Quick repricing or how to generate significant savings with tail suppliers (video)

Companies have increasingly large supply bases for various reasons: acquisitions, decentralized procurement, uncontrolled supplier portfolios, or process weakness. Due to the high number of suppliers, supply processes, and resource constraints, significant productivity goes unrealized. Strategic sourcing teams focus on high-value-add suppliers and strategic projects while plant buyers address tactical suppliers when issuing orders. At a time of increased cost pressures and complex supply chains, a significant amount of spend goes unaddressed year over year. With sourcing managers pushing their supply base to drive new innovations, achieve greater productivity, and keep the plants running, companies are continuing to ignore the tail spend and leave savings unrealized. That’s why Kepler has developed a Quick Repricing offering to generate savings with…

The use of machine learning to generate quick savings and gain in price consistency

The purpose of this article is to share our experience in the use of machine learning to generate quick saving opportunities and homogenize purchased pricing across business units / regions in large groups.

  Context and challenges within a multi-BU group

It could rapidly become cumbersome for a large group and its supply management team to ensure consistency of prices on a specific category.


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    Why pay $5.6 dollars for an extruded tube of 10cm long and 2mm diameter and $20 for 20cm and 2.5mm diameter tube?


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    Or a certain price for a red product and double for its blue version?

Those gaps can be normal but need to be controlled and understood so that suppliers or R&D can be challenged.

Even with a corporate organization and systems, Business Units are often independent or even worse, siloed. They could implicitly refuse to play it global because they need to go fast and stay independent. But, beyond organization & cultural behaviors, main barriers are technical & IT related:


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    Products have different specifications & characteristics making the price comparison and alignment difficult


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    There is no common repository combining technical & economical information


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    History of pricing is rarely reviewed


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    ERP system not alerting on pricing gaps


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    Tail pricing is not properly controlled


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    Pricing revision routines can be different across the group


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    Multi contract with similar suppliers


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    Suppliers apply a different cost of doing business per BU / Region

For sure, the traditional should cost approach and solutions can help solve these issues.

However, they are often complex to implement on a large scale, because they require a lot of manufacturing process related information, strong technical expertise and deep & time-consuming cooperation with suppliers (open book policy is mandatory).

 Refer to our article “Supply Chain optimization: a total cost approach

  Value proposition

Many firms have developed an innovative & effective approach combining advanced analytics and predictive algorithms (derived from Artificial Intelligence) to generate quick savings opportunities, only by processing your existing data.

The methodology is based on 5 pillars:


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    Create a cost model based on shared cost drivers considering BU/Region specifics


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    Utilize benchmarking to identify gaps across BU/Regions and optimized sourcing strategies


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    Identify overpriced products and quantify savings opportunities


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    Utilize data mining to determine optimization levers and prepare arguments for supplier negotiations


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    Specify the management systems & required needs to sustain the process 

The following benefits have been observed:


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    Identify savings opportunities (negotiation, VAVE, resourcing)


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    Generate quick wins through “analytics based” negotiations with suppliers


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    Control supplier quotes on new projects


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    Improve Sourcing Strategies through optimized supplier allocation per cluster


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    Drive continuous improvement through a robust cost model, increased skills, and improved cross-functional collaboration

  Machine learning based costing solutions

Machine learning based costing solution which estimates the price of a new product or service by processing current/historical data through a sophisticated algorithm, such as “Random Forrest”. Random Forrest is a nonparametric statistical method that performs learning on multiple decision trees driven on slightly different subsets of data generated by Bootstrap techniques (Ref. Breiman, L., Random Forests. Machine Learning. 45, 5-32 (2001)).

This type of methods allows to estimate the price of a product/service based on pre-identified parameters called “cost-drivers”. The estimation is very quick and accurate (30% of increased accuracy in comparison to traditional statistical methods).

The main advantages of machine learning based costing compared to traditional methods are:


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    The user does not need to be an expert of the manufacturing process of the product. The estimate is based exclusively on the product characteristics (“cost drivers”) which are information you have access to internally (vs. asking to your supplier)


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    It can mix an infinite number of cost drivers, those cost drivers can be continuous or discrete, technical (weight, function, color, raw material type, …) or commercial (country, volumes, supplier…)


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    Ability to process databases for which the number of variables largely exceeds the number of observations


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    Ability to identify and weight automatically the most important parameters, and therefore the cost drivers that impact the most


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    Ability to interpret results


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    Ability to manage missing values / incomplete database

For all these reasons machine learning based software helps create a very robust and ready-to-use costing model.

Beyond the above, machine learning based costing solution is processing all current purchasing prices and identifying inconsistencies / gaps versus estimates, which makes it easy to identify savings opportunities, including negotiations with suppliers.

Finally, some solutions have integrated benchmark functionalities which allow to compare each BU/Region for a specific category (even through products that have different design & characteristics).

They are increasingly utilized in industry and therefore is, every day, adding external benchmark knowledge for each commodity (IP and confidentialities being respected).

This allows to create benchmarking communities and share further (life sciences, automotive…)..

  Examples

Advantages

Limitations

Examples of software

Analytical Model

  • Explanatory and operations- centered model
  • “Best Landed Cost” Estimation and Target Price Definition
  • Allows to optimize the prices in production and to control the plans of progresses suppliers
  • Difficulty accessing process references and maintaining them over time
  • Intrusive approach towards suppliers
  • Expert model with little diffuse
  • Timeout for setting and performing encryption
  • Precision ?
  • Siemens PLM
  • A Priori
  • Facton
  • Statistical Parametric Model

    • Easy and quick to use
    • Estimated price coherence, and accuracy (conditional)
    • Non-intrusive approach to suppliers
    • Product and service applications
    • Very relevant in the upstream phases of the life cycle and for the analyses of coherence
    • Requires minimal data and quality history
    • Model that is not very “explanatory” to moderate supplier progress plans
    • Less relevant model for setting target prices and “Best Landed Cost”
    • Difficulty in modeling qualitative parameters
  • Seer
  • EstimFEC
  • Non-Parametric Statistical Model

    « Random Forests »

    • Easy and quick to use
    • Consistency of the estimated price, and precision increased by 30% compared to parametric models (conditional)
    • Non-intrusive approach to suppliers
    • Product and service applications
    • Very relevant in the upstream phases of the life cycle
    • Relevant also in the downstream phases for the analysis of price coherences and the identification of opportunities thanks to the explanatory properties of the forests
    • Integrates a lot of cost drivers, including qualitative ones
    • Detects technological breakthroughs
    • Prioritizes cost drivers
    • Manages missing values and can work with a limited sample
  • Model less relevant for setting target prices and “Best Landed Cost”
  • easyKost
  • Algorithms and Artificial Intelligence: New Horizons for Cost Estimation and Modeling?

    The progress made in the last 20 years in the field of statistics have made it possible to develop predictive algorithms that are much more efficient, especially in terms of precision. What are the possible applications in the field of cost estimation and modeling? While traditional analytical models based on the manufacturing processes of the product or service are still widely used in our Cartesian society, statistical models are gradually imposing themselves to their formidable efficiency. But rather than an opposition, these two methods are enriched and complement each other.

    Traditional costing models

    As a reminder, there are now 3 main methods used to estimate the cost of a product:

    1

    The analogical method

    This method estimates the cost of a new product compared to similar products produced or purchased in the past. This method is unreliable, but can be used in extremely upstream phases (study of opportunity) when the characteristics of the project or the service are not yet known. We will not detail this type of basic estimate in this article.

    2

    The analytical method

    It estimates the cost of a product by modeling the industrial production process. This method is based on the cost structure of the product of which it estimates each intermediate element, based on the materials & components involved, process costs (machine and labor), and related structural costs. This method has several advantages:

    • It allows to estimate an optimized and theoretical cost of production by modeling a virtual factory on the basis of the best ratio (labor cost, TRS, Scraps, …).
    • It allows to give an ambitious cost target and to identify the “Best Landed Cost” for a given product.
    • It also makes it possible to identify in a concrete way the sources of non-performance of the suppliers (on which process step, which cost item, which indicator …) and to engage with them a continuous improvement process to capture productivity.
    • This method is therefore particularly useful in the downstream phases of the life cycle (production, continuous improvement, product redesign, etc.).

    However, the analytical method has some disadvantages or constraints to its implementation:

    • It requires a good understanding of the manufacturing processes involved as well as key parameters (TRS, Scraps, cycle time …). So much information is not always easy to collect and capitalize with suppliers.
    • The determination of the “Best Landed Cost” requires feeding these tools with benchmark data on production parameters, and keeping these benchmarks up to date.
    • If the standard processes can be modeled more or less quickly (injection, extrusion, casting, cutting, striking, surface treatment …), the encryption of a complex product is often tedious. It requires a specialized expertise that only a few people master in the company…
    • As a result, encryption cells quickly experience bottlenecks, with processing delays incompatible with agile development and time-to-market constraints.
    • Finally, if these models have a real relevance to give cost targets, they often lack precision, because they do not take into account the hazards or certain external factors (balance of power, market effects, …) especially since many suppliers have a very low level of maturity on the control of their industrial cost price (PRI).

    Existing software solutions on the market address some of these problems by offering in particular integrated benchmarks on several manufacturing processes with benchmark data per country. Some editors have also developed interfaces that provide CAD file reading, which allows automating the proposal of manufacturing processes (virtual factory). However, these kinds of software remain heavy and long to set up and are used only by a few experts.

    3

    The parametric method

    This method estimates the cost of a product or service by statistical modeling. This method uses similar product or service histories to define equations or statistical laws that allow to model the evolution of the cost according to certain parameters known as “cost drivers”. These models are mostly based on linear, multilinear, polynomial or logarithmic regressions. These estimation methods have several advantages:

    • They make it possible to estimate the cost of a new product / service based on simple and known characteristics of the company (weight, size, volumes, country of production, key elements of the specification …) without necessarily knowing the details of the manufacturing process or external benchmarks. It is therefore a very quick and simple method to implement.
    • On the other hand, based on the observation of products / services actually manufactured or purchased in the past, the estimated cost is potentially more consistent and precise than a “theoretical” analytical model, provided that there is sufficient quality history.
    • These statistical methods are particularly useful in the early phases of life cycle (opportunity, feasibility, detailed design …) because they make it possible to make the right decisions quickly for an optimized design and thus to secure the margin while accelerating the “time to market “.
    • Further downstream, they also make it possible to quickly analyze the consistency or the inconsistencies in the current prices, thanks to the dispersion analyses with respect to the predictive model. Thus, they reveal aberrant products or services, at an abnormally high cost, for example, with regard to the predictive model. This gives optimization leads for buyers (renegotiation, change of supplier) or for R & D (redesign).

    On the other hand, these methods have several limitations:

    • Traditional statistical models (based on regressions) hardly take into account the qualitative parameters (except to reduce the size of the database).
    • They do not manage properly the missing data and therefore, require very clean databases.
    • They mismanage “breaks” or threshold effects. For example, the price can have a linear behavior over a certain range, then a radically different behavior from a certain threshold (size, weight, volume …) because the manufacturing process can change.
    • All these elements directly affect the accuracy of traditional parametric models and therefore their use.

    Artificial Intelligence paves the way for a fourth model of cost modeling

    The advances made in algorithmic and machine learning in recent years largely solve the disadvantages of traditional parametric methods and improve their performance and their field of application.

    Among the recent statistical methods, the random forest algorithm, formally proposed in 2001 by Leo Breiman and Adèle Cutler (Breiman, L., Random Forests, Machine Learning, 45, 5-32 (2001) is a non-parametric approach that performs learning on multiple decision trees driven on slightly different subsets of data generated by Bootstrap techniques.

    1/ What are the advantages?

    The main advantages of this artificial intelligence algorithm are:

    • Ability to model a very large number of parameters (“cost drivers”) and particularly qualitative or “symbolic” parameters
    • Ability to process databases where the number of variables largely exceeds the number of observations
    • Ability to identify and weight automatically the most important parameters, and thus the “cost drivers” that impact most the cost of the product
    • Ability to manage missing values / incomplete databases
    • Robustness to outliers
    • Ability to identify behavioral breaks in variables
    • Interpretation of the tree
    • Precision increased by 30 to 40% compared to traditional methods

    2/ What are the applications?

    The applications of these algorithms are numerous, especially in the medical, insurance, marketing targeting (with uplift methods).

    The application of random forests in the field of cost estimation solves many of the disadvantages of traditional parametric approaches and opens to new opportunities for companies interested in efficiency and competitiveness.

    A precise estimate of costs is now possible, even with a limited number of observations (a few dozen), limiting the resources used to collect and capitalize the data. On the other hand, the price of complex systems can be modeled from easily accessible functional cost drivers, making encryption particularly simple and fast. Thus, for an equipment manufacturer, we were able to model the cost of an air conditioning system almost exclusively from functional or environmental parameters such as the volume to be air-conditioned, the number of openings, the time required to reach the target temperature, etc.

    For this reason, random forests have begun to be used by some companies in the early phases of the product life cycle, including:

    • Gain productivity on their encryption activities (saving time and resources that they can focus on technological innovation figures)
    • Respond more quickly to their clients’ tenders and especially use this time saving to better optimize their proposal
    • Secure and optimize their margin on new business

    It is not surprising that the first users were sectors with strong encryption and product development activities (automotive, capital goods, consumer goods, etc.).

    The second step was to use these algorithms to perform consistency or price inconsistency analyzes by identifying products with large discrepancies between the actual price and the estimated price. The explanatory properties of random forests (classification with similar products) make it possible to argue with suppliers during negotiations and thus to generate savings in purchases.

    Finally, once the model is perfectly calibrated, it becomes a cost control tool to validate the fair price offered by the supplier. This reduces the bargaining process.

    3/ What are the opportunities?

    The opportunities offered by random forests in the field of cost estimation and optimization are therefore enormous and far from being fully exploited. Beyond cost optimization, the self-learning of the algorithm on the data of companies and their suppliers makes it possible to consider intelligent contributions such as the automatic preparation of negotiations (objectives, levers arguments …), the proposing optimized designs or redesigns, recommending the most adapted purchasing strategies anticipating supplier behavior …

    In conclusion, the 2 approaches are complementary in their use:

    Advantages

    Limitations

    Examples

    of software

    Analytical Model

    • Explanatory and operations- centered model
    • “Best Landed Cost” Estimation and Target Price Definition
    • Allows to optimize the prices in production and to control the plans of progresses suppliers
    • Difficulty accessing process references and maintaining them over time
    • Intrusive approach towards suppliers
    • Expert model with little diffuse
    • Timeout for setting and performing encryption
    • Precision ?
    • Siemens PLM
    • A Priori
    • Facton

    Statistical Parametric Model

    • Easy and quick to use
    • Estimated price coherence, and accuracy (conditional)
    • Non-intrusive approach to suppliers
    • Product and service applications
    • Very relevant in the upstream phases of the life cycle and for the analyses of coherence
    • Requires minimal data and quality history
    • Model that is not very “explanatory” to moderate supplier progress plans
    • Less relevant model for setting target prices and “Best Landed Cost”
    • Difficulty in modeling qualitative parameters
    •  Seer
    • EstimFEC

    Non-Parametric Statistical Model

    « Random Forests »

    • Easy and quick to use
    • Consistency of the estimated price, and precision increased by 30% compared to parametric models (conditional)
    • Non-intrusive approach to suppliers
    • Product and service applications
    • Very relevant in the upstream phases of the life cycle
    • Relevant also in the downstream phases for the analysis of price coherences and the identification of opportunities thanks to the explanatory properties of the forests
    • Integrates a lot of cost drivers, including qualitative ones
    • Detects technological breakthroughs
    • Prioritizes cost drivers
    • Manages missing values and can work with a limited sample
    • Model less relevant for setting target prices and “Best Landed Cost”
    • easyKost

    Conclusions

    In conclusion, it would be futile to oppose the analytical and statistical methods of cost estimation. They complement each other in their use and purpose. The statistical method, which is more consistent because it is based on the observation of the actual data, makes it possible to obtain a rapid and precise evaluation to make the right decisions in the product design or redesign processes. Simple to implement, it allows to model many families of products and services in a non-intrusive way and without needing to acquire an advanced technological expertise. The analytical method allows to obtain an encryption precisely reflecting the reality (or the simulation) of a manufacturing process. More tedious to implement, on the other hand it allows to define targets of cost to be reached with explanatory factors based on the observed industrial parameters and benchmarks. In this sense, it is more appropriate to quantify technological breakthroughs and to lead industrial suppliers’ progress plans to bring them to the target. It is also more relevant to quantify technological innovations on which the company does not have a history.

    Nevertheless, self-learning algorithms and deep learning open new horizons and fields of application for the use of statistical models, notably through the sharing of information between companies or between them and their suppliers.

    9 Best Practices to Boost Supplier Innovation

    Capturing and successfully implementing supplier innovation is now considered a strategic issue for most companies, who see it as a major value creation driver. Here are some good practices, results of recent projects, as well as recent activities of our Think Tank.

    Rule #1: Organize and promote Business Intelligence to efficiently engage the stakeholders of the innovation ecosystem.

    Seeking innovation from suppliers involves first to be able to clearly communicate the strategic lines of innovation. This is certainly not to curb creativity, but to guide the proposals towards the strategic priorities of the company (miniaturization, energy reduction, connectivity, CO2 reduction …). The innovation plan or technological roadmap is a key tool to project and share that vision through a multi-business governance.

    Then it is appropriate to map the innovation ecosystem with care. This is not limited to suppliers but includes a more complete set of partners (suppliers, customers, universities, laboratories, start-ups, consumer associations, etc.).

    Finally, the areas of innovation can be promoted and shared with the innovation ecosystem stakeholders through various communication channels (strategic meetings, innovation portals, open innovation, forums, etc.).

    In conclusion, to capture and successfully implement innovation depends primarily on a clear and understandable organization of the relationship between partners to better use the energies and skills of each.


    Business intelligence innovation ecosystem

    Rule #2: Identify and select the right suppliers to succeed in implementing innovation

    Too often, companies rely on a traditional model of SRM (Supplier Relationship Management) to identify preferred suppliers for innovation. These models are often guided by a desire to rationalize the number of suppliers and lead to selection of suppliers able to meet all the required criteria.

    But, the larger suppliers are not necessarily the most relevant for innovation! Successful innovation comes also from smaller suppliers as they are more agile and able to accompany you in the evolution of your business model (especially guided by the digital revolution). The integration of these “nuggets” required to know how to adapt procurement strategies include being able to enhance the contribution of these suppliers to enable them to grow with you.

    We selected 3 major criteria to identify the right partners:


    Right suppliers
    • Skills: technological, quality, industrial, financial…
    • Ability to cooperate: which is expressed by the strategic alignment and agreement on the main principles of cooperation and governance of innovation projects
    • Fit: the compatibility of cultures, but especially the desire and commitment of top Management

    « Big is Beautiful » not necessarily an ideal to choose your partners in co-innovation. Successful innovation is first a story of men and women and cooperation between talent!

    Rule #3: Share the right information with suppliers to ensure strategic alignment

    Driving supplier innovation and co-innovation is first knowing to be open and not hesitating to share information deemed sensitive (hence the importance of rule # 2): market visions, consumer developments, innovation plans, key elements of the technology roadmap, etc.! Of course, this must be mutually agreeable.

    If spontaneous events can be organized (conventions, open innovation, etc.), it is important to conduct strategic reviews with key suppliers (usually 2 times a year) to ensure the involvement of top management and alignment of the two companies.

    Innovation initially come from exchange and must be driven by the Top Management of the two Parties.

    Rule #4: Create a climate of trust to co-generate ideas with partners

    It’s well known. A majority of innovations come from the exchange and confrontation of ideas. There are multiple ways to organize this cogeneration: specific meetings, challenges, tech-days, creative sessions, collaborative platforms, etc.

    In any case, it is important to agree in advance on a collaboration contract which sets rules for collaboration (particularly in terms of intellectual property, risk sharing, etc.). Many attempts died or were given birth to in pain and frustration because the terms of the collaboration were not clearly defined from the start and the leaders of both companies were not specifically involved.


    Co-generate ideas

    Creating an environment of trust with partners is a fundamental prerequisite to successfully and sustainably cogenerate ideas!

    Rule #5: Structuring idea consideration to choose the right innovations.

    Generating ideas is not the most complicated part. It is then required to evaluate and sort ideas easily using rational criteria. This requires the establishment of standard documentation sheets where the factors are described (customer value, differentiation, competitiveness etc …) as well as the level of risk (technological, industrial, etc …). A fluid process must help sort innovations quickly to give quick feedback to discuss with suppliers.

    A good practice is often to make the assessment in 2 stages (filter 1 and filter 2) which allows you to quickly rule out invalid ideas to focus on those with a real interest.


    Structuring idea consideration

    Example of Filter 1


    Structuring idea consideration

    Example of Filter 2

    Evaluating ideas with agility to successfully implement the proper transformation and keep the motivation of partners!

    Rule #6: Promote and structure the internal exchanges to better drive innovation.

    The establishment of a multi-business governance (marketing, R & D, Purchasing …) is a key success factor to organize the promotion and driving of innovation with suppliers or ecosystem partners.

    This governance is fundamental to aligning technology roadmaps, procurement strategies and supplier roadmaps. Some companies can significantly transform their organizations. For example, a leading automotive supplier brought together under one organization (“Engineering Procurement”) part of its R & D and Procurement / Suppliers Development teams to drive supplier innovation and ensure alignment with operational roadmaps.

    The internal multi-business governance is essential to efficiently drive supplier innovation and align it with product strategy.


    Promote internal exchanges

    Rule #7: Valuing in-house innovation to create desire.

    Fostering an innovation culture requires the establishment of a change management policy which is based around three axes:



    • Create the right mind-set: communications, celebration of success, highlighting suppliers, etc.
    • Demystify: organize industrial visits / labs, share benchmarks, communicate on simple innovations, etc. Innovation is within everyone’s reach!
    • Create desire: create challenges, incentivize managers on the resources dedicated to innovation, create a synthetic indicator, etc. …

    Innovation is primarily a state of mind!

    Rule #8: Promote individual involvement in innovation to increate initiatives

    Innovation cannot be forced. It must be at the initiative of everyone and the more the better. If communication and promotion of innovation contribute to create this state of mind, this is not enough. It should also be relayed by HR and managerial actions.

    • Empowering managers: train and coach them so they can relay to the teams; enable them to grant time for innovation
    • Integrate criteria of innovation in recruitment, assessment, and in newcomer integration programs.

    Individual involvement in innovation

    Human Resources has a key role in spreading innovation culture!

    Rule #9: Develop the right skills to capture and retain the value created

    Successful innovation with suppliers also requires the know how to develop the right skills:


    Capture retain innovation
    • Develop technical skills: the classic mistake is to focus suppliers innovation to address a lack of internal technological skills! On the contrary, you have to be able to always control innovation from the supplier to be able to capture the fair share of value but also to keep it on time. The mapping of knowledge is an important prerequisite before committing to such an approach.
    • Develop life skills: the skills required to stimulate and drive supplier innovation are not those traditionally expected of buyers. Functional and technical expertise give way to leadership skills, Business Development, “Intelligent Risk Taker”, etc.

    Successful supplier innovation also finds ways to change through development of individual and collective skills.

    Conclusion

    To conclude, we say that successful supplier innovation relies on organizing an ecosystem that is structured, but also agile enough to make new opportunities emerge and become reality. These new patterns of cooperation are both complex and dynamic. They disrupt the traditional relationship patterns between companies and their suppliers. Companies, including their purchasing teams, must redeploy and equip themselves to face these new challenges. This is a major challenge to ensure the contribution of the procurement team to value creation!

    9 Tips for Procurement to Impact Revenue Generation

    Over the past 20 years, the Sourcing function has evolved from Cost Reduction To Total Cost Management To Risk Management gaining more and more strategic impact. However, Sourcing initiatives are still contained to bottom line impact. In this article, we will share real cases coming from various industry sectors (automotive, pharmaceutical, aerospace…), where Sourcing Organizations have generated Revenue Impact through specific initiatives or organization design.


    Purchasing to generate strategic market impact

    Procurement to create a competitive advantage

    This paper describes our 9 tips for Procurement to Generate Revenue Impact. They are broken into 3 categories…

    1

    Product

    How Procurement can generate revenue by influencing the new products that your company will launch

    2

    Business Growth

    By influencing processes that can exist today in your company how procurement can generate revenue

    3

    Future

    Procurement can make profit by influencing, and participating in the dynamic nature of how the business world is likely to change, but how?


    Procurement product business growth future

    Click/Tap on each tip to read more

    Tip #1: Design to Value

    Tip #2: Engineered Procurement Organization for Speed to Market

    Tip #3: Innovation Ecosystem

    Tip #4: Lean Bidding through Parametrical Costing

    Tip #5: Success in Growth Markets

    Tip #6: M&A

    Tip #7 to 9: Procurement 4.0

    Conclusion: What does it mean for the future of Procurement?

    We see Procurement evolving toward an increased synchronization with the overall business and a closer collaboration with the Sales organization. Traditional Category Management is expected to evolve toward a more value stream organized function allowing for breakthrough solutions to be quickly implemented Procurement will be more of a Business Partner contributing to the business model evolution by identifying and matching the internal and external opportunities. This will also impact the required skills of Sourcing professional: soft skills will take more importance than traditional functional skills.


    Future of Procurement