Supply Chain Potential: Feasible and Optimal Supply Chain Planning

A Supply Chain (SC) is a system of organizations, people, technologies, activities, information and resources involved in moving materials, products and services all the way through the manufacturing process, from the original supplier of materials supplier to the end customer.

Supply Chain Management (SCM) is about planning, coordinating, execution and control of the organizational entities with their supporting resource (human, machines), process and technology in moving material, products and services through different interconnected system entities to the end customer.

Supply Chain key objective is to ensure time and place utility at the lowest possible cost: just enough at the right time and right place to satisfy customer demand with a view to strive for maximum possible potentiality.  It impacts directly the organization:

• Growth and its viability by ensuring efficient and effective supply chain aligned with marketing, sales and financial strategic plans
• Profitability by ensuring the lowest possible operating costs in delivering a balance between efficiency and effectiveness, minimizing/eliminating waste and errors. Optimum throughput will ensure financial inflows and reduce inventory levels
• Optimum inventory turns within optimum efficiency and effectives conditions: inventory turns to need to be associated to optimum inventory targets
• Optimum return on invested capital: striving for the highest possible potential within the efficient and effectiveness objectives: striving for optimum plant/equipment effectiveness: optimum return invested capital whereby long batch runs versus optimized batch runs will maximize throughput (batch size impacts return on invested capital, machine set-up due to change over reduces the return on invested capital)

Most organizations have a hard time minimizing the discrepancies between their capacity (what is possible or constraints) and the demands of their customers. These inefficiencies can have a massive negative impact on an organization. In this competitive environment, organizations cannot afford the luxury of being inefficient when it comes to their capacity planning (potentiality) — the balancing act of ensuring available capacity meets required capacity while maximizing the return on an organization’s assets, minimizing costs, and effectively managing risks.

An organization’s supply chain must be flexible enough to accommodate fluctuations in demand, while also guarding against investments in capacity or production before it is needed. As a result, most of them are under-utilizing resources or are unable to fulfil customer demand due to complexity: this implies poor Supply Chain Governance in terms of planning, coordinating and control achieving sub-optimum balance between efficiency and effectiveness (the right things the right way).

The performance of the system depends on how well the parts fit together, not how well they perform individually and the ‘whole’ system exhibits emergent properties that are not to be found in its parts.

The Supply Chain governance framework key objective is to always strive for maximum potentiality and optimum efficiency and effectiveness:

• Potentiality: This is what Supply Chain governors should be doing by developing resources and removing constraints, although still operating within the bounds of what is already known to be feasible. A bottleneck (manufacturing or handling resource) is a resource with capacity less or equal to demand while a constraint is a limiting factor to organization’s performance, an obstacle to the organization achieving its goal. These are imposed constraint such as a policy or method (way of working) that influence system output.
• Capability: This is what system should be presently doing with existing resources, under existing constraints, if there is management focus to eliminate waste, increase output resulting in improvements in productivity and efficiency.
• Actuality:  Current state of Supply Chain variables (output, Equipment effectiveness, forecast accuracy), with existing resources, under existing constraints

1. Productivity: is the ratio of actuality and capability
2. Performance: is the ratio of actuality and potentiality

Feasible and optimum planning should be based on capability while management effort should strive for maximizing potentiality (leverage capability). Key performance indicators should compare actuality to capability and actuality to potentiality (what is possible).

A bottleneck (resource) is a resource with capacity less or equal to demand while a constraint is a limiting factor to organization’s performance, an obstacle to the organization achieving its goal. A constraint can be a bottleneck, but a bottleneck is not always a constraint.

Constraints fall into four different groups:

• Constraints of foundation (where we are limited in something that is usually seen as a foundation element for success); internet selling does but provides the buyers to touch and feel.  Policy decision such as aligning product development around a specific operating system (Nokia).
• Constraints of resource such as facilities, manufacturing resources, people etc..  A constraint related to manufacturing resource relates to what generates output.
• Constraints of method (where we are limited by having to do something in a certain way). Typically, entrenched business processes based on ERP system ways of working that influence effective exploitation of resources and time.
• Constraints of time (where we are limited in the amount of time we have to do something). Management Constraint; limitation of the system due to management capacity or management attention. Management has a limited availability to deal with issues and control in their system sphere. Capacity management implies how many different issues can be handled over a period of time. A fundamental aspect to viability relates to autonomy; the ability of operational units to operate in autonomy within predefined control criteria freeing up management effort to deal with more complex and serious issues.

For effective planning it is fundamental to achieve a plan that is both feasible and optimal which will increase performance, reduce costs, avoid costly mistakes and boost confidence among organization areas such as marketing, production and finance and finally shareholders:

1.  Capacity planning — this implies achieving system balance (continuous balancing act based on feedback loops) by ensuring available capacity meets required capacity (demand driven capacity requirements) while maximizing the return on an organization’s assets, minimizing costs, and effectively managing risks.
2. Flexibility – supply chain must be flexible enough to accommodate fluctuation in demand considering current investment in productive capacity.
3. Feasible capacity plans can be executed within the realities of the business. They must not reflect unrealistic objectives for which organization will never be able to execute given all their important constraints. A feasible plan must account for the constraints, trade-offs, regulations, policies, and the financial requirements of a business.

o   Constraint: a constraint is anything that limits an organization from being able to produce more of what it strives for. Some constraint examples are labour, production equipment, warehouse space, transport limitation and third-party supplier capacity limits, way of working (process).

o   Regulations: Organization such as the chemical industry, pharmaceutical and food industry are heavily regulated from a compliance perspective when it comes to their production.  Production flow are impacted by batch control, compliance and traceability: higher inventory levels to off-set validation time (quality checks)

o   Financials: what is produced (product), quantity that is produced (batch size) and quality impacts the financial situation of the organization such as cashflow perspective. If an organization fails to convert its output to income fast enough, they could find their survival being threatened. An organization may be able to physically increase production by running over-time shifts, but this may not be practical from a financial standpoint.

·         For a plan to be feasible, it must be able to meet the output given the constraints over a specified time period. A feasible plan represent reality, it must reflect organization’s system constraints (bottlenecks decide the overall constraint), and it the biggest point of failure when it comes to effective capacity planning. This is where time granularity plays a fundamental role. Data granularity for 24-month plan very different to data granularity for the next 60 days or two weeks plan.

The second fundamental aspect is to understand time granularity. Time granularity is fundamental in effective planning. Time granularity with respect to internal production and external sourcing  planning impact influences planning in a different manner:

·       >  External sourcing one has JIT supply within narrow band, short term semi confirmed production requirements, medium to long term sourcing requirement. Some external suppliers have long lead times due to shipment methods (sea container) limiting planning flexibility

·      >   Production planning withing short horizon 0-3 weeks considers already scheduled firm orders work in process , scheduled orders that are sequenced based on ideal setup matrix.

>  4 week to 2 months bottleneck resource constrained but not sequenced optimized with capacity bucket equivalent to shift size

>  Medium term planning based on daily bucket time granularity. As time granularity increases bucket definition size increases. Next 3-6 months bucket definition is daily while longer time frame time bucket can be weekly.

Backet constrained provides accurate enough and feasible plan that considers capacity constraints.

The third fundamental aspect with respect to creating an effective capacity plan is optimization: to determine an optimum plan within defined capacity considering time granularity.

Most organizations fail to create optimal capacity plans, they just fail to integrate it with feasibility. MRP execution adopts infinite capacity and its outcome is an unfeasible plan fuelling chaotic management. Many don’t have adequate planning systems, or many have invested millions but with poor or limited usage.

An optimal plan allows organizations to maximize or minimize the objectives they are trying to meet such as satisfying demand within different capacity constraints variants (shift definition) that have corresponding cost implication.

SAP 4Hanna Advanced Planning now offer PPO optimizer .  The optimizer profile which is identical profile used in IBP Supply optimizations . Linear optimization, also known as linear programming (LP), is a mathematical method for achieving the best outcome in a model represented by linear constraints. This approach is particularly important in today's dynamic business landscape where companies aim to optimize efficiency and minimize costs to remain competitive. The goal of linear optimization is to maximize or minimize a specific objective, such as profit or efficiency, while adhering to defined constraints like resource limitations or budget restrictions. The objective function represents the goal that the optimization aims to achieve. The objective function, represented as a linear function, can be used for minimization (minimizing costs) or maximization (maximizing profit or maximizing efficiency). Cost factors such demand delays, undercapacity utilisation, capacity expansion cost , production cost and transport costs.

The key outcome is feasible plan considering defined bucket size constraints that is actually feasible. The feasible plan also creates critical dependant demand for sub-assemblies, components and raw materials.

The optimal and feasible plan should highlight demand that cannot be satisfied and must be feedback to demand management. Demand management must have a clear understanding of what can be realized and what is not feasible in terms of products, markets, and timing).

The value of an optimal and feasible plan is also a critical feedback to demand management:

·     

>    >    Unconstrained demand versus constrained demand by different capacity variants and cost implication

·     >   This then enable marketing to have clear view demand profiles versus supply profiles and their impact on feasibility and profitability

For an organization to be able to generate optimal and feasible plan it requires effective Supply Chain Governance Framework (SCGF) and supporting planning systems (IT architecture) that can generate optimal plans and provide clear performance guidance in terms of what it has planned versus actual outcome. This is fundamental for learning and adaptation.

Most organization do not have effective governance in place, most have poor understanding in  dealing with constraints and most have close to zero initiative to maximize potentiality.

Effective Supply Chain governance must co-exist with system governance (the whole system): it is of no value to having an effective SCGF in place while the system is a disaster. Nokia Supply Chain governance might be effective, but it will not change the destiny of Nokia due to its disastrous governance.

The SCGF must be supported by smart Supply Chain Planning System (SCPS) that are able to generate optimal plans within the capacity constraints which are feasible. The SCPS must be able to generate plans that are aligned with time granularity (planning rolling waves) and respective feasibility:

• 1 to 4 year constrained plan by product family/market/facility based on expected demand and facility constrains, projected investment projection (shut down, new facilities or consolidation)
• 12 months constrained and optimal plan (network plan) by product/facility/supplier as per demand requirements (forecasting and marketing initiatives) matched by key resource capacity variants (shifts) which is fundamental for shift planning (increase or decrease)
• The selected 12-month plan (rolling wave review every 3 months) driving sourcing and procurement rolling into the production plan
• The time granularity for actual production: example 3 month window (qty based on lot size rules for batch size and rough-cut scheduling) and detailed scheduling production plan 3-4 weeks within exact constrains, routings (operations and BoM assignment) and sequence optimization

·         To absorb demand flexibility and protect detail scheduled plans the use of rule based available to promise systems (gATP) can deal with flexibility without disturbing feasible and optimal plans. This will then ensure customer delivery is met in full and on time and must where required not fulfill demand or rather postpone demand that is not covered within the demand characteristics used by optimum and feasible plan.

Many organizations have invested millions in Supply Chain Planning Systems and ERP, some with success while others with dismal results.

Fundamental to understand that ERP system utilize MRP logic which generates chaotic planning results: it cannot generate feasible and optimum plans resulting in a chaotic situation. Some organization have heavily invested in Supply Chain Planning System such as SAP APO that not only have MRP type deterministic heuristics but also optimization toolset to enable optimum planning such as linear programming that permit defining objective function to maximize profit or service levels.

Majority organizations have failed to utilize SAP APO toolset correctly such as the SNP optimizer which utilizes linear programming techniques to achieve feasible and optimum plans with corresponding scenario as per defined objective function. Many do not feedback to Demand Management constrained supply: marketing assumes demand will be always matched. The SNP optimizer is a critical toolset to be able to create scenarios with different capacity variants and their corresponding cost implication.

Many have made substantial investment SCPS with poor result not due to system capability but due to lack of adequate and effective Supply Chain Governance Framework in place that is able to manage potentiality, system complexity, system dynamics feedback that causes system imbalance or sub-optimum outcome (less than ideal efficiency and effectiveness). Many fail to plan with the correct data set that considers time granularity and where data granularity flows to different data sets: Sales and Operation Plan that flows into Masters Production Schedule (MPS)  network plan, MPS that flow into detailed production plan by site by product , that flows into detailed scheduled production plan.

Many have invested in Sales and Operation Planning System (ideal for 1-4 year planning rough cut) but without an effective integration to SCPS that is able to use output of S&OP time granularity plan to a more detailed supply chain network plan which feasible and optimum plan in detail over next 12-month horizon.

This is critical for feedback, without feedback tools such as S@OP and demand management are of little value.

Feedback is critical to provide a view of what has occurred (actual) versus what was planned in terms of feasible (constrained) and optimum: this is critical to ensure that what is planned in terms of feasible and optimum is realized and is critical for learning and adaptation in order carry-out corrective action.

Finally, many have invested million in reporting data warehouse system but in general they are ‘so what’ performance indicators reducing the ability to true learning, understanding, and adaptation. Inventory KPI or on-time delivery, or backorders, equipment effective KPI in isolation provide limited value.

Concluding remarks:

Creating feasible (constrained) and optimum plans will leverage performance, but it requires effective Supply Chain Governance Framework and effective Supply Chain Planning Systems in place.

Foundation for Industry 4.0

IIndustry 4.0 (source Wikipedia)  is a name given to the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of things, cloud computing^[and cognitive computing. Industry 4.0 is commonly referred to as the fourth industrial revolution.

Industry 4.0 fosters what has been called a "smart factory". Within modular structured smart factories, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the Internet of Things, cyber-physical systems communicate and cooperate with each other and with humans in real-time both internally and across organizational services offered and used by participants of the value chain.

Industry 4.0 is a collective term for technologies and concepts of value chain organization. Based on the technological concepts of cyber-physical systems, the Internet of Things and the Internet of Services, it facilitates the vision of the Smart Factory. Within the modular structured Smart Factories of Industry 4.0. Industry 4.0 operates within this framework:

·         Cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions

·         Over the Internet of Things, Cyber-physical systems communicate and cooperate with each other and humans in real time

·         Via the Internet of Services, both internal and cross-organizational services are offered and utilized by participants of the value chain.

Industry 4.0 is based on six design principles. These principles support companies in identifying and implementing Industry 4.0 scenarios.

• Interoperability: the ability of cyber-physical systems (i.e. workpiece carriers, assembly stations and products), humans and Smart Factories to connect and communicate with each other via the Internet of Things and the Internet of Services
• Virtualization: a virtual copy of the Smart Factory which is created by linking sensor data (from monitoring physical processes) with virtual plant models and simulation models
• Decentralization: the ability of cyber-physical systems within Smart Factories to make decisions on their own. Decentralized decisions: The ability of cyber physical systems to make decisions on their own and to perform their tasks as autonomously as possible. Only in the case of exceptions, interferences, or conflicting goals, are tasks delegated to a higher level.
• Real-Time Capability: the capability to collect and analyze data and provide the insights immediately
• Service Orientation: offering of services (of cyber-physical systems, humans and Smart Factories) via the Internet of Services. Technical assistance:  The ability of assistance systems to support humans by aggregating and visualizing information comprehensively for making informed decisions and solving urgent problems on short notice. Second, the ability of cyber physical systems to physically support humans by conducting a range of tasks that are unpleasant, too exhausting, or unsafe for their human co-workers.
• Modularity: flexible adaptation of Smart Factories for changing requirements of individual modules
• Information transparency: The transparency afforded by Industry 4.0 technology provides operators with vast amounts of useful information needed to make appropriate decisions. Inter-connectivity allows operators to collect immense amounts of data and information from all points in the manufacturing process, thus aiding functionality and identifying key areas that can benefit from innovation and improvement. Information that is required by the sub-system within digital architecture.

There is a danger that that Industry 4.0 will just be another failure and big hype whereby significant investment fail to move it from trial and pilots into realization.

Forbers: Why Industrial IOT Is Usually A Failure - And How To Fix It

‘In mid-2017, Cisco produced a report of survey results indicating that companies considered 76% of their IOT initiatives failures, and a majority said that IOT initiatives looked good on paper, but turned out to be more complex than expected’

Reality is that most organization have failed to achieve efficacy in Industry 3.0 . 

Industry 3.0

Industry 3.0, 4.0 to +++(adaption of Machine Learning and Artificial Intelligence)  will still have to comply to key Supply Chain Process which are plan , make and deliver effectively and efficiently by maximizing system potential (constraints), minimize waste , quality product and on time delivery.

Most organization do not have an integrated system view cockpit/dashboard to provide linked metrics. Overall equipment effectives (OEE) is of little value on its own without correlation to inventory levels and backorder. High OEE correlating with rising inventory and backorder indicates poor effectiveness.

Traditional governance frameworks are compartmentalized rather than system wide framework that is able to deal with system level of disorder: from complex system to complicated to chaotic.

To ensure Industry 4.0 realization benefits the following solid foundation needs to be put in place:

       

•      Governance framework (based on Viable System Model) that can deal with complexity and better equipped to respond to signals from internet of services. This governance framework will enable effective use of input that flows from Industry 4.0 framework and ensures correct decision (do the right things)

 Industry 4.0 cybernetic Approach

•          Dashboard/Cockpit that shows correlated metrics and associated events to enable better decision making and next best actions
•          Getting Industry 3.0 working efficiently and effectively with the necessary subsystems (ERP ) device connectivity, lean manufacturing

Note: Governance framework and the cockpit is not only fundamental to Industry 4.0 but also critical to ensure viability, sustainability and adaptation: doing the right thing in terms of dealing with system complexity, maximizing system potential, viability, and sustainability.  

The performance of the system depends on how well the parts fit together, not how well they perform individually and the ‘whole’ system exhibits emergent properties that are not to be found in its parts.

Potentiality: This is what the system should be doing within the boundaries of existing system state (same function, structure, identity, and feedback’s) by developing its existing resources  and removing constraints or elevating constraints to maximize throughput, improvements to  management control and processes although still operating within the bounds of what is already known to be feasible.

A constraint is a limitation, imposed by outside circumstances or by system behavior (ourselves) , that materially affects our ability to do something and limits a person or an organization in achieving their goal. Constraints directly impact systemic capability and potentiality.

There is a subtle difference between a constraint and a bottleneck.

A bottleneck (resource) is a resource with capacity less or equal to demand while a constraint is a limiting factor to organization’s performance, an obstacle to the organization achieving its goal.

Wikipedia:  Cyber Physical Systems (CPS)

A cyber-physical (also styled cyber physical) system (CPS) is a mechanism that is controlled or monitored by computer-based algorithms, tightly integrated with the Internet and its users. In cyber-physical systems, physical and software components are deeply intertwined, each operating on different spatial and temporal scales, exhibiting multiple and distinct behavioral modalities, and interacting with each other in a lot of ways that change with context. Examples of CPS include smart grid, autonomous automobile systems, medical monitoring, process control systems, robotics systems, and automatic pilot avionics.

CPS involves transdisciplinary approaches, merging theory of cybernetics, mechatronics, design and process science. The process control is often referred to as embedded systems. In embedded systems, the emphasis tends to be more on the computational elements, and less on an intense link between the computational and physical elements. CPS is also similar to the Internet of Things (IoT), sharing the same basic architecture; nevertheless, CPS presents a higher combination and coordination between physical and computational elements.

About Viable System Model

The viable system model (VSM) is a model of the organisational structure of any autonomous system capable of producing itself. A viable system is any system organised in such a way as to meet the demands of surviving in the changing environment. One of the prime features of systems that survive is that they are adaptable. The VSM expresses a model for a viable system, which is an abstracted cybernetic (regulation theory) description that is claimed to be applicable to any organisation that is a viable system and capable of autonomy.

SAP IBP latest flavour – why bother

IBP is SAP latest offering regarding Supply Chain planning. In essence, it covers Demand Planning (forecasting) S&OP, order-based planning (network planning) inventory optimization and a fancy cockpit all operating within SAP managed cloud environment.

Generally, IBP value proposition is to provide functionality that ERP or SAP 4/HANNA lack the ability to effectively manage within the supply chain.

Why bother?

·        If you already have invested million in SAP-APO system which could include DP, gATP, SNP and PPDS then IBP migration only worthwhile if IBP provides real business benefits.

•       IBP Demand solution provides a better forecast accuracy: in many cases forecast accuracy is also influenced by how the demand planning solution is constructed rather than complex  forecasting algorithms
•          With respect to PP/DS, IBP does not support the production planning functionality, IBP does not support advanced availability check

·       IBP provides Demand planning, S&OP  and network planning (time series and order based planning) Order based planning includes optimizer and deployment.

Greenfield project where a customer does not have SAP APO investing in IBP is worthwhile if there are business needs to manage:

1. Forecasting
2. S&OP
3. Network planning (where the is network complexity that traditional MRP cannot manage)

Ideally, a customer should also consider SAP 4/HANNA to effectively manage integration with IBP which is managed via the SAP 4/HANNA add-on. This makes sense if you plan to use time series or order-based planning.

Moving to IBP from an existing SAP ERP and APO must carefully consider the eventual upgrade of SAP  to SAP 4/HANNA. Migrating to IBP will result in a high cost of ownership if there will be concurrent usage of both IBP and SAP APO until all APO functionality is switched off.

The benefit of moving to SAP 4/HANNA allows a more sensible migration to IBP. It Can avoid  concurrent systems and ideally a two-phased approach can be used:

PHASE 1:

This implies firstly migrating ERP to SAP 4/HANNA and then activating additional functionality.

• Taking care of production: move from APO PP/PPDS to SAP 4/HANNA PP/DS . Practically PP/DS resolves the problems with poor MRP planning plus if network optimization is not needed then PP/DS can practically manage the whole supply chain
• Taking care of availability check: move from APO gATP to SAP 4/HANNA Advanced ATP although missing functionality such as rule based ATP and CTP.

PHASE 2:

In this phase it possible to switch from APO to IBP to avoid concurrent usage specifically within the area of demand management and Supply Network planning (SNP) . Careful consideration and validation is needed with respect to SNP functionality specifically in the areas:

• CTM although order-based planning does offer some similar functionality
• SNP optimizer: verify order-based planning optimizer to determine value considering the complexity 

SAP EWM The Good , the Bad and the Ugly

The good, the bad and the ugly, one of the greatest westerns ever made considering that it was a so-called ‘spaghetti western’

How does this apply to SAP EWM ?

The Good

SAP realized shortcoming within the existing SAP Warehouse Management system ability to satisfy complex business needs for warehouse operations. Many customers resorted to best of breed products integrated with SAP requiring complex interfaces. Initially, EWM was released with Service Parts Execution and subsequently evolved to its own product known as Extended Warehouse Management. Started with release 5.0 and currently reached 9.5 (Jan 2019) . SAP EWM solution capability eventually matured to easily compete with best of breed solution in most cases more effective due to the tight integration with SAP ERP system. EWM basically can support a complex high volume warehouse that requires an efficient operating solution to satisfy business needs.

Subsequent with the migration to SAP 4/HANNA SAP offer embedded EWM solution reducing the need of having a separate server with installed SAP SCM system to operate EWM system.  Many multinational organizations might continue to operate EWM on a separate decentralized server system due to transactional volumes and performance needs.

The Bad (or rather the difficult):

Achieving warehousing operational efficiency requires top-notch skill set not only within SAP space but also warehousing processes and integrating technology. Warehouse technology may consist of extremely complex automated systems such as Swisslog autostore , to automated crane systems and coveyor systems. Then we have Radio frequency , bar-code labels , packaging ect… Complex make up of technological elements integrated within SAP processes.

Swisslog autostore

In order to achieve operational excellence in terms of efficiency and effectiveness some form of bespoke custom development is required. Most project implementation require bespoke developments increasing risk and costs. Right skill set fundamental for efficient implementation and off-shoring rarely provides good outcomes.

The Ugly:

With the migration to SAP 4/HANNA embedded EWM system, EWM is now also offered which consists of basic EWM and advanced EWM.

Advanced EWM is only available at additional extra licensing costs and the following is not available within basic EWM license

• Material Flow System (MFS)
• Wave Management
• Transportation Units/YARD mgt
• Labour mgt
• Value added service
• Warehouse Billing
• Kitting
• Dock appointment scheduling
• Slotting

Wave mgt and Transportation units are fundamental in 90% of warehousing operations and having basic EWM license the installed EWM system will fail to provide real operational business benefits and leverage operational efficacy. This shortcoming can be overcome by means of bespoke enhancement: using shipment instead of TU and using PPF for scheduling and automating picking in place of waves.

The lack of MFS in the basic EWM license is less of an issue in that many operations, the automated system PLC driven hardware normally controlled by operating system provided by hardware supplier. Most cases, the hardware suppliers usually support the system and guarantee a certain percentage uptime . The service level agreement then becomes an issue with utilizing EWM MFS, most prefer interfaces to the existing PLC operating system. Basic EWM has full ability to generate IDOC to interface hardware.

With respect to kitting, not many warehouse systems carry-out kitting therefore not a major impact. Where it is needed, simple workaround is to simply set-up IM triggered movements: 1 for receipt of kit header and 1 for issue of kit components mapped to respective delivery documents. Simple low cost effort, a custom programme can be done to trigger kitting in order to keep reference between incoming header and outgoing components, the same custom can either require manual insert of components or read a BoM of the header to derive components.

Additional information with respect to SAP 4/HANNA: not only there is EWM embedded but also the availability of advanced production (technically APO PPDS embedded) and Advanced Available to Promise  (partially old APO gATP) but lacking CTP and rule-based availability check. They all require separate license implying that to work efficiently basic SAP 4/HANNA will not be sufficient to work smart 

ITS ALL ABOUT PICKING : SAP EWM view

In general, a warehouse and its contents are of no value, rather a major cost driver.

Its main purpose is to provide time and place utility to satisfy customer needs. Without the need for time and place utility a warehouse is of no purpose.

To provide time and place utility one of the fundamental aspect relates to efficacy, efficacy of picking, packing, labeling and shipment on time and in full of the correct ordered goods.

A high-volume warehouse will have many Transportation Units (TU) arriving throughout the day to load and deliver. These TU are of different size and will load packed boxes and pallets for delivery to different routes: deport for export, or from cross dock and de-consolidation or direct delivery.

This implies that the warehouse must synchronize picking so that goods are available with agreed loading time frame, correctly packed and labelled for loading.

From SAP perspective the key processes are as follows:

·         Sales orders are generated for customer to provide specific products at a desired date. Normally different order types are created to deal with exports, domestic ect..for ship to parties that are on a specific route. The sales order may have item quantities that may be defined as items, box or pallets. The sales order may contain specific batches or may contain a batch search strategy to determine ideal batches. The confirmed dates with respect to availability validate available qty and delivery date that is theoretical (rarely takes into consideration supply chain and warehouse issue)

·         Other types of outbound are triggered by stock transfer order for inter plant transfer. Quantities may be optimized based on type of planning system used of simply derived by MRP

·         Deliveries for the respective sales orders are then created based on available warehouse stock. Delivery creation considers the warehouse as block box: indifferent of availability single items, boxes and cartons, indifferent of the transportation unit capacity (possible up to a point if APO Transportation load Builder) , indifferent of load optimization or load sequencing

Deliveries are the key enables for commencing EWM warehouse picking operations. Different type of deliveries will determine if for cross plant transfer, domestic or export. In high volume warehouse picking by deliveries not an option. This will result in in operational delay, confusion and inefficiencies.

The ideal operational process flow is to ensure operational efficiency. This is done as follows:

ASSIGNMENT OF DELIVERIES TO SHIPMENTS

1.      Collect all the deliveries and assign to a Transportation unit. Each transportation unit represent a specific vehicle.

Normally all organization negotiate with shipping agent’s shipment service which include type of vehicle and route schedule. Each transportation unit will be responsible for delivering according to specific route and loading volume is approximated. This can be done via the following functionality:

o   In ERP to limited extent. No truck load optimization is possible load sequencing. Shipment creation allows selection and deliveries to be included with following selection criteria:

Delivery selection

Transportation criteria

Once created system generates shipment document with assigned deliveries

Shipment document is then replicated to EWM by means of IDOC which in turn creates a Transportation unit with assigned deliveries.

The above creation limitation does not have any form of load optimization and delivery grouping simplistically grouped.

o   Creation of Transportation unit in the EWM system. This is possible from shipping cockpit functionality that displays all the deliveries, planner then selects deliveries to be grouped in newly created TU.

Shipment cockpit via transaction NBWC (not part of SAP GUI transactions)

Deliveries displayed in cockpit

Transportation Unit creation criteria

Created Transportation unit with assigned deliveries. The transportation unit are not optimized in terms delivery route and load optimization.

o   Manually create Transportation Unit via transaction /SCWM/TU and assign deliveries.

TU creation criteria

Assign deliveries

Created TU with assigned deliveries

o   Alternative way via third party solution. This implies sending delivery documents via IDOC or other to external system that manages routing and load optimization. This third-party software will then have grouped deliveries in unique shipping units that are optimized by route and are loading volume optimized. These shipping units with assigned deliveries, product and qty are then interfaced (custom) ideally into EWM system by creating a Transportation Unit with assigned deliveries in the EWM system. Possible vendor : https://ortec.com/

Note: in all cases it possible to split deliveries across multiple TU due to load optimization and constraints.

Up to this process step SAP functionality has all the capabilities to create shipment but not perfectly optimized unless third party tool is used.

Depending on the business complexity with respect to delivery route management and load optimization it may be the standard offering within SAP and EWM may be adequate with a simple enhancement if needed.

Once we have the TU with assigned deliveries EWM can then really earn its value by triggering the follow-on step. This where EWM has all the functionality to carry-out effective and efficient picking and shipment.

At this point we have in the system many TU but it does not mean that the warehouse items are ideally stocked. TU are now structured as follows:

TU per vehicle and has expected planned date and time (approximate)

Deliveries

Deliveries may allow for peace’s, carton, packs a and pallet. Also, it may be that pallets are stored in high bay warehouse where some form of FEFO logic is needed.

EWM offers replenishment that can either based on min levels or order-based replenishment. The selection of how to manage the replenishment criteria depends has pros and cons.

Important to note that both methods trigger replenishment activities that may not be completed on time to satisfy TU:

·         Planned replenishment check all stock levels and is indifferent to outbound requirements. Limit this to single peace picking areas such a vertical carousel or pick to lite systems

·         Order based considers deliveries and is indifferent to planned shipments

·         Ideally order based should be based on planned TU, but TU is not part of selection criteria

Moving on:

PRE PLANNING THE SHIPMENTS

Once the TU are in place with expected date and time carry-out the following steps:

        Yard management. 

      This is an optional process, dependency is based on complexity, volume of vehicles and control that is required. Yard functionality to be used for check in of vehicle, weighing, sealing and check-out

                                                          Functions related to yard mgt

Shipping cockpit assign door

In the shipping cockpit

Assign door. This will be the door TU will arrive to be loaded. Important to setup goods issue bins to relevant door. This is fundamental for warehouse task such that pick destination bin displays goods issue bin associated to bin.

Staging bay door relationship.

Shipping cockpit the clearly displays that TU assigned to door

TU visibility and status of door assignment.

 Assignment of the door to TU automatically assigns door to delivery.

At this point in time may be necessary to modify TU date and time. Note: if this is done once picking is started then WO date are not adjusted therefore enhancement is required. This is fundamental to re-sequencing WO picking which is based on system guided picking.

       Assign wave. Once door is assigned assign wave to the TU. Wave creation will then trigger picking and this is where efficacy is gained in that picking managed because automated. Even is not all warehouse are created, planned job (say every 10 minutes) will re-release the wave to create any outstanding picks.

Wave can either be manually assigned or automatically. Once wave is created depending on type of release warehouse tasks are created where picking strategies are successful.

Possible option is to create wave that are not released: this will then require further step within the cockpit to release the wave. Decision of not releasing wave on creation depends on timing and pick volumes. Create wave not released enables replenishment to already consider these TU that have wave assigned but not yet started. Release the wave within narrow window to trigger picking.

It is now fundamental to set-up replenishment to run based on orders but with wave template defined. This implies that deliveries are selected that are assigned to TU and have specific wave template assigned. Consider enhancement to sort deliveries for replenishment based on TU sequence. This ensure that replenishment tasks sequenced accordingly.

Further enhancement is required to consider batch search strategies associated to outbound delivery must be considered when carry-out replenishment. No use having replenishment for batches that do not fit batch search strategy.

Further enhancement is needed if there is a need for multi-level replenishment: pallet > carton>peace’s.

Very important: activate wave mgt for replenishment, this is fundamental to automate steps and avoids excessive human effort. Operator effort purely manage exception where Ware task creation fails.

       Carry-out picking. Wave release will trigger warehouse tasks creation. Efficacy all depends on smart pick strategies are defined. Wave via background job will automatically be released in background to create any missing warehouse tasks due to replenishment tasks running in background. Warehouse order creation rules, queue assignment is fundamental to ensure warehouse order efficacy. This is to ensure that picking tasks are assigned to correct type of resource.

Warehouse order, associated to wave, queue with assigned warehouse tasks. Sequencing of WO must correspond to TU sequencing. This to ensure that when relevant truck arrives products are ready to be loaded.

99% picking activity either carried out via RF, automated crane system, pick to lite, voice pick. the remaining 1% managed manually only for exception. High number manual process confirms badly designed signed.

5.       Labeling

Once picking commences labels are needed. Normally if full pallet picking is carried, re-use of existing HU may be possible. It may that customer specific labels may be needed. Mass printing of labels will be disastrous for any warehouse operation. Labels need to be printed on demand associated to resource that is doing the picking.  This why small label printers attached to belts of RF operator or fork lift trucks will ensure correct label for correct item that is picked.

         Documentation

Many EWM implementations carry-out the usual error by printing delivery notes and packing list from ERP system instead of EWM. This causes all sort of problem in that there is dependency for goods issue to be carried out in ERP to print documentation. Ideal solution is to print documentation from EWM system rather than ERP system.

  Loading

EWM offers the ability to create loading WT, important to understand need for this kind of detail. It may result in unnecessary system effort. Simply update by updating loading in TU to indicate loading complete. Loading complete status can be used to automatically trigger printing of delivery note and packing lists.

  Close shipment

This tasks is needed to free-up door and indicate completion of TU activity.

There may be requirement to update seal number, this may be done at TU level and vehicle level as well as update from weighing station.

Departure from checkpoint then closes the TU and is further critical event to trigger update to ERP system of the TU so that shipment is created and if necessary shipping cost generated.  

Conclusion
 In any warehouse the real value is picking. Warehousing and shipment process to satisfy time and place utility which generates income (billing). Therefore, efficacy within warehousing solution is critical:

·         Efficacy in system operation

·         Efficacy in actual warehouse operation by resource.