What we do

The next step in your Digitisation

 

We help our customers with the next step in their digitisation, by finding the benefit of applying AI and Machine Learning in or with their Business Critical systems.

Predict - Better basis for decisions, now and in the future

AI and Machine Learning improve, rationalise and streamline time-consuming and resource-intensive business processes. Man is freed from extensive manual work of handling thousands of objects. Units with a large number of continuously changing parameters and can instead engage in more creative strategic work where people are more important.

 

AI / Machine Learning for Supply Chain

Supply chain generates very large volumes of complex data. Machine learning analyzes the information and finds insights that streamline supply chain management (SCM).

 

Opimize Supply Chain

Machine learning can analyze events and times for products that move throughout the supply chain.

Supply chain compares data with benchmark and history data to identify potential waiting times or bottlenecks and provides suggestions for measures to increase efficiency.

Forecast likely demand from customers

Data can be retrieved from many different areas such as market, seasonal campaigns, sales, history analysis, etc.

Machine learning combines this data to predict the demand for specific goods and helps control logistics and manufacturing for these products.

Plan Supply Chain for goods based on demand

Efficient supply chain provides products in the right place at the right time

Machine learning calculates customer requirements and optimizes supply chain upstream.

Machine learning matches the delivery of goods with market demand.

 

Control of suppliers and agreements

Supplier management is one of the most challenging parts of SCM.

Machine learning can analyze the type of contract and other parameters that lead to the best delivery from the supplier and use this information for future contracts and deliveries.

Ensure quality from suppliers, products and machines

Quality is central to SCM as faulty products create unnecessary extra work and increased costs.

Machine learning can monitor how quality varies over time and suggest improvements.

This does not only apply to materials and products.

Machine learning can handle other areas such as shipping, supplier and 3rd party quality.

Why Machine Learning is Important for the Supply Chain

Machine learning - strengthens SCM:

  • Supply chain organizations can reduce their inventory as machine learning optimizes the flow of articles and goods from one place to another

  • Costs are reduced as machine learning drives quality improvement and reduced waste

  • Products are delivered to the market "just in time" for sale through optimization of the supply chain

  • Control of suppliers is facilitated through simpler administration

  • Product owners gain more insight through meaningful information that supports continuous improvement and easier problem solving

Utmaningar med Machine Learning i Supply Chain

Machine learning bygger på tillförlitlig högkvalitativ och aktuell information.

Utebliven tillgång till rätt data kan skapa problem för machine learning i supply chain.

Ett beslutsamt sätt att hämta och analysera data är hög prioritet för supply chain managers:

  • Alla parter i supply chain ska leverera information på ett likartat sätt.

  • Där det är möjligt ska SCM integreras med leverantörers- och tillverkares system för automatisk överföring av data

  • Supply chain information bör kontrolleras periodiskt för att säkerställa kvalitet.

  • Machine learning modeller bör testas och kontrolleras för att säkerställa att utdata och förslag är i linje med verksamhetens behov och förväntningar.

Use Case for Machine Learning in Retail and Manufacturing Supply Chain

There are many good use cases for optimizing the supply chain via machine learning:

  • Stock analysis can identify when products decrease in popularity and when the end is approaching in the market

  • Price analysis can compare costs in the supply chain and profit levels in retail to optimize price and demand among customers

  • Upstream delays can be identified, enabling continuous planning or alternative supply

  • Retailers can link campaigns to demand and delivery planning so stores do not run out of stock

  • Retailers can reduce inventory costs by reducing inventory levels.

  • Analysis of the price of raw materials and external conditions can optimize cost and time for manufacturers

  • Manufacturers can reduce the time for launch in the market by optimizing contracts and reducing lead times upstream in the organization

 

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Machine Learning for Purchasing

 

The right basis for Strategy

Many purchasing organizations structure and manage comprehensive strategic information. It is common to handle large numbers of suppliers with many parameters, the picture quickly becomes complex, which makes control and operation resource-intensive.

Purchasing seeks the greatest possible outcome and efficiency. To achieve this, strategy and the ability to choose the right are required. It can be compared to a balance wave, hitting it in the right direction increases the chances of making the best overall decision in each situation.

Strategy work creates an action plan. The action plan is planned and budgeted to achieve the goals as efficiently as possible. With limited resources and time for strategy work, it is of utmost importance to choose the right focus at the right time.

An important part of strategy work is to work to have as much digitized information as possible.

Strategy governs the choice of Tactics based on the best available information and conditions in Operation. Tactics lead the operational work that is strongly dependent on complete, correct and updated information. Operational and tactical work comprises the bulk of the working time in the regular operations.

Have the right information

The goal is to have current and correct information in order to have the best possible basis when decisions are to be made. Information consists of both quantitative and qualitative parameters that explain the supplier's condition from different perspectives. If the condition deteriorates, the risk of ending up in a weak position increases, which can lead to a business risk. Traditional valuation model is based on volumes, values, etc. and has difficulty managing the state of all parameters.

Parameters often contain requirements that must be met. There can be a great risk if parameters fail, even stock market values may be at stake.

Your Information 

Most organizations have digitized their processes to some degree. Many people use Business Intelligence (BI) to see status and history where system support is not enough.

Purchasing organizations lose many percent efficiency due to various shortcomings in sufficient, accurate and up-to-date information.

Status and history are not sufficient information. Artificial Intelligence (AI) and Machine Learning (ML) take your analysis to the next level. By realizing proactive work, a comprehensive picture is given of where changes exist and will exist and where measures should be implemented. This cannot be done due to extensive complexity and lack of time and resources.

See the whole picture

Purchasing can also not be controlled effectively on individual parameters that BI-based solutions offer. AI and Machine Learning continuously provide a full overview and control of all objects with its parameters that provide the opportunity to control and prioritize where operations are to be performed.

It is common for the information to be in different systems. Compiling this complex information provides new conditions for controlling and managing the issues associated with the information.

Profitable

Machine Learning has a very large savings potential as the status changes over time and new parameters are added.

Machine Learning enables control of the business by the information goes from passive to active and makes action go from reactive to proactive.

Frequent reading of state parameters through automatic signals and updates of changes can automatically lead to operational activities, from visualization to action.

Integrate predictive analytics into your business

 

To take full advantage of artificial intelligence and predictive analytics, there are four elements that must be considered.

1. The right questions

Successful projects with predictive analytics begin with a good model to try. Even though we will use machine learning with algorithms that will make their own associations between different data points, we need to define the business challenge that will go in goal. This helps to create clarity in the goal.

2. Correct data 

Developments in IT over the past decade have enabled us to gain insights from large amounts of unstructured data with greater precision. However, we need to process data to reach convincing conclusions. The next step after defining questions that you want answered with predictive analytics, is to find out what data is available and if it is sufficient to answer your questions.

3. The right technology

Computer technology is a fast-growing industry. Data is created at such a fast pace that we need to continuously increase our ability to capture, store and do something with it.

Many of the leading standardized analysis tools have already launched predictive analytics tools, using various methods.

4. The right skills

To decide which solution best suits your business, it is more important than ever to have a team with expertise and experience in place to ask the right questions and identify the best solution.​