate. Employee involvement of any degree results in greater motivation, performance, and sense of responsibility for the long term sustained success of the enterprise.
Total employee involvement may be implemented through a variety of initiatives:
Interview and short surveys that ask employees for specific opportunities and recommendations to improve the business—provided leadership is willing to actively respond to the data
Internal training that gives team members the skills to contribute more to organizational improvements and transformation
A steering committee team that finds ways to get members involved and helps them contribute
An HR team that helps contributors expand their responsibilities over time
Benefits of Employee Involvement
With total employee involvement, all team members have the opportunity to help their organization grow, reach its objectives, and overcome obstacles. They are able to use more of their talents and intellect, along with feeling connected to other team members who are doing the same. This also leads to benefits for the organization, such as:
High Level Retention: When your team members see that you are interested and care about their talents and contributions, they’ll be less likely to want to go to another company. Plus, they may tell others about their job satisfaction, which can attract new talent.
Higher Motivation: Letting individual contributors use their abilities and autonomy will motivate them to work smarter and be engaged. Plus, managers who believe in total employee involvement communicate much more with their team members, which improves morale and encourages creativity and inspiration.
Enhanced Talent Development: Through greater communication, training and coaching, this management approach will help lead to colleagues who understand your organization, customers, and industry much better. As they learn, team members will feel free to give feedback, share insights, and take responsibility to implement business improvements. Over time, people will gain greater passion for continuous improvement, which will motivate them to continue learning and contributing to the success of the enterprise.
Easier Change Management: All organizations go through the never-ending cycle of change. Team members who don’t understand the reasons for changes tend to resist them. In contrast, members who are already involved in organizational improvements are likely to adapt to change more quickly and actually be change agents for the business.
Focus on Results: When team members are informed about your organization’s goals and objectives and involved in obtaining them, they will be more focused on critical K.P.I.’s and create value for all stakeholders. Managers will have less need to push contributors to focus on objectives.
Disadvantages of Employee Involvement in Decision Making?
Total employee involvement can be difficult and a disadvantage if you believe that your team members are skeptical and adversarial or act out of ill will toward the organization due to past mistreatments.
Theories about human nature hypothesize that drive for achievement is always suspect because of unconscious drives and motives. These theories suggest that individuals have no inherent motivation of their own to take responsibility for anyone or anything besides themselves. Leaders who subscribe to such philosophies will see total employee involvement as risky and speculative.
Another disadvantage is that in some crises, delicate situations, or circumstances which require confidentiality, leaders simply need to provide explicit direction to people without collaboration. For example, a leader may need to inform the workforce that they must follow certain government regulations. Not everything can be debated or contested.
Sometimes leaders will need to take a middle approach in some situations in which they can consult with team members for opinions and guidance but must ultimately make a final decision on their own. There is still a risk that team members may feel that their input isn’t being respected if they feel that policies are always flowing from the top down.
Total quality management (TQM) is an older framework for organizational management. It started to become less popular in the 1990s, but some of its eight essential principles are still relevant to various forms of employee involvement.
One of those principles speaks directly to total employee involvement. It was defined as having every team member put some of their own ideas and efforts into shared goals and objectives. TQM proclaims that employees should be given the opportunity to self-manage, be empowered to make important decisions, and be treated in such a way that they might be confident and feel respected in the workplace.
How Can Leaders Improve Employee Involvement?
Experimenting with employee involvement may be an effective strategy for your organization. But there are some important considerations to keep in mind as you improve or implement greater levels of employee involvement.
Open Communication: Give all team members an easy method to reach out to leaders and give them ideas, express concern, and discuss opportunities.
Better Feedback Mechanisms: Improve the ways that you get feedback from individual contributors. Allow for anonymous input and be sure to respond to feedback and comments on all inquiries.
Improved Onboarding: Provide thorough training and explain expectations to new employees, get their questions answered, and provide the resources they need to start contributing more quickly.
Peer Recognition: Create processes and spaces that encourage team members to publicly recognize the contributions of their peers and offer suggestions to enhance performance.
Shared Stories: Set up online communities where colleagues can tell each other stories and share their experiences. Let them give each other insights about opportunities for improvement in a natural way.
- 4 What is man machine analysis and why it is important? Discuss that how man machine analysis is conducted to get desired results. (20)
This introduction to machine learning provides an overview of its history, important definitions, applications and concerns within businesses today.
What is machine learning?
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
IBM has a rich history with machine learning. One of its own, Arthur Samuel, is credited for coining the term, “machine learning” with his research (PDF, 481 KB) (link resides outside IBM) around the game of checkers. Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer. Compared to what can be done today, this feat almost seems trivial, but it’s considered a major milestone within the field of artificial intelligence. Over the next couple of decades, the technological developments around storage and processing power will enable some innovative products that we know and love today, such as Netflix’s recommendation engine or self-driving cars.
Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them.
Machine Learning vs. Deep Learning vs. Neural Networks
Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, deep learning is actually a sub-field of machine learning, and neural networks is a sub-field of deep learning.
The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as “scalable machine learning” as Lex Fridman notes in this MIT lecture (01:08:05) (link resides outside IBM). Classical, or “non-deep”, machine learning is more dependent on human intervention to learn. Human experts determine the set of features to understand the differences between data inputs, usually requiring more structured data to learn.
“Deep” machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the set of features which distinguish different categories of data from one another. Unlike machine learning, it doesn’t require human intervention to process data, allowing us to scale machine learning in more interesting ways. Deep learning and neural networks are primarily credited with accelerating progress in areas, such as computer vision, natural language processing, and speech recognition.
Neural networks, or artificial neural networks (ANNs), are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network. The “deep” in deep learning is just referring to the depth of layers in a neural network. A neural network that consists of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm or a deep neural network. A neural network that only has two or three layers is just a basic neural network.
A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labelled or unlabeled, your algorithm will produce an estimate about a pattern in the data.
An Error Function: An error function serves to evaluate the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.
An Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this evaluate and optimize process, updating weights autonomously until a threshold of accuracy has been met.
Machine learning methods
Machine learning classifiers fall into three primary categories.
Supervised machine learning
Supervised learning, also known as supervised machine learning, is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights until the model has been fitted appropriately. This occurs as part of the cross validation process to ensure that the model avoids overfitting or underfitting. Supervised learning helps organizations solve for a variety of real-world problems at scale, such as classifying spam in a separate folder from your inbox. Some methods used in supervised learning include neural networks, naïve bayes, linear regression, logistic regression, random forest, support vector machine (SVM), and more.
Unsupervised machine learning
Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. Its ability to discover similarities and differences in information make it the ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, image and pattern recognition. It’s also used to reduce the number of features in a model through the process of dimensionality reduction; principal component analysis (PCA) and singular value decomposition (SVD) are two common approaches for this. Other algorithms used in unsupervised learning include neural networks, k-means clustering, probabilistic clustering methods, and more.
Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of having not enough labeled data (or not being able to afford to label enough data) to train a supervised learning algorithm.
For a deep dive into the differences between these approaches, check out “Supervised vs. Unsupervised Learning: What’s the Difference?”
Reinforcement machine learning
Reinforcement machine learning is a behavioral machine learning model that is similar to supervised learning, but the algorithm isn’t trained using sample data. This model learns as it goes by using trial and error. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem.
The IBM Watson® system that won the Jeopardy! challenge in 2011 makes a good example. The system used reinforcement learning to decide whether to attempt an answer (or question, as it were), which square to select on the board, and how much to wager—especially on daily doubles.
Speech recognition: It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability which uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting.
Customer service: Online chatbots are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.
Computer vision: This AI technology enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry.
Recommendation engines: Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.
Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.
Challenges of machine learning
As machine learning technology advances, it has certainly made our lives easier. However, implementing machine learning within businesses has also raised a number of ethical concerns surrounding AI technologies. Some of these include:
While this topic garners a lot of public attention, many researchers are not concerned with the idea of AI surpassing human intelligence in the near or immediate future. This is also referred to as superintelligence, which Nick Bostrum defines as “any intellect that vastly outperforms the best human brains in practically every field, including scientific creativity, general wisdom, and social skills.” Despite the fact that Strong AI and superintelligence is not imminent in society, the idea of it raises some interesting questions as we consider the use of autonomous systems, like self-driving cars. It’s unrealistic to think that a driverless car would never get into a car accident, but who is responsible and liable under those circumstances? Should we still pursue autonomous vehicles, or do we limit the integration of this technology to create only semi-autonomous vehicles which promote safety among drivers? The jury is still out on this, but these are the types of ethical debates that are occurring as new, innovative AI technology develops.
AI impact on jobs
While a lot of public perception around artificial intelligence centers around job loss, this concern should be probably reframed. With every disruptive, new technology, we see that the market demand for specific job roles shift. For example, when we look at the automotive industry, many manufacturers, like GM, are shifting to focus on electric vehicle production to align with green initiatives. The energy industry isn’t going away, but the source of energy is shifting from a fuel economy to an electric one. Artificial intelligence should be viewed in a similar manner, where artificial intelligence will shift the demand of jobs to other areas. There will need to be individuals to help manage these systems as data grows and changes every day. There will still need to be resources to address more complex problems within the industries that are most likely to be affected by job demand shifts, like customer service. The important aspect of artificial intelligence and its effect on the job market will be helping individuals transition to these new areas of market demand.
Privacy tends to be discussed in the context of data privacy, data protection and data security, and these concerns have allowed policymakers to make more strides here in recent years. For example, in 2016, GDPR legislation was created to protect the personal data of people in the European Union and European Economic Area, giving individuals more control of their data. In the United States, individual states are developing policies, such as the California Consumer Privacy Act (CCPA), which require businesses to inform consumers about the collection of their data. This recent legislation has forced companies to rethink how they store and use personally identifiable data (PII). As a result, investments within security have become an increasing priority for businesses as they seek to eliminate any vulnerabilities and opportunities for surveillance, hacking, and cyberattacks.
Bias and discrimination
Instances of bias and discrimination across a number of intelligent systems have raised many ethical questions regarding the use of artificial intelligence. How can we safeguard against bias and discrimination when the training data itself can lend itself to bias? While companies typically have well-meaning intentions around their automation efforts, Reuters (link resides outside IBM) highlights some of the unforeseen consequences of incorporating AI into hiring practices. In their effort to automate and simplify a process, Amazon unintentionally biased potential job candidates by gender for open technical roles, and they ultimately had to scrap the project. As events like these surface, Harvard Business Review (link resides outside IBM) has raised other pointed questions around the use of AI within hiring practices, such as what data should you be able to use when evaluating a candidate for a role.
Bias and discrimination aren’t limited to the human resources function either; it can be found in a number of applications from facial recognition software to social media algorithms.
As businesses become more aware of the risks with AI, they’ve also become more active this discussion around AI ethics and values. For example, last year IBM’s CEO Arvind Krishna shared that IBM has sunset its general purpose IBM facial recognition and analysis products, emphasizing that “IBM firmly opposes and will not condone uses of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values and Principles of Trust and Transparency.”
Since there isn’t significant legislation to regulate AI practices, there is no real enforcement mechanism to ensure that ethical AI is practiced. The current incentives for companies to adhere to these guidelines are the negative repercussions of an unethical AI system to the bottom line. To fill the gap, ethical frameworks have emerged as part of a collaboration between ethicists and researchers to govern the construction and distribution of AI models within society. However, at the moment, these only serve to guide, and research (link resides outside IBM) (PDF, 1 MB) shows that the combination of distributed responsibility and lack of foresight into potential consequences isn’t necessarily conducive to preventing harm to society.
- 5 Explain Transportation Method. Discuss the mechanism of Transportation Method with examples. (20)
Operations Research (OR) is a state of art approach used for problem-solving and decision making. OR helps any organization to achieve their best performance under the given constraints or circumstances. The prominent OR techniques are,
One of the problems the organizations face is the transportation problem. It originally means the problem of transporting/shipping the commodities from the industry to the destinations with the least possible cost while satisfying the supply and demand limits. It is a special class of linear programming technique that was designed for models with linear objective and constraint functions. Their application can be extended to other areas of operation, including
Scheduling and Time management
Enterprise resource planning
LP transportation problem arc and node diagram
The notations of the representation are:
m sources and n destinations
(i , j) joining source (i) and destination (j)
cij 🡪 transportation cost per unit
xij 🡪 amount shipped
ai 🡪 the amount of supply at source (i)
bj 🡪 the amount of demand at destination (j)
Transportation problem works in a way of minimizing the cost function. Here, the cost function is the amount of money spent to the logistics provider for transporting the commodities from production or supplier place to the demand place. Many factors decide the cost of transport. It includes the distance between the two locations, the path followed, mode of transport, the number of units that are transported, the speed of transport, etc. So, the focus here is to transport the commodities with minimum transportation cost without any compromise in supply and demand. The transportation problem is an extension of linear programming technique because the transportation costs are formulated as a linear function to the supply capacity and demand.
Transportation problem exists in two forms.
It is the case where the total supply equals the total demand.
It is the case where either the demand is greater than the supply, or vice versa.
In most cases, the problems take a balanced form. It is because usually, the production units work, taking the inventory and the demand into consideration. Overproduction increases the inventory cost whereas under production is challenged by the demand. Hence the trade-off should be carefully examined. Whereas, the unbalanced form exists in a situation where there is an unprecedented increase or decrease in demand.
Let us understand this in a much simpler way with the help of a basic example.
Let us assume that there is a leading global automotive supplier company named JIM. JIM has it’s production plants in many countries and supplies products to all the top automotive makers in the world. For instance, let’s consider that there are three plants in India at places M, N, and O. The capacity of the plants is 700, 300, 550 per day. The plant supplies four customers A, B, C, and D, whose demand is 650, 200, 450, 250 per day. The cost of transport per unit per km in INR and the distance between each source and destination in Kms are given in the tables below.
cost per km
distance between source and demand
Here, the objective is to determine the unknown while satisfying all the supply and demand restrictions. The cost of shipping from a source to a destination is directly proportional to the number of units shipped.
Many sophisticated programming languages have evolved to solve OR problems in a much simpler and easier way. But the significance of Microsoft Excel cannot be compromised and devalued at any time. It also provides us with a greater understanding of the problem than others. Hence we will use Excel to solve the problem.
It is always better to formulate the working procedure in steps that it helps in better understanding and prevents from committing any error.
Steps to be followed to solve the problem:
Create a transportation matrix (define decision variables)
Define the objective function
Formulate the constraints
Solve using LP method
Creating a transportation matrix:
A transportation matrix is a way of understanding the maximum possibilities the shipment can be done. It is also known as decision variables because these are the variables of interest that we will change to achieve the objective, that is, minimizing the cost function.
From / To A B C D Supply
M xma xmb xmc xmd 700
N xna xnb xnc xnd 300
O xoa xob xoc xod 550
Demand 650 200 450 250
Define the objective function:
An objective function is our target variable. It is the cost function, that is, the total cost incurred for transporting. It is known as an objective function because our interest here is to minimize the cost of transporting while satisfying all the supply and demand restrictions.
The objective function is the total cost. It is obtained by the sum product of the cost per unit per km and the decision variables (highlighted in red), as the total cost is directly proportional to the sum product of the number of units shipped and cost of transport per unit per Km.
The column “Total shipped” is the sum of the columns A, B, C, and D for respective rows and the row “Total Demand” is the sum of rows M, N, and O for the respective columns. These two columns are introduced to satisfy the constraints of the amount of supply and demand while solving the cost function.
Formulate the constraints:
The constraints are formulated concerning the demand and supply for respective rows and columns. The importance of these constraints is to ensure they satisfy all the supply and demand restrictions.
For example, the fourth constraint, xma + xna + xoa = 650 is used to ensure that the number of units coming from plants M, N, and O to customer A should not go below or above the demand that A has. Similarly the first constraint xma + xmb + xmc + xmd = 700 will ensure that the capacity of the plant M will not go below or above the given capacity hence, the plant can be utilized to its fullest potential without compromising the inventory.
Solve using LP method:
The simplest and most effective method to solve is using solver. The input parameters are fed as stated below and proceed to solve.
This is the best-optimized cost function, and there is no possibility to achieve lesser cost than this having the same constraints.
From the solved solution, it is seen that plant M ships 100 units to customer A, 350 units to C and 250 units to D. But why nothing to customer B? And a similar trend can be seen for other plants as well.
What could be the reason for this? Yes, you guessed it right! It is because some other plants ship at a profitable rate to a customer than others and as a result, you can find few plants supplying zero units to certain customers.
So, when will these zero unit suppliers get profitable and can supply to those customers? Wait! Don’t panic. Excel has got away for it too. After proceeding to solve, there appears a dialogue box in which select the sensitivity report and click OK. You will get a wonderful sensitivity report which gives details of the opportunity cost or worthiness of the resource.
Basic explanation for the report variables,
Cell: The cell ID in the excel
Name: The supplier customer pairing
Final value: Number of units shipped (after solving)
Reduced cost: How much should the transportation cost per unit per km should be reduced to make the zero supplying plant profitable and start supplying
Objective coefficient: Current transportation cost per unit per Km for each supplier customer pair
Allowable Increase: It tells us the maximum cost of the current transportation cost per unit per Km can be increased which doesn’t make any changes to the solution
Allowable Decrease: It tells how much maximum the current transportation cost per unit per Km can be lowered which doesn’t make any changes to the solution
Here, look into the first row of the sensitivity report. Plant M supplies to customer A. Here, the transportation cost per unit per Km is ₹14 and 100 units are shipped to customer A. In this case, the transportation cost can increase a maximum of ₹6, and can lower to a maximum of ₹1. For any value within this range, there will not be any change in the final solution.
Now, something interesting. Look at the second row. Between MB, there is not a single unit supplied to customer B from plant M. The current shipping cost is ₹22 and to make this pair profitable and start a business, the cost should come down by ₹6 per unit per Km. Whereas, there is no possibility of increasing the cost by even a rupee. If the shipping cost for this pair comes down to ₹16, we can expect a business to begin between them, and the final solution changes accordingly.
The above example is a balanced type problem where the supply equals the demand. In case of an unbalanced type, a dummy variable is added with either a supplier or a customer based on how the imbalance occurs.
Thus, the transportation problem in Excel not only solves the problem but also helps us to understand how the model works and what can be changed, and to what extent to modify the solution which in turn helps to determine the cost and an optimal supplier.